论文广场 - AcademicHub

01.

arXiv (CS.LG) 2026-06-19 DOI: arXiv:2605.09609

Minimal Filling Architectures of Polynomial Neural Networks: Counterexamples, Frontier Search, and Defects

作者:

arXiv:2605.09609v2 Announce Type: replace Abstract: We provide counterexamples to the unimodal minimal filling architecture conjecture for polynomial neural networks (PNNs) with power activation functions. Fixing the input and output widths, the conjecture states that any minimal filling architecture has unimodal widths for the hidden layers. We found counterexamples via a frontier search, recursive dimension bounds on neurovarieties, and symbolic computation. Notably, several subarchitectures of our main example exhibit large defect, in contrast with the predominantly small-defect behavior observed in prior literature.

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02.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.16672

Sinkhorn-CPD: Robust point cloud registration via unbalanced entropic optimal transport

作者:

Jin Zhang ↗Mingyang Zhao ↗Bing Liu ↗Xin Jiang ↗

Coherent Point Drift (CPD) is widely used for rigid point cloud registration because of its soft correspondences and closed-form parameter updates. However, CPD's target-side marginal constraint forces every observation, including outliers, to receive exactly unit probability mass. This assumption degrades registration accuracy under heavy outliers and partial overlap. Optimal transport (OT) methods can handle missing mass through unbalanced formulations, but require hand-tuned annealing schedules. In this paper, we propose Sinkhorn-CPD, which replaces CPD's target-side marginal constraint with dual Kullback-Leibler penalties, allowing the algorithm to discard outliers on both sides. The resulting formulation is a fully unbalanced entropic optimal transport problem, which can be efficiently solved by generalized Sinkhorn iterations. Moreover, Sinkhorn-CPD preserves the closed-form Procrustes and variance updates of CPD. In our method, the variance sigma^2 plays the role of the entropic regularization parameter, which induces an automatic annealing schedule from diffuse to sharp correspondences without manual temperature tuning. Experiments on synthetic, cross-category, and scan-to-CAD benchmarks show that Sinkhorn-CPD achieves state-of-the-art accuracy, with strong robustness to outliers and partial overlap.

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03.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.15574

Toward the Whole Picture: Accumulative Fingerprint Mapping and Reconstruction for Small-Area Mobile Sensors

作者:

Xiongjun Guan ↗Jianjiang Feng ↗Jie Zhou ↗

Small-area fingerprint sensing on mobile devices creates a fundamental mismatch between acquisition and recognition: each touch captures only a tiny, pose-varying local patch, while reliable biometric matching ultimately requires a stable and sufficiently complete fingerprint representation. Existing pipelines largely cope with this mismatch by treating repeated touches as independent partial templates, which leads to repeated registration, repeated matching, and no guarantee of adequate global coverage. In this paper, we advocate a different formulation, namely accumulative fingerprint mapping and reconstruction for small-area mobile sensing. Rather than matching every partial patch separately, the proposed perspective converts a sequence of local observations into a unified fingerprint state that is progressively refined as new touches arrive and can be matched only once after consolidation. As a concrete baseline, we present a classical pipeline that performs patch-wise structural feature extraction, feature-level registration and fusion, fingerprint map construction, and phase-based ridge reconstruction. More importantly, we position this baseline within a broader mobile fingerprint framework that integrates structured token learning, two-stage pose reasoning, and diffusion-based generative reconstruction. This viewpoint reframes mobile fingerprint recognition from multi-capture multi-match processing to accumulative map building, state refinement, and one-shot matching, offering a principled route toward efficient, pose-robust, and deployment-friendly biometrics for small-area mobile platforms. The baseline implementation has been publicly released at https://github.com/XiongjunGuan/FpReconstruction.

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04.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.16153

A Comprehensive Survey of Medical Image Segmentation: Challenges, Benchmarks, and Beyond

作者:

Pengyu Zhu ↗Xiaojing Zhang ↗Kunbo Zhang ↗Chunyan Zhang ↗Zhenyu Wang ↗

Medical image segmentation plays a critical role in clinical diagnostics, treatment planning, disease monitoring, and neurological disorder identification. This article presents a comprehensive review of its systematic development, covering widely used public datasets, representative methods built on the U-Net, Transformer, and SAM architectures, and key evaluation metrics with their differences, followed by an analysis of major challenges from multiple perspectives. Unlike surveys that focus on a single model family or a specific clinical application, this review organizes U-Net-, Transformer-, and SAM-based methods within a unified analytical framework, with a particular focus on their effectiveness in improving segmentation accuracy and efficiency. This work aims to guide future research and support clinical translation of medical image segmentation, with all related resources publicly available in our GitHub repository: https://github.com/andrew-pengyu/Awsome_MedSeg/tree/main.

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05.

arXiv (CS.AI) 2026-06-25 DOI: arXiv:2606.24956

Convex–Concave Quadratic Spectral Filtering for Graph Neural Networks

作者:

Ranhui Yan ↗Jia Cai ↗Mengzhu Chen ↗Haodong Yang ↗

arXiv:2606.24956v1 Announce Type: cross Abstract: Spectral graph neural networks (GNNs) interpret message passing as frequency-selective filtering. While low-order spectral filters are efficient, their limited selectivity often leads to weak attenuation outside the passband, whereas high-order alternatives introduce optimization challenges. We propose DCQ-GNN, a spectral GNN based on a compact bank of adaptive convex–concave quadratic filters. By restricting the filter order to two while explicitly exploiting complementary curvature, DCQ-GNN improves spectral selectivity as quantified by Dirichlet energy and entropy measures without resorting to high-order polynomial expansions. The model fuses filter outputs through a node-adaptive gating mechanism to enable node-wise structure-aware spectral selection. We provide a formal spectral analysis grounded in Dirichlet energy attenuation, von Neumann entropy, and curvature polarity, and derive explicit characterizations of filter behavior across varying levels of homophily and structural perturbations. Extensive benchmarks on 10 datasets show that DCQ-GNN ties for the top average rank (3.0) on heterophilic graphs and obtains the second-best rank (4.2) on homophilic graphs, remaining competitive with representative high-order polynomial spectral filters. Furthermore, under strong structural perturbations, DCQ-GNN exhibits substantially smaller performance degradation compared to both first-order and high-order baselines. These results demonstrate that curvature-aware quadratic banks provide a robust and efficient alternative to high-order spectral models while preserving optimization stability and computational efficiency.

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06.

arXiv (CS.LG) 2026-06-15 DOI: arXiv:2606.14169

Machine Learning for Biomedical Raman Spectroscopy: From Spectral Acquisition to Clinical Translation

作者:

arXiv:2606.14169v1 Announce Type: new Abstract: Raman spectroscopy provides label-free, chemically specific characterization of biological systems and has become an important tool for cancer diagnosis, molecular subtyping, microbiological identification, and intraoperative decision support. Biomedical Raman spectra are, however, high-dimensional, noisy, and affected by fluorescence background, acquisition variability, and biological heterogeneity, making robust computational analysis essential. This review examines the role of machine learning across the biomedical Raman spectroscopy pipeline, from preprocessing and signal correction to unsupervised structure discovery, supervised diagnosis and molecular stratification, representation and transfer learning, explainability, biomarker discovery, and multimodal integration with imaging, pathology, and molecular profiling. Emphasis is placed on the use of machine learning not only for diagnostic classification, but also for biologically interpretable and clinically actionable analysis. We also discuss the main barriers to clinical translation, including limited dataset sizes, inter-instrument variability, inconsistent preprocessing, insufficient external validation, reproducibility concerns, and limited sharing of software, data, and metadata. We argue that progress will require methodological advances together with standardization, robust validation, explainability, and deployment-ready analytical frameworks. By integrating methodological, biomedical, and translational perspectives, this review outlines key directions for developing reliable and clinically deployable Raman-AI systems.

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07.

medRxiv (Medicine) 2026-06-23 DOI: HASH:044547b6c8f11eaef18364d39f57062a

Linking mpox wastewater surveillance with reported clinical cases in three countries in Sub-Saharan Africa

作者:

Sgarabotto ↗Tiwari ↗Kabena ↗Lyimo ↗Lompo ↗Shea ↗Ngelesi ↗Mushumbusi ↗J. P ↗Zakaria ↗Msoma ↗Kabore ↗…

The emergence of the novel monkeypox virus (MPXV) clade Ib in the Democratic Republic of the Congo (DRC) and neighboring countries in late 2023 highlighted the need for rapid, scalable surveillance approaches to support outbreak detection and response. As part of the ODIN-Mpox project, wastewater surveillance (WWS) systems were established as an emergency public health measure in three Sub-Saharan African countries (DRC, Tanzania, and Burkina Faso) to evaluate the feasibility of wastewater-based monitoring for mpox and strengthen local surveillance capacity. Between January 2025 and April 2026, 117 wastewater samples were collected from selected sites and analyzed for MPXV DNA using targeted qPCR assays. Clinical mpox data were obtained from national surveillance systems and WHO reports to assess epidemiological linkages between wastewater detections and reported infections. Six wastewater samples tested positive for MPXV DNA. During the study period, DRC experienced the highest disease burden, with weekly reported cases peaking at about 3,000 in January 2025, while Tanzania reported a peak of 20 weekly cases in March 2025. No confirmed clinical cases were reported in Burkina Faso. No clear relationship was observed between reported case numbers and qPCR Ct values in positive wastewater samples. Despite the low detection frequency, the project demonstrated the operational feasibility of implementing MPXV wastewater surveillance in resource-limited settings and established laboratory capacity for environmental monitoring of emerging infectious diseases. Given the early stage of WWS implementation in the region, the study identified opportunities for further system strengthening, including optimization of sample processing and reporting workflows, improved access to laboratory supplies, and enhanced integration of environmental and clinical surveillance data streams. These findings highlight the value of WWS as a complementary component of integrated public health surveillance systems and emphasize the need for continued investment in laboratory capacity, harmonized methodologies, governance frameworks, and knowledge exchange to enhance outbreak preparedness and response in low-resource settings.

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08.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2606.20236

A Multi-Agent system for Multi-Objective constrained optimization

作者:

Federica Filippini ↗

arXiv:2606.20236v1 Announce Type: new Abstract: Many decision-making problems in computing and networking systems can be naturally formulated as cost-minimization problems under performance constraints. In dynamic environments, reinforcement learning (RL) is often used to solve such problems at runtime by embedding both costs and constraint violations into a single scalar reward through weighted penalty terms, following a Lagrangian-inspired formulation. However, in this context the behavior of the learned policy critically depends on the choice of these weights, which are typically selected manually. This makes it difficult to identify an appropriate trade-off between optimizing the primary objective and effectively avoiding constraint violations, particularly in non-stationary environments where their relative importance may change. This paper presents MAMO (Multi-Agent system for Multi-Objective constrained optimization), an approach to tackle this balancing problem through multi-agent RL. MAMO decouples task execution from objective design by formulating the selection of reward weights as a learning problem, providing a !rst step towards more autonomous and robust RL-based solutions for constrained optimization problems in dynamic environments.

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09.

arXiv (CS.CL) 2026-06-24 DOI: arXiv:2606.21447

Precision Recall Controllable Radiology Report Generation via Hybrid Natural Language and Clinical Reward Learning

作者:

Ling Chen ↗Ruinan Jin ↗Jun Luo ↗Hanliang Chen ↗Quirin Strotzer ↗Rongkai Yan ↗Yuan Xue ↗Luciano Prevedello ↗Dufan Wu ↗

Automated radiology report generation (RRG) has gained increasing attention because it can reduce the heavy workload of clinical report writing. However, most existing methods mainly optimize for natural language generation (NLG) metrics that focus on language fluency, while providing little control over clinically important factors such as precision and recall. As consequence, generated reports may be fluent but not well aligned with different clinical needs. To address this challenge, we propose a reinforcement learning framework for precision recall controllable RRG, where a control parameter explicitly adjusts the trade-off between clinical precision and recall during inference. This design allows the model to flexibly generate reports according to different clinical requirements. To ensure clinical correctness, we introduce a clinical reward into the training objective, which helps improve clinical efficacy (CE) beyond standard language-based optimization. In addition, we apply a group-relative training strategy that normalizes rewards within each training group, reducing reward variance and improving training stability. Extensive experiments on the MIMIC-CXR dataset show that our method consistently outperforms state-of-the-art approaches in both NLG and CE evaluation metrics, while providing reliable control over the CE precision recall trade-off.

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10.

arXiv (quant-ph) 2026-06-16 DOI: arXiv:2606.16418

A short proof of the modified Kretschmann-Schlingemann-Werner conjecture

作者:

Satvik Singh ↗

arXiv:2606.16418v1 Announce Type: new Abstract: Let $\Phi_1, \Phi_2 : \mathbb{M}_d(\mathbb{C})\to \mathbb{M}_n(\mathbb{C})$ be two quantum channels with respective Stinespring isometries $V_1, V_2 : \mathbb{C}^{d}\to \mathbb{C}^{n} \otimes \mathbb{C}^{m}$ on any common dilation space $\mathbb{C}^{m}$. We prove that there exists a unitary $U$ on $\mathbb{C}^{m}$ such that $\|V_1-({\bf1}\otimes U)V_2\|_\infty\leq\sqrt{2\|\Phi_1-\Phi_2\|_\diamond},$ thus resolving vom Ende's modification of the Kretschmann-Schlingemann-Werner conjecture in the affirmative.

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11.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2512.04524

Prototype-Based Semantic Consistency Alignment for Domain Adaptive Retrieval

作者:

Tianle Hu ↗Weijun Lv ↗Na Han ↗Xiaozhao Fang ↗Jie Wen ↗Jiaxing Li ↗Guoxu Zhou ↗

arXiv:2512.04524v4 Announce Type: replace-cross Abstract: Domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, enabling effective retrieval while mitigating domain discrepancies. However, existing methods encounter several fundamental limitations: 1) neglecting class-level semantic alignment and excessively pursuing pair-wise sample alignment; 2) lacking either pseudo-label reliability consideration or geometric guidance for assessing label correctness; 3) directly quantizing original features affected by domain shift, undermining the quality of learned hash codes. In view of these limitations, we propose Prototype-Based Semantic Consistency Alignment (PSCA), a two-stage framework for effective domain adaptive retrieval. In the first stage, a set of orthogonal prototypes directly establishes class-level semantic connections, maximizing inter-class separability while gathering intra-class samples. During the prototype learning, geometric proximity provides a reliability indicator for semantic consistency alignment through adaptive weighting of pseudo-label confidences. The resulting membership matrix and prototypes facilitate feature reconstruction, ensuring quantization on reconstructed rather than original features, thereby improving subsequent hash coding quality and seamlessly connecting both stages. In the second stage, domain-specific quantization functions process the reconstructed features under mutual approximation constraints, generating unified binary hash codes across domains. Extensive experiments validate PSCA's superior performance across multiple datasets.

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12.

arXiv (CS.CV) 2026-06-11 DOI: arXiv:2509.11548

How Auxiliary Reasoning Unleashes GUI Grounding in VLMs

作者:

Weiming Li ↗Yan Shao ↗Jing Yang ↗Yujing Lu ↗Ling Zhong ↗Yuhan Wang ↗Min Yu ↗Tongxiao Ruan ↗Manni Duan ↗

Graphical user interface (GUI) grounding is a fundamental task for building GUI agents. However, general vision-language models (VLMs) struggle with this task due to a lack of specific optimization. We identify a key gap in this paper: while VLMs exhibit significant latent grounding potential, as demonstrated by their performance measured by Pointing Game, they underperform when tasked with outputting explicit coordinates. To address this discrepancy and bypass the high data and annotation costs of current fine-tuning approaches, we propose three zero-shot auxiliary reasoning methods. By providing explicit spatial cues such as axes, grids and labeled intersections as part of the input image, these methods enable VLMs to better articulate their implicit spatial understanding capabilities. We evaluate these methods on four GUI grounding benchmarks across seven open-source and proprietary VLMs. Experimental results show substantial gains from auxiliary reasoning. Mark-Grid Scaffold boosts Gemini-3.1-Pro from 11.72\% under direct inference to 95.20\% on ScreenSpot-v2, achieves state-of-the-art performance on ScreenSpot, and approaches the strongest fine-tuned methods on ScreenSpot-v2 and UI-I2E-Bench. Our code is available at https://github.com/liweim/AuxiliaryReasoning.

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13.

arXiv (quant-ph) 2026-06-24 DOI: arXiv:2606.24713

Symmetric mass generation of interacting chiral fermions on a one-dimensional lattice without fermion doubling

作者:

V. A. Zakharov ↗Atsushi Ueda ↗Frank Verstraete ↗C. W. J. Beenakker ↗

arXiv:2606.24713v1 Announce Type: cross Abstract: Symmetric mass generation is the interaction-induced opening of a fermion gap without spontaneous symmetry breaking. The anomaly-free 3-4-5-0 model of Wang and Wen provides a minimal one-dimensional setting for this phenomenon, but a direct lattice realization faces two obstacles: fermion doubling for local chiral discretizations and perturbative irrelevance of the six-fermion gapping interaction. We address both obstacles. First, we formulate the model on a strictly one-dimensional tangent-fermion lattice, where a nonlocal hopping produces a single chiral branch without a mirror partner while retaining an efficient tensor-network representation. Second, we add a Hubbard-type density-density interaction (Luttinger parameter $K$) that reduces the scaling dimension of the 3-4-5-0 interaction from $5$ to $5K$, making it relevant for $K

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14.

arXiv (CS.CL) 2026-06-11 DOI: arXiv:2606.11817

Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code

作者:

Yitong Zhang ↗Shiteng Lu ↗Jia Li ↗

Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code. Meanwhile, Grammar-Constrained Decoding (GCD) has been widely adopted to improve the reliability of LLM-generated code by enforcing syntactic validity. In this paper, we reveal a counterintuitive risk: this reliability-oriented technique can itself become an attack surface. We uncover a new jailbreak attack, termed CodeSpear, that exploits GCD to induce LLMs into generating malicious code. Our experiments show that simply applying a benign code grammar constraint can effectively jailbreak LLMs. To address this vulnerability, we propose CodeShield, a safety alignment approach that robustly preserves safe behavior even under attacker-controlled grammar constraints. CodeShield aligns the model in the code modality by teaching it to generate honeypot code under GCD. Such code is semantically harmless, so it does not implement the malicious request, and structurally diverse, so it is difficult to suppress through grammar tightening. At the same time, CodeShield still preserves natural-language refusals when natural language is available. Experiments on 10 popular LLMs across 4 benchmarks show that CodeSpear outperforms representative jailbreak baselines and increases the attack success rate by more than 30 percentage points on average. CodeShield also restores safety under CodeSpear while preserving benign utility. Our findings reveal a fundamental risk of GCD and call for greater attention to its potential security implications.

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15.

arXiv (CS.CL) 2026-06-15 DOI: arXiv:2606.14150

Small LLMs: Pruning vs. Training from Scratch

作者:

Yufeng Xu ↗Taiming Lu ↗Kunjun Li ↗Jiachen Zhu ↗Mingjie Sun ↗Zhuang Liu ↗

Pruning promises a shortcut to strong small language models. In this work, we examine this promise by pruning Llama-3.1-8B at pruning ratios of 0.5–0.8 with six methods spanning depth, width, and sparse granularities, under two controlled token-matched settings. (1) With the same training token budget, pruned initialization consistently outperforms random initialization. This shows that the parent model provides a strong starting point, although the advantage narrows as the training token budget grows and as the pruning ratio rises, nearly vanishing at the highest pruning ratio we study. (2) When training from scratch is instead given the full token budget consumed by the whole pipeline, pruning at finer granularities still retains an advantage, while coarser structured pruning can be matched or surpassed. This suggests that the parent model transfers knowledge that additional training tokens alone cannot fully recover, but only at fine granularity. Taken together, our results yield a clear recommendation: with a large pretrained model in hand and a limited training token budget, pruning is better than training from scratch; when the training budget is not limited, training from scratch can be competitive for coarser pruning, so a large pretrained parent is not always necessary.

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16.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.12767

Constructing Evaluation Datasets for Procedural Reasoning: Balancing Naturalness, Grounding, and Multi-Hop Coverage

作者:

Sarah Elshabrawy ↗Rahul K. Dass ↗Ashok K. Goel ↗

arXiv:2606.12767v1 Announce Type: new Abstract: Evaluating procedural reasoning in AI-supported learning systems requires question-answer datasets that are both learner-like and grounded in the instructional knowledge the system is expected to use. We study how TMK-based question generation strategies affect dataset quality for procedural and multi-hop reasoning. We compare three strategies: strict generation from Task-Method-Knowledge (TMK) models, transcript-first generation with post-hoc TMK filtering, and TMK-aware generation that combines transcripts with structured guidance. To evaluate generated items, we introduce a grounding validation framework based on closed-set evidence units extracted from TMK models. The framework measures whether answers are supported by the underlying representation, whether questions are self-contained, and whether they target multi-hop procedural reasoning. Across 23 instructional topics and 690 generated question-answer pairs, strict TMK generation achieves the strongest overall quality, with 96.5% grounded questions and 92.6% usable questions. Transcript-first generation produces more learner-like questions but more context-dependent or weakly grounded items, while TMK-aware generation yields high raw multi-hop coverage but lower grounding. These results show that procedural richness and natural phrasing do not guarantee representational grounding, motivating explicit representation-aware validation for evaluation datasets in AI-supported learning.

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17.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.15886

Text region detection in historical astronomical diagrams

作者:

Zeynep Sonat Baltac{\i}↗Rapha\"el Baena ↗Fei Meng ↗Somk\'eo Norindr ↗Florence Somer ↗Matthieu Husson ↗Mathieu Aubry ↗

Text detection is a crucial task in the analysis of historical documents. While datasets and benchmarks exist for text detection in manuscripts and maps, the study of text in mathematical diagrams has received little attention. To address this, we introduce a large-scale, diverse, open-access dataset of 948 historical astronomical diagrams containing 10,940 oriented polygonal text regions. Our dataset spans ten centuries (8th to 18th) and seven main linguistic traditions: Arabic and Persian (115), Chinese (332), Byzantine (233), Latin (185), Hebrew (48), and Sanskrit (35). It captures a wide range of diagram styles and textual content, from symbols to multi-line paragraphs. Each text instance is annotated with ordered polygons that precisely delineate text regions and encode the reading direction. In addition, we annotated the 2,293 regions in Latin diagrams with 20 class labels. We evaluated several strong baselines on our dataset, including TESTR, DeepSolo++, and Poly-DETR, a simple extension of DINO-DETR that we design to predict ordered polygon vertices. Poly-DETR achieves state-of-the-art performance on the MTHv2 and cBAD2019 benchmarks and provides a solid, simple baseline on our dataset. Code and dataset available online.

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18.

arXiv (CS.CL) 2026-06-25 DOI: arXiv:2605.04926

Unintended Negative Impacts of Promotional Language in Patent Evaluation

作者:

Bingkun Zhao ↗Chenwei Zhang ↗Hao Peng ↗

Promotional language has been increasingly used to aid the communication of innovative ideas in science. Yet, less is known about its role in the context of technological innovation. Here, we use a validated and domain-diagnosed lexicon of 135 promotional words to study the association between promotional language and patent evaluation outcomes among 2.7 million USPTO patent applications. Our large-scale study reveals three unexpected findings. First, in contrast to scientific evaluation, we find that a higher frequency of promotional words is negatively associated with the probability of an application being (i) granted a patent, (ii) transferred ownership, and (iii) successfully appealed. This promotional penalty holds even after accounting for a range of confounding factors and is largely robust across different technological areas. Among matched samples, the difference in the success rate between the lowest and highest promotional density quintile is 5.5, 5.9, and 5.3 percentage points for patentability, transferability, and rejection reversal. Second, contrary to institutional skepticism, we show that promotional language is not a mask of weak technology, but objectively reflects the degree of combinatorial novelty and future citation impact. Third, digging into the mechanisms, we find that the tolerance to promotional framing is strongly moderated by human factors, with men and experienced examiners showing a higher acceptance of promotional narratives than women and novice examiners. By revealing an emerging paradox in the patent system, our study offers theoretical and practical implications for improving patent evaluation through more objective scrutiny of linguistic patterns in patent filings.

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19.

bioRxiv (Bioinfo) 2026-06-15 DOI: HASH:afb0144a38d292cd77f6bf7a01f30320

Multiple Fault Analysis and Drug Therapy on Signaling Pathways Using Dynamic Bayesian Network-based Model

作者:

Chowdhury ↗Maitra ↗Agarwal ↗Sur ↗Sarkar ↗Majumder ↗Lodh ↗

Cell growth is an intricate biological phenomenon that is closely regulated by the interplay between various growth factors and transcription factors. Signaling pathways are the main mediators in this event, which provide the driving force for mitosis or sometimes meiosis. However, when malfunctions occur within the biological network, they can cause uncontrolled cell division, regardless of external stimuli. By employing Dynamic Bayesian Networks (DBNs), these malfunctions can be explicitly simulated, offering insights into their effects on cellular behavior and growth regulation. To a significant extent, the resultant outcomes can be mitigated through the use of reduced drug combinations. This study delves into the intricacies of signaling pathway behavior under the influence of concurrent malfunctions. Initially, we replicate the effects of these dysfunctions within DBNs. Subsequently, drug therapy is applied to alleviate their impact. Our methodology introduces a parameter known as efficiency_score, enabling the identification of optimized drug combinations without prior knowledge of specific dysfunctions. Particularly relevant in the context of realistic cancer conditions, these tailored drug inhibition points demonstrate enhanced efficacy compared to conventional treatments. Leveraging GPU acceleration throughout the modeling process accelerates the analysis of multiple faults within the biological networks, rendering our approach notably faster and more efficient.

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20.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.16626

Using AI in engineering education: a balancing act, driven by clear purpose

作者:

Olya Kudina ↗

arXiv:2606.16626v1 Announce Type: cross Abstract: Based on a questionnaire of 100 higher-education students, predominantly from engineering-related fields, and a critical review of recent literature, this chapter examines how students use and perceive Large Language Models (LLMs) in engineering education. Students primarily value LLMs for writing support, conceptual clarification, coding assistance, and brainstorming, while simultaneously expressing concerns about inaccuracies, bias, overreliance, academic integrity, and the burden of verification. Through an analysis of two dominant metaphors, namely LLMs as an "oracle" and as a "tutor," the chapter shows how these systems cultivate expectations of authority, expertise, and personalized learning that often exceed their actual capabilities. The chapter further argues that students' attachment to the promises of efficiency and personalized support reflects a form of "cruel optimism," where the perceived benefits of LLMs often depend on the very skills, vigilance, and expertise that students are still developing. Overall, the chapter argues for a purpose-driven and context-sensitive approach to AI integration in engineering education, emphasizing critical AI literacy, reflective assessment design, pedagogical caution, and consideration of broader ethical and environmental impacts.

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21.

medRxiv (Medicine) 2026-06-24 DOI: HASH:30bb1492f55fcc2c069cab7d6c0c9dca

Deleterious mitochondrial heteroplasmy drives high-risk clonal hematopoiesis and hematological malignancy

作者:

Arking ↗D. E ↗McDonald ↗Shi ↗Puiu ↗J. V ↗Pasca ↗Hong ↗Y. S ↗Gondek ↗

Abstract Mitochondrial DNA (mtDNA) heteroplasmy, the coexistence of multiple mtDNA variants within cells, accumulates with age and is associated with hematological malignancies and mortality. However, whether predicted deleterious heteroplasmies causally contribute to cancer or merely represent passenger mutations remains unresolved. Here, leveraging ~36,000 first-degree relative pairs from the UK Biobank and All of Us Research Program cohorts, we deconvolute overall heteroplasmy metrics into those that are shared across family members (representing inherited variants) and those that are not (representing de novo variants) to establish a Mendelian randomization framework for assessing causality. We show that shared heteroplasmies exhibit strong purifying selection, with reduced predicted deleteriousness compared to not shared variants, and that 90% of an individual's deleterious heteroplasmy burden is somatically acquired. Critically, shared deleterious heteroplasmy burden, fixed at conception and thus temporally upstream of potential confounders, is significantly associated with hematological malignancies (RR=2.81, 95% CI 1.29-6.13), with effect sizes concordant with the not shared heteroplasmy burden. Furthermore, shared deleterious heteroplasmy specifically associates with high-risk clonal hematopoiesis of indeterminate potential (CHIP), particularly spliceosome mutations, suggesting mitochondrial dysfunction promotes clonal expansion of specific CHIP subtypes. Finally, we identify ultra-rare individual mtDNA variants associated with hematological malignancies, a hallmark of driver mutations. These findings establish mtDNA heteroplasmies, including inherited variants, as causal contributors to hematological malignancy risk and demonstrate that most disease-relevant burden is acquired during life, identifying potential opportunities for prevention and therapeutic intervention in individuals at elevated risk for hematological cancer, particularly of myeloid origin.

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22.

arXiv (CS.LG) 2026-06-17 DOI: arXiv:2606.17419

Generalization Guarantees for Multi-Input Neural Operator Learning in Sobolev Spaces

作者:

Yahong Yang ↗Zecheng Zhang ↗Wei Zhu ↗Wenjing Liao ↗Hao Liu ↗

arXiv:2606.17419v1 Announce Type: new Abstract: We develop approximation and generalization error estimates for multi-input neural operators, with the output error measured in Sobolev norms. In contrast to standard operator-learning settings with a single input function, our framework allows multiple input functions defined on possibly different domains, with different dimensions and Sobolev regularities. The derived rates explicitly quantify the contribution of each input space to the final error bound. In particular, in the balanced regime, the approximation and generalization rates are governed by the interaction between the input dimensions, regularities, and Sobolev orders, while the dependence on the model complexity retains a $\log\log/\log$-type structure. Our analysis provides a general theoretical framework for multi-input operator learning, including Sobolev training, and is applicable to operator learning problems arising from partial differential equations and scientific computing.

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23.

medRxiv (Medicine) 2026-06-24 DOI: HASH:883cc765b0dfbbeba7dd523d4fefebc8

Cognitive-emotional responses to ultrasonic neuromodulation of anterior cingulate cortex

作者:

Cooper ↗B. S ↗Koppelmans ↗Riis ↗T. S ↗Feldman ↗D. A ↗Kwon ↗Brashear ↗Guynn ↗Okifuji ↗Kubanek ↗…

The anterior cingulate cortex (ACC) is a key brain center involved in cognitive and emotional processing that is implicated in a variety of neuropsychiatric disorders including chronic pain and depression. Circuit-targeted diagnosis and treatment of these disorders will require the capacity to precisely modulate ACC subregions. Toward that end, we recently developed and validated a novel low-intensity transcranial focused ultrasound device that can noninvasively and directly modulate ACC subdivisions in humans with millimeter precision. Here we describe the subjective reports of 36 individuals diagnosed with either chronic pain or major depression who received repeated brief stimulation trials (807 active, 797 sham; duration 30s-3min) spanning the dorsoventral extent of the ACC. Sonication immediately altered cognitive-emotional states (odds ratio 5.6, active versus sham), eliciting a positive-valence experience more often than negative (29% versus 8%) in both diagnostic groups. Sham-adjusted response rate varied across ACC targets, with the largest effects (Cohen's d ~ 0.8) observed in pregenual and subgenual ACC in subjects with chronic pain and depression, respectively. These rapid trial-by-trial responses to ACC stimulation predicted subsequent improvements in pain and depression severity at 24 hours. Collectively, these findings reveal that transcranial ultrasound can robustly evoke immediate, target-specific, clinically meaningful changes in cognitive-emotional state, demonstrating the potential of ultrasonic neuromodulation as a tool for individualized probing of circuit function and dysfunction.

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24.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.11909

Embodied-BenchClaw: An Autonomous Multi-Agent System for Embodied Spatial Intelligence Benchmark Construction

作者:

Baoyang Jiang ↗Fengchun Zhang ↗Leyuan Wang ↗Haotian Li ↗Yida Wang ↗Zhe Ji ↗Jinshan Lai ↗Xi Ren ↗Jianwei Hu ↗Qiang Ma ↗

arXiv:2606.11909v1 Announce Type: new Abstract: Benchmarks are essential for evaluating embodied spatial intelligence, yet their construction is labor-intensive, hard to reuse, and difficult to maintain. Existing embodied benchmarks are often static and may quickly become saturated as models improve, limiting their ability to distinguish new capabilities. We propose Embodied-BenchClaw, an autonomous agentic system for constructing embodied spatial intelligence benchmarks. Given a user-specified evaluation intent, Embodied-BenchClaw automatically produces a complete and continually updatable benchmark package through a five-stage pipeline: intent blueprinting, data collection, structuring and cleaning, benchmark synthesis, and evaluation reporting. The pipeline is coordinated by three agents for planning, construction, and evaluation. To improve reusability and reliability, Embodied-BenchClaw introduces an extensible Skill Library and process quality control, enabling benchmark construction to be composable, verifiable, and repairable. We instantiate multiple benchmarks covering indoor spatial reasoning, outdoor spatial reasoning, robotic manipulation, quadruped robot navigation, UAV/aerial-view understanding, and static benchmark enhancement. These benchmarks span diverse embodied carriers, data sources, and spatial capabilities. Experiments with human evaluation, judge-based assessment, consistency checks, cost analysis, and ablations show that Embodied-BenchClaw can construct verifiable, executable, maintainable, and diagnostically useful embodied spatial benchmarks with reduced manual effort.

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25.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2602.07343

Seeing Roads Through Words: A Language-Guided Framework for RGB-T Driving Scene Segmentation

作者:

Ruturaj Reddy ↗Hrishav Bakul Barua ↗Junn Yong Loo ↗Thanh Thi Nguyen ↗Ganesh Krishnasamy ↗

Robust semantic segmentation of road scenes under adverse illumination, lighting, and shadow conditions remain a core challenge for autonomous driving applications. RGB-Thermal fusion is a standard approach, yet existing methods apply static fusion strategies uniformly across all conditions, allowing modality-specific noise to propagate throughout the network. Hence, we propose CLARITY that dynamically adapts its fusion strategy to the detected scene condition. Guided by vision-language model (VLM) priors, the network learns to modulate each modality's contribution based on the illumination state while leveraging object embeddings for segmentation, rather than applying a fixed fusion policy. We further introduce two mechanisms - one which preserves valid dark-object semantics that prior noise-suppression methods incorrectly discard, and a hierarchical decoder that enforces structural consistency across scales to sharpen boundaries on thin objects. Experiments on the MFNet dataset demonstrate that CLARITY establishes a new state-of-the-art (SOTA), achieving 62.3% mIoU and 77.5% mAcc.

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