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01.
arXiv (quant-ph) 2026-06-16

Forbidden transitions in superconducting artificial atoms

作者:
Alberto Del \'Angel↗Th\'eo S\'epulcre↗Ricardo Guti\'errez-J\'auregui↗Anton Frisk Kockum↗

arXiv:2606.06069v2 Announce Type: replace Abstract: Artificial atoms built from Josephson junctions have become a powerful tool to explore the limits of quantum optics due to their strong coupling to electromagnetic fields and their sensitivity to changes at the single-photon level. This sensitivity to quantum fluctuations complements their metrological and computational use, which are based on the precise oscillating frequency of the underlying supercurrents. We present here a theory for Josephson junctions immersed in electromagnetic fields where focus is shifted from temporal correlations and towards spatial ones. Unlike the commonly used circuit and black-box descriptions, our work is based on a microscopic model that enables systematically accounting for the effect of the spatial and vectorial profile of an electromagnetic field over a junction. As an example of the interactions that emerge in such a setup, we investigate the possibility of driving a junction via a quadrupole transition, using typical experimental parameters in existing devices. With the transition being dependent on the gradient of the electric field – rather than its intensity – the junction can be excited in a region where the electric field vanishes.

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02.
arXiv (CS.AI) 2026-06-25

Rational Neural Networks have Expressivity Advantages

作者:
Maosen Tang↗Alex Townsend↗

arXiv:2602.12390v2 Announce Type: replace-cross Abstract: We study neural networks with trainable low-degree rational activation functions and show that they are more expressive and parameter-efficient than modern piecewise-linear and smooth activations such as ELU, LeakyReLU, LogSigmoid, PReLU, ReLU, SELU, CELU, Sigmoid, SiLU, Mish, Softplus, Tanh, Softmin, Softmax, and LogSoftmax. For an error target of $\varepsilon>0$, we establish approximation-theoretic separations: Any network built from standard fixed activations can be uniformly approximated on compact domains by a rational-activation network with only $\mathrm{poly}(\log\log(1/\varepsilon))$ overhead in size, while the converse provably requires $\Omega(\log(1/\varepsilon))$ parameters in the worst case. This exponential gap persists at the level of full networks and extends to gated activations and transformer-style nonlinearities. In practice, rational activations integrate seamlessly into standard architectures and training pipelines, allowing rationals to match or outperform fixed activations under identical architectures and optimizers.

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03.
arXiv (CS.CL) 2026-06-24

Layer-wise Probing of wav2vec 2.0 and Whisper for Consonant Cluster Reduction in African American English

作者:
Hamid Mojarad↗Kevin Tang↗

Self-supervised and supervised speech models are increasingly used to investigate which linguistic information their internal representations encode, and at what level of abstraction they encode it. One underexplored phenomenon is consonant cluster reduction (CCR) in African American English (AAE), a widespread phonological process and a source of automatic speech recognition (ASR) disparity. To examine how CCR is represented, we conduct speaker-independent layer-wise probing of wav2vec2-base and Whisper-small using two tasks: segmental reduction detection and segmental restoration of underlying cluster identity. Both models distinguish reduced and canonical forms with high accuracy. Crucially, reduced segments retain cues to their underlying stops, indicating that CCR is encoded as structured gradient phonological variation rather than simple segmental deletion. These results demonstrate structured phonological encoding of AAE CCR patterns in modern speech models.

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04.
PLOS Medicine 2026-05-06

Pathways of emergency care for severely ill children in Nigerian and Ugandan hospitals: A process mapping study

作者:
Rami Subhi↗

by Rami Subhi, Abiodun Sogbesan, Dan Muramuzi, Mikael Burhin, Ayobami A. Bakare, Adegoke G. Falade, Freddy E. Kitutu, Freddie Ssengooba, Carina King, Sumit Kane, Belinda Dawson-McClaren, Hamish R. Graham, the MOXY-Implementation Research Collaboration Background Child mortality remains high in countries with weak emergency care systems. Facility organisation for paediatric emergency care is heterogeneous and under-described. We examined how hospitals in Uganda and Nigeria are organised to deliver emergency care for neonates and children. Methods and findings We conducted a qualitative, multi-method study in 26 purposively selected secondary and tertiary facilities in Uganda and Nigeria from October 2023 to December 2024. Embedded researchers documented patient pathways, resources for care, and care processes for severely ill children (

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05.
arXiv (CS.AI) 2026-06-25

Type Checking Project Haystack Grids using JSON Schema and Pydantic

作者:
Thomas Hirsch↗Samina Kadkhoda Masoumali↗Gerald Schweiger↗

arXiv:2606.24891v1 Announce Type: cross Abstract: Ontologies enable scalable energy services in buildings by supporting interoperability and automation. Project Haystack is a building ontology that is widely adopted due to its flexible, tag-based semantic model, openness, and extensibility, but suffers from ambiguous tag usage and limited automated validation. Although Project Haystack is formally open, its reliance on custom file formats and domain-specific languages that originate from the Haxall ecosystem creates a de facto barrier to integration. In this paper, we address these limitations by introducing a Python-based toolchain for Haystack. We present (i) a parser for Haystack definition files (Trio file format), and (ii) a code generator that derives Pydantic models and JSON Schema definitions from these parsed specifications. The resulting models enable static type checking and enable structural validation of Haystack grids within Python, as well as schema-based validation of JSON representations outside the Python ecosystem. All tools, generated models, and schemas are released publicly under an open-source license, with the goal of strengthening the Haystack ecosystem and opening a practical pathway beyond its current technical boundaries.

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

Hierarchical GRU with Input-Conditioned Slot Queries for Ball Action Anticipation

作者:
Parthsarthi Rawat↗

We present a hierarchical model for ball action anticipation in football broadcast video. Given a 30-second observation window, the system predicts actions occurring in the subsequent 5-second window across 10 classes. A shared local Transformer encodes clip-level features within each 5-second sub-window; a GRU then aggregates temporal context across all sub-windows; finally, a Transformer decoder with K input-conditioned event slots decodes the anticipation target via three decoupled heads (objectness, class, temporal offset). We introduce frequency-reweighted Hungarian matching that systematically favours rare action classes, and Gaussian soft targets for temporal bin supervision. On the SoccerNet Ball Action Anticipation benchmark, our method achieves 17.91% mAP on the test server.

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07.
arXiv (CS.AI) 2026-06-25

Logit Distance Bounds Representational Similarity

作者:
Beatrix M. G. Nielsen↗Emanuele Marconato↗Luigi Gresele↗Andrea Dittadi↗Simon Buchholz↗

arXiv:2602.15438v3 Announce Type: replace-cross Abstract: For a broad family of discriminative models that includes autoregressive language models, identifiability results imply that if two models induce the same conditional distributions, then their internal representations are equal up to an invertible linear transformation. We ask whether an analogous conclusion holds approximately when the distributions are close instead of equal. Building on the observation of Nielsen et al. (2025) that closeness in KL divergence need not imply high linear representational similarity, we study a distributional distance based on logit differences and show that closeness in this distance does yield linear similarity guarantees. Specifically, we define a representational dissimilarity measure based on the models' identifiability class and prove that it is bounded by the logit distance. We further show that, when model probabilities are bounded away from zero, KL divergence upper-bounds logit distance; yet the resulting bound fails to provide nontrivial control in practice. As a consequence, KL-based distillation can match a teacher's predictions while failing to preserve linear representational properties, such as linear-probe recoverability of human-interpretable concepts. In distillation experiments on synthetic and image datasets, logit-distance distillation yields students with higher linear representational similarity and better preservation of the teacher's linearly recoverable concepts.

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

LatentGym: A Testbed For Cross-Task Experiential Learning With Controllable Latent Structure

作者:
Daksh Mittal↗Tommaso Castellani↗Thomson Yen↗Naimeng Ye↗Fangyu Wu↗Minghui Chen↗Tiffany Cai↗Emmanouil Koukoumidis↗William Zeng↗Hongseok Namkoong↗

arXiv:2606.15306v1 Announce Type: cross Abstract: We envision continually learning agentic systems that become more useful over time: as they encounter sequences of related tasks, they should infer the hidden structure shared across those tasks and use it to improve future decisions. This cross-task experiential learning capability is pivotal in domains such as personalization and interactive assistance, but existing training/evaluation frameworks do not provide shared, controllable latent structures and cannot measure whether or why agents improve. We introduce LatentGym: a controllable suite in which each environment is organized around a ground-truth latent variable governing the structure across tasks. Our construction yields metrics that separate exploration (whether the agent's actions gather information about the latent) from exploitation (whether the agent uses what it has gathered). We demonstrate our suite on empirical studies addressing three questions: how and why frontier models fail to adapt across related tasks; whether post-training on related task sequences improves general cross-task adaptation, and where those gains come from; and how design choices such as inter-task feedback shape training dynamics and generalization. Together, these results establish a controlled foundation for studying how LLM agents learn from experience across tasks, and for designing agents that adapt more reliably in sequential, personalized, and interactive settings.

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09.
arXiv (CS.LG) 2026-06-12

FedBiCross: Personalized One-Shot Federated Learning on Medical Images

作者:
Yuexuan Xia↗Yinghao Zhang↗Yalin Liu↗Hong-Ning Dai↗Yong Xia↗

arXiv:2601.01901v4 Announce Type: replace Abstract: Data-free knowledge distillation-based one-shot federated learning (OSFL) trains a model in a single communication round without sharing raw data, making OSFL attractive for privacy-sensitive medical applications. However, existing methods aggregate predictions from all clients to form a global teacher. Under non-IID data, conflicting predictions dilute each other during averaging, yielding less informative soft labels that weaken distillation. We propose FedBiCross, a personalized OSFL framework with three stages: (1) clustering clients by model output similarity to form coherent sub-ensembles, (2) bi-level cross-cluster optimization that learns adaptive weights to selectively leverage beneficial cross-cluster knowledge while suppressing negative transfer, and (3) personalized distillation for client-specific adaptation. Experiments on four medical image datasets demonstrate that FedBiCross consistently outperforms state-of-the-art baselines across different non-IID degrees.

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11.
bioRxiv (Bioinfo) 2026-06-16

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets

作者:
Gao↗Cui↗

Large-scale clinical and biomedical datasets increasingly contain both diverse subgroup attributes (e.g., demographic or clinical subgroups) and multiple prediction targets. Although various machine learning approaches can address subgroup differences or multi-target prediction, they often consider these aspects independently rather than jointly. To more effectively capture the shared and subgroup-specific information in such complex datasets, we propose the Integrative Transfer Network (ITN), a deep neural network designed to leverage data across subgroups and multiple related outcomes simultaneously. In extensive experiments, including time-to-event and classification tasks where demographic subgroups and multiple disease endpoints are prevalent, ITN demonstrates consistent improvements in subgroup-specific prediction by borrowing strength from other subgroups and outcomes. We envision ITN as a unified framework for learning from heterogeneous datasets where subgroup-specific insights are critical.

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12.
arXiv (CS.LG) 2026-06-18

Reliable Neural-Codec Text-to-Speech by ASR Self-Verification and Distillation: Near-Zero Catastrophic Failures Across Models and Codecs

作者:
Ali Asaria↗Tony Salomone↗Deep Gandhi↗

arXiv:2606.18323v1 Announce Type: cross Abstract: Open autoregressive neural-codec text-to-speech (TTS) models sound excellent on typical inputs yet suffer stochastic catastrophic failures: on a meaningful fraction of utterances they emit silence, terminate early, or collapse into repetitive or hallucinated content. We show this failure mode is cheap to remove. Under a single format-robust metric (a catastrophic-failure rate via an ASR round-trip), best-of-N ASR self-verification drives failures to near-zero: no observed failures remain by N=2 on a standard corpus (LibriSpeech) and by N=4 on a hard prompt set. This is not an artifact of one model: the reduction replicates across four open codec-TTS systems and three neural codecs (XCodec2, SNAC, Mimi), reaching the near-zero floor by N=2 on three of the four. We then make the fix free at inference time by distilling the self-verified behaviour into the model, which recovers much of the robustness in single-shot decoding, closing ~52-58% of the failure mass on hard inputs at no test-time cost. The distillation gain concentrates where it is needed (hard inputs); on already-reliable prose there is no headroom and no detectable change. A controlled comparison adds a clean negative: offline direct preference optimization (DPO/IPO) does not beat plain supervised distillation, and an online iterative variant is promising but not statistically separable at our evaluation size. We report honestly the one model that resists (a larger Llasa where scale did not obviously help) and a rare-word capability ceiling that no self-distillation method overcomes

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13.
arXiv (CS.AI) 2026-06-12

CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction

作者:
Yinghao Ma↗Haiwen Xia↗Hewei Gao↗Weixiong Chen↗Yuxin Ye↗Yuchen Yang↗Sungkyun Chang↗Mingshuo Ding↗Yizhi Li↗Ruibin Yuan↗Simon Dixon↗Emmanouil Benetos↗…

arXiv:2603.00610v3 Announce Type: replace-cross Abstract: While music generation models have evolved to handle complex multimodal inputs mixing text, lyrics, and reference audio, evaluation mechanisms have lagged behind. In this paper, we bridge this critical gap by establishing a comprehensive ecosystem for music reward modeling under Compositional Multimodal Instruction (CMI), where the generated music may be conditioned on text descriptions, lyrics, and audio prompts. We first introduce CMI-Pref-Pseudo, a large-scale preference dataset comprising 110k pseudo-labeled samples, and CMI-Pref, a high-quality, human-annotated corpus tailored for fine-grained alignment tasks. To unify the evaluation landscape, we propose CMI-RewardBench, a unified benchmark that evaluates music reward models on heterogeneous samples across musicality, text-music alignment, and compositional instruction alignment. Leveraging these resources, we develop CMI reward models (CMI-RMs), a parameter-efficient reward model family capable of processing heterogeneous inputs. We evaluate their correlation with human judgment scores on musicality and alignment on CMI-Pref along with previous datasets. Further experiments demonstrate that CMI-RM not only correlates strongly with human judgments, but also enables effective inference-time scaling via top-k filtering. Code is available at GitHub (https://github.com/Haiwen-Xia/CMI-RewardBench). Model weights: CMI-RM (https://huggingface.co/HaiwenXia/CMI-RM). Datasets: CMI-Pref-Pseudo (https://huggingface.co/datasets/HaiwenXia/cmi-pref-pseudo) and CMI-Pref (https://huggingface.co/datasets/HaiwenXia/cmi-pref)

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14.
arXiv (CS.AI) 2026-06-15

Metabolic cost of information processing in Poisson variational autoencoders

作者:
Hadi Vafaii↗Jacob L. Yates↗

arXiv:2602.13421v2 Announce Type: replace-cross Abstract: Computation in biological systems is fundamentally energy-constrained, yet standard theories of computation treat energy as freely available. Here, we argue that variational free energy minimization under a Poisson assumption offers a principled path toward an energy-aware theory of computation. Our key observation is that the Kullback-Leibler (KL) divergence term in the Poisson free energy objective becomes proportional to the prior firing rates of model neurons, yielding an emergent metabolic cost term that penalizes high baseline activity. This structure couples an abstract information-theoretic quantity – the *coding rate* – to a concrete biophysical variable – the *firing rate* – which enables a trade-off between coding fidelity and energy expenditure. Such a coupling arises naturally in the Poisson variational autoencoder (P-VAE) – a brain-inspired generative model that encodes inputs as discrete spike counts and recovers a spiking form of *sparse coding* as a special case – but is absent from standard Gaussian VAEs. To demonstrate that this metabolic cost structure is unique to the Poisson formulation, we compare the P-VAE against Grelu-VAE, a Gaussian VAE with ReLU rectification applied to latent samples, which controls for the non-negativity constraint. Across a systematic sweep of the KL term weighting coefficient $\beta$ and latent dimensionality, we find that increasing $\beta$ monotonically increases sparsity and reduces average spiking activity in the P-VAE. In contrast, Grelu-VAE representations remain unchanged, confirming that the effect is specific to Poisson statistics rather than a byproduct of non-negative representations. These results establish Poisson variational inference as a promising foundation for a resource-constrained theory of computation.

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15.
Nature Medicine 2026-06-25

Metabolic determinants of cancer immunotherapy outcomes identified by plasma profiling

作者:
Déborah Suissa↗

Immune-checkpoint inhibitors benefit a subset of patients with advanced cancer, and the metabolic determinants of response remain unclear. Here, using targeted metabolomics and metagenomics, we profiled 4,336 plasma samples from 1,714 patients across five tumor types and 16 cohorts spanning Europe and North America, longitudinally sampled during five immune-checkpoint inhibitor-based treatment modalities, including fecal microbiota transplantation. A multimodal machine-learning framework integrating 154 metabolites with clinical variables identified five metabolites, age, body mass index and renal function as predictors of 12-month progression-free survival. The model achieved areas under the curve of 0.88 in training and 0.73 in validation cohorts of 105 and 30 patients, respectively and generalized across seven external cohorts. Histidine was a favorable prognostic feature of survival, whereas long-chain fatty acids and succinate were negatively associated with outcome. Histidine supplementation enhanced antitumor immunity in mice. Histidine-rich diets improved progression-free survival in patients lacking dysbiotic microbiome signatures associated with histidine catabolism. Mass-spectrometry-based metabolomic analysis of plasma samples from multiple cohorts of patients treated with immunotherapy across five distinct tumor types, followed by machine learning enabled identification of metabolic signatures, as well as functional exploration, reveals association of increased plasma histidine levels with prolonged survival and its potential for therapeutic intervention.

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16.
arXiv (CS.LG) 2026-06-11

FlexiBrain: Resolution-Agnostic Voxel-Level Encoding for Native fMRI

作者:
Mo Wang↗Wenhao Ye↗Junfeng Xia↗Minghao Xu↗Hongkai Wen↗Quanying Liu↗

arXiv:2606.11500v1 Announce Type: cross Abstract: The success of large-scale deep learning models in neuroscience is fundamentally constrained by severe data heterogeneity. Native fMRI data aggregated from diverse sources exhibit substantial variation in both spatial and temporal resolutions. Consequently, most existing frameworks rely on lengthy, rigid preprocessing pipelines that enforce uniformity across datasets. This practice introduces two critical limitations: (1) potential degradation of subject-specific anatomical information; (2) significant computational overhead, often requiring hours of processing per subject. Here, we propose FlexiBrain, a resolution-agnostic voxel-level encoding framework for native fMRI based on Mamba-JEPA. FlexiBrain defines patch sizes in real-world physical units and employs a dynamic patch resizing, thereby bypassing destructive spatial standardization while enabling direct ingestion of data in native space. We instantiate the framework using an efficient Mamba-JEPA backbone to model high-dimensional 4D fMRI signals. Across five diverse downstream neuroscience tasks, FlexiBrain consistently outperforms recent state-of-the-art methods, achieving gains of up to 12 percentage points without external data augmentation. Importantly, FlexiBrain functions as a seamless plug-in module, substantially reducing preprocessing costs and accelerating the development of robust voxel-level fMRI foundation models. Code is available at https://github.com/OneMore1/FlexiBrain.

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17.
arXiv (CS.CV) 2026-06-17

OmniDrive: An LLM-Choreographed Multi-Agent World Model with Unified Latent Co-Compression for Multi-View Driving Video Generation

作者:
Zijie Meng↗Yufei Liu↗Chengqian Ma↗Zhiyu Li↗Jiyuan Liu↗Wenhua Nie↗Bingcai Wei↗Shuqin Chen↗Weichen Xu↗Jiquan Yuan↗Miao Zhang↗

Generative world models for autonomous driving face two unresolved tensions: heterogeneous control injection, where free-form language, HD-maps, trajectories, and camera poses reside in incompatible representational spaces, and post-hoc cross-view fusion, where per-camera latents fail to encode global 3-D geometry. We trace both to a single root cause: the absence of a shared symbolic interlingua aligning language, geometry, and pixels at the latent-token level. We present DRIVE-CHOREO, an LLM-choreographed multi-agent world model that recasts controllable multi-view video generation as latent choreography. Three Qwen2.5-VL agents - a Director parsing user intent into a structured WorldScript, a Cartographer grounding it into spatially-anchored layout tokens, and an Auditor feeding cross-view critiques back as auxiliary supervision - jointly author a single position-aware token sequence. This sequence is co-compressed with the multi-view video via a view-time permutation that enforces inter-camera geometry within the convolutional receptive field of a 3-D VAE. On nuScenes, DRIVE-CHOREO sets new state-of-the-art multi-view consistency and BEV mAP (21.6) with competitive FVD (45.7); a detector trained purely on our synthetic data gains +2.4 NDS on the real validation split, validating downstream utility.

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18.
arXiv (CS.AI) 2026-06-19

Learner-based Concept Drift Detection: Analysis and Evaluation

作者:
Md Moman Ul Haque Khan↗Samira Sadaoui↗

arXiv:2606.20216v1 Announce Type: cross Abstract: Machine learning algorithms deployed for evolving streaming environments must handle the non-stationary data distributions, commonly referred to as concept drift. The presence of concept drift poses a major challenge for many real-world applications because it can severely degrade their predictive performance, hindering their ability to support robust decision-making. Consequently, the timely and efficient detection of drift events is critical for sustaining high accuracy over time. This study examines theoretically the concept drift characteristics and numerous drift detection algorithms across several categories. Furthermore, we evaluate their performance on both synthetic and real-world datasets exhibiting diverse streaming scenarios and drift characteristics, such as abrupt and gradual changes. This study aims to enhance understanding of the complex notion of concept drift characteristics and behavior of drift detectors, along with their applicability to diverse contexts.

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

Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting

作者:
Seunghan Lee↗Jaehoon Lee↗Jun Seo↗Sungdong Yoo↗Minjae Kim↗Tae Yoon Lim↗Dongwan Kang↗Hwanil Choi↗SoonYoung Lee↗Wonbin Ahn↗

arXiv:2603.14709v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enhances zero-shot time series (TS) forecasting by leveraging external knowledge bases, yet existing approaches overlook input-level relevance when fusing retrieved samples with the query. We argue that not all retrievals are equally useful, and irrelevant ones can degrade performance. To this end, we propose Cross-RAG, a zero-shot RAG-based forecasting framework that selectively attends to query-relevant retrieved samples via query–retrieval cross-attention. By modeling input-level relevance between the query and retrieved samples, Cross-RAG jointly incorporates three sources of information: 1) the query itself, 2) the retrieved samples, and 3) their relational interactions. In particular, this input-aware design enables Cross-RAG to remain stable as the number of retrieved samples $k$ grows, whereas prior methods without cross-attention require careful $k$ tuning to avoid degradation from irrelevant retrievals. Extensive experiments demonstrate that Cross-RAG consistently improves zero-shot forecasting performance across multiple TSFM backbones and various RAG methods, with additional analyses confirming its effectiveness across various retrieval scenarios. Code is available at https://github.com/seunghan96/cross-rag/.

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20.
bioRxiv (Bioinfo) 2026-06-18

Population-associated molecular variation in histologically normal breast tissue is context-dependent and associated with distinct transcriptional states

作者:
Hulsy↗W. D↗Salazar↗Chalkia↗Chavez↗Zhao↗Halkia↗Razorenova↗O. V↗Nikolaidis↗

Population-associated molecular variation in breast tissue may contribute to differences in tissue biology and disease susceptibility, yet the extent to which such variation is shaped by underlying tissue states remains unclear. Here, we performed RNA-seq and lipidomic profiling of histologically normal breast tissue samples from African American (AA) and Caucasian White (CW) individuals, followed by conceptual integration of the resulting transcriptomic and lipidomic patterns. Unsupervised analysis revealed two distinct baseline transcriptional states (G1 and G2) that defined the primary axis of molecular variation across the cohort and corresponded to epithelial-enriched (G1) and vascular-enriched (G2) tissue contexts as determined by cell-type deconvolution. Global comparisons between AA and CW samples showed minimal transcriptomic differences, with only a single gene reaching significance after multiple testing correction. However, when stratified by baseline tissue state, 191 genes were differentially expressed within G1, with coordinated upregulation of extracellular matrix organization and proliferative/cytoskeletal processes in AA samples. These patterns were consistently supported across multiple enrichment approaches. No comparable population-associated differences were observed within G2. Lipidomic analyses showed partial but non-significant trends consistent with transcriptomic structure, suggesting that lipid variation provides complementary but limited support for baseline molecular differences, likely reflecting constraints of bulk tissue composition. Together, these findings suggest that population-associated molecular differences in normal breast tissue are context-dependent and emerge within specific baseline transcriptional states, where distinct biological programs can coexist and be differentially modulated. These findings highlight the importance of tissue heterogeneity in shaping molecular variation and its potential relevance to disease-associated tissue states.

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21.
arXiv (quant-ph) 2026-06-24

Conditional channel entropy sets fundamental limits on thermodynamic quantum information processing

作者:
Himanshu Badhani↗Siddhartha Das↗

arXiv:2604.01217v2 Announce Type: replace Abstract: The thermodynamic resourcefulness of quantum channels primarily depends on their underlying causal structure and their ability to generate quantum correlations. We quantify this interplay within the resource theory of athermality for bipartite quantum channels in the presence of a side channel acting as memory, referred to as the resource theory of conditional athermality. For channels with trivial output Hamiltonians, we characterize the optimal one-shot rates for distilling the identity gate from a given channel, as well as the cost of simulating the channel using the identity gate, under conditional Gibbs-preserving superchannels. We show that these rates have a direct trade-off relation with the conditional channel entropies, attributing operational significance to signaling in quantum processes. Furthermore, we establish an asymptotic equipartition property for the conditional channel min-entropy for classes of channels that are either tele-covariant or no-signaling from the non-conditioning input to the conditioning output. As a consequence, we demonstrate asymptotic reversibility of the resource theory for these channels. The asymptotic conditional athermality capacity of a tele-covariant channel is half the superdense coding capacity of its Choi state. Our work establishes the conditional channel entropy as a primitive information-theoretic concept for quantum processes, elucidating its potential for wider applications in quantum information science.

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22.
arXiv (CS.LG) 2026-06-12

Auditing Discriminatory Patterns in Mortgage Lending Through Association Rules and Fair Binning

作者:
Archit Rathod↗Dhwani Chande↗Het Nagda↗

arXiv:2606.12435v1 Announce Type: cross Abstract: Mortgage lending in the United States exhibits persistent racial and gender disparities. We investigate whether standard data preprocessing steps, specifically attribute binning, amplify these disparities in downstream pattern mining. Using 103,481 cleaned mortgage applications from the HMDA 2023 dataset (Chicago metropolitan area), we build a three-stage pipeline: (1) a PySpark data cleaning and binning pipeline that implements both standard equal-frequency binning and the epsilon-biased fair binning algorithm from Asudeh et al. [1], (2) FP-Growth association rule mining that compares denial patterns under both binning regimes, and (3) K-Means clustering with a per-cluster disparate impact audit. Our standard binning shows 9.63% racial bias in income discretization, consistent with the 8-10% reported in prior work. Fair binning with seven race groups is infeasible at epsilon=0.03 and only succeeds at epsilon=0.08 with a Price of Fairness of 29.4%. FP-Growth reveals that high debt-to-income ratio is the dominant denial predictor (67.2% confidence, 2.81 lift), while racial bias does not appear as explicit high-support rules. However, K-Means clustering followed by a disparate impact audit flags 10 out of 45 cluster-group pairs, showing that Black applicants face significantly higher denial rates than White applicants even among financially similar groups.

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23.
medRxiv (Medicine) 2026-06-16

Wildfire pollution exposure during childhood adversely affects cognitive and neural development

作者:
Judd↗kievit↗

Air pollution has well-documented negative cardiovascular and respiratory consequences. However, the impact of particulate matter pollution (PM2.5) on brain development is unclear. Animal studies suggest that exposure to early-life PM2.5 can cause adverse neurodevelopmental outcomes, but in vivo human work has been hampered by cross-sectional designs and heavily confounded PM2.5 exposure measures. Here we use an innovative natural experimental design to isolate the effects of wildfire pollution on neurocognitive development in a large cohort of children (N>9000, 4 waves, age 9-16). Doing so, we find that greater wildfire PM2.5 exposure is robustly associated with slower brain development and shallower cognitive improvement across early adolescence. Our study underscores the urgent public health concern that wildfire PM2.5 poses for childhood development.

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24.
Nature (Science) 2026-06-25

Base editing reveals an essential role for NANOG in human embryogenesis

作者:
Oliver J. Bower↗

Understanding how the first cell lineages in human development are specified and maintained has fundamental importance and clinical implications for regenerative medicine, infertility and pregnancy loss. While mouse models have provided valuable insights into transcription factors regulating early development, translating these findings to human embryos has been limited by ethical, technical and biological constraints. Functional studies of transcription factors in human embryos have been hindered by nuclease-based genome-editing approaches that induce genotoxicity1-3. To overcome this, we applied adenine base editing (ABE8e)4,5 to precisely target an exon splice donor site, resulting in a splicing defect and functional knockout of NANOG, representing the first application of base editing to study a developmental regulator in human embryos. This approach did not trigger genotoxicity and showed limited off-target editing. Loss of NANOG disrupts pluripotent epiblast specification and instead cells differentiate toward a primitive endoderm (yolk sac) or trophectoderm (placental) transcriptional programme. Retention of primitive endoderm differentiation in NANOG-edited human embryos reveals a functional compensation distinct from mouse, underscoring the importance of directly investigating human development. Our findings demonstrate an essential role for NANOG in human pluripotency and epiblast specification, and highlight the utility of base editing for functional interrogation of human development.

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25.
arXiv (CS.LG) 2026-06-16

Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding

作者:
Jiamin Xu↗Kyra Gan↗

arXiv:2602.00781v2 Announce Type: replace Abstract: Online reinforcement learning in non-episodic, finite-horizon MDPs remains underexplored and is challenged by the need to estimate returns to a fixed terminal time. Existing infinite-horizon methods, which often rely on discounted contraction, do not naturally account for this fixed-horizon structure. We introduce a modified Q-function: rather than targeting the full-horizon, we learn a K-step lookahead Q-function that truncates planning to the next K steps. To further improve sample efficiency, we introduce a thresholding mechanism: actions are selected only when their estimated K-step lookahead value exceeds a time-varying threshold. We provide an efficient tabular learning algorithm for this novel objective, proving it achieves fast finite-sample convergence: it achieves minimax optimal constant regret for $K=1$ and $\mathcal{O}(\max((K-1),C_{K-1})\sqrt{SAT\log(T)})$ regret for any $K \geq 2$. We numerically evaluate the performance of our algorithm under the objective of maximizing reward. Our implementation adaptively increases K over time, balancing lookahead depth against estimation variance. Empirical results demonstrate superior cumulative rewards over state-of-the-art tabular RL methods across synthetic MDPs and RL environments: JumpRiverswim, FrozenLake and AnyTrading. Code is provided on \href{https://github.com/jamie01713/K-Step-Lookahead}{github}.

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