2026 October - 50 articles
Machine learning models are increasingly deployed in high-stakes decision systems where predictive performance alone may provide an insufficient basis for responsible model selection. Although explainable artificial intelligence (XAI) offers techniqu...
Whole-slide images and computed tomography scans are commonly decomposed into patches or slices, whereas diagnostic labels are assigned only at the study level, so that which patches or slices support the diagnosis remains unknown. Multiple-instance...
Despite recent advances in multilingual artificial intelligence, Indigenous languages of the Americas remain underrepresented in natural language processing (NLP) because of limited corpora, annotations, benchmarks, and reusable resources. This syste...
Generative models hold great potential but only if one can trust the evaluation of the data they generate. It is common to visualize and evaluate quality on two-dimensional visualizations of high-dimensional data. We show that many commonly used qual...
Physics-informed neural networks (PINNs) approximate solutions of differential equations by embedding governing equations and boundary conditions into the training objective. Controlled PINN comparisons are difficult because geometry representation,...
Electricity theft is a worldwide challenge that causes significant losses for utility companies, with annual revenue losses estimated at USD 89.3 billion globally. The widespread rollout of Advanced Metering Infrastructure (AMI) and smart meters has...
Deep neural decoders have demonstrated strong performance in electroencephalography (EEG)-based motor-imagery (MI) brain–computer interfaces (BCIs), yet their internal representations remain difficult to interpret, limiting neuroscientific insi...
Context selection for large language models (LLMs) requires distinguishing source informativeness from the performance a fixed model can realize. We develop a framework separating the Blackwell order under optimal decision rules from a model-specific...
This paper proposes a formal framework for analyzing extractability in language models based on the minimum number of attempts required to extract a set of sequences with a given confidence. It unifies previously proposed notions and mitigates the th...
We learn compatible destination and owner views in bounded, transcript-free records. Structural source/type/version addressing selects the first relation; authoritative, requested-attribute filtering reduces ordinary candidates to a mean of 3.28. Lea...
Structural dam safety control is fundamentally based on visual inspections and on the analysis of monitoring data and their comparison with the expected behavior for a given load combination, which in turn is calculated with a predictive model. In re...
Nutritional estimation pipelines built on large language models are entering biomarker research, but free-text meal descriptions have no ground truth, so outputs cannot be validated against a reference standard. What can be established without one is...
Large language models can produce convincing explanations that rely on invented facts, unsupported assumptions, or overlooked alternatives. This paper presents a method for checking whether such explanations are supported by the available evidence. T...
Automated microscopic image analysis is essential for quantitative microbiology, yet manual acquisition and generic recognition models struggle with morphological variability and scene artifacts. We address both ends of this pipeline. First, we prese...
Physics-informed neural networks usually incorporate differential equations primarily through the loss function, while the neural architecture itself is selected independently of the underlying numerical structure. This work develops a scheme-induced...
Retrospective clinical research depends on reliable extraction of structured variables from unstructured medical documentation, but manual chart abstraction is slow and vulnerable to inter-rater variability. We compared zero-shot extraction of clinic...
Background: Multi-agent large language model (LLM) systems increasingly rely on structured interaction, debate, and final model-based evaluation, yet an unsupported causal attribution introduced by one participant may propagate through the interactio...
Model validation is essential for evaluating how reliably a machine learning model performs on unseen data, especially in imbalanced classification where performance estimates can be affected by an underrepresentation of the minority class. Cross-val...
The rapid adoption of multimodal learning systems across high-stakes applications has intensified concerns about fairness and social bias, yet existing research remains scattered across modalities, tasks, protected attributes, mitigation strategies,...
In multi-stage manufacturing, conformity is confirmed only at the final test, after the cost has been incurred. On an automotive assembly line (16,500 workpieces), a regression model per checkpoint predicts the final measurement; a threshold turns it...
Automated binary semantic segmentation of focal liver lesions (FLLs) in multi-phasic computed tomography (CT) scans is important for knowledge extraction of hepatic malignancy, staging disease, and planning treatment. However, standard models struggl...
Retrieval-augmented generation and structured agent memory are commonly treated as competing ways to ground large language models. Our results show that their relative value changes with the task and that neither retrieved similarity nor graph associ...
The reciprocal relationship between large language models (LLMs) and knowledge graphs (KGs) opens new opportunities for knowledge discovery from scientific literature; however, most approaches address only one direction of this interaction. In comple...
Compressed-TinyML pipelines apply distillation, quantization and pruning to fit lightweight anomaly detectors onto resource-constrained sensor nodes. Such frameworks are routinely called privacy-aware on the strength of on-device inference or noisy f...
High-stakes and regulated decision-support systems require predictive models that are simultaneously accurate and interpretable, yet accuracy is frequently obtained through opaque models whose behaviour is difficult to audit. This paper develops an e...
Zebra finches (Taeniopygia guttata) possess a diverse vocal repertoire that plays an important role in their social communication, yet automated classification of their vocalizations remains largely unexplored. This study presents a biologically info...
Biomedical question answering requires multi-hop reasoning over implicit relationships—e.g., identifying gene-associated diseases to retrieve target drugs. However, closed-book frontier language models achieve only ~14.57% precision on two-hop...
Occupation-aligned course recommendation requires connecting occupational requirements with relevant educational content. CASPER-O addresses this task through occupation-specific retrieval, ESCO-based queries (CASPER Query), and expert-grounded encod...
Artificial intelligence (AI) models, particularly those based on deep learning, have achieved strong performance across a wide range of real-world applications. However, as models increase in scale and complexity, their behavior and internal decision...
Gait Anomaly Detection is crucial for early diagnosis of neurodegenerative conditions and for monitoring rehabilitation progress. Surface electromyography provides a direct view into muscle synergy and motor intention, but the field suffers from a ch...
To improve physical processes, domain experts use experimental test setups to gather data. After sampling, the data is analysed using a regression model, for example. The objective of this work is to investigate the performance of different combinati...
Agricultural knowledge recommendation differs from standard item ranking because the desired output is often an actionable field plan rather than a document, a passage, or an isolated tip. A useful recommendation must match the crop, growth stage, pr...
African swine fever (ASF) remains a major challenge for veterinary surveillance because of its complex spatial spread across neighbouring regions. We present a Bayesian susceptible–infected–susceptible (SIS) model on a graph in which Euro...
With the advent of the big data era, data becomes increasingly important, especially for many machine learning tasks where data quality is vital. However, missing data is inevitable in many real-world applications, making its handling a challenging p...
Continuous optimization now draws on a large and growing number of competing metaheuristic families, including differential-evolution lineages, covariance-matrix adaptation strategies, swarm-intelligence methods, and simulated annealing, yet no singl...
Credit-scoring systems must translate predictive risk into calibrated, uncertainty-aware, and governable decisions rather than ranking applicants alone. This study aims to develop and evaluate a governed credit-scoring architecture integrating predic...
Intrinsic-dimension (ID) estimates and active-unit diagnostics address different aspects of variational-autoencoder bottleneck selection. We evaluate search policies on four image datasets, five training seeds and five regularisation weights under ex...
Physics-informed anomaly detectors for industrial control systems are usually validated on a single synthetic or testbed dataset, which can yield optimistic estimates that do not transfer to deployment-like conditions. This validation-to-deployment d...
Large language models (LLMs) are increasingly proposed for clinical summarization, yet evaluations relying on textual similarity overlook clinically consequential failure modes such as fabrication, omission, contradiction, and negation inversion. We...
Growing deployment of Convolutional Neural Networks in safety-critical domains has intensified the need for transparent, discriminative explanations, yet gradient-based methods such as Grad-CAM compute explanations for a single class score, producing...
We examine how developer origin, citizenship-perspective instructions, and English versus Mandarin presentation shape sentiment scores from large language models (LLMs) for nationality-matched actions. Across two experiments using the same eleven-mod...
Passive acoustic monitoring (PAM) has emerged as a non-intrusive method for detecting and classifying marine mammal vocalizations in near real time. Yet reliable classification from PAM remains challenging, particularly for whistles—transient,...
Reliable mobility management is critical for high-mobility 5G/6G applications such as smart cities, intelligent transportation systems (ITS), and connected vehicles, where reactive 3GPP handover mechanisms become unreliable as user equipment (UE) vel...
Public surgical segmentation datasets contain information about the operative view that extends beyond their pixel labels. RoboSurg-VQA organises this information as a closed-set visual question answering (VQA) benchmark while retaining the source of...
Reported gains from multimodal fusion, feature-attribution explanations, and model-confidence scores are only as trustworthy as the evaluation protocol and preprocessing pipeline behind them, yet these are rarely audited together. We develop a benchm...
Structured clinical variables may complement fundus photographs, but the most appropriate point at which to integrate them into a retinal foundation model remains uncertain. This study compares an image-only baseline with three strategies for adding...
From the Environmental and Remote Sensing community, the need for AI models that are accurate but also interpretable is increasing. However, usually Deep and Machine Learning techniques are uninterpretable. In this study, we propose to use Genetic Pr...
Network-on-Chip (NoC) fabrics are the dominant interconnect for many-core processors, yet widely used routing methods—dimension-order routing and turn-model-based adaptive variants—assume mostly local, low-variance congestion. Modern work...
Accurate prediction of drug-pair-associated polypharmacy side effects remains an important challenge in computational pharmacovigilance. Standard binary cross-entropy (BCE) may provide insufficient emphasis on difficult positive examples. We evaluate...
Unattended ground sensors (UGSs) are passive systems used to detect and classify nearby activity such as the movement of military vehicles or personnel. However, UGS performance is limited in complex environments. This work explores the application o...
