arXiv subjects

Bo Zhou

Publications and source records attributed to Bo Zhou.

At least 55 records · Page 3Linked to original sources

arXiv2026

SPECTRA: An Efficient Spectral-Informed Neural Network for Sensor-Based Activity Recognition

Real time sensor based applications in pervasive computing require edge deployable models to ensure low latency privacy and efficient interaction. A prime example is sensor based human activity recognition where models must balance accuracy with stringent resource constraints. Yet many deep learning approaches treat temporal sensor signals as black box sequences overlooking spectral temporal structure while demanding excessive computation. We present SPECTRA a deployment first co designed spectral temporal architecture that integrates short time Fourier transform STFT feature extraction depthwise separable convolutions and channel wise self attention to capture spectral temporal dependencies under real edge runtime and memory constraints. A compact bidirectional GRU with attention pooling summarizes within window dynamics at low cost reducing downstream model burden while preserving accuracy. Across five public HAR datasets SPECTRA matches or approaches larger CNN LSTM and Transformer baselines while substantially reducing parameters latency and energy. Deployments on a Google Pixel 9 smartphone and an STM32L4 microcontroller further demonstrate end to end deployable realtime private and efficient HAR.

cs.LG↗

arXiv2026

Central-to-Local Adaptive Generative Diffusion Framework for Improving Gene Expression Prediction in Data-Limited Spatial Transcriptomics

Spatial Transcriptomics (ST) provides spatially resolved gene expression profiles within intact tissue architecture, enabling molecular analysis in histological context. However, the high cost, limited throughput, and restricted data sharing of ST experiments result in severe data scarcity, constraining the development of robust computational models. To address this limitation, we present a Central-to-Local adaptive generative diffusion framework for ST (C2L-ST) that integrates large-scale morphological priors with limited molecular guidance. A global central model is first pretrained on extensive histopathology datasets to learn transferable morphological representations, and institution-specific local models are then adapted through lightweight gene-conditioned modulation using a small number of paired image-gene spots. This strategy enables the synthesis of realistic and molecularly consistent histology patches under data-limited conditions. The generated images exhibit high visual and structural fidelity, reproduce cellular composition, and show strong embedding overlap with real data across multiple organs, reflecting both realism and diversity. When incorporated into downstream training, synthetic image-gene pairs improve gene expression prediction accuracy and spatial coherence, achieving performance comparable to real data while requiring only a fraction of sampled spots. C2L-ST provides a scalable and data-efficient framework for molecular-level data augmentation, offering a domain-adaptive and generalizable approach for integrating histology and transcriptomics in spatial biology and related fields.

cs.LG↗

arXiv2026

The Art of Efficient Reasoning: Data, Reward, and Optimization

Large Language Models (LLMs) consistently benefit from scaled Chain-of-Thought (CoT) reasoning, but also suffer from heavy computational overhead. To address this issue, efficient reasoning aims to incentivize short yet accurate thinking trajectories, typically through reward shaping with Reinforcement Learning (RL). In this paper, we systematically investigate the mechanics of efficient reasoning for LLMs. For comprehensive evaluation, we advocate for more fine-grained metrics, including length distribution conditioned on correctness and performance across a wide spectrum of token budgets ranging from 2k to 32k. First, we reveal that the training process follows a two-stage paradigm: length adaptation and reasoning refinement. Through extensive experiments (about 0.2 million GPU hours) in a unified protocol, we deconstruct training prompts and rollouts, reward shaping, and optimization strategies. A central finding is to maintain a sufficient density of positive reward signals and avoid the short-is-correct trap. Moreover, the learned length bias generalizes across domains and difficulty levels. We distill these findings into valuable insights and practical guidelines, and validate them across the Qwen3 models ranging from 0.6B to 30B, demonstrating the robustness and generalization. Weights are available at https://wutaiqiang.github.io/project/Art

cs.CL↗

arXiv2026

FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classification

Federated learning (FL) enables privacy-preserving collaborative model training without direct data sharing. Model-heterogeneous FL (MHFL) extends this paradigm by allowing clients to train personalized models with heterogeneous architectures tailored to their computational resources and application-specific needs. However, existing MHFL methods predominantly rely on centralized aggregation, which introduces scalability and efficiency bottlenecks, or impose restrictions requiring partially identical model architectures across clients. While peer-to-peer (P2P) FL removes server dependence, it suffers from model drift and knowledge dilution, limiting its effectiveness in heterogeneous settings. To address these challenges, we propose FedSKD, a novel MHFL framework that facilitates direct knowledge exchange through round-robin model circulation, eliminating the need for centralized aggregation while allowing fully heterogeneous model architectures across clients. FedSKD's key innovation lies in multi-dimensional similarity knowledge distillation, which enables bidirectional cross-client knowledge transfer at batch, pixel/voxel, and region levels for heterogeneous models in FL. This approach mitigates catastrophic forgetting and model drift through progressive reinforcement and distribution alignment while preserving model heterogeneity. Extensive evaluations on fMRI-based autism spectrum disorder diagnosis and skin lesion classification demonstrate that FedSKD outperforms state-of-the-art heterogeneous and homogeneous FL baselines, achieving superior personalization (client-specific accuracy) and generalization (cross-institutional adaptability). These findings underscore FedSKD's potential as a scalable and robust solution for real-world medical federated learning applications.

cs.LG↗

arXiv2026

Maximal Ancillarity, Semiparametric Efficiency, and the Elimination of Nuisances

Restricting statistical experiments via nuisance-ancillary $σ$-fields yields nuisance-free experiments. However, a moot point with ancillarity is that maximal ancillary $σ$-fields are typically not unique. There are exceptions, though, among which the limiting experiments in a locally asymptotically normal (LAN) context. Building on this, we address the maximal ancillarity uniqueness problem by adopting a Hájek-Le Cam asymptotic perspective and define the concept of sequences of locally asymptotically maximal nuisance-ancillary $σ$-fields. We then show that any semiparametrically efficient procedure admits versions that are measurable with respect to such $σ$-fields while enjoying strict finite-sample nuisance-ancillarity, hence eliminating the nuisance without the hassle of estimating it. This is in sharp contrast with classical tangent space projections, which also achieve semiparametric efficiency but only enjoy asymptotic nuisance-ancillarity -- at the price, moreover, of adequately estimating the nuisance. When the nuisance is the density of some noise or innovation driving the data-generating process of a LAN experiment, we show that a sequence of locally asymptotically maximal nuisance-ancillary $σ$-fields is generated by the so-called center-outward residual ranks and signs based on measure transportation results. Restricting local experiments to such $σ$-fields yields sequences of finite-sample nuisance-free (here, distribution-free) restrictions of the original local LAN experiments that nevertheless achieve the semiparametric efficiency bounds of the original ones.

math.ST↗

arXiv2026

Three-Body Barrier Dynamics of Double-Alpha Decay in Heavy Nuclei

The simultaneous emission of two $α$ particles--double-$α$ decay--represents a long-predicted but unobserved mode of nuclear radioactivity. Here we formulate this process as a genuine three-body problem within the hyperspherical coordinate framework and evaluate decay probabilities by numerically solving the corresponding hyperradial Schrödinger equation, combined with large-scale random sampling of the potential parameters; the latter treatment ensures that the present results are more convincing. Inspired by this, we demonstrate that the penetrability ratio between simultaneous and sequential $α$ emission exhibits a strikingly linear dependence on $ZQ_{αα}^{-1/2}$, extending the barrier penetration dynamics into the correlated few-body regime. The nuclei $^{108}$Xe, $^{218}$Ra, $^{224}$Pu, $^{222}$U, $^{216}$Rn, and $^{220}$Th are suggested as the most promising candidates for the observation of double-$α$ decay, with predicted half-lives potentially accessible within present detection limits. Our results provide a unified framework for multi-$α$ decay and open a pathway to probing nuclear clustering and few-body correlations in heavy nuclei.

nucl-th↗

arXiv2026

Self-locking non-volatile coding metasurfaces via origami-based mechanical bits

Digital coding metasurfaces have revolutionized electromagnetic (EM) manipulation, yet typical tunable approaches based on active components suffer from the "volatility" bottleneck. While mechanical modulation provides a potential solution, current implementations generally lack inherent state-locking capability, rendering them vulnerable to environmental disturbances and actuation errors. Inspired by the concept of mechanical bits (MBs), this paper presents a self-locking non-volatile coding metasurface platform enabled by Kresling origami-based MBs, where the continuous mechanical deformation of individual meta-atoms is discretized into robust binary geometric states protected by intrinsic energy barriers. The bistable states are strictly mapped to 1-bit EM coding phases via tailored metallic patterns integrated onto a multimaterial 3D printed Kresling origami array. Building upon this concept, both transmission- and reflection-type prototypes are proposed and experimentally demonstrated, exhibiting exceptional wavefront manipulation capabilities through near-field holographic imaging and far-field beam steering. In addition, the lightweight origami unit (1.5 g) exhibits an exceptional load-bearing capacity, supporting over 100 times its own weight. These results bridge mechanical logic with EM information processing, establishing a universal physical paradigm for constructing low-power, highly robust coding metasurfaces resilient to extreme environments.

physics.optics↗

arXiv2026

Identifying $α$-cluster configurations in $^{20}$Ne via ultracentral Ne+Ne Collisions

The initial-state geometry in relativistic heavy-ion collisions provides a novel probe to nuclear cluster structure. For $^{20}$Ne, a novel approach is proposed to distinguish between the cluster configurations (5$α$ versus $α+ ^{16}$O) in order to gain insight into nuclear structure transitions governed by many-body quantum correlations. Through analytical calculations with the microscopic Brink model and event-by-event simulations using the hydrodynamic framework, we establish the normalized symmetric cumulant NSC (3, 2) and the Pearson coefficient $ρ_2 (v_{2}^{2},\ δ[p_{\mathrm{T}}])$ as quantitative discriminators to reveal enhanced cluster degrees of freedom in the ground state of $^{20}$Ne. The ultracentral Ne+Ne collisions at the LHC can experimentally identify these two competing configurations via these flow correlation observables, opening a new paradigm for probing clustering in light nuclei.

nucl-th↗

arXiv2026

From Neck to Head: Bio-Impedance Sensing for Head Pose Estimation

We present NeckSense, a novel wearable system for head pose tracking that leverages multi-channel bio-impedance sensing with soft, dry electrodes embedded in a lightweight, necklace-style form factor. NeckSense captures dynamic changes in tissue impedance around the neck, which are modulated by head rotations and subtle muscle activations. To robustly estimate head pose, we propose a deep learning framework that integrates anatomical priors, including joint constraints and natural head rotation ranges, into the loss function design. We validate NeckSense on 7 participants using the current SOTA pose estimation model as ground truth. Our system achieves a mean per-vertex error of 25.9 mm across various head movements with a leave-one-person-out cross-validation method, demonstrating that a compact, line-of-sight-free bio-impedance wearable can deliver head-tracking performance comparable to SOTA vision-based methods.

cs.CV↗

arXiv2026

Discovery and Reinforcement of Tool-Integrated Reasoning Chains via Rollout Trees

Tool-Integrated Reasoning has emerged as a key paradigm to augment Large Language Models (LLMs) with computational capabilities, yet integrating tool-use into long Chain-of-Thought (long CoT) remains underexplored, largely due to the scarcity of training data and the challenge of integrating tool-use without compromising the model's intrinsic long-chain reasoning. In this paper, we introduce DART (Discovery And Reinforcement of Tool-Integrated Reasoning Chains via Rollout Trees), a reinforcement learning framework that enables spontaneous tool-use during long CoT reasoning without human annotation. DART operates by constructing dynamic rollout trees during training to discover valid tool-use opportunities, branching out at promising positions to explore diverse tool-integrated trajectories. Subsequently, a tree-based process advantage estimation identifies and credits specific sub-trajectories where tool invocation positively contributes to the solution, effectively reinforcing these beneficial behaviors. Extensive experiments on challenging benchmarks like AIME and GPQA-Diamond demonstrate that DART significantly outperforms existing methods, successfully harmonizing tool execution with long CoT reasoning.

cs.CL↗

arXiv2026

Segmental Advantage Estimation: Enhancing PPO for Long-Context LLM Training

Training Large Language Models (LLMs) for reasoning tasks is increasingly driven by Reinforcement Learning with Verifiable Rewards (RLVR), where Proximal Policy Optimization (PPO) provides a principled framework for stable policy updates. However, the practical application of PPO is hindered by unreliable advantage estimation in the sparse-reward RLVR regime. This issue arises because the sparse rewards in RLVR lead to inaccurate intermediate value predictions, which in turn introduce significant bias when aggregated at every token by Generalized Advantage Estimation (GAE). To address this, we introduce Segmental Advantage Estimation (SAE), which mitigates the bias that GAE can incur in RLVR. Our key insight is that aggregating $n$-step advantages at every token(as in GAE) is unnecessary and often introduces excessive bias, since individual tokens carry minimal information. Instead, SAE first partitions the generated sequence into coherent sub-segments using low-probability tokens as heuristic boundaries. It then selectively computes variance-reduced advantage estimates only from these information-rich segment transitions, effectively filtering out noise from intermediate tokens. Our experiments demonstrate that SAE achieves superior performance, with marked improvements in final scores, training stability, and sample efficiency. These gains are shown to be consistent across multiple model sizes, and a correlation analysis confirms that our proposed advantage estimator achieves a higher correlation with an approximate ground-truth advantage, justifying its superior performance.

cs.LG↗

arXiv2026

TransDOA: Calibrating Array Imperfections via Transformer-based Transfer Learning

In practical scenarios, processes such as sensor design, manufacturing, and installation will introduce certain errors. Furthermore, mutual interference occurs when the sensors receive signals. These defects in array systems are referred to as array imperfections, which can significantly degrade the performance of Direction of Arrival (DOA) estimation. In this study, we propose a deep-learning based transfer learning approach, which effectively mitigates the degradation of deep-learning based DOA estimation performance caused by array imperfections. In the proposed approach, we highlight three major contributions. First, we propose a Vision Transformer (ViT) based method for DOA estimation, which achieves excellent performance in scenarios with low signal-to-noise ratios (SNR) and limited snapshots. Second, we introduce a transfer learning framework that extends deep learning models from ideal simulation scenarios to complex real-world scenarios with array imperfections. By leveraging prior knowledge from ideal simulation data, the proposed transfer learning framework significantly improves deep learning-based DOA estimation performance in the presence of array imperfections, without the need for extensive real-world data. Finally, we incorporate visualization and evaluation metrics to assess the performance of DOA estimation algorithms, which allow for a more thorough evaluation of algorithms and further validate the proposed method. Our code can be accessed at https://github.com/zzb-nice/DOA_est_Master.

eess.SP↗

arXiv2026

AT$^2$PO: Agentic Turn-based Policy Optimization via Tree Search

LLM agents have emerged as powerful systems for tackling multi-turn tasks by interleaving internal reasoning and external tool interactions. Agentic Reinforcement Learning has recently drawn significant research attention as a critical post-training paradigm to further refine these capabilities. In this paper, we present AT$^2$PO (Agentic Turn-based Policy Optimization via Tree Search), a unified framework for multi-turn agentic RL that addresses three core challenges: limited exploration diversity, sparse credit assignment, and misaligned policy optimization. AT$^2$PO introduces a turn-level tree structure that jointly enables Entropy-Guided Tree Expansion for strategic exploration and Turn-wise Credit Assignment for fine-grained reward propagation from sparse outcomes. Complementing this, we propose Agentic Turn-based Policy Optimization, a turn-level learning objective that aligns policy updates with the natural decision granularity of agentic interactions. ATPO is orthogonal to tree search and can be readily integrated into any multi-turn RL pipeline. Experiments across seven benchmarks demonstrate consistent improvements over the state-of-the-art baseline by up to 1.84 percentage points in average, with ablation studies validating the effectiveness of each component. Our code is available at https://github.com/zzfoutofspace/ATPO.

cs.AI↗

arXiv2026

ConMax: Confidence-Maximizing Compression for Efficient Chain-of-Thought Reasoning

Recent breakthroughs in Large Reasoning Models (LRMs) have demonstrated that extensive Chain-of-Thought (CoT) generation is critical for enabling intricate cognitive behaviors, such as self-verification and backtracking, to solve complex tasks. However, this capability often leads to ``overthinking'', where models generate redundant reasoning paths that inflate computational costs without improving accuracy. While Supervised Fine-Tuning (SFT) on reasoning traces is a standard paradigm for the 'cold start' phase, applying existing compression techniques to these traces often compromises logical coherence or incurs prohibitive sampling costs. In this paper, we introduce ConMax (Confidence-Maximizing Compression), a novel reinforcement learning framework designed to automatically compress reasoning traces while preserving essential reasoning patterns. ConMax formulates compression as a reward-driven optimization problem, training a policy to prune redundancy by maximizing a weighted combination of answer confidence for predictive fidelity and thinking confidence for reasoning validity through a frozen auxiliary LRM. Extensive experiments across five reasoning datasets demonstrate that ConMax achieves a superior efficiency-performance trade-off. Specifically, it reduces inference length by 43% over strong baselines at the cost of a mere 0.7% dip in accuracy, proving its effectiveness in generating high-quality, efficient training data for LRMs.

cs.AI↗

arXiv2026

Extremal $Q$-index problem in outerplanar graphs

Outerplanar Turán problem has received considerable attention recently. We study the spectral version via $Q$-index. We determine the unique graph that maximizes the $Q$-index among all $n$-vertex connected outerplanar graphs which are respectively forbidden to contain: (i) a fixed cycle; and (ii) the disjoint union of paths of a given order.

math.CO↗

arXiv2026

AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning

Robust anatomical segmentation of chest X-rays (CXRs) remains challenging due to the scarcity of comprehensive annotations and the substantial variability of real-world acquisition conditions. We propose AnyCXR, a unified framework that enables generalizable multi-organ segmentation across arbitrary CXR projection angles using only synthetic supervision. The method combines a Multi-stage Domain Randomization (MSDR) engine, which generates over 100,000 anatomically faithful and highly diverse synthetic radiographs from 3D CT volumes, with a Conditional Joint Annotation Regularization (CAR) learning strategy that leverages partial and imperfect labels by enforcing anatomical consistency in a latent space. Trained entirely on synthetic data, AnyCXR achieves strong zero-shot generalization on multiple real-world datasets, providing accurate delineation of 54 anatomical structures in PA, lateral, and oblique views. The resulting segmentation maps support downstream clinical tasks, including automated cardiothoracic ratio estimation, spine curvature assessment, and disease classification, where the incorporation of anatomical priors improves diagnostic performance. These results demonstrate that AnyCXR establishes a scalable and reliable foundation for anatomy-aware CXR analysis and offers a practical pathway toward reducing annotation burdens while improving robustness across diverse imaging conditions.

cs.CV↗

arXiv2025

ProDM: Synthetic Reality-driven Property-aware Progressive Diffusion Model for Coronary Calcium Motion Correction in Non-gated Chest CT

Coronary artery calcium (CAC) scoring from chest CT is a well-established tool to stratify and refine clinical cardiovascular disease risk estimation. CAC quantification relies on the accurate delineation of calcified lesions, but is oftentimes affected by artifacts introduced by cardiac and respiratory motion. ECG-gated cardiac CTs substantially reduce motion artifacts, but their use in population screening and routine imaging remains limited due to gating requirements and lack of insurance coverage. Although identification of incidental CAC from non-gated chest CT is increasingly considered for it offers an accessible and widely available alternative, this modality is limited by more severe motion artifacts. We present ProDM (Property-aware Progressive Correction Diffusion Model), a generative diffusion framework that restores motion-free calcified lesions from non-gated CTs. ProDM introduces three key components: (1) a CAC motion simulation data engine that synthesizes realistic non-gated acquisitions with diverse motion trajectories directly from cardiac-gated CTs, enabling supervised training without paired data; (2) a property-aware learning strategy incorporating calcium-specific priors through a differentiable calcium consistency loss to preserve lesion integrity; and (3) a progressive correction scheme that reduces artifacts gradually across diffusion steps to enhance stability and calcium fidelity. Experiments on real patient datasets show that ProDM significantly improves CAC scoring accuracy, spatial lesion fidelity, and risk stratification performance compared with several baselines. A reader study on real non-gated scans further confirms that ProDM suppresses motion artifacts and improves clinical usability. These findings highlight the potential of progressive, property-aware frameworks for reliable CAC quantification from routine chest CT imaging.

cs.CV↗

arXiv2025

Local Asymptotic Normality for Multi-Armed Bandits

Van den Akker, Werker, and Zhou (2025) showed that the limit experiment, in the sense of H\a'{a}jek-Le Cam, for (contextual) bandits whose arms' expected payoffs differ by $O(T^{-1/2})$, is Locally Asymptotically Quadratic (LAQ) but highly non-standard, being characterized by a system of coupled stochastic differential equations. The present paper considers the complementary case where the arms' expected payoffs are fixed with a unique optimal (in the sense of highest expected payoff) arm. It is shown that, under sampling schemes satisfying mild regularity conditions (including UCB and Thompson sampling), the model satisfies the standard Locally Asymptotically Normal (LAN) property.

math.ST↗