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01.
PLOS Medicine 2026-06-04

Comparative impacts and cost-effectiveness of tuberculosis systematic screening strategies in prisons in Brazil, Colombia, and Peru: A mathematical modeling study

作者:
Yiran E. Liu↗

by Yiran E. Liu, José Victor Bortolotto Bampi, Ronan F. Arthur, Argita D. Salindri, Caroline Busatto, Pedro Avedillo Jiménez, Daniele Maria Pelissari, Fernanda Dockhorn Costa Johansen, Robert Arana-Narvaez, Alvaro Fernando Moreno Roca, Wilfredo Santos Solís Tupes, Esther Mori Jiu, Christian Alfredo Moreno Roca, Erika Albertina Abregú Contreras, Valentina Antonieta Alarcón Guizado, Julián Trujillo Trujillo, Belkys Marcelino, Mónica Alonso Gonzalez, Mayra Cecilia Córdova Ayllon, Ted Cohen, Moises A. Huaman, Jeremy D. Goldhaber-Fiebert, Julio Croda, Jason R. Andrews Background Incarceration is a leading driver of tuberculosis in Latin America. Systematic screening in prisons may reduce tuberculosis burden, but optimal strategies and cost-effectiveness remain uncertain. We examined the population-wide health impacts and cost-effectiveness of systematic screening in prisons in Brazil, Colombia, and Peru, comparing different timepoints, frequencies, and screening algorithms. Methods and findings Using dynamic transmission models calibrated to Brazil, Colombia, and Peru, we simulated annual or biannual (twice-yearly) prison-wide screening, alone or combined with entry and exit screening from 2026 to 2035. We evaluated four algorithms: (1) symptom screening, (2) chest X-ray with computer-aided detection (CXR-CAD), (3) symptoms and CXR-CAD (follow-up testing if either is positive), and (4) GeneXpert Ultra (Xpert) with pooled sputum. Individuals screening positive then received individual Xpert. We projected impacts on within-prison and population-level tuberculosis incidence in 2035, along with discounted costs (2023 US dollars) and disability-adjusted life years (DALYs). Model projections showed that combined entry, exit, and biannual screening with CXR-CAD was highly impactful and cost-effective across countries, reducing tuberculosis incidence by 61%–87% in prisons and 18%–28% population-wide. Compared to only biannual CXR-CAD (the next best strategy), the incremental cost per DALY averted of adding entry and exit screening was $2,984 (Brazil), $2,925 (Colombia), and $645 (Peru). Adding symptom screening to CXR-CAD marginally increased benefit and was only cost-effective in Peru’s higher-incidence prisons. Biannual screening alone remained cost-effective at prison incidence levels well below national averages, as well as at far lower willingness-to-pay thresholds. In settings without CXR-CAD, pooled Xpert was an impactful, cost-effective alternative. Key limitations include the model’s simplified representation of tuberculosis disease states and lack of stratification by age, gender/sex, HIV, or drug resistance. Conclusions These modeling results support immediate national-level adoption of prison-wide tuberculosis screening twice-yearly and at entry and exit, using CXR-CAD or pooled Xpert.

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02.
arXiv (CS.CL) 2026-06-16

AmchiBias: Measuring Stereotypical Bias in Goan Identity Groups with a Minimal Pair Dataset in English and Konkani

作者:
Michelle Barbosa↗Sebastian Pad\'o↗Franziska Weeber↗

Socio-cultural stereotypical bias is an important consideration in the development and deployment of NLP systems. It is however often considered only at the national level, despite rich subnational socio-cultural structures. We present AmchiBias, the first benchmark for measuring socio-cultural stereotypical bias for the Indian state of Goa with its unique historically multicultural setting. It covers various Goan identity groups and comprises 313 minimal pairs across eight sociodemographic dimensions in both English and Devanagari Konkani. We then evaluate stereotypical bias in five multilingual encoder models on this benchmark. We find near-chance scores in Konkani, reflecting language incompetence for general multilingual models and a lack of Goan cultural competence for Indian language models. Queried in English, models with a stronger Indian language coverage show higher bias for pan-Indian groups than hyperlocal Goan groups. This suggests the English signal reflects pan-Indian pretraining associations rather than genuine Goan cultural knowledge. Our findings highlight a critical gap in low-resource multilingual NLP evaluation for hyperlocal community identities.

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03.
arXiv (CS.CV) 2026-06-15

S$^2$COPE: Self-Supervised Concept Discovery via Preference Learning

作者:
Shilong Xiang↗Zirui Zhang↗Chengzhi Mao↗

Current representation learning paradigms force a fundamental compromise: self-supervised methods scale to massive datasets but yield opaque features, whereas interpretable models remain bottlenecked by the need for dense human annotation. We introduce Self-Supervised Concept discOvery via Preference lEarning (\model), a label-free framework that resolves this dilemma. Instead of treating Vision-Large-Language Models (VLLMs) as static feature extractors, \model leverages them as active participants in a self-supervised preference optimization loop. By autonomously hypothesizing, validating, and reinforcing candidate visual attributes directly from raw imagery, our framework discovers novel, structured concepts without a single label. Extensive experiments across natural, medical, and physics domains demonstrate that \model successfully extracts domain-specific concepts where standard VLLMs often fail to generate. By amortizing concept discovery directly into the VLLM backbone through our self-supervised preference objective – rather than relying on static generation and disjoint filtering – we achieve up to a 24-point absolute improvement in downstream top-1 classification accuracy on unseen data. Our work suggest that interpretability can emerge through a model's autonomous interaction with incidental visual structures, without any human supervision.

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04.
arXiv (CS.CL) 2026-06-18

Fair Cognitive Impairment Detection Through Unlearning

作者:
William Nguyen↗Jiali Cheng↗Hadi Amiri↗

Mild Cognitive Impairment (MCI) is a medical condition characterized by a noticeable decline in memory, language, or thinking abilities. MCI detection from spontaneous speech is promising for scalable screening. However, learned models often exploit demographic cues correlated with labels, resulting in a large performance gap across subgroups. We present a multimodal framework that combines (i) cross-model fusion between modalities (speech, text, and image), and (ii) unlearning using gradient reversal that discourages the shared embedding from encoding task-irrelevant demographic attributes. Evaluated on the multilingual benchmarks TAUKADIAL and PREPARE, our method outperforms the state-of-the-art multilingual and multimodal baseline in MCI classification while substantially reducing the performance gap across patient subgroups (sex and language). We further analyze transfer across datasets, showing that demographic unlearning helps learn more robust representations for MCI detection.

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05.
medRxiv (Medicine) 2026-06-10

Global and local genetic overlap among ME/CFS, irritable bowel syndrome and psychiatric traits: a hypothesis-generating analysis

作者:
Lee↗

Background. Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and irritable bowel syndrome (IBS) frequently co-occur following infection, yet shared genetic architecture at the locus level has not been systematically characterised. Aims. To estimate global and local genetic correlations between ME/CFS (including infection-onset subgroup), IBS, major depressive disorder (MDD) and loneliness/isolation, and characterise ME/CFS cell-type heritability enrichment. Method. GWAS summary statistics: DecodeME (15,579 ME/CFS; 9,738 infection-onset), FinnGen R9 (9,296 IBS), PGC MDD Wave 2 (45,396) and UK Biobank loneliness (N=455,364). LDSC for global correlations; LAVA for local correlations across 2,495 loci; MAGMA for cell-type enrichment (Descartes Human atlas); coloc.abf for colocalisation. Results. All pairwise global correlations were significant after Bonferroni correction, including ME/CFS-all-MDD (rg=0.598, 95% CI 0.46-0.74) and ME/CFS-all-IBS (rg=0.573, 0.39-0.75). Of 4,232 local tests, 16 reached FDR

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06.
arXiv (CS.AI) 2026-06-16

A Perception vs. Distortion Perspective on Score-Based Generative Channel Estimation

作者:
Marco Skocaj↗Lukas Eller↗Mate Boban↗

arXiv:2606.16815v1 Announce Type: cross Abstract: Driven by their remarkable success in computer vision and inverse problem solving, score-based models are increasingly applied to wireless communications, where they show promise across a range of physical-layer tasks. However, despite this growing interest, the current literature often lacks a rigorous analysis of when score-matching offers a tangible advantage over traditional discriminative learning. This paper aims to address this gap through the use-case of channel estimation, a fundamental inverse problem in wireless systems. We present a theoretically grounded interpretation of score-based channel estimation through the lens of the perception-distortion tradeoff, identifying the conditions where score matching excels as well as its key limitations. In particular, by modeling downstream wireless tasks (e.g., capacity maximization) as functionals of the channel estimation process, we quantify the excess risk incurred by standard distortion-minimization approaches. Extensive numerical results show that under high predictive uncertainty, the large excess risk gap can be offset by score-based estimation, enabling near Bayesian-optimal precoding via the learned posterior, whereas in the low predictive uncertainty regime, discriminative distortion-minimization approaches are preferable due to lower complexity and more efficient use of model capacity.

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07.
arXiv (quant-ph) 2026-06-11

Entanglement generation between field modes mediated by a fluctuating conducting wall

作者:
Luca Giovanni Cammarata↗Tommaso Fazio↗Roberto Passante↗Lucia Rizzuto↗

arXiv:2606.12338v1 Announce Type: cross Abstract: We consider a movable conducting plate of finite mass, between two fixed ones, whose mechanical degrees of freedom are treated quantum-mechanically and bound to its equilibrium position by a harmonic potential. The movable wall is thus subjected to quantum fluctuations of its position. This creates a system of two sub-cavities separated by the movable fluctuating plate, and two massless one-dimensional scalar fields, one in each sub-cavity. This system is described by an appropriate generalization of the Law Hamiltonian. The presence of the movable wall yields an effective plate-fields interaction, as well as an effective interaction between the field modes. We obtain, at the second order in perturbation theory, the ground state of the interacting system and the reduced density operator of the fields in each sub-cavity by tracing out the wall's degrees of freedom. We calculate the entanglement between two field modes, one in each cavity, by evaluating analytically the negativity; we then evaluate numerically also the total multimode negativity. Our results show that in both cases the fields in the two sub-cavities are entangled, in contrast to the case in which the wall is fixed in space. We discuss the amount of the field entanglement present as a function of relevant physical parameters of the system such as the mass and oscillation frequency of the movable wall, its distance from the fixed walls and the frequencies of the field modes considered.

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08.
arXiv (CS.CV) 2026-06-16

FUSE: Quantifying Uncertainty in Vision-Language Models by Bayesian Fusing Epistemic and Aleatoric Uncertainty

作者:
Harry Zhang↗Luca Carlone↗

Vision-language models (VLMs) are playing an increasingly important role across multiple domains. In many applications, such as robotics, it is crucial to quantify the uncertainty in the output of these models. } We develop FUSE, a probabilistic framework for capturing two complementary sources of uncertainty in vision-language modeling: (i) aleatoric embedding-level uncertainty derived from input data vision-language ambiguity, and (ii) epistemic model-level uncertainty estimated from the semantic response diversity of VLMs. Our approach formulates a Bayesian fusion mechanism that analytically combines these uncertainty sources to produce a scalar measure of uncertainty. This measure can be used to reliably predict the model's output correctness for downstream applications. We demonstrate that our method outperforms baselines and achieves SOTA uncertainty calibration.

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09.
arXiv (CS.CV) 2026-06-15

Prompt2Effect: Training-Free Image-to-Video Model Specialization via LoRA Generation

作者:
Xiaomeng Yang↗Yanyu Li↗Gordon Guocheng Qian↗Ivan Skorokhodov↗Viacheslav Ivanov↗Avalon Vinella↗Xuan Zhang↗Yanzhi Wang↗Sergey Tulyakov↗Anil Kag↗

Personalizing Image-to-Video (I2V) diffusion models with specific visual effects is increasingly demanded for high-end video generation. Current practice requires training a separate Low-Rank Adaptation (LoRA) module for each effect, incurring substantial data curation and iterative optimization costs that hinder interactive control. We present Prompt2Effect, a weight-driven hypernetwork that amortizes per-effect training by directly synthesizing effect-specific LoRA weights in a single forward pass. Unlike prior hypernetworks that regress adapter weights purely from semantics, Prompt2Effect is explicitly conditioned on the frozen base model weights, grounding weight prediction in the structural geometry of each layer. Furthermore, instead of predicting raw LoRA matrices, we introduce an SVD-canonicalized parameterization that resolves factorization ambiguity and stabilizes large-scale weight synthesis. Together, these design principles enable accurate and scalable LoRA prediction for high-dimensional I2V diffusion models. Extensive experiments demonstrate that Prompt2Effect achieves on-par or superior video quality and effect alignment compared to conventional LoRA fine-tuning, while reducing the computational cost from 56 GPU training hours to 3.3 seconds of hypernetwork inference. When used as initialization for subsequent fine-tuning, our predicted weights further improve final performance and accelerate optimization by approximately 10x.

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10.
arXiv (CS.CV) 2026-06-19

3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning

作者:
Zhentao Liu↗Huangxuan Zhao↗Wenhui Qin↗Zhenghong Zhou↗Xinggang Wang↗Wenping Wang↗Xiaochun Lai↗Chuansheng Zheng↗Dinggang Shen↗Zhiming Cui↗

Digital Subtraction Angiography (DSA) is one of the gold standards for vascular disease diagnosis. With the help of a contrast agent, time-resolved 2D DSA images deliver comprehensive blood flow information and can be utilized to reconstruct 3D vessel structures for medical assessment. Current commercial DSA systems typically require hundreds of scanning views to perform reconstruction, resulting in substantial radiation exposure. In this study, we propose a neural rendering-based optimization framework tailored for high-quality sparse-view DSA reconstruction to reduce radiation dosage. Our approach, termed vessel probability guided attenuation learning, represents DSA imaging as a complementary weighted combination of static and dynamic attenuation fields, with the weights derived from the time-independent vessel probability field. Functioning as a foreground mask, vessel probability provides proper gradients for both static and dynamic fields adaptive to different scene types. This mechanism enables self-supervised decomposition between static backgrounds and dynamic contrast agent flow, and significantly improves reconstruction quality. Our model is trained by minimizing the discrepancy between synthesized projections and real captured DSA images. We further employ two training strategies to improve reconstruction quality: (1) coarse-to-fine progressive training for better geometry and (2) temporal perturbed rendering loss for temporal consistency. Experimental results have demonstrated high-quality 3D vessel reconstruction and 2D DSA image synthesis.

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11.
medRxiv (Medicine) 2026-06-11

Maternal deaths associated factors in the Conflict-Affected North West Region of Cameroon. Lessons from a cross-sectional survey

作者:
ACHUONDOU↗E. E↗Ayaba↗U. W↗Kuma↗A. C↗Talla↗K. E↗

Background Maternal mortality is a significant global public health crisis, particularly in sub-Saharan Africa and conflict-affected regions. Cameroon's maternal mortality ratio is high at 406 deaths per 100,000 live births, while the ongoing Anglophone conflict has further exacerbated maternal healthcare delivery in the North West Region (NWR){middle dot} Despite the evidence-based interventions like partographs, obstetric kits, birth preparedness plans, and active management of the third stage of labour, implementation gaps persist across health facilities. Objective The study aimed to assess factors related to preventable maternal deaths in the NWR of Cameroon by exploring maternal health service usage, implementation of obstetric measures, demand-side challenges, accessibility barriers, and health system weaknesses. Methodology The study employed a quantitative descriptive cross-sectional survey design{middle dot} Data was collected with structured questionnaires from postpartum women and healthcare workers in selected health facilities and catchment communities in the NWR{middle dot} Also, a multistage sampling technique was adopted, and Cochran's formula generated a sample size of 109 respondents{middle dot} In addition, data were analysed using SPSS version 27 and Stata version 18, employing descriptive and inferential statistics. Results In this study, while 70{middle dot}64 percent of females attended at least 4 ANC visits, only 38{middle dot}53 percent met WHO ANC adequacy requirements. Facility delivery was 96{middle dot}33 percent, yet only 38{middle dot}46 percent received completed delivery plans. Conflict-related challenges affected access, with 44{middle dot}95 percent reporting insecurity-associated movement difficulties, while 44{middle dot}95 percent reported increased transportation expenses due to the conflict. Near-miss complications were reported among 27.52 percent of participants. Delivery record reviews indicated that obstetric kits were utilised in 81{middle dot}76 percent of deliveries, partographs were accessible in 86{middle dot}49 percent of records but correctly filled in just 60{middle dot}81 percent , while oxytocin administration was 95{middle dot}95 percent. Integrated Health Centres showed poorer adherence with intrapartum interventions compared with District and Regional Hospitals (p

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12.
arXiv (CS.LG) 2026-06-16

Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning

作者:
Dmitriy Bystrov↗Daniil Medyakov↗Dmitry Bylinkin↗Aleksandr Beznosikov↗

arXiv:2606.14970v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) has become a central application of modern optimization, enabling pretrained models to adapt to diverse downstream tasks and domain-specific data. A major obstacle in large-scale fine-tuning is the memory overhead of backpropagation, which requires storing activations, gradients, and optimizer states. Zeroth-order (ZO) optimization offers a memory-efficient alternative, but its performance is highly sensitive to the stepsize and smoothing parameter, often requiring costly task-specific tuning. Parameter-free (PF) optimization addresses this issue by adapting algorithmic parameters without prior knowledge of problem-dependent constants. Moreover, large-scale fine-tuning can benefit from geometry-aware updates that account for the heterogeneous structure of parameter blocks, which can be modeled through methods that exploit linear minimization oracle (LMO). In this work, we study PF adaptation for LMO-based ZO optimization and introduce $\texttt{AdaNAGED}$, a method that unifies gradient-free training, adaptive tuning, and non-Euclidean update geometry. We establish convergence guarantees and validate the method on large-scale LLM fine-tuning task with $\texttt{OPT}-1.3\mathrm{B}$ model.

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13.
arXiv (CS.LG) 2026-06-19

Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning

作者:
Asaf Cassel↗Aviv Rosenberg↗

arXiv:2606.20107v1 Announce Type: new Abstract: Optimal Reinforcement Learning (RL) algorithms typically rely on carefully constructed count-based uncertainty estimates to drive exploration. Although theoretically sound, such estimates are hard to compute in practical settings and therefore offer limited insight for designing exploration heuristics. Meanwhile, ensembling has emerged as a practical approach, but remains without theoretical justification. Building on a recent ensemble-based method for Multi-Armed Bandits, we propose a quantile-based ensemble method for finite-horizon Markov Decision Processes (MDPs). Our simple count-free approach achieves optimal variance-dependent regret bounds, providing theoretical grounding for ensemble-based exploration in RL.

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15.
arXiv (CS.CV) 2026-06-16

TUNI: Unifying Pre-training and Fine-tuning with Modality-Aware Mutual Learning and Rectification for RGB-T Semantic Segmentation

作者:
Xiaodong Guo↗Xianda Guo↗Tong Liu↗Zhihong Deng↗Yanlun Peng↗Xiang Li↗Wujie Zhou↗

RGB-thermal (RGB-T) semantic segmentation improves the environmental perception of autonomous platforms in challenging conditions. Prevailing RGB-T segmentation frameworks suffer from suboptimal multi-modal feature extraction and fusion, unbalanced modality dependency, and inadequate utilization of thermal information. To address these challenges, we propose TUNI, a unified pre-training and fine-tuning framework for efficient and real-time RGB-T semantic segmentation. It pre-trains an RGB-T encoder that incorporates an RGB-T local module that selectively emphasizes salient consistent and distinct local features across modalities, thereby integrating cross-modal feature extraction and fusion in a unified manner. To alleviate the modality bias issue during RGB-T pre-training, modality-inverted contrastive mutual learning is introduced to enable knowledge exchange between two RGB-dominated and thermal-dominated encoders. In the fine-tuning phase, modality rectification learning fully exploits residual thermal information by focusing on correct yet divergent prediction regions between two modality-specific decoders. We further develop three TUNI variants, covering lightweight, balanced, and high-performance requirements. Extensive experiments on five RGB-T semantic segmentation datasets demonstrate that TUNI achieves superior accuracy, generalization, and compactness compared with 15 state-of-the-art models. The code is available at https://github.com/xiaodonguo/TUNI-v2.

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16.
medRxiv (Medicine) 2026-06-17

LLM-Driven Extraction of NI-RADS and Imaging Tumor Characteristics to Enhance Oropharyngeal Cancer Survivorship Surveillance

作者:
Song↗Shbita↗Jang↗I. J. H↗Starostina↗Lewis↗Sahli↗Floyd↗W. R↗Mahin↗Rinsurongkawong↗Barbon↗…

Abstract Purpose Radiologic surveillance is essential for oropharyngeal cancer (OPC) survivors, guiding recurrence detection and follow-up strategies. The Neck Imaging Reporting and Data System provides a standardized framework for post-treatment risk reporting at both the primary tumor site (pNI-RADs) and cervical lymph nodes (nNI-RADS). Comprehensive surveillance additionally requires assessment of disease status, including the primary tumor, nodal involvement, and distant metastases. These clinical results are often embedded as unstructured data within free-text radiology reports. We hypothesized that a large language model (LLM) can reliably extract NI-RADS score criteria and summarize key imaging features from unstructured radiology text, achieving high concordance with expert review. Methods Previously untreated OPC patients who received definitive cancer therapy were identified. Eligible imaging reports included post-treatment head and neck CT, MRI, or FDG PET/CT scans containing narrative and impression text. Examinations lacking narrative or impression text, containing pre-existing NI-RADS annotations, or involving non-surveillance imaging modalities were excluded. A total of 200 reports were randomly selected from 7,076 eligible examinations for manual abstraction using a three-reviewer consensus framework to establish a reference dataset. Using the Palantir Foundry Pipeline Builder, a GPT-5-based LLM was deployed to extract pNI-RADS and nNI-RADS scores, and key imaging features of disease status from these reports. Performance was evaluated using exact agreement and F1-based metrics. Results Agreement for no evidence of disease (score of 1) was 93.3% (126/135; F1 = 0.94) and 90.3% (130/144; F1 = 0.93) for pNI-RADS and nNI-RADS, respectively. For NI-RADS [≥]2, exact category agreement was 73.1% (38/52; macro-F1 = 0.75) for pNI-RADS and 64.3% (27/42; macro-F1 = 0.56) for nNI-RADS. Quadratic weighted {kappa} was 0.81 and 0.59, respectively. For post-treatment disease surveillance variables, agreement was 94.9% (149/157; F1 = 0.87) for primary tumor presence, 89.1% (164/184; F1 = 0.87) for nodal disease presence, and 94.7% (126/133; F1 = 0.70) for distant metastasis detection. Specificity was high across disease-status variables (0.95-0.99), with negative predictive values of 0.95 for primary tumor, 0.87 for nodal disease, and 0.99 for distant metastasis. Conclusions Our LLM-based information retrieval and classification approach for radiographic treatment response from unstructured, multidimensional imaging reports achieved high performance for disease exclusion and moderate performance for detecting suspected residual and/or new disease. This pipeline supports scalable and standardized surveillance data capture for longitudinal monitoring, clinical analytics, and survivorship research in head and neck oncology.

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17.
arXiv (CS.LG) 2026-06-11

Deterministic Policy Gradient for Learning Equilibrium in Time-Inconsistent Control Problems

作者:
Xin Guo↗Yijie Huang↗Xiang Yu↗

arXiv:2606.11798v1 Announce Type: cross Abstract: In this paper, we develop a continuous-time model-free reinforcement learning algorithm to learn deterministic equilibrium policies in general time-inconsistent control problems. Utilizing the extended Hamilton-Jacobi-Bellman system, we recast the original time-inconsistent problem into an equivalent two-stage problem. In the first stage, for given auxiliary functions, we employ the deterministic policy gradient approach to learn an optimal policy in an auxiliary time-consistent control problem. In the second stage, given the updated policy, we exploit the inner fixed point iterations and some martingale characterizations to learn the auxiliary functions. As a theoretical contribution, we provide some mild model assumptions and establish the convergence of inner fixed point iterations. By repeating this actor-critic style of iterations across two stages, our algorithm aims to learn the equilibrium under different sources of time-inconsistency in a unified manner. The superior effectiveness of the proposed algorithm are illustrated in two classical financial applications with time-inconsistency: mean-variance portfolio management and optimal tracking portfolio under non-exponential discounting.

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18.
arXiv (quant-ph) 2026-06-16

Boson Sampling as a Probe of Chaotic and Integrable Quantum Dynamics in a Photonic Chip

作者:
Yuancheng Zhan↗Khen Cohen↗Norman T. W. Koo↗Kian Hwee Lim↗Hui Zhang↗Lingxiao Wan↗Sanghoon Chae↗Ai Qun Liu↗Victor M Bastidas↗Yaron Oz↗Leong-Chuan Kwek↗

arXiv:2605.25398v2 Announce Type: replace Abstract: Quantum chaos plays a key role in understanding complex quantum dynamics, while integrated photonics offers unique advantages for quantum applications, including high-speed operation, scalability, and programmable unitary transformations. However, integrated photonic approaches to probing quantum chaos remain largely unexplored, owing to the absence of a clear connection between programmable photonic dynamics and established chaos diagnostics. In this work, we establish Fock-state boson sampling as a practical probe of quantum chaos by exploiting the sensitivity of multiphoton interference to the random-matrix properties of underlying single-particle unitary dynamics. More importantly, we design and fabricate a programmable quantum photonic chip to experimentally implement this framework, achieving the first integrated-photonic demonstration of quantum-chaos probes based on boson sampling. Experimental results show that the three complementary probes proposed in this work, namely the distance to Porter–Thomas statistics, Shannon entropy, and Out-of-Time-Ordered-Correlator-equivalent observables, exhibit close agreement with theoretical predictions and consistently distinguish chaotic and integrable dynamics. Our work provides a scalable route for investigating complex quantum dynamics on programmable photonic platforms while leveraging the intrinsic advantages of boson sampling through multiphoton interference and complex output statistics.

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19.
arXiv (CS.CL) 2026-06-16

Compositional Reasoning Depth Predicts Clinical AI Failure: Empirical Evidence Consistent with Transformer Compositionality Limits in Electronic Health Record Question Answering

作者:
Sanjay Basu↗

Aggregate accuracy benchmarks conceal a systematic structure in how large language models fail at electronic health record (EHR) question answering: questions requiring more inferential steps produce disproportionately more errors. Motivated by theoretical results on transformer compositionality limits, we introduce a pre-specified hop-count taxonomy – the number of distinct reasoning steps required to answer a clinical question from an EHR – as a principled predictor of model failure. We annotate 313 clinician-generated MedAlign EHR question-answer pairs across four hop levels and evaluate 301 questions in a within-model ablation (claude-sonnet-4-6, zero-shot vs. extended thinking) and cross-architecture replications (gpt-4o and gpt-5.4-2026-03-05, zero-shot). All three models, spanning two providers and two OpenAI generations (GPT-4 and GPT-5), show monotone accuracy decline with hop count: Claude Sonnet zero-shot falls from 30.6% (hop=1) to 17.6% (hop=4) (Cochran-Armitage z=-2.30, p=0.011; OR per hop 0.72, 95% CI [0.56,0.92], p=0.008); GPT-4o replicates this (37.8% to 14.7%; OR 0.58 [0.45,0.75], p

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20.
arXiv (quant-ph) 2026-06-12

A Quantum Algorithm for Random Number Generation

作者:
Aastha Kataria↗Devansh Desai↗Ashwini Dalvi↗Sagar Korde↗Abhijeet Pasi↗Irfan N A Siddavatam↗Sudhir Ranjan Jain↗

arXiv:2606.13034v1 Announce Type: new Abstract: We present a quantum algorithm for random number generation that achieves a provable quadratic speedup over classical Markov chain mixing, building on the Diaconis-Shahshahani Fourier analysis of the top-to-random card shuffle. The algorithm integrates three quantum primitives into a unified mixing circuit: the Quantum Fourier Transform (QFT), which diagonalizes the Markov transition operator; controlled phase rotations, which encode the shuffle eigenvalue spectrum; and the Grover diffusion operator, which acts as a quantum analogue of the Aldous-Diaconis strong uniform stopping time by reflecting amplitudes about their mean at each iteration. For an n-qubit register, the mixing time is O(\sqrt{n \log n}) iterations. Extending to m qudits of local dimension d reduces this to O(\sqrt{\log_d N}) iterations, where N = d^m, compared to the classical O(n \log n) bound. The qudit formulation further reduces QFT circuit depth from O(\log^2 N) to O(\log_d^2 N) gates per layer by encoding the same N-state space using m = \log_d N subsystems instead of \log_2 N qubits. We validate both variants on IBM superconducting hardware.

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21.
medRxiv (Medicine) 2026-06-17

Non-Medical COVID-19 Impacts and Hearing Status: A Global Study of Differential Health Impact Among Deaf, Hard of Hearing, and Hearing Populations

作者:
Siddiqi↗Murray↗Hall↗Koplitz↗Dye↗T. D. V↗

Background: Deaf and hard of hearing (HoH) experienced complex challenges during the COVID19 pandemic, including obscured visual communication from mask mandates, inaccessible public health messaging, and inadequate interpreter availability. We examined whether hearing status predicted nonmedical COVID19 impact on a global level. Methods: We conducted a nested cross-sectional analysis within a global study collecting data across two waves (April to May 2020 and July to August 2022) from 184 countries. Participants (N=7,998) were categorized as Deaf (n=304), Hard of Hearing (HoH; n=951), or Hearing (n=6,743). The primary outcome was a composite COVID-related non-medical Personal Impact TScore derived from 14 items across employment, resource access, and healthcare domains. Multinomial logistic regression models progressively adjusted for demographic, structural, and psychosocial variables. Results: Deaf participants reported substantially higher rates of pandemic-related job loss (28.9% vs. 9.6% hearing), healthcare cancellations (39.9% vs. 24.6%), and inability to obtain basic supplies. Over half (55.9%) of Deaf participants scored above the median composite impact index, compared to 39.2% of hearing participants. In the fully adjusted model, Deaf status remained an independent predictor of high non-medical impact (aOR=1.6, 95% CI: 1.1 to 2.4). HoH status showed no statistically significant difference from hearing participants in any model. Conclusions: People identifying as Deaf experienced significant disparities during COVID19 when compared with HoH or hearing people, driven by language access barriers and institutional exclusion rather than hearing loss per se. These experiences underscore the importance for systemic interventions centering on accessible communication, Deaf-centered needs, and reducing audism in Deaf-hearing interaction.

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22.
medRxiv (Medicine) 2026-06-10

Epidemiology of Cervical Precancerous Lesions: Prevalence and Predictors from Pap Smear Screening in Hawassa City Hospitals, Sidama Region, Ethiopia. Institutional-Based Cross-sectional Study

作者:
Fisshatsion↗A. B↗Zewude↗Y. A↗Nisro↗A. M↗Abebe↗R. F↗

Background: Cervical cancer is the fourth most common cancer in women worldwide and remains a major public health challenge. In Ethiopia, it is the second leading cause of cancer deaths, with around 8,000 new cases and 6,000 deaths each year. Region?specific data on the prevalence and predictors of precancerous lesions remain scarce, yet such information is vital for guiding targeted reproductive health strategies. This study therefore examined the prevalence and predictors of cervical precancerous lesions among women aged 21-60 years undergoing Pap smear screening in public hospitals in Hawassa City, Sidama Region. Methods: An institution-based cross-sectional study was conducted among 241 women attending Pap smear screening at public hospitals in Hawassa City from March to August 2025. Sociodemographic and clinical data were collected via interviews and medical records. Lesions were classified based on the standardized international framework for reporting cervical cytology results from Pap smears per the Bethesda system. Multivariable logistic regression identified predictors p

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23.
bioRxiv (Bioinfo) 2026-06-19

Perturbation Curve models continuous transcriptional response trajectories and improves prediction of genetic modulations

作者:
Zhong↗wang↗Yang↗Yu↗Qi↗Jiang↗

Single-cell CRISPR screens, Perturb-seq, have revolutionized functional genomics by revealing biological causality. However, although perturbation assignments are typically represented as discrete labels, the cell-level effective strength of perturbations is often continuous and diverse. Current analytical frameworks struggle to decouple the variability in perturbation strength from the diversity of downstream responses. Here, we present Perturbation Curve (PertCurve), a nonlinear, curve-based computational framework that models the trajectories of transcriptomic responses by explicitly incorporating diverse perturbation magnitudes and strengths. By ordering cells by perturbation strength, we demonstrate that PertCurve accurately recapitulates the response magnitudes and reveals the distinct modularity and asynchrony patterns of downstream gene behaviors. These patterns are categorized into archetypes, including proportional, sensitive, and threshold responses. By applying this framework across CRISPRi/a modalities, we identify universal response patterns in viral infection, apoptosis, and proliferation genes, and reveal previously overlooked context-specific regulatory features in cell differentiation. Finally, incorporating PertCurve into perturbation prediction models and evaluation metrics enhances predictive performance, delivering actionable insights for refining established models.

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24.
arXiv (CS.CV) 2026-06-16

VEPHand: View-Efficient Photometric Hand Performance Capture at Scale

作者:
Zhengyang Shen↗Kai-Hung Chang↗Erroll Wood↗Deying Kong↗Bo Peng↗Timo Bolkart↗Jinlong Yang↗Bowen Zhao↗Danhang Tang↗Sasa Petrovic↗Emre Aksan↗J\'er\'emy Riviere↗…

Robust, high-fidelity 3D hand capture, while fundamental to digital human creation, remains challenging with practical multi-view systems that balance rich photometry with the geometric ambiguities of reconstruction arising from limited viewpoint density. This paper presents an end-to-end pipeline for dynamic hand performance capture and registration, specifically designed for view-efficient setups ($\sim$20 views). We address key challenges with two primary innovations. First, to overcome reconstruction difficulties like limited view overlap and background clutter, our mask-free neural method robustly extracts detailed hand geometry and appearance from unmasked images using scene parameterization and scenario-specific density regularization. Second, addressing registration challenges such as accurately capturing non-linear skin deformations and ensuring plausible results during severe self-contact, we propose a physics-inspired framework. It aligns reconstructions to a personalized hand model by optimizing intrinsic volumetric offsets within its canonical tetrahedral mesh, alongside pose parameters. This approach, supported by robust losses and optimization, captures fine surface deformations, ensures plausible results under severe articulation and self-contact, and demonstrates strong tolerance to input noise. We demonstrate the scalability and robustness of our automated pipeline on an extensive dataset of over 12,000 sequences, from which we also derive a large-scale, high-quality synthetic 2D/3D hand dataset for training downstream tasks. This showcases its effectiveness for single hands, intricate two-hand interactions, and natural hand-object manipulations. Our method achieves state-of-the-art reconstruction fidelity in view-efficient, unmasked scenarios and highly accurate registration. Our project page are available at https://zyshen021.github.io/VEPHand/.

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25.
Nature (Science) 2026-06-16

Daily briefing: How many elementary particles are there?

作者:
Flora Graham↗

Estimates range from 17 to 995.5. Plus, one man with paralysis is using a brain–computer interface at home and GLP-1 obesity drugs appear to boost testosterone and sperm quality. Estimates range from 17 to 995.5. Plus, one man with paralysis is using a brain–computer interface at home and GLP-1 obesity drugs appear to boost testosterone and sperm quality.

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