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.

Knowledge Graphs (KGs), with their intricate hierarchies and semantic relationships, present unique challenges for graph representation learning, necessitating tailored approaches to effectively capture and encode their complex structures into useful numerical representations. The fractal-like nature of these graphs, where patterns repeat at various scales and complexities, requires specialized algorithms that can adapt and learn from the multi-level structures inherent in the data. This similarity to fractals requires methods that preserve the recursive detail of knowledge graphs while facilitating efficient learning and extraction of relational patterns. In this study, we explore the integration of similarity group with attention mechanisms to represent knowledge graphs in complex spaces. In our approach, SimE, we make use of the algebraic (bijection) and geometric (similarity) properties of the similarity transformations to enhance the representation of self-similar fractals in KGs. We empirically validate the capability of providing representations of bijections and similarities in benchmark KGs. We also conducted controlled experiments that captured one-to-one, one-to-many, and many-to-many relational patterns and studied the behavior of state-of-the-art models including the proposed SimE model. Because of the lack of benchmark fractal-like KG datasets, we created a set of fractal-like testbeds to assess the subgraph similarity learning ability of models. The observed results suggest that SimE captures the complex geometric structures of KGs whose statements satisfy these algebraic and geometric properties. In particular, SimE is competitive with state-of-the-art KG embedding models and is able to achieve high values of Hits@1. As a result, SimE is capable of effectively predicting correct links and ranking them with the highest ranks. SimE is publicly available on GitHub https://github.com/NIMI-research/SimE.

Chimeric antigen receptor (CAR) T cells and bispecific T cell engagers have become integral components in the treatment of relapsed/refractory multiple myeloma. We report a 63-year-old male who received ciltacabtagene autoleucel CAR T cells and the GPRC5D texttimes CD3 bispecific talquetamab for early relapse of his multiple myeloma. Nine months after CAR T therapy, he developed a symptomatic leukemic peripheral T cell lymphoma with cutaneous and intestinal involvement. Longitudinal single-cell RNA and T cell receptor sequencing of peripheral blood and bone marrow revealed two hyperexpanded CAR-carrying T cell clones. These expanded clones exhibited an exhausted effector-memory T cell transcriptional signature, and the neoplasm itself was sensitive to dexamethasone treatment. The immunophenotypic and transcriptional alterations of these abnormal T cells resembled those of T-large granular lymphocytic leukemia. Spatial transcriptomes of skin lesions confirmed the aberrant CAR-expressing T cells. Whole-genome sequencing revealed three distinct integration sites, within the introns of ZGPAT, KPNA4 and polycomb-associated noncoding RNAs. Before and after CAR T whole-genome analyses implicated clonal outgrowth of a TET2-mutated precursor propelled by additional subclone-specific loss of heterozygosity and other secondary mechanisms. This case highlights the evolution of a CAR-carrying peripheral T cell lymphoma following CAR T cell and bispecific T cell engager therapy, offering critical insights into the clonal evolution from a predisposed hematopoietic precursor to a mature neoplasm.

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences, by improving weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. The latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous, small sample sizes and data with limited annotations. This paper reviews how AI is being used to analyze extreme climate events (like floods, droughts, wildfires, and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating real-time information, and deploying understandable models, all crucial steps for gaining stakeholder trust and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy to enhance disaster readiness and risk reduction.

We propose a new, more general definition of extended probability measures. We study their properties and provide a behavioral interpretation. We put them to use in an inference procedure, whose environment is canonically represented by the probability space (Ω, ℱ, P), when both P and the composition of Ω are unknown. We develop an ex ante analysis — taking place before the statistical analysis requiring knowledge of Ω — in which the true composition of Ω is progressively learned. We describe how to update extended probabilities in this setting and introduce the concept of lower extended probabilities. We apply our findings to a species sampling problem and to the study of the boomerang effect (the empirical observation that sometimes persuasion yields the opposite effect: the persuaded agent moves their opinion away from the opinion of the persuading agent).

Understanding why some individuals age faster than others is essential to evolutionary biology and geroscience, but measuring variation in biological age is difficult. One solution may lie in measuring gut microbiome composition because microbiota change with many age-related factors (e.g., immunity and behavior). Here we create a microbiome-based age predictor using 13,563 gut microbial profiles from 479 wild baboons collected over 14 years. The resulting “microbiome clock” predicts host chronological age. Deviations from the clock’s predictions are linked to demographic and socio-environmental factors that predict baboon health and survival: animals who appear old-for-age tend to be male, sampled in the dry season (for females), and high social status (both sexes). However, an individual’s “microbiome age” does not predict the attainment of developmental milestones or lifespan. Hence, the microbiome clock accurately reflects age and some social and environmental conditions, but not the pace of development or mortality risk.Competing Interest StatementThe authors have declared no competing interest.

In diesem Beitrag wird die Verwendung von auf großen Sprachmodellen (LLMs) basierenden Systemen in Bildungskontexten einer ethischen Betrachtung unterzogen. Diese geht von den technischen als auch von den gesellschaftlichen Bedingungen aus und fragt nach den Gefahren, die mit der Nutzung solcher Systeme einhergehen. Die Abhängigkeiten, die nicht nur auf der Ebene der Techniknutzung, sondern darüber hinaus auch im gesellschaftlichen und ökonomischen Gefüge erzeugt werden, werden anhand der von Kate Crawford identifizierten Dimensionen der wahren Kosten von KI-Systemen illustriert. Diese Gefahren werden mit dem Nutzen solcher Systeme kontrastiert, der darin besteht, Bildungskontexte in Schulen und Hochschulen beispielsweise durch personalisierte Tutoringsysteme zu bereichern. Die häufig ausgeklammerte Frage nach den in LLMs enthaltenen Menschenbildern wird am Schluss unter Einbeziehung der vorangegangenen Abwägung diskutiert. Unter Abschätzung von Risiken und Chancen wird resümiert, dass auf LLMs basierende Systeme in Bildungskontexten konstruktiv eingesetzt werden sollten, damit künftige Generationen einen souveränen Umgang mit diesen erlernen.

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.

In this paper, we introduce AutoRDF2GML, a framework designed to convert RDF data into data representations tailored for graph machine learning tasks. AutoRDF2GML enables, for the first time, the creation of both content-based features---i.e., features based on RDF datatype properties---and topology-based features---i.e., features based on RDF object properties. Characterized by automated feature extraction, AutoRDF2GML makes it possible even for users less familiar with RDF and SPARQL to generate data representations ready for graph machine learning tasks, such as link prediction, node classification, and graph classification. Furthermore, we present four new benchmark datasets for graph machine learning, created from large RDF knowledge graphs using our framework. These datasets serve as valuable resources for evaluating graph machine learning approaches, such as graph neural networks. Overall, our framework effectively bridges the gap between the Graph Machine Learning and Semantic Web communities, paving the way for RDF-based machine learning applications.

We present a novel microservice-based system, that facilitates a polyparadigmatic ontology term search (leveraging semantic search via vector embeddings, keyword search, and attribute filters). The search index strategy intends to preserve important semantic aspects of the ontological context of a term (selected attributes and term relationships) using structured search fields and multilevel vector spaces assembling hyper-level vector spaces. The flexible, yet simple query API allows fine-grained search requests based on a combination of fuzzy and exact filters. The architecture is based on a highly automatable and flexible Docker Compose setup strategy. While deploying the system for a local ontology is only one command away, the setup also allows ingesting a configurable subset of over 1,800 published ontologies in over 12,000 versions via DBpedia Archivo.

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

Due to their low efficiency, neural retrieval models are usually evaluated on small corpora (e.g. MS MARCO or BEIR subsets) or in re-ranking scenarios using a more efficient first-stage retriever. To estimate their effectiveness on larger corpora independently of a first-stage retriever, we propose a new corpus subsampling strategy based on the top-k results of the pooled systems that contributed to the relevance judgments of a corpus. Our experiments on nine TREC tasks covering different corpus sizes show that using the top-1,000 or even only the top-100 pools provides a reliable effectiveness estimate for neural models. This reduces the required experimental resources for large corpora by a factor of up to 1,000 and enables a “green” IR evaluation.

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.

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.

With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models. Despite recent progress in deep learning to ecosystem monitoring, there is a need for datasets specifically designed to analyse compound heatwave and drought extreme impact. Here, we introduce the DeepExtremeCubes database, tailored to map around these extremes, focusing on persistent natural vegetation. It comprises over 40,000 globally sampled small data cubes (i.e. minicubes), with a spatial coverage of 2.5 by 2.5 km. Each minicube includes (i) Sentinel-2 L2A images, (ii) ERA5-Land variables and generated extreme event cube covering 2016 to 2022, and (iii) ancillary land cover and topography maps. The paper aims to (1) streamline data accessibility, structuring, pre-processing, and enhance scientific reproducibility, and (2) facilitate biosphere dynamics forecasting in response to compound extremes.

This paper introduces an artificial neural network (ANN) approach to estimate the autoregressive process AR(1) when the autocorrelation parameter is near one. Traditional ordinary least squares (OLS) estimators suffer from biases in small samples, necessitating various correction methods proposed in the literature. The ANN, trained on simulated data, outperforms these methods due to its nonlinear structure. Unlike competitors requiring simulations for bias corrections based on specific sample sizes, the ANN directly incorporates sample size as input, eliminating the need for repeated simulations. Stability tests involve exploring different ANN architectures and activation functions and robustness to varying distributions of the process innovations. Empirical applications on financial and industrial data highlight significant differences among methods, with ANN estimates suggesting lower persistence than other approaches.

Mesenchymal stem cell therapy shows promising results for difficult-to-treat diseases, but standardized manufacturing requires robust quality control through automated cell confluence monitoring. While deep learning can automate confluence estimation, research on cost-effective dataset curation and the role of foundation models in this task remains limited. We systematically investigate the most effective strategies for confluence estimation, focusing on active learning-based dataset curation, goal-specific labeling, and leveraging foundation models for zero-shot inference. Here, we show that zero-shot inference with the Segment Anything Model (SAM) achieves excellent confluence estimation without any task-specific training, outperforming fine-tuned smaller models. Further, our findings demonstrate that active learning does not significantly improve model dataset curation compared to random selection in homogeneous cell datasets. We show that goal-specific, simplified labeling strategies perform comparably to precise annotations while substantially reducing annotation effort. These results challenge common assumptions about dataset curation: neither active learning nor extensive fine-tuning provided significant benefits for our specific use case. Instead, we found that leveraging SAMtextquoterights zero-shot capabilities and targeted labeling strategies offers the most cost-effective approach to automated confluence estimation. Our work provides practical guidelines for implementing automated cell monitoring in MSC manufacturing, demonstrating that extensive dataset curation may be unnecessary when foundation models can effectively handle the task out of the box.Competing Interest StatementThe authors have declared no competing interest.

When a retrieval system receives a query it has encountered before, previous relevance feedback, such as clicks or explicit judgments can help to improve retrieval results. However, the content of a previously relevant document may have changed, or the document might not be available anymore. Despite this evolved corpus, we counterfactually use these previously relevant documents as relevance signals. In this paper we proposed approaches to rewrite user queries and compare them against a system that directly uses the previous qrels for the ranking. We expand queries with terms extracted from the previously relevant documents or derive so-called keyqueries that rank the previously relevant documents to the top of the current corpus. Our evaluation in the CLEF LongEval scenario shows that rewriting queries with historical relevance feedback improves the retrieval effectiveness and even outperforms computationally expensive transformer-based approaches.

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.

IntroductionFeedback and reinforcement signals in the brain act as natures sophisticated teaching tools, guiding neural circuits to self-organization, adaptation, and the encoding of complex patterns. This study investigates the impact of two feedback mechanisms within a deep liquid state machine architecture designed for spiking neural networks.MethodsThe Reinforced Liquid State Machine architecture integrates liquid layers, a winner-takes-all mechanism, a linear readout layer, and a novel reward-based reinforcement system to enhance learning efficacy. While traditional Liquid State Machines often employ unsupervised approaches, we introduce strict feedback to improve network performance by not only reinforcing correct predictions but also penalizing wrong ones.ResultsStrict feedback is compared to another strategy known as forgiving feedback, excluding punishment, using evaluations on the Spiking Heidelberg data. Experimental results demonstrate that both feedback mechanisms significantly outperform the baseline unsupervised approach, achieving superior accuracy and adaptability in response to dynamic input patterns.DiscussionThis comparative analysis highlights the potential of feedback integration in deepened Liquid State Machines, offering insights into optimizing spiking neural networks through reinforcement-driven architectures.

Most car-following models were originally developed for lane-based traffic. Over the past two decades, efforts have been made to calibrate car-following models for non-lane-based traffic. However, traffic conditions with varying vehicle dimensions, intermittent following, and multiple leaders often occur, making subjective Leader–Follower (LF) pair identification challenging. In this study, we analyze Vehicle Following (VF) behavior in traffic with a lack of lane discipline using high-resolution microscopic trajectory data collected in Chennai, India. The paper’s main contributions are threefold. Firstly, three criteria are used to identify LF pairs from the driver’s perspective, taking into account the intermittent following, lack of lane discipline due to consideration of lateral separation, and the presence of in-between vehicles. Secondly, the psycho-physical concept of the regime in the Wiedemann-99 model is leveraged to determine the traffic-dependent “influence zone” for LF identification. Thirdly, a joint and consistent framework is proposed for identifying LF pairs and estimating VF parameters. The proposed methodology outperforms other heuristic-based LF identification methods from the literature in terms of quantitative and qualitative performance measures. The proposed approach can enable robust and more realistic LF identification and VF parameter calibration with practical applications such as level of service (LOS) analysis, capacity, and travel time estimation.

We study different domination problems of attacking and non-attacking rooks and queens on polyominoes and polycubes of all dimensions. Our main result proves that maximum independent domination is NP-complete for non-attacking queens and for non-attacking rooks on polycubes of dimension three and higher. We also analyze these problems for polyominoes and convex polyominoes, conjecture the complexity classes, and provide a computer tool for investigation. We have also computed new values for classical queen domination problems on chessboards (square polyominoes). For our computations, we have translated the problem into an integer linear programming instance. Finally, using this computational implementation and the game engine Godot, we have developed a video game of minimum domination of queens and rooks on randomly generated polyominoes.

Metal ions, as abundant and vital cofactors in numerous proteins, are crucial for enzymatic activities and protein interactions. Given their pivotal role and catalytic efficiency, accurately and efficiently identifying metal-binding sites is fundamental to elucidating their biological functions and has significant implications for protein engineering and drug discovery. To address this challenge, we present SuperMetal, a generative AI framework that leverages a score-based diffusion model coupled with a confidence model to predict metal-binding sites in proteins with high precision and efficiency. Using zinc ions as an example, SuperMetal outperforms existing state-of-the-art models, achieving a precision of 94 % and coverage of 90 %, with zinc ions localization within 0.52 textpm 0.55 AA of experimentally determined positions, thus marking a substantial advance in metal-binding site prediction. Furthermore, SuperMetal demonstrates rapid prediction capabilities (under 10 s for proteins with $$backslashsim$$2000 residues) and remains minimally affected by increases in protein size. Notably, SuperMetal does not require prior knowledge of the number of metal ions---unlike AlphaFold 3, which depends on this information. Additionally, SuperMetal can be readily adapted to other metal ions or repurposed as a probe framework to identify other types of binding sites, such as protein-binding pockets.

The continuously growing size of Neural Network (NN) models makes the design of lightweight neural network accelerators for edge devices an emerging subject in recent research. Previous works explored different lightweight technologies or even emerging neural network structures, such as quantization, approximate computing, neuromorphic computing, etc., to reduce hardware resource consumption in accelerator designs. This inspired our interest in exploring the potential of other emerging network structures in hardware accelerator designs. Kolmogorov-Arnold Network (KAN) [1] is a recently proposed novel neural network structure by replacing the multiplication and activation function in Artificial Neural Networks (ANN) with learnable nonlinear functions, which has the potential to transform the paradigm of neural network design. However, considering the complexity of the nonlinear function on hardware, the design of the lightweight hardware accelerator of KAN lacks thoroughly related research.

How do drivers perceive risk? Understanding what situations and factors cause drivers to perceive situations as critical can improve our understanding of road user behavior and inform automated driving technology. To investigate the factors that shape drivers’ risk perception, we conducted an eye-tracking study with 27 participants who watched dashcam videos and continuously rated the perceived risk of various driving situations. Using the resulting dataset, we developed a computer vision-based machine learning approach that generates explainable predictions of perceived risk from video and eye-tracking data. Our SHAP analysis reveals that the proximity of objects and number of cars in a scene are the most significant contributors to perceived risk. Most interestingly, while people tend to sample similar objects in critical situations, their risk perception remains highly personal making visual sampling behavior a weak predictor of perceived risk. Overall, our explanations reveal non-linear insights beyond previous work, suggesting that risk perception is not only shaped by visual input, but primarily by cognitive processes which is in line with theoretical models of situation awareness. The dataset, source code, and a comprehensive usage guide are publicly available: https://osf.io/cwd6h/.

Visual analytics (VA) is a paradigm for insight generation by using visual analysis techniques and automated reasoning by transforming data into hypotheses and visualization to extract new insights. The insights are fed back into the data to enhance it until the desired insight is found. Many applications use this principle to provide meaningful mechanisms to assist decision-makers in achieving their goals. This process can be affected by various uncertainties that can interfere with the user decision-making process. Currently, there are no methodical description and handling tool to include uncertainty in VA systematically. We provide a unified workflow to transform the classic VA cycle into an uncertainty-aware visual analytics (UAVA) cycle consisting of five steps. To prove its usability, three real-world applications represent examples of the UAVA cycle implementation and the described workflow.

Chatbots based on large language models are becoming an important tool in modern software development, yet little is known about how programming beginners interact with this new technology to write code and acquire new knowledge. Thus, we are missing key ingredients to develop guidelines on how to adopt chatbots for becoming productive at programming.

Plant traits play a pivotal role in steering ecosystem dynamics. As plant canopies have evolved to interact with light, spectral data convey information on a variety of plant traits. Machine learning techniques have been used successfully to retrieve diverse traits from hyperspectral data. Nonetheless, the efficacy of machine learning is restricted by limited access to high-quality reference data for training. Previous studies showed that aggregating data across domains, sensors, or growth forms provided by collaborative efforts of the scientific community enables the creation of transferable models. However, even such curated databases are still sparse for several traits. To address these challenges, we investigated the potential of filling such data gaps with simulated hyperspectral data generated through the most widely-used radiative transfer model (RTM) PROSAIL. We coupled trait information from the TRY plant trait database with information on plant communities from the sPlot database, to build a realistic input trait dataset for the RTM-based simulation of canopy spectra. Our findings indicate that simulated data can alleviate the effects of data scarcity for highly underrepresented traits. In most other cases, however, the effects of including simulated data from RTMs are negligible or even negative. While more complex RTM models promise further improvements, their parameterization remains challenging. This highlights two key observations: firstly, RTM models, such as PROSAIL, exhibit limitations in producing realistic spectra across diverse ecosystems; secondly, real-world data repurposed from various sources exhibit superior retrieval success compared to simulated data. As a result, we advocate to emphasize the importance of active data sharing over secrecy and overreliance on modeling to address data limitations.

In recent years, data-driven stories have found a firm footing in journalism, business, and education. They leverage visualization and storytelling to convey information to broader audiences. Likewise, immersive technologies, like augmented and virtual reality devices, provide excellent potential for exploring and explaining data, thus inviting research on how data-driven storytelling transfers to immersive environments. To gain a better understanding of this exciting novel research area, we conducted a scoping review on the emerging notion of immersive data-driven storytelling, extended by surveying immersive data journalism and by analyzing immersive games, selected based on community reviews and tags. We present our methodology for the survey and discuss prominent themes that coalesce the knowledge we extracted from the literature, journalism, and games. These themes include, among others, the spatial embodiment of narration, the incorporation of the users and their context into narratives, and the balance between guiding the user versus promoting serendipity. Our discussion of these themes reveals research opportunities and challenges that will inform the design of immersive data-driven stories in the future.

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).

Given a finite set of points in (mathbb R^d), Tverberg’s theorem guarantees the existence of partitions of this set into parts whose convex hulls intersect. We introduce a graph structured on the family of Tverberg partitions of a given set of points, whose edges describe closeness between different Tverberg partitions. We prove bounds on the minimum and maximum degrees of this graph, the number of vertices of maximal degree, its clique number, and its connectedness.

Pathogenic mutations that cause rhodopsin misfolding lead to a spectrum of currently untreatable blinding diseases collectively termed retinitis pigmentosa. Small molecules to correct rhodopsin misfolding are therefore urgently needed. In this study, we utilized virtual screening to search for drug-like molecules that bind to the orthosteric site of rod opsin and improve its folding and trafficking. We identified and validated the biological effects of 2 non-retinoid compounds with favorable pharmacological properties that cross the blood–retina barrier. These compounds reversibly bind to unliganded rod opsin, each with a Kd comparable to 9-cis-retinal and improve opsin stability. By improving the internal protein structure network (PSN), these rod opsin ligands also enhanced the plasma membrane expression of total 36 of 123 tested clinical RP variants, including the most prevalent P23H variant. Importantly, these compounds protected retinas against light-induced degeneration in mice vulnerable to bright light injury and prolonged survival of photoreceptors in a retinitis pigmentosa mouse model for rod opsin misfolding.

We present mdciao, an open-source command line tool and Python Application-Programming-Interface (API) for easy, one-shot analysis and representation of molecular dynamics (MD) simulation data. Building upon the widely used concept of residue-residue contact-frequencies, mdciao offers a wide spectrum of further analysis and representations, enriched with available domain specific annotations. The user-friendly interface offers pre-packaged solutions for non-expert users, while keeping customizability for expert ones. Emphasis has been put into automatically producing annotated, production-ready figures and tables. Furthermore, seamless on-the-fly query and inclusion of domain-specific generic residue numbering for GPCRs, GAIN-domains, G-proteins, and kinases is made possible through online lookups. This allows for easy selection and comparison across different systems, regardless of sequence identity, target residues or domains. Finally, the fully documented Python API allows users to include the basic or advanced mdciao functions in their analysis workflows, and provides numerous examples and Jupyter Notebook Tutorials. The source code is published under the GNU Lesser General Public License v3.0 or later and hosted on https://github.com/gph82/mdciao, and the documentation, including guides and examples, can be found at https://www.mdciao.org

The diurnal temperature range (DTR), the difference between daily maximum and minimum temperature, is important for the impact of extreme temperatures, but despite physical links to aerosol forcing previous studies have struggled to attribute observed DTR changes to aerosols. Using causal inference, we can clearly identify aerosols as a driver of European DTR change since 1940. Following a decrease from the 1940s, since the 1980s the European DTR has increased by about 0.5K due to a reduction in European aerosol emissions leading to cooler nights relative to days. Agreement between causal effects estimated from observations with those estimated for two CMIP6 models evaluates the models' microphysical and radiative parameterizations. From causal effects, we also derive effective radiative forcing estimates of aerosols on surface shortwave during European summer, which amount to [−1.7; −1.5] Wm−2 in observations and one model, while it is less negative in the other model ([−0.9; −0.8] Wm−2).

An animal is a planar shape formed by attaching congruent regular polygons along their edges. In 1976, Harary and Harborth gave closed isoperimetric formulas for Euclidean animals. Here, we provide analogous formulas for hyperbolic animals. We do this by proving a connection between Sturmian words and the parameters of a discrete analogue of balls in the graph determined by hyperbolic tessellations. This reveals a complexity in hyperbolic animals that is not present in Euclidean animals.

AbstractTemporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort.

Abstract The chemokine-like receptor 1 (CMKLR1) is activated by the adipokine and chemoattractant protein chemerin. Cryo-EM structures of chemerin-9-CMKLR1-Gi have been published, where chemerin-9 is the nonapeptide of the C terminus of chemerinS157. Chemerin-9 is as active as the full-length protein in Ca2+-release but shows differences in equilibrium read-outs. An equally potent cyclic chemerin-9 variant (cC9) was reported previously. Now, we have built a computational model of CMKLR1 to investigate the binding mode of cC9 and chemerinS157 in comparison to chemerin-9. Differences were investigated using CMKLR1 variants. Double-mutant cycle analysis identified CMKLR1-F2.53 as the relevant position for Phe8-binding of cC9. Energy contribution revealed slight differences in Phe8-binding to CMKLR1-F2.53 and space for larger residues. This was confirmed as the chemerin-9 variant with 1-naphthyl-L-alanine at position 8 showed a 4-fold increased potency of 2 nM (pEC50=8.6±0.15). While chemerin-9 and cC9 share their interactions at the CMKLR1, chemerinS157 tolerates most mutations of CMKLR1 in the deep binding site. The computational model of chemerinS157 suggests a β-sheet interaction between the N-terminal CMKLR1-segment I25VVL28 and the β-sheet D108KVLGRLVH116 of ChemS157, which was confirmed experimentally. Our data add to the knowledge by identifying the binding mode of chemerinS157 and cC9 at CMKLR1, facilitating the future structure-based drug design.

In many recent works,the potential of Exploratory Landscape Analysis (ELA) features to numerically characterize single-objective continuous optimization problems has been demonstrated. These numerical features provide the input for all kinds of machine learning tasks in the domain of continuous optimization problems, ranging, i.a., from High-level Property Prediction to Automated Algorithm Selection and Automated Algorithm Configuration. Without ELA features, analyzing and understanding the characteristics of single-objective continuous optimization problems is — to the best of our knowledge — very limited.Yet, despite their usefulness, as demonstrated in several past works, ELA features suffer from several drawbacks. These include, in particular, (1.) a strong correlation between multiple features, as well as (2.) its very limited applicability to multiobjective continuous optimization problems. As a remedy, recent works proposed deep learning-based approaches as alternatives to ELA. In these works, among others point-cloud transformers were used to characterize an optimization problem's fitness landscape. However, these approaches require a large amount of labeled training data.Within this work, we propose a hybrid approach, Deep-ELA, which combines (the benefits of) deep learning and ELA features. We pre-trained four transformers on millions of randomly generated optimization problems to learn deep representations of the landscapes of continuous single- and multi-objective optimization problems. Our proposed framework can either be used out-of-the-box for analyzing single- and multiobjective continuous optimization problems, or subsequently fine-tuned to various tasks focusing on algorithm behavior and problem understanding.

Machine learning models for multi-omics data often trade off predictive accuracy against biological interpretability. An emerging class of deep learning architectures structurally encode biological knowledge to improve both prediction and explainability. Opportunities and challenges remain for broader adoption.

Immunotherapy is currently one of the most promising treatment options for malignant melanoma. To uncover new immunological targets for future treatment approaches, single-cell transcriptomic and epigenomic analyses were performed on human primary melanoma and melanocytic nevus samples. The detailed methods of this study are described in the Supplementary Material.

Excited-state nonadiabatic simulations with quantum mechanics/molecular mechanics (QM/MM) are essential to understand photoinduced processes in explicit environments. However, the high computational cost of the underlying quantum chemical calculations limits its application in combination with trajectory surface hopping methods. Here, we use FieldSchNet, a machine-learned interatomic potential capable of incorporating electric field effects into the electronic states, to replace traditional QM/MM electrostatic embedding with its ML/MM counterpart for nonadiabatic excited state trajectories. The developed method is applied to furan in water, including five coupled singlet states. Our results demonstrate that with sufficiently curated training data, the ML/MM model reproduces the electronic kinetics and structural rearrangements of QM/MM surface hopping reference simulations. Furthermore, we identify performance metrics that provide robust and interpretable validation of model accuracy.

Abstract argumentation frameworks are by now a major research area in knowledge representation and reasoning. Various aspects of AFs have been extensively studied over the last 25 years. Contributing to understanding the expressive power of AFs, researchers found lower and upper bounds for the maximal number of extensions, that is, acceptable points of view, in AFs. One of the classical and most important concepts in AFs are so-called complete extensions. Surprisingly, the exact bound for the maximal number of complete extensions in an AF has not yet been formally established, although there is a reasonable conjecture tracing back at least to 2015. Recently the notion of modularization was introduced and it was shown that this concept plays a key role for the understanding of relations between semantics as well as intrinsic properties. In this paper, we will use this property to give a formal proof of the conjecture regarding complete semantics.

Monitoring the status of large computing systems is essential to identify unexpected behavior and improve their performance and uptime. However, due to the large-scale and distributed design of such computing systems as well as a large number of monitoring parameters, automated monitoring methods should be applied. Such automatic monitoring methods should also have the ability to adapt themselves to the continuous changes in the computing system. In addition, they should be able to identify behavioral anomalies in useful time, to perform appropriate reactions. This work proposes a general lightweight and unsupervised method for near real-time anomaly detection using operational data measurement on large computing systems. The proposed model requires as little as 4 hours of data and 50 epochs for each training process to accurately resemble the behavioral pattern of computing systems.

Lipocalin family proteins have been shown to bind a vast array of small molecules and have subsequently been adapted to selectively bind specific ligands. In this study, candesartan, an antihypertension drug, was identified to bind mouse and human siderocalin in biomolecular NMR experiments, allowing for structural insights into the candesartan-siderocalin interaction. The ligand binding site was determined through an integrative structural biology approach using in silico ligand docking guided by NMR experiments. Building on this structurally informed binding model, we used rational protein design to modulate the binding pocket for increased or decreased ligand binding affinity. The predicted mutations were evaluated in vitro using isothermal titration calorimetry. This resulted in a mutant with a 50-fold increase in binding affinity in addition to a second mutant with a five-fold decrease in binding affinity. Thus, siderocalins have potential as a scaffold for creation of various ligand binding-based tools, including drug scavengers.

The advent of endovascular thrombectomy has significantly improved outcomes for stroke patients with intracranial large vessel occlusion, yet individual benefits can vary widely. As demand for thrombectomy rises and geographic disparities in stroke care access persist, there is a growing need for predictive models that quantify individual benefits. However, current imaging methods for estimating outcomes may not fully capture the dynamic nature of cerebral ischemia and lack a patient-specific assessment of thrombectomy benefits. Our study introduces a deep learning approach to predict individual responses to thrombectomy in acute ischemic stroke patients. The proposed models provide predictions for both tissue and clinical outcomes under two scenarios: one assuming successful reperfusion and another assuming unsuccessful reperfusion. The resulting simulations of penumbral salvage and difference in NIHSS at discharge quantify the potential individual benefits of the intervention. Our models were developed on an extensive dataset from routine stroke care, which included 405 ischemic stroke patients who underwent thrombectomy. We used acute data for training (n = 304), including multimodal CT imaging and clinical characteristics, along with post hoc markers like thrombectomy success, final infarct localization, and NIHSS at discharge. We benchmarked our tissue outcome predictions under the observed reperfusion scenario against a thresholding-based clinical method and a generalised linear model. Our deep-learning model showed significant superiority, with a mean Dice score of 0.48 on internal (n = 50) and 0.52 on external (n = 51) test data, versus 0.26/0.36 and 0.34/0.35 for the baselines, respectively. The NIHSS sum score prediction achieved median absolute errors of 1.5 NIHSS points on the internal test dataset and 3.0 NIHSS points on the external test dataset, outperforming other machine learning models. By predicting the patient-specific response to thrombectomy for both tissue and clinical outcomes, our approach offers an innovative biomarker that captures the dynamics of cerebral ischemia. We believe this method holds significant potential to enhance personalised therapeutic strategies and to facilitate efficient resource allocation in acute stroke care.

Wikidata, now a decade old, is the largest public knowledge graph, with data on more than 100 million concepts contributed by over 560,000 editors. It is widely used in applications and research. At its launch in late 2012, however, it was little more than a hopeful new Wikimedia project, with no content, almost no community, and a severely restricted platform. Seven years earlier still, in 2005, it was merely a rough idea of a few PhD students, a conceptual nucleus that had yet to pick up many important influences from others to turn into what is now called Wikidata. In this paper, we try to recount this remarkable journey, and we review what has been accomplished, what has been given up on, and what is yet left to do for the future.

Problem-solving strategies have been investigated in various informatics education contexts. However, no substantial research has yet been conducted on the problem-solving behavior of students in the field of Machine Learning (ML). This study aims to bridge this gap by analyzing the self-directed problem-solving processes employed by students in grades 8 to 10 $$(n = 93)$$(n=93)when developing decision trees as classification models. A digital multi-touch puzzle game and a custom-developed toolchain were utilized to visually capture and subsequently analyze students' gameplay behaviors using quantitative content analysis techniques. The results of this study indicate that learners within the examined age group predominantly employ exploratory problem-solving strategies in the self-directed construction of decision trees. In contrast, structured approaches are employed much less frequently and demonstrate lower persistence, yet they are significantly more correlated with successful game completion. These findings underscore the necessity of developing learning environments that promote the application and facilitate the persistence of structured problem-solving strategies, enabling learners to engage with the functioning and development of decision trees in a systematic and purposeful manner.