中 EN
登录 注册账户

Academic Intelligence · Curated Daily

探索全球前沿学术脉络

AcademicHub 汇聚顶级期刊与预印本平台的实时文献。定制您的专属科研雷达，利用大语言模型自动生成交叉领域文献分析简报。

登录 注册账户
01.
arXiv (CS.LG) 2026-06-17

A Diffusion Approximation for Temporal-Difference Learning with Linear Features under Markovian Noise

作者:
M. Forzo↗E. Monzio Compagnoni↗A. Russo↗A. Pacchiano↗

arXiv:2606.18183v1 Announce Type: cross Abstract: Temporal difference (TD) learning with linear function approximation is a core method for policy evaluation. Its classical continuous-time description is an ordinary differential equation (ODE), which captures the asymptotic mean dynamics but neglects stochastic fluctuations determining the error floor. We introduce a stochastic differential equation (SDE) approximation for linear TD(0) under Markovian noise. The resulting model distinguishes the contraction dynamics governed by the projected Bellman operator from the influence of Markovian sampling. As a consequence, the model explains the constant-stepsize error floor through the interaction between Markovian long-run covariance and the contraction geometry of the projected Bellman operator.

阅读与讨论 → 访问原文 →
02.
bioRxiv (Bioinfo) 2026-06-16

DMcloud: Macromolecular Structure Modeling Using Local Structure Fitting for Medium to Low Resolution cryo-EM maps

作者:
Terashi↗Wang↗Zhang↗Zhu↗Hong Park↗Kihara↗

Cryogenic electron microscopy (cryo-EM) has become an essential experimental approach in structural biology for determining macromolecular structures. When the resolution of a cryo-EM map is worse than approximately 5[A], fitting known or predicted molecular models into the map becomes a common strategy for interpretation. However, accurately fitting biomolecular models into cryo-EM maps, particularly for large macromolecular complexes, remains challenging when the input structure models contain errors or are in a conformation different from that represented in the map. Here, we present DMcloud, a method for local structure fitting of proteins and nucleic acids in cryo-EM maps. Instead of forcing an entire input model into the map, DMcloud divides input structures into local regions, identifies regions that are supported by the density, removes unsupported regions, and assembles the retained regions into a final model. We benchmarked DMcloud on 176 cryo-EM maps, including intermediate and high-resolution maps that include proteins, DNAs, or RNAs. For EM maps in the 5.0-10.0 [A] and 2.5-5.0 [A] resolution ranges, DMcloud achieved average sequence modeling coverage of 0.49 and 0.70, respectively. For DNA/RNA maps, DMcloud achieved an average sequence coverage of 0.75. Across all datasets, DMcloud consistently outperformed existing methods in model accuracy, map-model correlation, and modeling coverage.

阅读与讨论 → 访问原文 →
03.
arXiv (CS.CL) 2026-06-19

Omnilingual SONAR: Cross-Lingual and Cross-Modal Sentence Embeddings Bridging Massively Multilingual Text and Speech

作者:
Omnilingual SONAR Team↗Jo\~ao Maria Janeiro↗Pere-Llu\'is Huguet Cabot↗Ioannis Tsiamas↗Yen Meng↗Vivek Iyer↗Guillem Ram\'irez↗Loic Barrault↗Belen Alastruey↗Xiang "Tony" Cao↗Yu-An Chung↗Marta R. Costa-Jussa↗…

Cross-lingual sentence encoders typically cover only a few hundred languages and often trade downstream quality for stronger alignment, limiting their adoption. We introduce OmniSONAR, a new family of omnilingual, cross-lingual and cross-modal sentence embedding models that natively embed text, speech, code, and mathematical expressions in a single semantic space, while delivering state-of-the-art downstream performance at the scale of thousands of languages, from high-resource to extremely low-resource varieties. To reach this scale without representation collapse, we use progressive training. We first learn a strong foundational space for 200 languages with an LLM-initialized encoder-decoder, combining token-level decoding with a novel split-softmax contrastive loss and synthetic hard negatives. Building on this foundation, we expand to several thousands language varieties via a two-stage teacher-student encoder distillation framework. Finally, we demonstrate the cross-modal extensibility of this space by seamlessly mapping 177 spoken languages into it. OmniSONAR halves cross-lingual similarity search error on the 200-language FLORES dataset and reduces error by a factor of 15 on the 1,560-language BIBLE benchmark. It also enables strong translation, outperforming NLLB-3B on multilingual benchmarks and exceeding prior models (including much larger LLMs) by 15 chrF++ points on 1,560 languages into English BIBLE translation. OmniSONAR also performs strongly on MTEB and XLCoST. For speech, OmniSONAR achieves a 43% lower similarity-search error and reaches 97% of SeamlessM4T speech-to-text quality, despite being zero-shot for translation (trained only on ASR data). Finally, by training an encoder-decoder LM, Spectrum, exclusively on English text processing OmniSONAR embedding sequences, we unlock high-performance transfer to thousands of languages and speech for complex downstream tasks.

阅读与讨论 → 访问原文 →
04.
arXiv (CS.AI) 2026-06-11

A Physics-Inspired Optimizer: Velocity Regularized Adam

作者:
Pranav Vaidhyanathan↗Lucas Schorling↗Natalia Ares↗Maike Osborne↗

arXiv:2505.13196v3 Announce Type: replace-cross Abstract: We introduce Velocity-Regularized Adam (VRAdam), a physics-inspired optimizer for training deep neural networks that draws on ideas from quartic terms for kinetic energy with its stabilizing effects on various system dynamics. Previous algorithms, including the ubiquitous Adam, operate at the so-called adaptive edge of stability regime during training, leading to rapid oscillations and slowed convergence of loss. However, VRAdam adds a higher order penalty on the learning rate based on the velocity such that the algorithm automatically slows down whenever weight updates become large. In practice, we observe that the effective dynamic learning rate shrinks in high-velocity regimes, and damping oscillations. By combining this velocity-based regularizer for global damping with per-parameter scaling of Adam, we create a powerful hybrid optimizer. For this optimizer, we provide rigorous theoretical analysis of operation at the edge of stability from a physical and control perspective for the momentum. Furthermore, we derive convergence bounds with the rate $\mathcal{O}(\ln(N)/\sqrt{N})$ for a stochastic non convex objective under mild assumptions. We demonstrate that VRAdam exceeds the performance against standard optimizers including AdamW. We benchmark various tasks such as image classification, language modeling, and generative modeling using diverse architectures and training methodologies including Convolutional Neural Networks (CNNs), Transformers, and GFlowNets.

阅读与讨论 → 访问原文 →
05.
arXiv (quant-ph) 2026-06-12

Multiple Topological Haldane Phases for Symmetry-Protected Quantum Information Processing

作者:
Jo\~ao Pedro Gama D'Elia↗Irene D'Amico↗Thereza Paiva↗

arXiv:2606.12685v1 Announce Type: new Abstract: Symmetry-protected topological phases have attracted significant interest at the fundamental level and as a potential platform for quantum information processing, owing to their protected edge states and resilience to perturbations. Applying these features for practical and efficient quantum computation is highly desirable, but remains an open challenge. Here, we demonstrate the partitioning into multiple independent Haldane phase subsystems of a single spin-1/2 ladder system and propose this as a scalable architecture for gate-based quantum computation, which takes advantage of the symmetry-protected topological order. We encode qubits in the two topological states of the $S^{z}=0$ sector of each subsystem. Finite-size effects, typically viewed as detrimental, instead provide a controllable energy splitting that enables single-qubit rotations using only local magnetic fields. An Ising-type interaction between neighboring subsystem edges generates entangling gates, enabling universal quantum computation driven by two control parameters that are easily accessible experimentally. Our results demonstrate how symmetry-protected topological phases can be directly harnessed for circuit-model quantum computation in realistic systems.

阅读与讨论 → 访问原文 →
06.
arXiv (CS.AI) 2026-06-17

Graph neural networks at war: integrating cybersecurity and drone intelligence in the Israeli-Iranian conflict

作者:
Sozan Sulaiman Maghdid↗Tarik Ahmed Rashid↗Shavan Askar↗

arXiv:2606.17119v1 Announce Type: cross Abstract: Physical cyber systems have brought about new threats and challenges in detection and immediate response. This study examines how Graph Neural Networks (GNNs) can be used to aid cybersecurity and drone management in a physical cyber system comprising of cyber intrusions and unmanned aerial vehicles (UAVs). By providing a bridge between structural understanding of graphical neural networks, this work has provided an integrated procedure that allows intrusion detection systems to educate on underlying network structures, identify malicious activity, and facilitates drone response measures. Based on an emulation-based case study, cyberattacks models were created to provoke the responses of the drones, which proved that graph-based learning can assist with the situational awareness, swarm coordination, and adaptive maneuver. According to the performance valuation, this method has a detection rate of 94.2, average area under the receiver operating characteristic (ROC) of 0.955 and an average response time of 1.4 seconds. Comparative experiments reveal that proposed GraphSAGE network is more effective than the Graphical Convolutional Networks (GCNs) and Graphical Attention Networks (GATs) in the identical situation. Such findings prove that graphical neural networks can be used to avert intrusion and response of dynamic cyber-physical systems.

阅读与讨论 → 访问原文 →
07.
arXiv (CS.LG) 2026-06-15

Denoising Score Matching with Random Features: Insights on Diffusion Models from Precise Learning Curves

作者:
Anand Jerry George↗Rodrigo Veiga↗Nicolas Macris↗

arXiv:2502.00336v3 Announce Type: replace Abstract: We theoretically investigate the phenomena of generalization and memorization in diffusion models. Empirical studies suggest that these phenomena are influenced by model complexity and the size of the training dataset. In our experiments, we further observe that the number of noise samples per data sample ($m$) used during Denoising Score Matching (DSM) plays a significant and non-trivial role. We capture these behaviors and shed insights into their mechanisms by deriving asymptotically precise expressions for test and train errors of DSM under a simple theoretical setting. The score function is parameterized by random features neural networks, with the target distribution being $d$-dimensional Gaussian. We operate in a regime where the dimension $d$, number of data samples $n$, and number of features $p$ tend to infinity while keeping the ratios $\psi_n=\frac{n}{d}$ and $\psi_p=\frac{p}{d}$ fixed. By characterizing the test and train errors, we identify regimes of generalization and memorization as a function of $\psi_n,\psi_p$, and $m$. Our theoretical findings are consistent with the empirical observations.

阅读与讨论 → 访问原文 →
08.
arXiv (CS.AI) 2026-06-11

DuoBench: A Reproducible Benchmark for Bimanual Manipulation in Simulation and the Real World

作者:
Tobias J\"ulg↗Seongjin Bien↗Simon Hilber↗Yannik Blei↗Pierre Krack↗Maximilian Li↗Sven Parusel↗Rudolf Lioutikov↗Florian Walter↗Wolfram Burgard↗

arXiv:2606.11901v1 Announce Type: cross Abstract: Bimanual robot systems substantially expand manipulation capabilities, but coordinating two arms introduces additional control complexity and failure modes that are not well captured by existing benchmarks. We introduce DuoBench, an extensible benchmarking framework for bimanual manipulation policies on the FR3 Duo platform. DuoBench comprises eleven tasks spanning four coordination categories, implemented in simulation and partially reproduced in the real world through reproducible task recipes with 3D-printable assets. In addition, we propose a stage-based evaluation scheme that supports fine-grained semantic failure analysis beyond binary success and provide human-teleoperated datasets for all benchmark tasks. We benchmark several dual-arm imitation-learning and vision-language-action policies in simulation and on real hardware. Our results show that current policies remain challenged by bimanual manipulation, particularly in early interaction stages, parallel arm execution, and transfer between simulation and real-world settings. DuoBench provides a reproducible testbed for diagnosing these failure modes and studying future methods for dual-arm policy learning. Code, datasets, and videos are available at https://duobench.github.io/

阅读与讨论 → 访问原文 →
09.
arXiv (CS.LG) 2026-06-15

A Complexity Measure for Active Learning in Multi-group Mean Estimation

作者:
Abdellah Aznag↗Rachel Cummings↗Adam N. Elmachtoub↗

arXiv:2606.14690v1 Announce Type: new Abstract: We study a max-risk objective for active learning in a multi-group mean estimation $d$-armed bandits: a learner adaptively allocates a budget of $T$ samples across $d$ groups to minimize the worst-case uncertainty index $\max_{k\in[d]}\sigma_k^2/n_k$, where $\sigma_k$ is the standard deviation of the distribution of arm $d$, and $n_k$ is the number of times arm $d$ is sampled. We develop a local minimax framework and prove the first general lower bound for this objective, valid for any finite-variance hypothesis class. The bound separates difficulty into three orthogonal factors: a budget term, a heteroscedasticity index measuring how unevenly the uncertainty is spread across arms, and a model-dependent complexity measure, the Variance Local Curvature ($\mathrm{VLC}$), which captures how much information a local change of variance creates inside the hypothesis class. For smooth classes, the $\mathrm{VLC}$ is a reparametrization of a variance–Fisher information, with closed-form values for common families. Benchmarking against the strongest available upper bound shows near-optimality up to logarithmic factors in broad regimes, and pinpoints a systematic gap in highly heterogeneous instances. Our proof introduces two key ingredients: a loss-induced $\ell_1$ geometry on the decision space, and a representation-based instance generator that reduces hard-instance construction to an explicit random matrix calculation.

阅读与讨论 → 访问原文 →
10.
arXiv (CS.CV) 2026-06-12

PROBE: Probabilistic Occupancy BEV Encoding with Analytical Translation Robustness for 3D Place Recognition

作者:
Jinseop Lee↗Byoungho Lee↗Gichul Yoo↗

We present PROBE (PRobabilistic Occupancy BEV Encoding), a learning-free LiDAR place recognition descriptor that models each BEV cell's occupancy as a Bernoulli random variable. Rather than relying on discrete point-cloud perturbations, PROBE analytically marginalizes over continuous Cartesian translations via the polar Jacobian, yielding a distance-adaptive angular uncertainty $\sigma_\theta = \sigma_t / r$ in $\mathcal{O}(R{\cdot}S)$ time. The primary parameter $\sigma_t$ represents the expected translational uncertainty in meters, a sensor-independent physical quantity that enhances cross-sensor generalization while reducing the need for extensive per-dataset tuning. Pairwise similarity combines a Bernoulli-KL Jaccard with exponential uncertainty gating and FFT-based height cosine similarity for rotation alignment. Evaluated on four datasets spanning four diverse LiDAR types, PROBE achieves the highest accuracy among handcrafted descriptors in multi-session evaluation and competitive single-session performance relative to both handcrafted and supervised baselines. The source code and supplementary materials are available at https://sites.google.com/view/probe-pr.

阅读与讨论 → 访问原文 →
11.
arXiv (CS.CL) 2026-06-12

Marginal Alignment Does Not Guarantee Joint-Distribution Fidelity: An Official-Reference Audit of Nemotron-Personas-Korea with Cross-Locale Replication

作者:
Joonhyung Bae↗

Synthetic persona datasets cite alignment with official demographics as a basis for trust, yet downstream users consume them as joint structures across age, sex, region, occupation, education, name, and institutional status. Marginal alignment does not imply that these joints are preserved. We propose the Independence-Assumption Footprint (IAF), an audit primitive that operates on the attribute combinations a dataset card itself documents as treated independently. For each such combination, IAF compares the synthetic joint against an external official or institutional reference, using direct joint tables where available and rule-implied checks otherwise. Applied to NVIDIA Nemotron-Personas-Korea (one million Korean synthetic personas), IAF finds that NPK aligns with KOSIS marginals while three joints fail. The major-by-occupation distribution against the KEIS graduate universe carries a large conditional mismatch. The age profile of military service is institutionally inconsistent. Female representation in male-dominated occupations is substantially over-flattened toward parity, with the strict screening verdict mapping-dependent and age-robust under direct standardisation. A transferability demonstration across six further NPK locales finds locale-dependent rather than universal diagnostics, with reference-taxonomy cardinality confounding cross-locale flag counts. For synthetic personas used as silicon samples, marginal claims must therefore be paired with disclosure-anchored joint audits before reuse. The released audit artefacts (reference manifests, occupational crosswalks, derived metrics, reproducibility scripts) instantiate this protocol on the NPK family and are released for retargeting at other synthetic persona resources.

阅读与讨论 → 访问原文 →
12.
arXiv (CS.CL) 2026-06-12

LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories

作者:
Baochang Ren↗Xinjie Liu↗Xi Chen↗Yanshuo Liu↗Chenxi Li↗Daqi Gao↗Zeqin Su↗Jintao Xing↗Zirui Xue↗Rui Li↗Xiangyu Zhao↗Shuofei Qiao↗…

Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach. AI can help read literature, generate hypotheses, and plan protocols, yet the execution of those protocols at the bench still requires a human operator. Vision-Language-Action (VLA) models provide one possible interface between written protocols and robot execution, but existing policies are trained mostly on household and tabletop demonstrations and rarely encounter the instruments, transparent liquids, or fixed protocol workflows found in scientific laboratories. Closing this gap requires both laboratory-specific supervision and a unified learning framework that can accommodate the diverse robot embodiments used to execute experimental protocols. We therefore identify data and embodiment as central bottlenecks alongside model design. To address the data side, we build RoboGenesis, a simulation-based workflow and data engine that composes configured laboratory workflows from atomic skills, validates and filters rollouts, and exports structured demonstrations across supported robot profiles. On the policy side, we present LabVLA, trained with a two-stage recipe: FAST action token pretraining first makes the Qwen3-VL-4B-Instruct backbone action aware before any continuous control is learned, and flow matching posttraining then attaches a DiT action expert under knowledge insulation. On the LabUtopia benchmark, LabVLA achieves the highest average success rate among all evaluated baselines under both in-distribution and out-of-distribution settings.

阅读与讨论 → 访问原文 →
13.
arXiv (CS.LG) 2026-06-18

Acceleration of an algebraic multigrid pressure solver using graph neural networks

作者:
Eric Chill\'on↗Artur K. Lidtke↗Nguyen Anh Khoa Doan↗Bernat Font↗

arXiv:2606.19251v1 Announce Type: cross Abstract: Solving the pressure-Poisson equation remains the primary computational bottleneck in incompressible unstructured flow solvers primarily due to the inherent sensitivity of traditional linear solvers to mesh irregularities. This work introduces a data-driven algebraic multigrid (AMG) smoother that uses a modified graph convolutional isomorphism network (GCIN). The graph neural network predicts optimal polynomial coefficients to construct a sparse pseudo-inverse operator across diverse grid topologies. The coefficients are optimized to reduce the residual after each V-cycle iteration. By directly capturing the algebraic structure of the system from the sparse coefficient matrix, the proposed method maintains the solver's linearity while adapting to local anisotropies in unstructured grids. Our framework demonstrates significant performance gains by reducing the number of V-cycles required for a given tolerance and delivering wall-clock speedups from 4% to 37% across diverse benchmarks. Notably, the model exhibits robust generalization by maintaining efficiency on meshes up to 128 times larger than those seen in training, and by accelerating the solver's convergence on unseen industry-relevant problems such as the AirfRANS dataset.

阅读与讨论 → 访问原文 →
14.
arXiv (CS.CV) 2026-06-17

Beyond MACs: Hardware Efficient Architecture Design for Vision Backbones

作者:
Moritz Nottebaum↗Matteo Dunnhofer↗Christian Micheloni↗

Vision backbone networks play a central role in modern computer vision. Enhancing their efficiency directly benefits a wide range of downstream applications. To measure efficiency, many publications rely on MACs (Multiply Accumulate operations) as a predictor of execution time. In this paper, we experimentally demonstrate the shortcomings of such a metric, especially in the context of edge devices. By contrasting the MAC count and execution time of common architectural design elements, we identify key factors for efficient execution and provide insights to optimize backbone design. Based on these insights, we present LowFormer, a novel vision backbone family. LowFormer features a streamlined macro and micro design that includes Lowtention, a lightweight alternative to Multi-Head Self-Attention. Lowtention not only proves more efficient, but also enables superior results on ImageNet. Additionally, we present an edge GPU version of LowFormer, that can further improve upon its baseline's speed on edge GPU and desktop GPU. We demonstrate LowFormer's wide applicability by evaluating it on smaller image classification datasets, as well as adapting it to several downstream tasks, such as object detection, semantic segmentation, image retrieval, and visual object tracking. LowFormer models consistently achieve remarkable speed-ups across various hardware platforms compared to recent state-of-the-art backbones. Code and models are available at https://github.com/altair199797/LowFormer/blob/main/Beyond_MACs.md.

阅读与讨论 → 访问原文 →
15.
arXiv (CS.AI) 2026-06-11

Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification

作者:
Veerendhra Kumar Dangeti↗Xiao Gu↗Ying Weng↗Shreyank N Gowda↗

arXiv:2606.12252v1 Announce Type: cross Abstract: Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required for repeated model development and deployment. This challenge is particularly evident in electrocardiogram classification, where large datasets and long training schedules make efficiency practically important. Progressive Data Dropout reduces training cost by excluding samples from gradient updates once they are learned, but it relies on model confidence and may retain samples that are difficult due to noise or ambiguity rather than useful signal. In this work, we introduce ERTS, an explainability-based reliability training signal for efficient ECG classification. ERTS uses explanation quality during training to distinguish between informative and unreliable uncertainty. Building on progressive data selection, we compute Grad-CAM attention maps for candidate samples and derive a focus score that measures whether model predictions are supported by coherent and localised patterns. Samples with low focus are filtered out, while those with meaningful attention are prioritised for gradient updates. We evaluate ERTS across three ECG datasets and multiple backbone architectures, showing consistent improvements in macro-F1 alongside reduced effective training cost. These results suggest that explanation quality can serve as a practical signal for improving both efficiency and reliability in clinical time-series learning. Code will be released.

阅读与讨论 → 访问原文 →
16.
medRxiv (Medicine) 2026-06-17

Multi-strain Probiotics Alter Gut Microbiota and Estrobolome Pathways in Primary Dysmenorrhea

作者:
Kiraman↗S. K↗Zakaria↗I. A↗Loh↗S. Y. E↗Norfuad↗F. A↗Abdul Ghani↗N. A↗Ahmad↗H. F↗…

Background: Exact cause of primary dysmenorrhoea is unknown but recent evidence uncovers a potential link between gut dysbiosis and benign gynaecological disorder via disruption of estrobolome. Methods: A randomized controlled trial to investigate the effects of multi-strain oral probiotics on primary dysmenorrhoea has been conducted. This is a secondary analysis comparing the stool microbiome in women with primary dysmenorrhoea and those without (control), and the effects of treatment with probiotics versus placebo. Results: Although microbial richness and evenness were comparable between groups (alpha diversity, p > 0.05), gut microbial community composition differed significantly (Bray Curtis PERMANOVA, p = 0.015), characterised by reduced Bifidobacterium adolescentis and Blautia and enrichment of Faecalibacterium in dysmenorrhoea, alongside condition-specific core taxa. Post-intervention analysis revealed significant shifts in microbial community structure between pre- and post-treatment groups (PERMANOVA, F = 2.11, p = 0.005), with probiotic supplementation inducing more consistent and directed microbiome changes than placebo, without altering alpha diversity (p > 0.05). Functional prediction showed no significant difference in overall beta glucuronidase pathway abundance (p > 0.05); however, dysmenorrhoea was associated with higher abundance of beta glucuronidase producing taxa (MaAsLin2, q < 0.05) that were differentially modulated by probiotic treatment. Conclusion: This discovery provides evidence on the microbial disruption in primary dysmenorrhoea as well as the benefit of probiotics to modulate the intestinal microbiota to improve the condition.

阅读与讨论 → 访问原文 →
17.
arXiv (CS.AI) 2026-06-16

Overcoming the Impedance Mismatch: A Theoretical Roadmap for Fusing Foundation Models and Knowledge Graphs

作者:
Sahil Rajesh Dhayalkar↗

arXiv:2606.15656v1 Announce Type: new Abstract: Modern artificial intelligence remains fundamentally divided between the continuous, probabilistic spaces of Foundation Models and the discrete, deterministic structures of Knowledge Graphs. While Retrieval-Augmented Generation (RAG) attempts to connect them by serializing graph data into text, we argue this lexical bridging is merely a superficial patch. In this paper, we formalize the underlying structural and geometric friction as the Impedance Mismatch. By categorizing current neuro-symbolic integration strategies into a three-tiered hierarchy, we demonstrate that neither surface-level prompt injection nor continuous representation alignment can preserve the strict logical motifs required for reliable multi-hop reasoning. We define the specific mathematical limits, such as the Lexical Bottleneck and Topological Collapse, that show current architectures will eventually hallucinate or conflate semantic nodes. To achieve true semantic fusion, we propose a rigorous theoretical roadmap. We advocate for natively internalizing discrete symbolic structures through Structured Residual Streams, utilizing Vector Symbolic Architectures for latent sub-graph injection, and performing model updates via Orthogonal Subspace Editing. This actionable framework paves the way for models that seamlessly fuse the precision of symbolic logic with the expressivity of parametric memory.

阅读与讨论 → 访问原文 →
18.
arXiv (CS.CV) 2026-06-16

Hierarchical Fine-Grained Aerial Object Detection

作者:
Yan Zhang↗Fang Xu↗Wen Yang↗Gui-Song Xia↗

Fine-grained aerial object detection, driven by the intrinsic granularity of real-world object categories, is crucial for advanced scene understanding in remote sensing. Existing methods largely inherit the paradigm of coarse-grained object detection, relying solely on single-label supervision and thus struggling to distinguish model-level categories with subtle structural differences. However, for each specific model (e.g., Boeing 787), structured prior knowledge such as attributes and hierarchies offers discriminative semantics across multiple granularities. Motivated by this, we present ExpertDet, a scheme that incorporates expert-informed cues to enhance fine-grained aerial object detection. Specifically, we design Vision-aware Masked Attribute Modeling (VMAM), which aligns attribute semantics with visual structures by reconstructing randomly masked attributes from visual cues, enabling the detector to capture subtle structural distinctions. We further propose Hierarchical Visual Instance Promotion (HierVIP), which builds a visual prototype tree based on hierarchical relations and imposes taxonomy-aware constraints to preserve cross-level semantic continuity while enhancing category discrimination. Moreover, we curate a new fine-grained object detection benchmark for Precise recognition of model-specific Ships and Planes from aerial imagery, PSP, covering 106 ship classes and 30 airplane models, respectively, featuring the most extensive collection of model-specific categories among existing aerial object detection datasets to date. We benchmark state-of-the-art object detection algorithms on the PSP benchmark. Extensive evaluation demonstrates that ExpertDet consistently outperforms other fine-grained competitors across hierarchy levels. The dataset, benchmark, and code are available at https://nnnnerd.github.io/PSP-Benchmark/.

阅读与讨论 → 访问原文 →
19.
medRxiv (Medicine) 2026-06-22

Anterior-superior hypothalamic enlargement as specific marker in episodic migraine: converging evidence from an independent discovery-replication design

作者:
Liu↗Peng↗Yin↗Wen↗Huang↗Kendrick↗K. M↗Becker↗Yao↗Ferraro↗

Background: Growing evidence implicates the hypothalamus as a key structure in migraine pathophysiology; however, our understanding of its precise role and of the specific nuclei involved remains limited. We combined MRI data from our laboratory with publicly available MRI datasets from OpenNeuro to examine hypothalamic subunit volumes in episodic migraine and assess the specificity of these alterations relative to chronic pain conditions. Methods: Structural MRI combined with an automated atlas-based segmentation algorithm and a discovery-replication design was employed to investigate cross-sectional volumetric differences across 5 bilateral hypothalamic subunits in two independent migraine cohorts: DS1-MIG (DS1-MIG-base, n = 111 patients, n = 35 controls) and DS2-MIG (n = 27 patients, n = 31 controls). The adjusted volumes were compared between groups using MANOVA as an omnibus test, followed by Welch t-tests to test univariate follow-up. Longitudinal volumetric changes were additionally assessed in DS1-MIG participants with available follow-up scans using linear mixed models. To assess the specificity of findings to migraine, the same pipeline was applied to two chronic pain datasets, one including patients with fibromyalgia (DS-FM, n = 33 patients, n = 33 controls) and the other including patients with trigeminal neuralgia (n = 119 patients, n = 55 controls). Results: MANOVA revealed significant multivariate group differences in the discovery and replication migraine cohorts (DS1-MIG-base: = .006; DS2-MIG: = .008). Follow-up univariate analyses identified a consistent enlargement of the left anterior-superior subunit across both cohorts (FDR = .023 in DS1-MIG-base and FDR = .046 in DS2-MIG), representing the only cross-cohort replication finding. Beyond this shared signature, DS2-MIG exhibited additional significant enlargements of the right anterior-inferior and right tubular-inferior subunits. Longitudinal analyses in DS1-MIG showed that hypothalamic subunit volumes remained broadly stable over time within both migraine patients and control participants. No significant volumetric alterations were detected in the fibromyalgia or trigeminal neuralgia cohorts, either in multivariate or univariate analyses, underscoring migraine-specific findings. Conclusions: These findings provide evidence for subunit-specific hypothalamic structural alterations in migraine localized in the left anterior hypothalamic subunit. The stability of these differences over time and their absence in other chronic pain conditions suggest a migraine-specific structural organisation of hypothalamic circuitry.

阅读与讨论 → 访问原文 →
20.
arXiv (CS.LG) 2026-06-19

Recurrent neural networks approximate continuous functions

作者:
Valentin Abadie↗Clemens Hutter↗Helmut B\"olcskei↗

arXiv:2606.20325v1 Announce Type: new Abstract: Classical approximation theorems ask for a new neural network whenever the target accuracy is improved. This paper studies the opposite possibility: can the network be chosen once and for all, and can accuracy be bought only by letting it run longer? We prove that this is possible for every continuous function on [-1,1]. More precisely, each such function is uniformly approximated by the time evolution of a single ReLU recurrent neural network with fixed weights and fixed hidden dimension. The mechanism behind the construction is a new intermediate model, the Turing machine with neural units (TMNU). This model retains the algorithmic freedom needed to implement polynomial approximation schemes, while remaining rigid enough to be simulated by RNNs with explicit bounds on hidden dimension and weight magnitude. The resulting convergence rates reflect the underlying polynomial approximation rates. We complement the construction with minimax lower bounds showing that runtime is not merely a proof artifact, but an unavoidable resource in this fixed-network approximation paradigm.

阅读与讨论 → 访问原文 →
21.
arXiv (quant-ph) 2026-06-17

Quantum Computing Algebra (QCA), the theory and implementation

作者:
Jaroslav Hrdina↗Dietmar Hildenbrand↗Oliver Rettig↗

arXiv:2606.17621v1 Announce Type: new Abstract: We present a real geometric algebra framework designed for the direct translation of the Dirac formalism into geometric algebra representations. Unlike previous approaches based on positive-definite signatures, QCA employs a split-signature construction that enables a natural realization of quantum states and operators while simplifying computational implementation. We further present an implementation of QCA using the GAALOP software and show how quantum gates and multi-qubit systems can be efficiently represented and generated computationally. As an application, we demonstrate the use of QCA in quantum game theory, where the real-algebraic formulation provides computational advantages for modeling entangled strategies and quantum interactions. The proposed framework establishes a practical bridge between the abstract formalism of quantum computation and efficient geometric algebra implementations.

阅读与讨论 → 访问原文 →
22.
medRxiv (Medicine) 2026-06-15

Cost-Performance Evaluation of Large Language Models for Aspect-Based Sentiment Analysis of HCAHPS Patient Comments: A Validation Study

作者:
Nawab↗Ramsey↗Asfandiyar↗Atreya↗Hijjawi↗Rokkam↗Ghayur↗Rajesh↗Yousuf↗Shah↗Z. A↗Misra↗…

Background: Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) free-text comments contain actionable feedback, but timely, scalable, and affordable sentiment analysis remains challenging for health systems that rely on third-party vendors. Objectives: To evaluate cost-performance tradeoffs between a cost-optimized and a flagship large language model (LLM) for aspect-based sentiment analysis of HCAHPS comments, using human inter-rater agreement as a reproducibility benchmark. Methods: We analyzed 512 free-text HCAHPS comments collected from two community hospitals in calendar year 2023. Six trained reviewers (medical students, recent medical graduates, and practicing internists) independently assigned positive, negative, or neutral labels to each comment-aspect pair; the majority label among three reviewers formed the consensus reference standard. Two OpenAI models - GPT-5-nano (cost-optimized) and GPT-5 (flagship) - were prompted in a zero-shot setting via the OpenAI API. We calculated pairwise Cohen's {kappa} to establish a human inter-rater baseline, then compared each model's labels to the consensus using Cohen's {kappa}, accuracy, weighted F1, and per-call cost and latency. Results: Mean human inter-rater agreement was {kappa} = 0.79 (substantial). Both LLMs exceeded this baseline (cost-optimized {kappa} = 0.85; flagship {kappa} = 0.85) with nearly identical accuracy (0.92) and weighted F1 (0.93 vs. 0.93). Performance was strong on positive (F1 ~ 0.97) and negative (F1 ~ 0.90) classes but poor on the underrepresented neutral class (F1

阅读与讨论 → 访问原文 →
23.
medRxiv (Medicine) 2026-06-18

Maternal and fetal HLA heterozygosity in preeclampsia: Insights from a large multi-ancestry pregnancy cohort

作者:
Cao↗Maher↗Hu↗Keating↗B. J↗Burwick↗R. M↗Karumanchi↗S. A↗Maxwell↗G. L↗Powe↗…

Preeclampsia (PE) is a leading cause of maternal and neonatal morbidity, with immune dysregulation at the maternal-fetal interface central to its pathogenesis. The highly polymorphic human leukocyte antigen (HLA) region mediates maternal immune tolerance of the semi-allogeneic fetus, yet the contribution of HLA diversity to PE risk remains poorly defined. Whether the HLA heterozygote advantage observed in other immune disorders is relevant to PE has not been systematically evaluated. Using data from the multi-ancestry TOPMed Boston-Colombia Collaborative for Adverse Pregnancy Outcomes (n = 12,790; 4,770 PE, 8,020 controls; 10,808 maternal, 1,982 fetal, including 1,848 pairs), we evaluated associations between heterozygosity across eight classical HLA loci and PE and four sub-phenotypes, adjusting for genetic ancestry. HLA heterozygosity was common across most loci (>80%). No individual maternal HLA locus was associated with overall PE; however, heterozygosity across class I loci showed a protective effect in preterm PE (OR=0.82, 95%CI:0.69-0.97), with a similar pattern for HLA-A heterozygosity (OR=0.78, 95%CI:0.64-0.96). In contrast, fetal heterozygosity at HLA-DQB1 was nominally associated with increased risk of PE (OR=1.36, 95%CI:1.03-1.79) and preterm PE (OR=1.73, 95%CI:1.13-2.73). No individual maternal or fetal HLA alleles were associated with PE. Maternal-fetal mismatch analysis demonstrated locus-specific associations with preterm PE, including increased risk with HLA-DQA1 mismatch and reduced risk with HLA-C mismatch. These findings highlight distinct maternal and fetal immunogenetic contributions to PE risk and underscore the importance of considering HLA diversity-rather than individual alleles alone-in studies of PE etiology.

阅读与讨论 → 访问原文 →
24.
medRxiv (Medicine) 2026-06-15

ECHOCARDIOGRAPHY ABNORMALITIES IN PREECLAMPSIA WITH SEVERE FEATURES.

作者:
Rathod↗A. T↗Sarojini↗PC↗S. R↗Deshmukh↗Fathima↗S. A↗

Purpose To determine the frequency of echocardiographic abnormalities in women with preeclampsia with severe features. To describe the spectrum and types of echocardiographic abnormalities associated with preeclampsia with severe features. Method This is a Prospective observational study conducted in Vani Vilas hospital attached to Bangalore Medical College and Research Institute, Bangalore from January 2023 to December 2025. 560 pregnant women diagnosed with severe preeclampsia(SPE) were included in the study. Chronic hypertension without superimposed preeclampsia, underlying cardiac diseases and previous history of peripartum cardiomyopathy were excluded from the study. Transthoracic echocardiography-TTE (2D ECHO) was done to evaluate cardiac structure and function. Echocardiographic abnormalities identified during the study were documented and analysed using descriptive statistical methods. Results Abnormalities in ECHO was noted in 23.03%. A unique finding was the documentation of elevated pulmonary artery systolic pressures (PASP) suggestive of Pulmonary Hypertension (PH) (PASP >35 mm HG) among 20.25% of the participants. It was also the commonest abnormality on ECHO. Mild PH was the commonest (15.71%), moderate PH was seen in 3.92% and severe PH in 0.71% of cases. Next most frequent abnormality was moderate to severe valvular regurgitation (10%), followed by left ventricular hypertrophy (5.53%). Diastolic dysfunction (DD) was seen in 3.92%, systolic dysfunction(SD) in 3.57%, chamber dilatation in 3.57% and LV global hypokinesia in 3.03% cases of SPE Conclusion Preeclampsia with severe features (SPE) is associated with 23.03% abnormalities on echocardiography. SPE is associated with systolic dysfunction, diastolic dysfunction, chamber dilatation, valvular regurgitation, left ventricular hypertrophy and pulmonary hypertension.

阅读与讨论 → 访问原文 →
25.
arXiv (CS.CV) 2026-06-16

An Adaptive Data cleaning Framework for Noisy Label Detection

作者:
Chen-Hsuan Fang↗Wei-Hsinag Chen↗Pin-Hsuan Yu↗Jung-Hua Wang↗Tsung-Wei Pan↗

Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets. In real-world applications, however, labels are often corrupted by ambiguity, human error, or dynamic environments. Over-parameterized DNNs easily memorize these noisy labels during training, degrading model accuracy and generalization. Existing data-cleaning and sample-selection strategies often rely on manually specified thresholds, prior knowledge of the noise ratio, or a single metric (either learning dynamics or geometric structure), making them unstable in complex data regimes. This paper proposes a self-adaptive data-cleaning framework that integrates local, global, and learning dynamics cues for robust noisy-label detection. Samples are mapped into a unified low-dimensional feature space through a modular feature concatenation paradigm. We provide two instantiations: a 2D metric integrating class-adaptive KNN-based local disagreement with k-means-based global centroid distance, and a 3D multi-metric that additionally incorporates a z-normalized score. Unlike conventional 1D Gaussian Mixture Models applied to a single scalar metric, our framework performs multi-metric clustering on the feature space to adaptively partition samples into clean-dominant and noise-dominant components without requiring manual thresholds or noise priors. Experiments on CIFAR-10, MNIST, and ImageNet-100 with 5% to 40% symmetric label noise show high recall across settings, including near-perfect recall (>=98%) on ImageNet-100 at 40% noise. Subsequent training yields accuracy gains across evaluated settings, especially under severe corruption on ImageNet-100. These findings suggest that multi-metric integration provides a threshold-free, practical, and low-tuning strategy for noisy label detection.

阅读与讨论 → 访问原文 →