2024

• Ahmadi, Najia ; Nguyen, Quang Vu ; Sedlmayr, Martin ; Wolfien, Markus: A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data. In: Scientific Reports vol. 14 (2024), Nr. 1, p. 2287

  The emergence of collaborations, which standardize and combine multiple clinical databases across different regions, provide a wealthy source of data, which is fundamental for clinical prediction models, such as patient-level predictions. With the aid of such large data pools, researchers are able to develop clinical prediction models for improved disease classification, risk assessment, and beyond. To fully utilize this potential, Machine Learning (ML) methods are commonly required to process these large amounts of data on disease-specific patient cohorts. As a consequence, the Observational Health Data Sciences and Informatics (OHDSI) collaborative develops a framework to facilitate the application of ML models for these standardized patient datasets by using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). In this study, we compare the feasibility of current web-based OHDSI approaches, namely ATLAS and “Patient-level Prediction” (PLP), against a native solution (R based) to conduct such ML-based patient-level prediction analyses in OMOP. This will enable potential users to select the most suitable approach for their investigation. Each of the applied ML solutions was individually utilized to solve the same patient-level prediction task. Both approaches went through an exemplary benchmarking analysis to assess the weaknesses and strengths of the PLP R-Package. In this work, the performance of this package was subsequently compared versus the commonly used native R-package called Machine Learning in R 3 (mlr3), and its sub-packages. The approaches were evaluated on performance, execution time, and ease of model implementation. The results show that the PLP package has shorter execution times, which indicates great scalability, as well as intuitive code implementation, and numerous possibilities for visualization. However, limitations in comparison to native packages were depicted in the implementation of specific ML classifiers (e.g., Lasso), which may result in a decreased performance for real-world prediction problems. The findings here contribute to the overall effort of developing ML-based prediction models on a clinical scale and provide a snapshot for future studies that explicitly aim to develop patient-level prediction models in OMOP CDM.
• Akshay, Akshay ; Katoch, Mitali ; Shekarchizadeh, Navid ; Abedi, Masoud ; Sharma, Ankush ; Burkhard, Fiona C ; Adam, Rosalyn M ; Monastyrskaya, Katia ; Gheinani, Ali Hashemi: Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis. In: Gigascience vol. 13 (2024)

  BACKGROUND: Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. RESULTS: To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating 4 essential functionalities-namely, Data Exploration, AutoML, CustomML, and Visualization-MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on 6 distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. CONCLUSION: MLme serves as a valuable resource for leveraging ML to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.
• Arya, Shreya ; Curry, Justin ; Mukherjee, Sayan: A Sheaf-Theoretic Construction of Shape Space. In: Foundations of Computational Mathematics (2024)
• Aydin, Irem ; Diebel-Fischer, Hermann ; Freiberger, Vincent ; Möller-Klapperich, Julia ; Buchmann, Erik ; Färber, Michael ; Lauber-Rönsberg, Anne ; Platow, Birte: Assessing Privacy Policies with AI: Ethical, Legal, and Technical Challenges.. In: Proceedings of the 1st International Conference on AI-based Systems and Services (AISyS’24), 2024
• Baader, Franz ; De Bortoli, Filippo: Logics with Concrete Domains: First-Order Properties, Abstract Expressive Power, and (Un)Decidability. In: SIGAPP Appl. Comput. Rev. vol. 24. New York, NY, USA, Association for Computing Machinery (2024), Nr. 3, pp. 5–17

  Concrete domains have been introduced in description logic (DL) to enable reference to concrete objects (such as numbers) and predefined predicates on these objects (such as numerical comparisons) when defining concepts. The primary research goal in this context was to find restrictions on the concrete domain such that its integration into certain DLs preserves decidability or tractability. In this article, we complement these investigations by studying the abstract expressive power of both first-order and description logics extended with concrete domains, i.e., we analyze which classes of first-order interpretations can be expressed using these logics, compared to what their counterparts without concrete domains can express. We demonstrate that, under natural conditions on the concrete domain D (which also play a role for decidability), extensions of first-order logic (FOL) or the well-known DL ALC with D share important formal characteristics with FOL, such as the compactness and the Löwenheim-Skolem properties. Nevertheless, these conditions do not ensure that the abstract expressive power of the extensions we consider is contained in that of FOL, though in some cases it is. To be more precise, we show, on the one hand, that unary concrete domains leave the abstract expressive power within FOL if we are allowed to introduce auxiliary predicates. As a by-product, we obtain (semi-)decidability results for some fragments of FOL extended with the concrete domains considered in this article. On the other hand, we show that the ability to express equality between elements of D, another condition employed in the context of showing decidability of ALC(D), is sufficient to push the abstract expressive power of most first-order fragments with concrete domains beyond that of FOL. While for such concrete domains D decidability is retained for ALC(D), we show that the availability of equality in D causes undecidability of the two-variable fragment of FOL(D), although the two-variable fragment of FOL is decidable.
• Baader, Franz ; De Bortoli, Filippo: The abstract expressive power of first-order and description logics with concrete domains.. In: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. Avila Spain : ACM, 2024
• Baader, Franz ; Fernández Gil, Oliver: Unification in the Description Logic $mathcalELH_mathcalR^+$ without the Top Concept Modulo Cycle-Restricted Ontologies.. In: Benzmüller, C. ; Heule, M. J. ; Schmidt, R. A. (eds.): Automated Reasoning - 12th International Joint Conference, IJCAR 2024, Nancy, France, July 1-6, 2024, Proceedings, Part II, Lecture Notes in Computer Science. vol. 14740 : Springer, 2024, pp. 279–297
• Baader, Franz ; Giesl, Jürgen: On the Complexity of the Small Term Reachability Problem for Terminating Term Rewriting Systems.. In: Rehof, J. (ed.): 9th International Conference on Formal Structures for Computation and Deduction (FSCD 2024), Leibniz International Proceedings in Informatics (LIPIcs). vol. 299. Dagstuhl, Germany : Schloss Dagstuhl -- Leibniz-Zentrum für Informatik, 2024 — ISBN 978-3-95977-323-2, pp. 16:1–16:18
• Barth, Lukas Silvester ; Fatemeh ; Fahimi ; Joharinad, Parvaneh ; Jost, Jürgen ; Keck, Janis: IsUMap: Manifold Learning and Data Visualization leveraging Vietoris-Rips filtrations (2024)
• Barth, Lukas Silvester ; Fatemeh ; Fahimi ; Joharinad, Parvaneh ; Jost, Jürgen ; Keck, Janis ; Mikhail, Thomas Jan: Fuzzy simplicial sets and their application to geometric data analysis (2024)
• Berthold, Matti ; Rapberger, Anna ; Ulbricht, Markus: Capturing Non-flat Assumption-based Argumentation with Bipolar SETAFs.. In: Proc. KR, 2024
• Bevendorff, Janek ; Casals, Xavier Bonet ; Chulvi, Berta ; Dementieva, Daryna ; Elnagar, Ashaf ; Freitag, Dayne ; Fröbe, Maik ; Korenčić, Damir ; Mayerl, Maximilian ; et al.: Overview of PAN 2024: Multi-author Writing Style Analysis, Multilingual Text Detoxification, Oppositional Thinking Analysis, and Generative AI Authorship Verification: Extended Abstract.. In: Advances in Information Retrieval : Springer Nature Switzerland, 2024 — ISBN 9783031560729, pp. 3–10
• Bevendorff, Janek ; Wiegmann, Matti ; Potthast, Martin ; Stein, Benno: Is Google getting worse? A longitudinal investigation of SEO spam in search engines.. In: Lecture Notes in Computer Science, Lecture notes in computer science. Cham : Springer Nature Switzerland, 2024, pp. 56–71
• Bevendorff, Janek ; Wiegmann, Matti ; Potthast, Martin ; Stein, Benno: Product Spam on YouTube: A Case Study.. In: Proceedings of the 2024 Conference on Human Information Interaction and Retrieval, CHIIR ’24. Sheffield, United Kingdom : Association for Computing Machinery, 2024 — ISBN 9798400704345, pp. 358–363

  YouTube videos are a popular medium for online product reviews. They are not only informative and entertaining, but may also be perceived as quite credible under the viewer’s impression of a personal product demonstration by an expert. As the world’s largest online video platform, YouTube’s content is included prominently in the results of most general-purpose web search engines. Consequently, online marketeers are using classic Search Engine Optimization (SEO) techniques also for placing their video content in search engines. Over the years, we have noticed an ever increasing noise floor of low-quality SEO content in product search results and in this study, we show that this trend has spilled over into videos as well. We examine YouTube video reviews for several thousand products retrieved from three commercial search engines and conduct spam detection experiments based directly on the videos’ subtitle transcripts rather than relying on metadata and comments. We find that at least a third of the retrieved videos can be regarded as spam or low-quality productions. We are further able to distinguish these spam product reviews accurately from higher-quality videos with a semi-supervised n-gram classification approach.
• Beylier, Charlotte ; Hofmann, Simon M. ; Scherf, Nico: Revealing the learning process in reinforcement learning agents through attention-oriented metrics (2024)
• Blümel, Lydia ; König, Matthias ; Ulbricht, Markus: Weak Admissibility for ABA via Abstract Set Attacks.. In: Proc. KR, 2024
• Bohlinger, Sandra ; Hummel, Sandra: Digital capacity building in teacher education: An environmental case study from Cambodia.. In: Doing Higher Education. Wiesbaden : Springer Fachmedien Wiesbaden, 2024, pp. 31–50
• Borgwardt, Stefan ; Bortoli, Filippo De ; Koopmann, Patrick: The Precise Complexity of Reasoning in $mathcalALC$ with $omega$-Admissible Concrete Domains (Extended Version) (2024)

  Concrete domains have been introduced in the context of Description Logics to allow references to qualitative and quantitative values. In particular, the class of ω-admissible concrete domains, which includes Allen's interval algebra, the region connection calculus (RCC8), and the rational numbers with ordering and equality, has been shown to yield extensions of ALC for which concept satisfiability w.r.t. a general TBox is decidable. In this paper, we present an algorithm based on type elimination and use it to show that deciding the consistency of an ALC(D) ontology is ExpTime-complete if the concrete domain D is ω-admissible and its constraint satisfaction problem is decidable in exponential time. While this allows us to reason with concept and role assertions, we also investigate feature assertions f(a,c) that can specify a constant c as the value of a feature f for an individual a. We show that, under conditions satisfied by all known ω-admissible domains, we can add feature assertions without affecting the complexity
• Buraglio, Giovanni ; Dvorak, Wolfgang ; König, Matthias ; Ulbricht, Markus: Justifying argument acceptance with collective attacks: Discussions and disputes.. In: Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence. Jeju, South Korea : International Joint Conferences on Artificial Intelligence Organization, 2024

  In formal argumentation one aims for intuitive and concise justifications for the acceptance of arguments. Discussion games and dispute trees are established methods to obtain such a justification. However, so far these techniques are based on instantiating the knowledge base into graph-based Dung style abstract argumentation frameworks (AFs). These instantiations are known to produce frameworks with a large number of arguments and thus also yield long discussion games and large dispute trees. To obtain more concise justifications for argument acceptance, we propose to instantiate the knowledge base as an argumentation framework with collective attacks (SETAF). Remarkably, this approach yields smaller frameworks compared to traditional AF instantiation, while exhibiting increased expressive power. We then introduce discussion games and dispute trees tailored to SETAFs, show that they correspond to credulous acceptance w.r.t. the well-known preferred semantics, analyze and tune them w.r.t. the size, and compare the two notions. Finally, we illustrate how our findings apply to assumption-based argumentation.
• Changat, Manoj ; Shanavas, Ameera Vaheeda ; Stadler, Peter F.: Transit Functions and Clustering Systems (2024)
• de Lima, Jo~ao Paulo C ; Khan, Asif Ali ; Carro, Luigi ; Castrillon, Jeronimo: Full-stack optimization for CAM-only DNN inference, arXiv (2024)

  The accuracy of neural networks has greatly improved across various domains over the past years. Their ever-increasing complexity, however, leads to prohibitively high energy demands and latency in von Neumann systems. Several computing-in-memory (CIM) systems have recently been proposed to overcome this, but trade-offs involving accuracy, hardware reliability, and scalability for large models remain a challenge. Additionally, for some CIM designs, the activation movement still requires considerable time and energy. This paper explores the combination of algorithmic optimizations for ternary weight neural networks and associative processors (APs) implemented using racetrack memory (RTM). We propose a novel compilation flow to optimize convolutions on APs by reducing their arithmetic intensity. By leveraging the benefits of RTM-based APs, this approach substantially reduces data transfers within the memory while addressing accuracy, energy efficiency, and reliability concerns. Concretely, our solution improves the energy efficiency of ResNet-18 inference on ImageNet by 7.5x compared to crossbar in-memory accelerators while retaining software accuracy.
• Derraz, Bouchra ; Breda, Gabriele ; Kaempf, Christoph ; Baenke, Franziska ; Cotte, Fabienne ; Reiche, Kristin ; Köhl, Ulrike ; Kather, Jakob Nikolas ; Eskenazy, Deborah ; et al.: New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology. In: npj Precision Oncology vol. 8 (2024), Nr. 1, p. 23

  Until recently the application of artificial intelligence (AI) in precision oncology was confined to activities in drug development and had limited impact on the personalisation of therapy. Now, a number of approaches have been proposed for the personalisation of drug and cell therapies with AI applied to therapy design, planning and delivery at the patient’s bedside. Some drug and cell-based therapies are already tuneable to the individual to optimise efficacy, to reduce toxicity, to adapt the dosing regime, to design combination therapy approaches and, preclinically, even to personalise the receptor design of cell therapies. Developments in AI-based healthcare are accelerating through the adoption of foundation models, and generalist medical AI models have been proposed. The application of these approaches in therapy design is already being explored and realistic short-term advances include the application to the personalised design and delivery of drugs and cell therapies. With this pace of development, the limiting step to adoption will likely be the capacity and appropriateness of regulatory frameworks. This article explores emerging concepts and new ideas for the regulation of AI-enabled personalised cancer therapies in the context of existing and in development governance frameworks.
• Dietrich, Konstantin ; Prager, Raphael Patrick ; Doerr, Carola ; Trautmann, Heike: Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization (2024)
• Dietrich, Konstantin ; Vermetten, Diederick ; Doerr, Carola ; Kerschke, Pascal: Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization (2024)
• Dimopoulos, Yannis ; Dvorak, Wolfgang ; König, Matthias ; Rapberger, Anna ; Ulbricht, Markus ; Woltran, Stefan: Redefining ABA+ semantics via abstract set-to-set attacks. In: Proc. Conf. AAAI Artif. Intell. vol. 38, Association for the Advancement of Artificial Intelligence (AAAI) (2024), Nr. 9, pp. 10493–10500

  Assumption-based argumentation (ABA) is a powerful defeasible reasoning formalism which is based on the interplay of assumptions, their contraries, and inference rules. ABA with preferences (ABA+) generalizes the basic model by allowing qualitative comparison between assumptions. The integration of preferences however comes with a cost. In ABA+, the evaluation under two central and well-established semantics---grounded and complete semantics---is not guaranteed to yield an outcome. Moreover, while ABA frameworks without preferences allow for a graph-based representation in Dung-style frameworks, an according instantiation for general ABA+ frameworks has not been established so far. In this work, we tackle both issues: First, we develop a novel abstract argumentation formalism based on set-to-set attacks. We show that our so-called Hyper Argumentation Frameworks (HYPAFs) capture ABA+. Second, we propose relaxed variants of complete and grounded semantics for HYPAFs that yield an extension for all frameworks by design, while still faithfully generalizing the established semantics of Dung-style Argumentation Frameworks. We exploit the newly established correspondence between ABA+ and HYPAFs to obtain variants for grounded and complete ABA+ semantics that are guaranteed to yield an outcome. Finally, we discuss basic properties and provide a complexity analysis. Along the way, we settle the computational complexity of several ABA+ semantics.
• Dvov rák, Wolfgang ; König, Matthias ; Ulbricht, Markus ; Woltran, Stefan: Principles and their computational consequences for argumentation frameworks with collective attacks. In: J. Artif. Intell. Res. vol. 79, AI Access Foundation (2024), pp. 69–136

  Argumentation frameworks (AFs) are a key formalism in AI research. Their semantics have been investigated in terms of principles, which define characteristic properties in order to deliver guidance for analyzing established and developing new semantics. Because of the simple structure of AFs, many desired properties hold almost trivially, at the same time hiding interesting concepts behind syntactic notions. We extend the principle-based approach to argumentation frameworks with collective attacks (SETAFs) and provide a comprehensive overview of common principles for their semantics. Our analysis shows that investigating principles based on decomposing the given SETAF (e.g. directionality or SCC-recursiveness) poses additional challenges in comparison to usual AFs. We introduce the notion of the reduct as well as the modularization principle for SETAFs which will prove beneficial for this kind of investigation. We then demonstrate how our findings can be utilized for incremental computation of extensions and show how we can use graph properties of the frameworks to speed up these algorithms.
• Ebel, Patrick: Generative AI and Attentive User Interfaces: Five Strategies to Enhance Take-Over Quality in Automated Driving (2024)
• Ebel, Patrick ; Goodridge, Courtney M ; Srinivasan, Aravinda Ramakrishnan ; Colley, Mark ; Sandhaus, Hauke ; Hock, Philipp ; Wintersberger, Philipp ; Janssen, Christian ; Bazilinskyy, Pavlo: Changing Lanes Toward Open Science: Openness and Transparency in Automotive User Research, OSF (2024)
• Eckardt, Jan-Niklas ; Hahn, Waldemar ; Röllig, Christoph ; Stasik, Sebastian ; Platzbecker, Uwe ; Müller-Tidow, Carsten ; Serve, Hubert ; Baldus, Claudia D. ; Schliemann, Christoph ; et al.: Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence. In: npj Digital Medicine vol. 7 (2024), Nr. 1, p. 76

  Clinical research relies on high-quality patient data, however, obtaining big data sets is costly and access to existing data is often hindered by privacy and regulatory concerns. Synthetic data generation holds the promise of effectively bypassing these boundaries allowing for simplified data accessibility and the prospect of synthetic control cohorts. We employed two different methodologies of generative artificial intelligence – CTAB-GAN+ and normalizing flows (NFlow) – to synthesize patient data derived from 1606 patients with acute myeloid leukemia, a heterogeneous hematological malignancy, that were treated within four multicenter clinical trials. Both generative models accurately captured distributions of demographic, laboratory, molecular and cytogenetic variables, as well as patient outcomes yielding high performance scores regarding fidelity and usability of both synthetic cohorts (n = 1606 each). Survival analysis demonstrated close resemblance of survival curves between original and synthetic cohorts. Inter-variable relationships were preserved in univariable outcome analysis enabling explorative analysis in our synthetic data. Additionally, training sample privacy is safeguarded mitigating possible patient re-identification, which we quantified using Hamming distances. We provide not only a proof-of-concept for synthetic data generation in multimodal clinical data for rare diseases, but also full public access to synthetic data sets to foster further research.
• Eichholz, Hannah Marie ; Cornelis, Alissa ; Wolf, Benjamin ; Grubitzsch, Hanna ; Friedrich, Philip ; Makky, Ahmad ; Aktas, Bahriye ; Käs, Josef Alfons ; Stepan, Holger: Anatomy of the fetal membranes: insights from spinning disk confocal microscopy. In: Arch. Gynecol. Obstet. vol. 309 (2024), Nr. 5, pp. 1919–1923

  PURPOSE: The fetal membranes are essential for the maintenance of pregnancy, and their integrity until parturition is critical for both fetal and maternal health. Preterm premature rupture of the membranes (pPROM) is known to be an indicator of preterm birth, but the underlying architectural and mechanical changes that lead to fetal membrane failure are not yet fully understood. The aim of this study was to gain new insights into the anatomy of the fetal membrane and to establish a tissue processing and staining protocol suitable for future prospective cohort studies. METHODS: In this proof of principle study, we collected fetal membranes from women undergoing vaginal delivery or cesarean section. Small membrane sections were then fixed, stained for nucleic acids, actin, and collagen using fluorescent probes, and subsequently imaged in three dimensions using a spinning disk confocal microscope. RESULTS: Four fetal membranes of different types were successfully processed and imaged after establishing a suitable protocol. Cellular and nuclear outlines are clearly visible in all cases, especially in the uppermost membrane layer. Focal membrane (micro) fractures could be identified in several samples. CONCLUSION: The presented method proves to be well suited to determine whether and how the occurrence of membrane (micro) fractures and cellular jamming correlate with the timing of membrane rupture and the mode of delivery. In future measurements, this method could be combined with mechanical probing techniques to compare optical and mechanical sample information.
• Eidi, Marzieh ; Mukherjee, Sayan: Higher Order Bipartiteness vs Bi-Partitioning in Simplicial Complexes (2024)
• Eisenhauer, Nico ; Frank, Karin ; Weigelt, Alexandra ; Bartkowski, Bartosz ; Beugnon, Rémy ; Liebal, Katja ; Mahecha, Miguel ; Quaas, Martin ; Al-Halbouni, Djamil ; et al.: A belowground perspective on the nexus between biodiversity change, climate change, and human well-being. In: Journal of Sustainable Agriculture and Environment vol. 3 (2024), Nr. 2, p. e212108

  Abstract Soil is central to the complex interplay among biodiversity, climate, and society. This paper examines the interconnectedness of soil biodiversity, climate change, and societal impacts, emphasizing the urgent need for integrated solutions. Human-induced biodiversity loss and climate change intensify environmental degradation, threatening human well-being. Soils, rich in biodiversity and vital for ecosystem function regulation, are highly vulnerable to these pressures, affecting nutrient cycling, soil fertility, and resilience. Soil also crucially regulates climate, influencing energy, water cycles, and carbon storage. Yet, climate change poses significant challenges to soil health and carbon dynamics, amplifying global warming. Integrated approaches are essential, including sustainable land management, policy interventions, technological innovations, and societal engagement. Practices like agroforestry and organic farming improve soil health and mitigate climate impacts. Effective policies and governance are crucial for promoting sustainable practices and soil conservation. Recent technologies aid in monitoring soil biodiversity and implementing sustainable land management. Societal engagement, through education and collective action, is vital for environmental stewardship. By prioritizing interdisciplinary research and addressing key frontiers, scientists can advance understanding of the soil biodiversity–climate change–society nexus, informing strategies for environmental sustainability and social equity.
• Elstner, Theresa ; Hanle, Bärbel ; Loebe, Frank ; Fröbe, Maik ; Kolyada, Nikolay ; Mohr, Janis ; Frochte, Jörg ; Hofmann, Sven ; Stein, Benno ; et al.: Classification of shared tasks used in teaching.. In: Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1. Milan Italy : ACM, 2024
• Ewald, Jan ; He, Ziyang ; Dimitriew, Wassili ; Schuster, Stefan: Including glutamine in a resource allocation model of energy metabolism in cancer and yeast cells. In: npj Systems Biology and Applications vol. 10, Springer Science and Business Media LLC (2024), Nr. 1
• Farzana, Sheikh Mastura ; Fröbe, Maik ; Granitzer, Michael ; Hendriksen, Gijs ; Hiemstra, Djoerd ; Potthast, Martin ; Zerhoudi, Saber: The First International Workshop on Open Web Search (WOWS).. In: Advances in Information Retrieval : Springer Nature Switzerland, 2024 — ISBN 9783031560699, pp. 426–431
• Farzana, Sheikh Mastura ; Fröbe, Maik ; Granitzer, Michael ; Hendriksen, Gijs ; Hiemstra, Djoerd ; Potthast, Martin ; Zerhoudi, Saber: The First International Workshop on Open Web Search (WOWS).. In: Goharian, N. ; Tonellotto, N. ; He, Y. ; Lipani, A. ; McDonald, G. ; Macdonald, C. ; Ounis, I. (eds.): Advances in Information Retrieval. Cham : Springer Nature Switzerland, 2024 — ISBN 978-3-031-56069-9, pp. 426–431

  We organize the first international Workshop on Open Web Search (WOWS) at ECIR 2024 with two calls for contributions. The first call targets scientific contributions on cooperative search engine development.This includes cooperative crawling of the web and cooperative deployment and evaluation of search engines. We specifically highlight the potential of enabling public and commercial organizations to use an indexed web crawl as a resource to create innovative search engines tailored to specific user groups, instead of relying on one search engine provider. The second call aims at gaining practical experience with joint, cooperative evaluation of search engine prototypes and their components using the Information Retrieval Experiment Platform (TIREx).
• Farzaneh, Hamid ; De Lima, Joao Paulo Cardoso ; Li, Mengyuan ; Khan, Asif Ali ; Hu, Xiaobo Sharon ; Castrillon, Jeronimo: C4CAM: A compiler for CAM-based in-memory accelerators.. In: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3. La Jolla CA USA : ACM, 2024
• Farzaneh, Hamid ; Lima, Jo~ao Paulo Cardoso De ; Khelejani, Ali Nezhadi ; Khan, Asif Ali ; Mayahinia, Mahta ; Tahoori, Mehdi ; Castrillon, Jeronimo: SHERLOCK: Scheduling Efficient and Reliable Bulk Bitwise Operations in NVMs.. In: Proceedings of the 61th Annual Design Automation Conference (DAC’24), DAC ’24. San Francisco, California : ACM, 2024
• Flegel, Thomas ; Neumann, Anja ; Holst, Anna-Lena ; Kretzschmann, Olivia ; Loderstedt, Shenja ; Tästensen, Carina ; Gutmann, Sarah ; Dietzel, Josephine ; Becker, Lisa Franziska ; et al.: Machine learning algorithms predict canine structural epilepsy with high accuracy. In: Frontiers in Veterinary Science vol. 11 (2024)

  Clinical reasoning in veterinary medicine is often based on clinicians’ personal experience in combination with information derived from publications describing cohorts of patients. Studies on the use of scientific methods for patient individual decision making are largely lacking. This applies to the prediction of the individual underlying pathology in seizuring dogs as well. The aim of this study was to apply machine learning to the prediction of the risk of structural epilepsy in dogs with seizures.

  Dogs with a history of seizures were retrospectively as well as prospectively included. Data about clinical history, neurological examination, diagnostic tests performed as well as the final diagnosis were collected. For data analysis, the Bayesian Network and Random Forest algorithms were used. A total of 33 features for Random Forest and 17 for Bayesian Network were available for analysis. The following four feature selection methods were applied to select features for further analysis: Permutation Importance, Forward Selection, Random Selection and Expert Opinion. The two algorithms Bayesian Network and Random Forest were trained to predict structural epilepsy using the selected features.

  A total of 328 dogs of 119 different breeds were identified retrospectively between January 2017 and June 2021, of which 33.2% were diagnosed with structural epilepsy. An overall of 89,848 models were trained. The Bayesian Network in combination with the Random feature selection performed best. It was able to predict structural epilepsy with an accuracy of 0.969 (sensitivity: 0.857, specificity: 1.000) among all dogs with seizures using the following features: age at first seizure, cluster seizures, seizure in last 24 h, seizure in last 6 month, and seizure in last year.

  Machine learning algorithms such as Bayesian Networks and Random Forests identify dogs with structural epilepsy with a high sensitivity and specificity. This information could provide some guidance to clinicians and pet owners in their clinical decision-making process.

• Freiberger, Vincent ; Buchmann, Erik: Fairness certification for natural Language Processing and Large Language Models, arXiv (2024)

  Natural Language Processing (NLP) plays an important role in our daily lives, particularly due to the enormous progress of Large Language Models (LLM). However, NLP has many fairness-critical use cases, e.g., as an expert system in recruitment or as an LLM-based tutor in education. Since NLP is based on human language, potentially harmful biases can diffuse into NLP systems and produce unfair results, discriminate against minorities or generate legal issues. Hence, it is important to develop a fairness certification for NLP approaches. We follow a qualitative research approach towards a fairness certification for NLP. In particular, we have reviewed a large body of literature on algorithmic fairness, and we have conducted semi-structured expert interviews with a wide range of experts from that area. We have systematically devised six fairness criteria for NLP, which can be further refined into 18 sub-categories. Our criteria offer a foundation for operationalizing and testing processes to certify fairness, both from the perspective of the auditor and the audited organization.
• Freiberger, Vincent ; Buchmann, Erik: Legally binding but unfair? Towards assessing fairness of privacy policies, arXiv (2024)

  Privacy policies are expected to inform data subjects about their data protection rights and should explain the data controller's data management practices. Privacy policies only fulfill their purpose, if they are correctly interpreted, understood, and trusted by the data subject. This implies that a privacy policy is written in a fair way, e.g., it does not use polarizing terms, does not require a certain education, or does not assume a particular social background. We outline our approach to assessing fairness in privacy policies. We identify from fundamental legal sources and fairness research, how the dimensions informational fairness, representational fairness and ethics / morality are related to privacy policies. We propose options to automatically assess policies in these fairness dimensions, based on text statistics, linguistic methods and artificial intelligence. We conduct initial experiments with German privacy policies to provide evidence that our approach is applicable. Our experiments indicate that there are issues in all three dimensions of fairness. This is important, as future privacy policies may be used in a corpus for legal artificial intelligence models.
• Fröbe, Maik ; Mackenzie, Joel ; Mitra, Bhaskar ; Nardini, Franco Maria ; Potthast, Martin: ReNeuIR at SIGIR 2024: The third workshop on reaching efficiency in neural information retrieval.. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. vol. 31. Washington DC USA : ACM, 2024, pp. 3051–3054
• Fröbe, Maik ; Reimer, Jan Heinrich ; MacAvaney, Sean ; Deckers, Niklas ; Reich, Simon ; Bevendorff, Janek ; Stein, Benno ; Hagen, Matthias ; Potthast, Martin: The information retrieval experiment platform (extended abstract).. In: Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence. Jeju, South Korea : International Joint Conferences on Artificial Intelligence Organization, 2024

  We have built TIREx, the information retrieval experiment platform, to promote standardized, reproducible, scalable, and blinded retrieval experiments. Standardization is achieved through integration with PyTerrier's interfaces and compatibility with ir_datasets and ir_measures. Reproducibility and scalability are based on the underlying TIRA framework, which runs dockerized software in a cloud-native execution environment. Using Docker images of 50 standard retrieval approaches, we evaluated all of them on 32 tasks (i.e., 1,600 runs) in less than a week on a midsize cluster (1,620 CPU cores and 24 GPUs), demonstrating multi-task scalability. Importantly, TIRA also enables blind evaluation of AI experiments, as the test data can be hidden from public access and the tested approaches run in a sandbox that prevents data leaks. Keeping the test data hidden from public access ensures that it cannot be used by third parties for LLM training, preventing future training-test leaks.
• Fröbe, Maik ; Scells, Harrisen ; Elstner, Theresa ; Akiki, Christopher ; Gienapp, Lukas ; Reimer, Jan Heinrich ; MacAvaney, Sean ; Stein, Benno ; Hagen, Matthias ; et al.: Resources for combining teaching and research in information retrieval coursework.. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. vol. 13. Washington DC USA : ACM, 2024, pp. 1115–1125
• Gohsen, Marcel ; Hagen, Matthias ; Potthast, Martin ; Stein, Benno: Task-Oriented Paraphrase Analytics, arXiv (2024)

  Since paraphrasing is an ill-defined task, the term ``paraphrasing'' covers text transformation tasks with different characteristics. Consequently, existing paraphrasing studies have applied quite different (explicit and implicit) criteria as to when a pair of texts is to be considered a paraphrase, all of which amount to postulating a certain level of semantic or lexical similarity. In this paper, we conduct a literature review and propose a taxonomy to organize the 25~identified paraphrasing (sub-)tasks. Using classifiers trained to identify the tasks that a given paraphrasing instance fits, we find that the distributions of task-specific instances in the known paraphrase corpora vary substantially. This means that the use of these corpora, without the respective paraphrase conditions being clearly defined (which is the normal case), must lead to incomparable and misleading results.
• González Laffitte, Marcos E ; Stadler, Peter F: Progressive multiple alignment of graphs. In: Algorithms vol. 17, MDPI AG (2024), Nr. 3, p. 116

  The comparison of multiple (labeled) graphs with unrelated vertex sets is an important task in diverse areas of applications. Conceptually, it is often closely related to multiple sequence alignments since one aims to determine a correspondence, or more precisely, a multipartite matching between the vertex sets. There, the goal is to match vertices that are similar in terms of labels and local neighborhoods. Alignments of sequences and ordered forests, however, have a second aspect that does not seem to be considered for graph comparison, namely the idea that an alignment is a superobject from which the constituent input objects can be recovered faithfully as well-defined projections. Progressive alignment algorithms are based on the idea of computing multiple alignments as a pairwise alignment of the alignments of two disjoint subsets of the input objects. Our formal framework guarantees that alignments have compositional properties that make alignments of alignments well-defined. The various similarity-based graph matching constructions do not share this property and solve substantially different optimization problems. We demonstrate that optimal multiple graph alignments can be approximated well by means of progressive alignment schemes. The solution of the pairwise alignment problem is reduced formally to computing maximal common induced subgraphs. Similar to the ambiguities arising from consecutive indels, pairwise alignments of graph alignments require the consideration of ambiguous edges that may appear between alignment columns with complementary gap patterns. We report a simple reference implementation in Python/NetworkX intended to serve as starting point for further developments. The computational feasibility of our approach is demonstrated on test sets of small graphs that mimimc in particular applications to molecular graphs.
• González Laffitte, Marcos E. ; Stadler, Peter F.: Progressive Multiple Alignment of Graphs. In: Algorithms vol. 17 (2024), Nr. 3

  The comparison of multiple (labeled) graphs with unrelated vertex sets is an important task in diverse areas of applications. Conceptually, it is often closely related to multiple sequence alignments since one aims to determine a correspondence, or more precisely, a multipartite matching between the vertex sets. There, the goal is to match vertices that are similar in terms of labels and local neighborhoods. Alignments of sequences and ordered forests, however, have a second aspect that does not seem to be considered for graph comparison, namely the idea that an alignment is a superobject from which the constituent input objects can be recovered faithfully as well-defined projections. Progressive alignment algorithms are based on the idea of computing multiple alignments as a pairwise alignment of the alignments of two disjoint subsets of the input objects. Our formal framework guarantees that alignments have compositional properties that make alignments of alignments well-defined. The various similarity-based graph matching constructions do not share this property and solve substantially different optimization problems. We demonstrate that optimal multiple graph alignments can be approximated well by means of progressive alignment schemes. The solution of the pairwise alignment problem is reduced formally to computing maximal common induced subgraphs. Similar to the ambiguities arising from consecutive indels, pairwise alignments of graph alignments require the consideration of ambiguous edges that may appear between alignment columns with complementary gap patterns. We report a simple reference implementation in Python/NetworkX intended to serve as starting point for further developments. The computational feasibility of our approach is demonstrated on test sets of small graphs that mimimc in particular applications to molecular graphs.
• Guzman, Grover E. C. ; Stadler, Peter F. ; Fujita, Andre: Cavity approach for the approximation of spectral density of graphs with heterogeneous structures. In: Phys. Rev. E vol. 109, American Physical Society (2024), Nr. 3, p. 034303
• Haase, Robert ; Tischer, Christian ; H�rich�, Jean-Karim ; Scherf, Nico: Benchmarking Large Language Models for Bio-Image Analysis Code Generation, Cold Spring Harbor Laboratory (2024)
• Hannemann, Anika ; Ewald, Jan ; Seeger, Leo ; Buchmann, Erik: Federated learning on transcriptomic data: Model quality and performance trade-offs, arXiv (2024)

  Machine learning on large-scale genomic or transcriptomic data is important for many novel health applications. For example, precision medicine tailors medical treatments to patients on the basis of individual biomarkers, cellular and molecular states, etc. However, the data required is sensitive, voluminous, heterogeneous, and typically distributed across locations where dedicated machine learning hardware is not available. Due to privacy and regulatory reasons, it is also problematic to aggregate all data at a trusted third party.Federated learning is a promising solution to this dilemma, because it enables decentralized, collaborative machine learning without exchanging raw data. In this paper, we perform comparative experiments with the federated learning frameworks TensorFlow Federated and Flower. Our test case is the training of disease prognosis and cell type classification models. We train the models with distributed transcriptomic data, considering both data heterogeneity and architectural heterogeneity. We measure model quality, robustness against privacy-enhancing noise, computational performance and resource overhead. Each of the federated learning frameworks has different strengths. However, our experiments confirm that both frameworks can readily build models on transcriptomic data, without transferring personal raw data to a third party with abundant computational resources.
• Heine, Anne-Marie ; Ulbricht, Markus: From Acceptance to Rejection in Abstract Argumentation.. In: Proc. KR, 2024
• Heins, Jonathan ; Schäpermeier, Lennart ; Kerschke, Pascal ; Whitley, Darrell: Dancing to the State of the Art? How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem (2024)
• Hellmuth, Marc ; Korchmaros, Annachiara ; Ram’irez-Rafael, José Antonio ; Schmidt, Bruno ; Stadler, Peter F. ; Thekkumpadan Puthiyaveedu, Sandhya: Partial Fitch Graphs: Characterization, Satisfiability and Complexity*. In: bioRxiv, Cold Spring Harbor Laboratory (2024)

  Horizontal gene transfer is an important contributor to evolution. Following Walter M. Fitch, two genes are xenologs if at least one HGT separates them. More formally, the directed Fitch graph has a set of genes as its vertices, and directed edges (x, y) for all pairs of genes x and y for which y has been horizontally transferred at least once since it diverged from the last common ancestor of x and y. Subgraphs of Fitch graphs can be inferred by comparative sequence analysis. In many cases, however, only partial knowledge about the textquotedblleftfulltextquotedblright Fitch graph can be obtained. Here, we characterize Fitch-satisfiable graphs that can be extended to a biologically feasible textquotedblleftfulltextquotedblright Fitch graph and derive a simple polynomial-time recognition algorithm. We then proceed to show that several versions of finding the Fitch graph with total maximum (confidence) edge-weights are NP-hard. In addition, we provide a greedy-heuristic for textquotedblleftoptimallytextquotedblright recovering Fitch graphs from partial ones. Somewhat surprisingly, even if ~ 80% of information of the underlying input Fitch-graph G is lost (i.e., the partial Fitch graph contains only ~ 20% of the edges of G), it is possible to recover ~ 90% of the original edges of G on average.Competing Interest StatementThe authors have declared no competing interest.
• Hendriksen, Gijs ; Dinzinger, Michael ; Farzana, Sheikh Mastura ; Fathima, Noor Afshan ; Fröbe, Maik ; Schmidt, Sebastian ; Zerhoudi, Saber ; Granitzer, Michael ; Hagen, Matthias ; et al.: The Open Web Index: Crawling and Indexing the Web for Public Use.. In: Advances in Information Retrieval. 46th European Conference on IR Research (ECIR 2024), Lecture Notes in Computer Science. Berlin Heidelberg New York : Springer, 2024
• Herrmann, Luisa ; Peth, Vincent ; Rudolph, Sebastian: Decidable (Ac)counting with Parikh and Muller: Adding Presburger Arithmetic to Monadic Second-Order Logic over Tree-Interpretable Structures.. In: Murano, A. ; Silva, A. (eds.): 32nd EACSL Annual Conference on Computer Science Logic (CSL 2024), Leibniz International Proceedings in Informatics (LIPIcs). vol. 288. Dagstuhl, Germany : Schloss Dagstuhl -- Leibniz-Zentrum für Informatik, 2024 — ISBN 978-3-95977-310-2, pp. 33:1–33:19
• Hoffmann, Katja ; Nesterow, Igor ; Peng, Yuan ; Henke, Elisa ; Barnett, Daniela ; Klengel, Cigdem ; Gruhl, Mirko ; Bartos, Martin ; Nüßler, Frank ; et al.: Streamlining intersectoral provision of real-world health data: a service platform for improved clinical research and patient care. In: Frontiers in Medicine vol. 11 (2024)

  textlesssectextgreatertextlesstitletextgreaterIntroductiontextless/titletextgreatertextlessptextgreaterObtaining real-world data from routine clinical care is of growing interest for scientific research and personalized medicine. Despite the abundance of medical data across various facilities — including hospitals, outpatient clinics, and physician practices — the intersectoral exchange of information remains largely hindered due to differences in data structure, content, and adherence to data protection regulations. In response to this challenge, the Medical Informatics Initiative (MII) was launched in Germany, focusing initially on university hospitals to foster the exchange and utilization of real-world data through the development of standardized methods and tools, including the creation of a common core dataset. Our aim, as part of the Medical Informatics Research Hub in Saxony (MiHUBx), is to extend the MII concepts to non-university healthcare providers in a more seamless manner to enable the exchange of real-world data among intersectoral medical sites.textless/ptextgreatertextless/sectextgreatertextlesssectextgreatertextlesstitletextgreaterMethodstextless/titletextgreatertextlessptextgreaterWe investigated what services are needed to facilitate the provision of harmonized real-world data for cross-site research. On this basis, we designed a Service Platform Prototype that hosts services for data harmonization, adhering to the globally recognized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) international standard communication format and the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Leveraging these standards, we implemented additional services facilitating data utilization, exchange and analysis. Throughout the development phase, we collaborated with an interdisciplinary team of experts from the fields of system administration, software engineering and technology acceptance to ensure that the solution is sustainable and reusable in the long term.textless/ptextgreatertextless/sectextgreatertextlesssectextgreatertextlesstitletextgreaterResultstextless/titletextgreatertextlessptextgreaterWe have developed the pre-built packages “ResearchData-to-FHIR,” “FHIR-to-OMOP,” and “Addons,” which provide the services for data harmonization and provision of project-related real-world data in both the FHIR MII Core dataset format (CDS) and the OMOP CDM format as well as utilization and a Service Platform Prototype to streamline data management and use.textless/ptextgreatertextless/sectextgreatertextlesssectextgreatertextlesstitletextgreaterConclusiontextless/titletextgreatertextlessptextgreaterOur development shows a possible approach to extend the MII concepts to non-university healthcare providers to enable cross-site research on real-world data. Our Service Platform Prototype can thus pave the way for intersectoral data sharing, federated analysis, and provision of SMART-on-FHIR applications to support clinical decision making.textless/ptextgreatertextless/sectextgreater
• Hoffmann, Mario ; Buchmann, Erik: ChatSEC: Spicing up Vulnerability Scans with AI for Heterogeneous University IT - Towards Enhancing Security Vulnerability Reports for Non-Experts.. In: Proceedings of the 1st International Conference on AI-based Systems and Services (AISyS’24), 2024
• Hoffmann, Mario ; Buchmann, Erik: ChatSEC - Towards Enhancing Security Vulnerability Reports for Non-Experts.. In: Proceedings of the 11th Workshop ``Mensch-Maschine-Interaktion in sicherheitskritischen Systemen’’ - Mensch und Computer (MuC’24), 2024
• Hummel, Sandra ; Aldrian, Sarah ; Sheehan, Bridget: Culture, Education and Digital Transformation in Sri Lanka.. In: Hummel, S. (ed.): Empowering Education in Cambodia and Sri Lanka: Quality Improvement in Teaching and Learning in the 21st Century. Wiesbaden : Springer Fachmedien Wiesbaden, 2024 — ISBN 978-3-658-43306-2, pp. 9–29

  This article provides a multifaceted examination of Sri Lanka's rich socio-cultural history, current socio-economic challenges, and the pivotal role of education, particularly focusing on teacher education. The text begins by exploring Sri Lanka's intricate history and cultural diversity, reflecting the influences from various ethnic communities and colonial rule. It then navigates through the country's recent developments, including the brutal civil war, economic crisis, and Covid-19 pandemic. In the educational landscape, the article delves into the structure and content of Sri Lanka's educational system, highlighting the commitment to universal and free education, a 93% literacy rate, and the diverse types of schools, but also pointing out disparities between urban and rural areas and gender biases. Special emphasis is placed on the structure, challenges, and potential reforms within teacher education. This includes detailed insights into pre-service and in-service teacher training, the unique challenges in peripheral areas, the need for innovative pedagogical strategies, and the role of various governmental and educational entities. The article concludes by synthesizing the insights into a call for a comprehensive, context-specific, and adaptive approach towards teacher education, aiming to create a robust and equitable system aligned with the needs of 21st-century learners. The analysis integrates historical, cultural, educational, and economic perspectives to provide a cohesive understanding of Sri Lanka's complex educational landscape and offers targeted recommendations for transformative change.
• Hummel, Sandra ; Bohlinger, Sandra ; Sheehan, Bridget ; Hummel, S. (ed.): Empowering Education in Cambodia and Sri Lanka: Quality Improvement in Teaching and Learning in the 21st Century. Wiesbaden : Springer Fachmedien Wiesbaden, 2024 — ISBN 978-3-658-43306-2

  This article discusses the role of the EU-funded project `CONTESSA – Contemporary teaching skills' (https://contessa-project.eu) in modernizing teacher training methodologies in Cambodia and Sri Lanka. Focusing on the pressing need to adapt to 21st-century teaching and learning paradigms, CONTESSA's design reflects an understanding of the technological disparities across these countries. While acknowledging the resource constraints, the program centers its e-learning curriculum on empowering teachers to enhance lesson planning and teaching quality. The program introduces multiple modules, each targeting specific pedagogical skills ranging from digital teaching, learner-centric approaches, embracing classroom diversity, to understanding gender-based nuances in education. Additionally, the article underscores the significance of training teacher trainers to ensure the program's sustainability, detailing a comprehensive Train-the-Trainer initiative that encompasses face-to-face workshops, tutorials, and a user manual. Derived from a four-year-long project, findings indicate the potential of the CONTESSA program in fostering a shift towards widely accessible teacher training, aiming to equip educators with crucial 21st-century teaching skills and promote holistic student learning. The program's broader vision emphasizes a collaborative community of practice, ensuring the professional growth of educators and impactful learning experiences for students.
• Ionescu, Bogdan ; Müller, Henning ; Druagulinescu, Ana Maria ; Idrissi-Yaghir, Ahmad ; Radzhabov, Ahmedkhan ; Herrera, Alba Garcia Seco de ; Andrei, Alexandra ; Stan, Alexandru ; Storaas, Andrea M. ; et al.: Advancing Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications with ImageCLEF 2024.. In: Goharian, N. ; Tonellotto, N. ; He, Y. ; Lipani, A. ; McDonald, G. ; Macdonald, C. ; Ounis, I. (eds.): Advances in Information Retrieval. Cham : Springer Nature Switzerland, 2024 — ISBN 978-3-031-56072-9, pp. 44–52

  The ImageCLEF evaluation campaign was integrated with CLEF (Conference and Labs of the Evaluation Forum) for more than 20 years and represents a Multimedia Retrieval challenge aimed at evaluating the technologies for annotation, indexing, and retrieval of multimodal data. Thus, it provides information access to large data collections in usage scenarios and domains such as medicine, argumentation and content recommendation. ImageCLEF 2024 has four main tasks: (i) a Medical task targeting automatic image captioning for radiology images, synthetic medical images created with Generative Adversarial Networks (GANs), Visual Question Answering and medical image generation based on text input, and multimodal dermatology response generation; (ii) a joint ImageCLEF-Touché task Image Retrieval/Generation for Arguments to convey the premise of an argument, (iii) a Recommending task addressing cultural heritage content-recommendation, and (iv) a joint ImageCLEF-ToPicto task aiming to provide a translation in pictograms from natural language. In 2023, participation increased by 67% with respect to 2022 which reveals its impact on the community.
• Jüttner, Victor ; Fleig, Arthur ; Buchmann, Erik: ChatAnalysis: Can GPT-4 Undermine Privacy in Smart Homes with Data Analysis?.. In: Proceedings of the 11th Workshop ``Mensch-Maschine-Interaktion in sicherheitskritischen Systemen’’ - Mensch und Computer (MuC’24), 2024
• Khan, Asif Ali ; De Lima, Jo~ao Paulo C ; Farzaneh, Hamid ; Castrillon, Jeronimo: The landscape of compute-near-memory and compute-in-memory: A research and commercial overview, arXiv (2024)

  In today's data-centric world, where data fuels numerous application domains, with machine learning at the forefront, handling the enormous volume of data efficiently in terms of time and energy presents a formidable challenge. Conventional computing systems and accelerators are continually being pushed to their limits to stay competitive. In this context, computing near-memory (CNM) and computing-in-memory (CIM) have emerged as potentially game-changing paradigms. This survey introduces the basics of CNM and CIM architectures, including their underlying technologies and working principles. We focus particularly on CIM and CNM architectures that have either been prototyped or commercialized. While surveying the evolving CIM and CNM landscape in academia and industry, we discuss the potential benefits in terms of performance, energy, and cost, along with the challenges associated with these cutting-edge computing paradigms.
• Kiesel, Johannes ; cCöltekin, cCaugri ; Heinrich, Maximilian ; Fröbe, Maik ; Alshomary, Milad ; De Longueville, Bertrand ; Erjavec, Tomavz ; Handke, Nicolas ; Kopp, Matyávs ; et al.: Overview of Touché 2024: Argumentation Systems.. In: Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part V. Glasgow, United Kingdom : Springer-Verlag, 2024 — ISBN 978-3-031-56068-2, pp. 466–473

  Decision-making and opinion-forming are everyday tasks that involve weighing pro and con arguments. The goal of Touché is to foster the development of support-technologies for decision-making and opinion-forming and to improve our understanding of these processes. This fifth edition of the lab features three shared tasks: (1) Human value detection (ValueEval), where participants detect (implicit) references to human values and their attainment in text; (2) Multilingual Ideology and Power Identification in Parliamentary Debates, where participants identify from a speech the political leaning of the speaker’s party and whether it was governing at the time of the speech (new task); and (3) Image retrieval or generation in order to convey the premise of an argument with visually. In this paper, we briefly describe the planned setup for the fifth lab edition at CLEF 2024 and summarize the results of the 2023 edition.
• Lehtonen, Tuomo ; Rapberger, Anna ; Toni, Francesca ; Ulbricht, Markus ; Wallner, Johannes P: Instantiations and computational aspects of non-flat assumption-based argumentation, arXiv (2024)

  Most existing computational tools for assumption-based argumentation (ABA) focus on so-called flat frameworks, disregarding the more general case. In this paper, we study an instantiation-based approach for reasoning in possibly non-flat ABA. We make use of a semantics-preserving translation between ABA and bipolar argumentation frameworks (BAFs). By utilizing compilability theory, we establish that the constructed BAFs will in general be of exponential size. In order to keep the number of arguments and computational cost low, we present three ways of identifying redundant arguments. Moreover, we identify fragments of ABA which admit a poly-sized instantiation. We propose two algorithmic approaches for reasoning in possibly non-flat ABA. The first approach utilizes the BAF instantiation while the second works directly without constructing arguments. An empirical evaluation shows that the former outperforms the latter on many instances, reflecting the lower complexity of BAF reasoning. This result is in contrast to flat ABA, where direct approaches dominate instantiation-based approaches.
• Lehtonen, Tuomo ; Rapberger, Anna ; Toni, Francesca ; Ulbricht, Markus ; Wallner, Johannes P.: On Computing Admissibility in ABA.. In: Proc. COMMA, 2024
• Lenhardt, J. ; Quaas, J. ; Sejdinovic, D.: Marine cloud base height retrieval from MODIS cloud properties using machine learning. In: Atmospheric Measurement Techniques (2024), p. to appear
• Lorenz, Martin ; Amorim, Tiago ; Dey, Debargha ; Sadeghi, Mersedeh ; Ebel, Patrick: Computational Models for In-Vehicle User Interface Design: A Systematic Literature Review.
• Lutz, Carsten ; Manière, Quentin: Adding Circumscription to Decidable Fragments of First-Order Logic: A Complexity Rollercoaster.
• Mackay, E. B. L. ; Murphy-Barltrop, C. J. R. ; Jonathan, P.: The SPAR Model: A New Paradigm for Multivariate Extremes: Application to Joint Distributions of Metocean Variables. In: Journal of Offshore Mechanics and Arctic Engineering vol. 147, ASME International (2024), Nr. 1
• Mehrdad, S. ; Handorf, D. ; Höschel, I. ; Karami, K. ; Quaas, J. ; Dipu, S. ; Jacobi, C.: Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach. In: EGUsphere vol. 2024 (2024), pp. 1–55
• Milosevic, Nikola ; M�ller, Gesine ; Huisken, Jan ; Scherf, Nico: Open Problem: Active Representation Learning (2024)
• Molinari, Chiara ; Solaini, Leonardo ; Rebuzzi, Francesca ; Tedaldi, Gianluca ; Angeli, Davide ; Petracci, Elisabetta ; Prascevic, Dusan ; Ewald, Jan ; Rahm, Erhard ; et al.: Genomic events stratifying prognosis of early gastric cancer. In: Gastric Cancer (2024)

  The purpose of the study was to conduct a comprehensive genomic characterization of gene alterations, microsatellite instability (MSI), and tumor mutational burden (TMB) in submucosal-penetrating (Pen) early gastric cancers (EGCs) with varying prognoses.
• Murphy-Barltrop, C. J. R. ; Wadsworth, J. L. ; Eastoe, E. F.: Improving Estimation for Asymptotically Independent Bivariate Extremes via Global Estimators for the Angular Dependence Function. In: Extremes (2024)

  Modelling the extremal dependence of bivariate variables is important in a wide variety of practical applications, including environmental planning, catastrophe modelling and hydrology. The majority of these approaches are based on the framework of bivariate regular variation, and a wide range of literature is available for estimating the dependence structure in this setting. However, such procedures are only applicable to variables exhibiting asymptotic dependence, even though asymptotic independence is often observed in practice. In this paper, we consider the so-called `angular dependence function'; this quantity summarises the extremal dependence structure for asymptotically independent variables. Until recently, only pointwise estimators of the angular dependence function have been available. We introduce a range of global estimators and compare them to another recently introduced technique for global estimation through a systematic simulation study, and a case study on river flow data from the north of England, UK.
• Murphy-Barltrop, C.J.R. ; Wadsworth, J.L.: Modelling Non-Stationarity in Asymptotically Independent Extremes. In: Computational Statistics and Data Analysis vol. 199, Elsevier BV (2024), p. 108025
• Murphy-Barltrop, Callum John Rowlandson ; Mackay, Ed ; Jonathan, Philip: Inference for Bivariate Extremes via a Semi-Parametric Angular-Radial Model. In: Extremes (2024)

  The modelling of multivariate extreme events is important in a wide variety of applications, including flood risk analysis, metocean engineering and financial modelling. A wide variety of statistical techniques have been proposed in the literature; however, many such methods are limited in the forms of dependence they can capture, or make strong parametric assumptions about data structures. In this article, we introduce a novel inference framework for bivariate extremes based on a semi-parametric angular-radial model. This model overcomes the limitations of many existing approaches and provides a unified paradigm for assessing joint tail behaviour. Alongside inferential tools, we also introduce techniques for assessing uncertainty and goodness of fit. Our proposed technique is tested on simulated data sets alongside observed metocean time series', with results indicating generally good performance.
• Murphy-Barltrop, Callum John Rowlandson ; Mackay, Ed ; Jonathan, Philip: Inference for bivariate extremes via a semi-parametric angular-radial model (2024)
• Murphy-Barltrop, Callum John Rowlandson ; Mackay, Ed ; Jonathan, Philip: Inference For Bivariate Extremes Via A Semi-Parametric Angular-Radial Model. In: Extremes (2024)

  The modelling of multivariate extreme events is important in a wide variety of applications, including flood risk analysis, metocean engineering and financial modelling. A wide variety of statistical techniques have been proposed in the literature; however, many such methods are limited in the forms of dependence they can capture, or make strong parametric assumptions about data structures. In this article, we introduce a novel inference framework for bivariate extremes based on a semi-parametric angular-radial model. This model overcomes the limitations of many existing approaches and provides a unified paradigm for assessing joint tail behaviour. Alongside inferential tools, we also introduce techniques for assessing uncertainty and goodness of fit. Our proposed technique is tested on simulated data sets alongside observed metocean time series', with results indicating generally good performance.
• Naumann, Lucas Fabian ; Irmai, Jannik ; Zhao, Shengxian ; Andres, Bjoern: Box facets and cut facets of lifted multicut polytopes, arXiv (2024)

  The lifted multicut problem is a combinatorial optimization problem whose feasible solutions relate one-to-one to the decompositions of a graph $G = (V, E)$. Given an augmentation $widehatG = (V, E cup F)$ of $G$ and given costs $c in mathbbR^E cup F$, the objective is to minimize the sum of those $c_uw$ with $uw in E cup F$ for which $u$ and $w$ are in distinct components. For $F = emptyset$, the problem specializes to the multicut problem, and for $E = tbinomV2$ to the clique partitioning problem. We study a binary linear program formulation of the lifted multicut problem. More specifically, we contribute to the analysis of the associated lifted multicut polytopes: Firstly, we establish a necessary, sufficient and efficiently decidable condition for a lower box inequality to define a facet. Secondly, we show that deciding whether a cut inequality of the binary linear program defines a facet is NP-hard.
• Nguyen Anh, Kiet ; Ulbricht, Markus: Preferred reasoning in ABA by cycle-breaking.. In: Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence. Jeju, South Korea : International Joint Conferences on Artificial Intelligence Organization, 2024

  We develop a fixed-parameter tractable (FPT) algorithm for skeptical preferred reasoning in assumption-based argumentation (ABA). To this end we make use of so-called backdoors, i.e. sets of assumptions that need to be evaluated s.t. the remaining ABA framework (ABAF) belongs to a computational beneficial sub-class. In order to identify such target classes, we employ a suitable notion of a dependency graph of an ABAF. We show that these graphs can be constructed in polynomial time and that one can efficiently check sufficient properties ensuring that reasoning in the underlying ABAF is tractable. After establishing the theoretical foundations, we test our implementation against the ASPforABA solver which convincingly won the ABA track of the ICCMA'23 competition. As it turns out, our algorithm outperforms ASPforABA on instances with small backdoor sizes.
• Niemier, Michael ; Enciso, Zephan ; Sharifi, Mohammad Mehdi ; Hu, X. Sharon ; O’Connor, Ian ; Graening, Alexander ; Sharma, Ravit ; Gupta, Puneet ; Castrillon, Jeronimo ; et al.: Smoothing Disruption Across the Stack: Tales of Memory, Heterogeneity, and Compilers.. In: Proceedings of the 2024 Design, Automation and Test in Europe Conference (DATE), DATE’24. Valencia, Spain : IEEE, 2024, pp. 1–10
• Nixon, Michelle Pistner ; McGovern, Kyle C. ; Letourneau, Jeffrey ; David, Lawrence A. ; Lazar, Nicole A. ; Mukherjee, Sayan ; Silverman, Justin D.: Scale Reliant Inference (2024)
• Olcese, Gaia ; Bates, Paul D ; Neal, Jeffrey C ; Sampson, Christopher C ; Wing, Oliver E J ; Quinn, Niall ; Murphy-Barltrop, Callum J R ; Probyn, Izzy: Developing a fluvial and pluvial stochastic flood model of Southeast Asia. In: Water Resour. Res. vol. 60, American Geophysical Union (AGU) (2024), Nr. 6

  AbstractFlood event set generation, as employed in catastrophe risk models, relies on gauge information that is not available in data‐scarce regions. To overcome this limitation, we develop a stochastic fluvial and pluvial flood model of Southeast Asia, using freely and globally available discharge data from the global hydrological model GloFAS and rainfall from the ERA5 reanalysis. We use a conditional multivariate statistical model to produce a synthetic catalog of 10,000 years of flood events. We calculate the flood population exposure associated with each flood event using freely available population data from WorldPop and generate exposure probability exceedance curves. We validate the population exposure curves against observed flood disaster data from EM‐DAT, showing that our methodology provides exposure estimates that are in line with historical observations. We find that there is a 1% probability that more than 30 million people will be exposed to flooding in a given year according to our event set. This number is roughly half the population living in the 100‐year return period flood zone of Fathom's hazard maps, suggesting most studies based on static flood maps overestimate exposure. This analysis provides significant progress over previous non‐stochastic studies which are only able to compute total or average exposure within a given floodplain area and demonstrates that a reanalysis‐based stochastic flood model can be designed to generate reliable estimates of population exposure probability exceedance. This study is a step toward a fully global catastrophe model for floods capable of providing exposure and loss estimates worldwide.
• Pahlke, Johannes ; Sbalzarini, Ivo: Proven Distributed Memory Parallelization of Particle Methods. In: ACM Trans. Parallel Comput.. New York, NY, USA, Association for Computing Machinery (2024)

  We provide a mathematically proven parallelization scheme for particle methods on distributed-memory computer systems. Particle methods are a versatile and widely used class of algorithms for computer simulations and numerical predictions in various applications, ranging from continuum fluid dynamics to discrete granular flows and molecular dynamics simulations. Particle methods naturally lend themselves to implementation on parallel computing systems. So far, however, a mathematical proof of correctness and equivalence to sequential implementations was only available for shared-memory parallelism. Here, we leverage a formal definition of the algorithmic class of particle methods to provide a proven parallelization scheme for distributed-memory computers. We prove that thus parallelized particle methods on distributed-memory computers are formally equivalent to their sequential counterpart for a well-defined class of particle methods, and we provide analytical expressions for the speed-up and scalability bounds of this class of algorithms in function of their parameters. The parallelization scheme analyzed here is the basis of many real-world software designs for parallel particle methods. The present analysis is, therefore, of direct relevance to existing and new parallel implementations of particle methods and places them on solid theoretical grounds, rationalizing best practices and providing useful scalability and speedup bounds for benchmarking.
• Parry, Andrew ; Fröbe, Maik ; MacAvaney, Sean ; Potthast, Martin ; Hagen, Matthias: Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models (2024)
• Pasche, Olivier C ; Wider, Jonathan ; Zhang, Zhongwei ; Zscheischler, Jakob ; Engelke, Sebastian: Validating deep-learning weather forecast models on recent high-impact extreme events, arXiv (2024)

  The forecast accuracy of deep-learning-based weather prediction models is improving rapidly, leading many to speak of a ``second revolution in weather forecasting''. With numerous methods being developed, and limited physical guarantees offered by deep-learning models, there is a critical need for comprehensive evaluation of these emerging techniques. While this need has been partly fulfilled by benchmark datasets, they provide little information on rare and impactful extreme events, or on compound impact metrics, for which model accuracy might degrade due to misrepresented dependencies between variables. To address these issues, we compare deep-learning weather prediction models (GraphCast, PanguWeather, FourCastNet) and ECMWF's high-resolution forecast (HRES) system in three case studies: the 2021 Pacific Northwest heatwave, the 2023 South Asian humid heatwave, and the North American winter storm in 2021. We find evidence that machine learning (ML) weather prediction models can locally achieve similar accuracy to HRES on record-shattering events such as the 2021 Pacific Northwest heatwave and even forecast the compound 2021 North American winter storm substantially better. However, extrapolating to extreme conditions may impact machine learning models more severely than HRES, as evidenced by the comparable or superior spatially- and temporally-aggregated forecast accuracy of HRES for the two heatwaves studied. The ML forecasts also lack variables required to assess the health risks of events such as the 2023 South Asian humid heatwave. Generally, case-study-driven, impact-centric evaluation can complement existing research, increase public trust, and aid in developing reliable ML weather prediction models.
• Rade, Michael ; Grieb, Nora ; Weiss, Ronald ; Sia, Jaren ; Fischer, Luise ; Born, Patrick ; Boldt, Andreas ; Fricke, Stephan ; Franz, Paul ; et al.: Single-cell multiomic dissection of response and resistance to chimeric antigen receptor T cells against BCMA in relapsed multiple myeloma. In: Nature Cancer (2024)

  Markers that predict response and resistance to chimeric antigen receptor (CAR) T cells in relapsed/refractory multiple myeloma are currently missing. We subjected mononuclear cells isolated from peripheral blood and bone marrow before and after the application of approved B cell maturation antigen-directed CAR T cells to single-cell multiomic analyses to identify markers associated with resistance and early relapse. Differences between responders and nonresponders were identified at the time of leukapheresis. Nonresponders showed an immunosuppressive microenvironment characterized by increased numbers of monocytes expressing the immune checkpoint molecule CD39 and suppressed CD8+ T cell and natural killer cell function. Analysis of CAR T cells showed cytotoxic and exhausted phenotypes in hyperexpanded clones compared to low/intermediate expanded clones. We identified potential immunotherapy targets on CAR T cells, like PD1, to improve their functionality and durability. Our work provides evidence that an immunosuppressive microenvironment causes resistance to CAR T cell therapies in multiple myeloma.
• Rade, Michael ; Kreuz, Markus ; Borkowetz, Angelika ; Sommer, Ulrich ; Blumert, Conny ; Füssel, Susanne ; Bertram, Catharina ; Löffler, Dennis ; Otto, Dominik J. ; et al.: A reliable transcriptomic risk-score applicable to formalin-fixed paraffin-embedded biopsies improves outcome prediction in localized prostate cancer. In: Molecular Medicine vol. 30 (2024), Nr. 1, p. 19

  Clinical manifestation of prostate cancer (PCa) is highly variable. Aggressive tumors require radical treatment while clinically non-significant ones may be suitable for active surveillance. We previously developed the prognostic ProstaTrend RNA signature based on transcriptome‐wide microarray and RNA-sequencing (RNA-Seq) analyses, primarily of prostatectomy specimens. An RNA-Seq study of formalin-fixed paraffin-embedded (FFPE) tumor biopsies has now allowed us to use this test as a basis for the development of a novel test that is applicable to FFPE biopsies as a tool for early routine PCa diagnostics.
• Rapberger, Anna ; Ulbricht, Markus: Repairing Assumption-Based Argumentation Frameworks.. In: Proc. KR, 2024
• Sadeghi, Mersedeh ; Pöttgen, Daniel ; Ebel, Patrick ; Vogelsang, Andreas: Explaining the Unexplainable: The Impact of Misleading Explanations on Trust in Unreliable Predictions for Hardly Assessable Tasks.. In: Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, UMAP ’24. Cagliari, Italy : Association for Computing Machinery, 2024 — ISBN 9798400704338, pp. 36–46

  To increase trust in systems, engineers strive to create explanations that are as accurate as possible. However, if the system’s accuracy is compromised, providing explanations for its incorrect behavior may inadvertently lead to misleading explanations. This concern is particularly pertinent when the correctness of the system is difficult for users to judge. In an online survey experiment with 162 participants, we analyze the impact of misleading explanations on users’ perceived and demonstrated trust in a system that performs a hardly assessable task in an unreliable manner. Participants who used a system that provided potentially misleading explanations rated their trust significantly higher than participants who saw the system’s prediction alone. They also aligned their initial prediction with the system’s prediction significantly more often. Our findings underscore the importance of exercising caution when generating explanations, especially in tasks that are inherently difficult to evaluate. The paper and supplementary materials are available at https://doi.org/10.17605/osf.io/azu72
• Salybekov, Amankeldi A. ; Wolfien, Markus ; Hahn, Waldemar ; Hidaka, Sumi ; Kobayashi, Shuzo: Artificial Intelligence Reporting Guidelines’ Adherence in Nephrology for Improved Research and Clinical Outcomes. In: Biomedicines vol. 12 (2024), Nr. 3

  The use of artificial intelligence (AI) in healthcare is transforming a number of medical fields, including nephrology. The integration of various AI techniques in nephrology facilitates the prediction of the early detection, diagnosis, prognosis, and treatment of kidney disease. Nevertheless, recent reports have demonstrated that the majority of published clinical AI studies lack uniform AI reporting standards, which poses significant challenges in interpreting, replicating, and translating the studies into routine clinical use. In response to these issues, worldwide initiatives have created guidelines for publishing AI-related studies that outline the minimal necessary information that researchers should include. By following standardized reporting frameworks, researchers and clinicians can ensure the reproducibility, reliability, and ethical use of AI models. This will ultimately lead to improved research outcomes, enhanced clinical decision-making, and better patient management. This review article highlights the importance of adhering to AI reporting guidelines in medical research, with a focus on nephrology and urology, and clinical practice for advancing the field and optimizing patient care.
• Schäpermeier, Lennart ; Kerschke, Pascal: Reinvestigating the R2 Indicator: Achieving Pareto Compliance by Integration (2024)
• Schlatt, Ferdinand ; Fröbe, Maik ; Scells, Harrisen ; Zhuang, Shengyao ; Koopman, Bevan ; Zuccon, Guido ; Stein, Benno ; Potthast, Martin ; Hagen, Matthias: Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders (2024)
• Schlatt, Ferdinand ; Fröbe, Maik ; Scells, Harrisen ; Zhuang, Shengyao ; Koopman, Bevan ; Zuccon, Guido ; Stein, Benno ; Potthast, Martin ; Hagen, Matthias: A systematic investigation of distilling large language models into cross-encoders for passage re-ranking, arXiv (2024)

  Cross-encoders distilled from large language models (LLMs) are often more effective re-rankers than cross-encoders fine-tuned on manually labeled data. However, the distilled models usually do not reach their teacher LLM's effectiveness. To investigate whether best practices for fine-tuning cross-encoders on manually labeled data (e.g., hard-negative sampling, deep sampling, and listwise loss functions) can help to improve LLM ranker distillation, we construct and release a new distillation dataset: Rank-DistiLLM. In our experiments, cross-encoders trained on Rank-DistiLLM reach the effectiveness of LLMs while being orders of magnitude more efficient. Our code and data is available at https://github.com/webis-de/msmarco-llm-distillation.
• Schmidt, Sebastian ; Zelch, Ines ; Bevendorff, Janek ; Stein, Benno ; Hagen, Matthias ; Potthast, Martin: Detecting Generated Native Ads in Conversational Search.. In: Companion Proceedings of the ACM on Web Conference 2024, WWW ’24. vol. 5 : ACM, 2024, pp. 722–725
• Schultz, Kristian ; Bej, Saptarshi ; Hahn, Waldemar ; Wolfien, Markus ; Srivastava, Prashant ; Wolkenhauer, Olaf: ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets. In: Pattern Recognition vol. 147 (2024), p. 110138

  Oversampling is commonly used to improve classifier performance for small tabular imbalanced datasets. State-of-the-art linear interpolation approaches can be used to generate synthetic samples from the convex space of the minority class. Generative networks are common deep learning approaches for synthetic sample generation. However, their scope on synthetic tabular data generation in the context of imbalanced classification is not adequately explored. In this article, we show that existing deep generative models perform poorly compared to linear interpolation-based approaches for imbalanced classification problems on small tabular datasets. To overcome this, we propose a deep generative model, ConvGeN that combines the idea of convex space learning with deep generative models. ConvGeN learns coefficients for the convex combinations of the minority class samples, such that the synthetic data is distinct enough from the majority class. Our benchmarking experiments demonstrate that our proposed model ConvGeN improves imbalanced classification on such small datasets, as compared to existing deep generative models, while being on par with the existing linear interpolation approaches. Moreover, we discuss how our model can be used for synthetic tabular data generation in general, even outside the scope of data imbalance, and thus improves the overall applicability of convex space learning.
• Seiler, Moritz Vinzent ; Kerschke, Pascal ; Trautmann, Heike: Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems (2024)
• Stein, David ; Andres, Bjoern: Partial Optimality in the Linear Ordering Problem.. In: Forty-first International Conference on Machine Learning, 2024
• Thakur, Nandan ; Bonifacio, Luiz ; Fröbe, Maik ; Bondarenko, Alexander ; Kamalloo, Ehsan ; Potthast, Martin ; Hagen, Matthias ; Lin, Jimmy: Systematic evaluation of neural retrieval models on the touché 2020 argument retrieval subset of BEIR.. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. vol. 618. Washington DC USA : ACM, 2024, pp. 1420–1430
• Tiemann, Jan ; McGinity, Matthew ; Sbalzarini, Ivo F. ; Günther, Ulrik: Live and Interactive 3D Photomanipulation under the Microscope using Virtual Reality.. In: Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, CHI EA ’24. New York, NY, USA : Association for Computing Machinery, 2024 — ISBN 9798400703317

  State-of-the-art microscopes, as used in cell biology, are not only capable of capturing 3D images, but also permit manipulation of (sub-)cellular structures using techniques such as optical traps, optogenetics or laser ablation. However, such microscopes are still controlled using 2D interfaces, prohibiting actual 3-dimensional manipulation.We present microscenery, a virtual reality (VR) microscope control software, designed to facilitate 3D laser ablation experiments. We combine microscopy automation with VR rendering and intuitive controller-based input to empower biologists with the precision of laser-based techniques while providing the full 3D spatial context of their sample.We describe the design goals and architecture of the software and illustrate the potential of the system by conducting a brief expert review study for 3D ablation experiments. Our results suggest VR is not only an effective interface for microscopic manipulations, but can enable novel experiments which are either impossible with traditional 2D interfaces, or prohibitively time-consuming.
• Ulbricht, Markus: Formal Argumentation in Symbolic AI.. In: Larson, K. (ed.): Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24 : International Joint Conferences on Artificial Intelligence Organization, 2024. — Early Career, pp. 8577–8582
• Vargis, Tom Richard ; Ghiasvand, Siavash: A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement (2024)
• Vassena, Nicola ; Stadler, Peter F.: Unstable cores are the source of instability in chemical reaction networks. In: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences vol. 480 (2024), Nr. 2285, p. 20230694

  In biochemical networks, complex dynamical features such as superlinear growth and oscillations are classically considered a consequence of autocatalysis. For the large class of parameter-rich kinetic models, which includes generalized mass action kinetics and Michaelis–Menten kinetics, we show that certain submatrices of the stoichiometric matrix, so-called unstable cores, are sufficient for a reaction network to admit instability and potentially give rise to such complex dynamical behaviour. The determinant of the submatrix distinguishes unstable-positive feedbacks, with a single real-positive eigenvalue, and unstable-negative feedbacks without real-positive eigenvalues. Autocatalytic cores turn out to be exactly the unstable-positive feedbacks that are Metzler matrices. Thus there are sources of dynamical instability in chemical networks that are unrelated to autocatalysis. We use such intuition to design non-autocatalytic biochemical networks with superlinear growth and oscillations.
• Vieira, Caio ; Castrillon, Jeronimo ; Beck, Antonio Carlos Schneider: Hyperdimensional Computing Quantization with Thermometer Codes.. In: 6th Workshop on Accelerated Machine Learning (AccML), co-located with 19th International Conference on High-Performance and Embedded Architectures and Compilers (HiPEAC). Munich, Germany, 2024, p. 7pp
• von Löhneysen, Sarah ; Mörl, Mario ; Stadler, Peter F.: Limits of experimental evidence in RNA secondary structure prediction. In: Frontiers in Bioinformatics vol. 4 (2024)
• Wang, Shuai ; Scells, Harrisen ; Zhuang, Shengyao ; Potthast, Martin ; Koopman, Bevan ; Zuccon, Guido: Zero-shot Generative Large Language Models for Systematic Review Screening Automation (2024)
• Wang, Xiangzun ; Cichos, Frank: Harnessing synthetic active particles for physical reservoir computing. In: Nat. Commun. vol. 15 (2024), Nr. 1, p. 774

  The processing of information is an indispensable property of living systems realized by networks of active processes with enormous complexity. They have inspired many variants of modern machine learning, one of them being reservoir computing, in which stimulating a network of nodes with fading memory enables computations and complex predictions. Reservoirs are implemented on computer hardware, but also on unconventional physical substrates such as mechanical oscillators, spins, or bacteria often summarized as physical reservoir computing. Here we demonstrate physical reservoir computing with a synthetic active microparticle system that self-organizes from an active and passive component into inherently noisy nonlinear dynamical units. The self-organization and dynamical response of the unit are the results of a delayed propulsion of the microswimmer to a passive target. A reservoir of such units with a self-coupling via the delayed response can perform predictive tasks despite the strong noise resulting from the Brownian motion of the microswimmers. To achieve efficient noise suppression, we introduce a special architecture that uses historical reservoir states for output. Our results pave the way for the study of information processing in synthetic self-organized active particle systems.
• Wang, Xiangzun ; Cichos, Frank: Harnessing synthetic active particles for physical reservoir computing. In: Nature Communications vol. 15, Springer Science and Business Media LLC (2024), Nr. 1
• Wiegmann, Matti ; Rakete, Jennifer ; Wolska, Magdalena ; Stein, Benno ; Potthast, Martin: If there’s a Trigger Warning, then where’s the Trigger? Investigating Trigger Warnings at the Passage Level, arXiv (2024)
• Wiegmann, Matti ; Reimer, Jan Heinrich ; Ernst, Maximilian ; Potthast, Martin ; Hagen, Matthias ; Stein, Benno: A Mastodon Corpus to Evaluate Federated Microblog Search.. In: Farzana, S. ; Fröbe, M. ; Granitzer, M. ; Hendriksen, G. ; Hiemstra, D. ; Potthast, M. ; Zerhoudi, S. (eds.): Proceedings of the First International Workshop on Open Web Search (WOWS 2024). vol. 3689 : CEUR Workshop Proceedings, 2024, pp. 37–49
• Zelch, Ines ; Hagen, Matthias ; Potthast, Martin: A User Study on the Acceptance of Native Advertising in Generative IR.. In: Proceedings of the 2024 Conference on Human Information Interaction and Retrieval, CHIIR ’24. Sheffield, United Kingdom : Association for Computing Machinery, 2024 — ISBN 9798400704345, pp. 142–152

  Commercial conversational search engines need a business model. Since advertising is the main source of revenue for “traditional” ten-blue-links web search, ads are not an unlikely option for conversational search either. In traditional web search, ads are usually placed above organic search results. However, large language models (LLMs) may be dynamically prompted to blend product placements with “organic” conversational responses, similar to native advertising in journalism. This type of advertising can be very difficult to recognize, depending on how subtly it is integrated and disclosed. To raise awareness of this potential development, we analyze the capabilities of current LLMs to blend ads with generative search results. In a user study, we ask people about the perceived quality of (emulated) search results in different advertising scenarios. In a substantial number of cases, our survey participants do not notice brand or product placements when they do not expect them. Thus, our results show the potential of LLMs to subtly mix advertising with generated search results. This warrants further investigation, for example, to develop appropriate advertising disclosure rules, and to detect advertising in generated results. Our research also raises broader concerns about whether commercial or open-source generative models can be trusted not to be fine-tuned to generate ads rather than “genuine” responses.

2023

• Allal, Loubna Ben ; Li, Raymond ; Kocetkov, Denis ; Mou, Chenghao ; Akiki, Christopher ; Ferrandis, Carlos Munoz ; Muennighoff, Niklas ; Mishra, Mayank ; Gu, Alex ; et al.: SantaCoder: don’t reach for the stars!, arXiv (2023)

  The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code. This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experiments conducted to de-risk the model architecture, and the experiments investigating better preprocessing methods for the training data. We train 1.1B parameter models on the Java, JavaScript, and Python subsets of The Stack and evaluate them on the MultiPL-E text-to-code benchmark. We find that more aggressive filtering of near-duplicates can further boost performance and, surprisingly, that selecting files from repositories with 5+ GitHub stars deteriorates performance significantly. Our best model outperforms previous open-source multilingual code generation models (InCoder-6.7B and CodeGen-Multi-2.7B) in both left-to-right generation and infilling on the Java, JavaScript, and Python portions of MultiPL-E, despite being a substantially smaller model. All models are released under an OpenRAIL license at https://hf.co/bigcode.

2022

• Ebert, Matthias P ; Meindl-Beinker, Nadja M ; Gutting, Tobias ; Maenz, Martin ; Betge, Johannes ; Schulte, Nadine ; Zhan, Tianzuo ; Weidner, Philip ; Burgermeister, Elke ; et al.: Second-line therapy with nivolumab plus ipilimumab for older patients with oesophageal squamous cell cancer (RAMONA): a multicentre, open-label phase 2 trial. In: Lancet Healthy Longev. vol. 3, Elsevier BV (2022), Nr. 6, pp. e417-e427

  BACKGROUND: The overall survival of patients with advanced and refractory oesophageal squamous cell carcinoma, mostly aged 65 years and older, is poor. Treatment with PD-1 antibodies showed improved progression-free survival and overall survival. We assessed the safety and efficacy of combined nivolumab and ipilimumab therapy in this population. METHODS: This multicentre, open-label, phase 2 trial done in 32 sites in Germany included patients aged 65 years and older with oesophageal squamous cell carcinoma and disease progression or recurrence following first-line therapy. Patients were treated with nivolumab (240 mg fixed dose once every 2 weeks, intravenously) in the safety run-in phase and continued with nivolumab and ipilimumab (nivolumab 240 mg fixed dose once every 2 weeks and ipilimumab 1 mg/kg once every 6 weeks, intravenously). The primary endpoint was overall survival, which was compared with a historical cohort receiving standard chemotherapy in the intention-to-treat population. This study is registered with ClinicalTrials.gov, NCT03416244. FINDINGS: Between March 2, 2018, and Aug 20, 2020, we screened 75 patients with advanced oesophageal squamous cell carcinoma. We enrolled 66 patients (50 [76%] men and 16 [24%] women; median age 70·5 years [IQR 67·0-76·0]), 44 (67%) of whom received combined nivolumab and ipilimumab therapy and 22 (33%) received nivolumab alone. Median overall survival time at the prespecified data cutoff was 7·2 months (95% CI 5·7-12·4) and significantly higher than in a historical cohort receiving standard chemotherapy (p=0·0063). The most common treatment-related adverse events were fatigue (12 [29%] of 42), nausea (11 [26%]), and diarrhoea (ten [24%]). Grade 3-5 treatment-related adverse events occurred in 13 (20%) of 66 patients. Treatment-related death occurred in one patient with bronchiolitis obliterans while on nivolumab and ipilimumab treatment. INTERPRETATION: Patients aged at least 65 years, with advanced oesophageal squamous cell carcinoma might benefit from combined nivolumab and ipilimumab therapy in second-line treatment. FUNDING: Bristol Myers Squibb.
• Fröbe, Maik ; Günther, Sebastian ; Probst, Maximilian ; Potthast, Martin ; Hagen, Matthias: Webis-MS-MARCO-Anchor-Texts-22, Zenodo (2022)

  The Webis MS MARCO Anchor Text 2022 dataset enriches Version 1 and 2 of the document collection of MS MARCO with anchor text extracted from six Common Crawl snapshots. The six Common Crawl snapshots cover the years 2016 to 2021 (between 1.7-3.4 billion documents each). We sampled 1,000 anchor texts for documents with more than 1,000 anchor texts at random and all anchor texts for documents with less than 1,000 anchor texts (this sampling yields that all anchor text is included for 94% of the documents in Version 1 and 97% of documents for Version 2). Overall, the MS MARCO Anchor Text 2022 dataset enriches 1,703,834 documents for Version 1 and 4,821,244 documents for Version 2 with anchor text. Cleaned versions of the MS MARCO Anchor Text 2022 dataset are available in ir_datasets, Zenodo and Hugging Face. The raw dataset with additional information and all metadata for the extracted anchor texts (roughly 100GB) is available on Hugging Face and files.webis.de. The details of the construction of the Webis MS MARCO Anchor Text 2022 dataset are described in the associated paper. If you use this dataset, please cite @InProceedingsfroebe:2022a, address = Berlin Heidelberg New York, author = Maik Fr``obe and Sebastian G''unther and Maximilian Probst and Martin Potthast and Matthias Hagen, booktitle = Advances in Information Retrieval. 44th European Conference on IR Research (ECIR 2022), editor = Matthias Hagen and Suzan Verberne and Craig Macdonald and Christin Seifert and Krisztian Balog and Kjetil Norvrag and Vinay Setty, month = apr, publisher = Springer, series = Lecture Notes in Computer Science, site = Stavanger, Norway, title = The Power of Anchor Text in the Neural Retrieval Era, year = 2022
• Heinz, Céline N ; Echle, Amelie ; Foersch, Sebastian ; Bychkov, Andrey ; Kather, Jakob Nikolas: The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups. In: Histopathology vol. 80, Wiley (2022), Nr. 7, pp. 1121–1127

  AIMS: Artificial intelligence (AI) provides a powerful tool to extract information from digitised histopathology whole slide images. During the last 5 years, academic and commercial actors have developed new technical solutions for a diverse set of tasks, including tissue segmentation, cell detection, mutation prediction, prognostication and prediction of treatment response. In the light of limited overall resources, it is presently unclear for researchers, practitioners and policymakers which of these topics are stable enough for clinical use in the near future and which topics are still experimental, but worth investing time and effort into. METHODS AND RESULTS: To identify potentially promising applications of AI in pathology, we performed an anonymous online survey of 75 computational pathology domain experts from academia and industry. Participants enrolled in 2021 were queried about their subjective opinion on promising and appealing subfields of computational pathology with a focus upon solid tumours. The results of this survey indicate that the prediction of treatment response directly from routine pathology slides is regarded as the most promising future application. This item was ranked highest in the overall analysis and in subgroups by age and professional background. Furthermore, prediction of genetic alterations, gene expression and survival directly from routine pathology images scored consistently high throughout subgroups. CONCLUSIONS: Together, these data demonstrate a possible direction for the development of computational pathology systems in clinical, academic and industrial research in the near future.
• Srivastava, Aarohi ; Rastogi, Abhinav ; Rao, Abhishek ; Shoeb, Abu Awal ; Abid, Abubakar ; Fisch, Adam ; Brown, Adam R ; Santoro, Adam ; Gupta, Aditya ; et al.: Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models, arXiv (2022)

  Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit ``breakthrough'' behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.

2021

• Huemer, Marie-Theres ; Bauer, Alina ; Petrera, Agnese ; Scholz, Markus ; Hauck, Stefanie M ; Drey, Michael ; Peters, Annette ; Thorand, Barbara: Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study. In: J. Cachexia Sarcopenia Muscle vol. 12, Wiley (2021), Nr. 4, pp. 1011–1023

  BACKGROUND: The coexistence of low muscle mass and high fat mass, two interrelated conditions strongly associated with declining health status, has been characterized by only a few protein biomarkers. High-throughput proteomics enable concurrent measurement of numerous proteins, facilitating the discovery of potentially new biomarkers. METHODS: Data derived from the prospective population-based Cooperative Health Research in the Region of Augsburg S4/FF4 cohort study (median follow-up time: 13.5 years) included 1478 participants (756 men and 722 women) aged 55-74 years in the cross-sectional and 608 participants (315 men and 293 women) in the longitudinal analysis. Appendicular skeletal muscle mass (ASMM) and body fat mass index (BFMI) were determined through bioelectrical impedance analysis at baseline and follow-up. At baseline, 233 plasma proteins were measured using proximity extension assay. We implemented boosting with stability selection to enable false positives-controlled variable selection to identify new protein biomarkers of low muscle mass, high fat mass, and their combination. We evaluated prediction models developed based on group least absolute shrinkage and selection operator (lasso) with 100$times$ bootstrapping by cross-validated area under the curve (AUC) to investigate if proteins increase the prediction accuracy on top of classical risk factors. RESULTS: In the cross-sectional analysis, we identified kallikrein-6, C-C motif chemokine 28 (CCL28), and tissue factor pathway inhibitor as previously unknown biomarkers for muscle mass [association with low ASMM: odds ratio (OR) per 1-SD increase in log2 normalized protein expression values (95% confidence interval (CI)): 1.63 (1.37-1.95), 1.31 (1.14-1.51), 1.24 (1.06-1.45), respectively] and serine protease 27 for fat mass [association with high BFMI: OR (95% CI): 0.73 (0.61-0.86)]. CCL28 and metalloproteinase inhibitor 4 (TIMP4) constituted new biomarkers for the combination of low muscle and high fat mass [association with low ASMM combined with high BFMI: OR (95% CI): 1.32 (1.08-1.61), 1.28 (1.03-1.59), respectively]. Including protein biomarkers selected in $geq$90% of group lasso bootstrap iterations on top of classical risk factors improved the performance of models predicting low ASMM, high BFMI, and their combination [delta AUC (95% CI): 0.16 (0.13-0.20), 0.22 (0.18-0.25), 0.12 (0.08-0.17), respectively]. In the longitudinal analysis, N-terminal prohormone brain natriuretic peptide (NT-proBNP) was the only protein selected for loss in ASMM and loss in ASMM combined with gain in BFMI over 14 years [OR (95% CI): 1.40 (1.10-1.77), 1.60 (1.15-2.24), respectively]. CONCLUSIONS: Proteomic profiling revealed CCL28 and TIMP4 as new biomarkers of low muscle mass combined with high fat mass and NT-proBNP as a key biomarker of loss in muscle mass combined with gain in fat mass. Proteomics enable us to accelerate biomarker discoveries in muscle research.
• Kestemont, M. ; Manjavacas, E. ; Markov, I. ; Bevendorff, J. ; Wiegmann, M. ; Stamatatos, E. ; Stein, B. ; Potthast, M.: Overview of the cross-domain authorship verification task at PAN 2021.. In: Faggioli, G. ; Ferro, N. ; Joly, A. ; Maistro, M. ; Piroi, F. (eds.): CLEF-WN 2021 - Proceedings of the Working Notes of CLEF 2021 - Conference and Labs of the Evaluation Forum, CEUR Workshop Proceedings. vol. 2936 : CEUR-WS, 2021. — 2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021 ; Conference date: 21-09-2021 Through 24-09-2021, pp. 1743–1759

  textcopyright 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).Idiosyncrasies in human writing styles make it difficult to develop systems for authorship identification that scale well across individuals. In this year's edition of PAN, the authorship identification track focused on open-set authorship verification, so that systems are applied to unknown documents by previously unseen authors in a new domain. As in the previous year, the sizable materials for this campaign were sampled from English-language fanfiction. The calibration materials handed out to the participants were the same as last year, but a new test set was compiled with authors and fandom domains not present in any of the previous datasets. The general setup of the task did not change, i.e., systems still had to estimate the probability of a pair of documents being authored by the same person. We attracted 13 submissions by 10 international teams, which were compared to three complementary baselines, using five diverse evaluation metrics. Post-hoc analyses show that systems benefitted from the abundant calibration materials and were well-equipped to handle the open-set scenario: Both the top-performing approach and the highly competitive cohort of runner-ups presented surprisingly strong verifiers. We conclude that, at least within this specific text variety, (large-scale) open-set authorship verification is not necessarily or inherently more difficult than a closed-set setup, which offers encouraging perspectives for the future of the field.

2020

• Mihindukulasooriya, Nandana ; Dubey, Mohnish ; Gliozzo, Alfio ; Lehmann, Jens ; Ngomo, Axel-Cyrille Ngonga ; Usbeck, Ricardo: SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge, arXiv (2020)

  Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given problem domain. The SeMantic AnsweR Type prediction task (SMART) was part of ISWC 2020 challenges. Question type and answer type prediction can play a key role in knowledge base question answering systems providing insights that are helpful to generate correct queries or rank the answer candidates. More concretely, given a question in natural language, the task of SMART challenge is, to predict the answer type using a target ontology (e.g., DBpedia or Wikidata).