arXiv subjects

Zhen Zhang

Publications and source records attributed to Zhen Zhang.

At least 217 records · Page 12Linked to original sources

arXiv2025

Emotion Recognition from Skeleton Data: A Comprehensive Survey

Emotion recognition through body movements has emerged as a compelling and privacy-preserving alternative to traditional methods that rely on facial expressions or physiological signals. Recent advancements in 3D skeleton acquisition technologies and pose estimation algorithms have significantly enhanced the feasibility of emotion recognition based on full-body motion. This survey provides a comprehensive and systematic review of skeleton-based emotion recognition techniques. First, we introduce psychological models of emotion and examine the relationship between bodily movements and emotional expression. Next, we summarize publicly available datasets, highlighting the differences in data acquisition methods and emotion labeling strategies. We then categorize existing methods into posture-based and gait-based approaches, analyzing them from both data-driven and technical perspectives. In particular, we propose a unified taxonomy that encompasses four primary technical paradigms: Traditional approaches, Feat2Net, FeatFusionNet, and End2EndNet. Representative works within each category are reviewed and compared, with benchmarking results across commonly used datasets. Finally, we explore the extended applications of emotion recognition in mental health assessment, such as detecting depression and autism, and discuss the open challenges and future research directions in this rapidly evolving field.

cs.CV↗

arXiv2025

A new Phase-Field Model for Anisotropic Surface Diffusion: Anisotropic Cahn-Hilliard Equation with Improved Conservation (ACH-IC)

As popular approximations to sharp-interface models, the Cahn-Hilliard type phase-field models are usually used to simulate interface dynamics with volume conservation. However, the convergence rate of the volume enclosed by the interface to its sharp-interface limit is usually at first order of the interface thickness in the classical Cahn-Hilliard model with constant or degenerate mobilities. In this work, we propose a variational framework for developing new Cahn-Hilliard dynamics with enhanced volume conservation by introducing a more general conserved quantity. In particular, based on Onsager's variational principle (OVP) and a modified conservation law, we develop an anisotropic Cahn-Hilliard equation with improved conservation (ACH-IC) for approximating anisotropic surface diffusion. The ACH-IC model employs a new conserved quantity that approximates a step function more effectively, and yields second-order volume conservation while preserving energy dissipation for the classical anisotropic surface energy. The second-order volume conservation as well as the convergence to the sharp-interface surface diffusion dynamics is derived through comprehensive asymptotic analysis. Numerical evidence not only reveals the underlying physics of the proposed model in comparison with the classical one, but also demonstrates its exceptional performance in simulating anisotropic surface diffusion dynamics.

math-ph↗

arXiv2025

Reasoning about Rare-Event Reachability in Stochastic Vector Addition Systems via Affine Vector Spaces

Rare events in Stochastic Vector Addition System (VAS) are of significant interest because, while extremely unlikely, they may represent undesirable behavior that can have adverse effects. Their low probabilities and potentially extremely large state spaces challenge existing probabilistic model checking and stochastic rare-event simulation techniques. In particular, in Chemical Reaction Networks (CRNs), a chemical kinetic language often represented as VAS, rare event effects may be pathological. We present two novel heuristics for priority-first partial state space expansion and trace generation tuned to the transient analysis of rare-event probability in VAS: Iterative Subspace Reduction (ISR) and Single Distance Priority (SDP). Both methods construct a closed vector space containing all solution states. SDP then simply prioritizes shorter distances to this ``solution space'', while ISR constructs a set of nested subspaces, where short and highly-probable satisfying traces are likely to pass through in sequence. The resulting partial state graph from each method contains likely traces to rare-event states, allowing efficient probabilistic model checking to compute a lower-bound probability of a rare event of interest. These methods are deterministic, fast, and demonstrate marked performance on challenging CRN models.

cs.FL↗

arXiv2025

Rotational Metric: A Solution to Einstein's Clock-Rate Problem and Its Magnetospheric Applications

The rotational metric provides an exact solution to Einstein's clock-rate problem in curved spacetime, specifically, whether time flows more slowly at the equator of a compact object such as a neutron star than at its poles. It features a curvature singularity, an event horizon, a potentially evolving ergosphere, a rigidly-rotating normal space, and two stationary limit surfaces. Although derived from the Schwarzschild metric through rotational transformations, it includes an additional ergosphere. Given the equivalence of inertia and gravity, this demonstrates how non-inertial transformations, such as rotational transformations, can introduce new spacetime structures into a gravitational system. In particular, the additional physical degrees of freedom carried by rotational transformations are `eaten' by the gravitational system to form an additional ergosphere. Furthermore, the rotational metric effectively models a rigidly-rotating gravitational system and is applicable for describing rotationally-induced gravitational effects in various rotating magnetospheres.

gr-qc↗

arXiv2025

Alpha clustering in warm and dense nuclear matter from heavy-ion collisions

Although light nuclear clusters are known to affect the properties of warm and dilute nuclear matter, their role in warm and dense nuclear matter remains unclear due to the lack of experimental evidence for their modifications by the Mott effect in such an environment. To address this issue, we resort to intermediate-energy heavy-ion collisions, where light clusters are mainly produced in the transiently formed warm and dense matter. A kinetic approach, which includes dynamically the formation and dissociation of light clusters, is employed to deduce the strength of the Mott effects and the $α$-particle fraction in warm and dense nuclear matter from the light-nuclei yields measured by the FOPI Collaboration in central Au$+$Au collisions at energies of $0.25A$ to $0.6A~\rm GeV$. We find an unexpectedly abundant $α$ clustering in this environment, which will have profound implications for modeling the nuclear equation of state and describing supernovae and neutron star mergers.

nucl-th↗

arXiv2025

Thermodynamically Consistent Modeling and Stable ALE Approximations of Reactive Semi-Permeable Interfaces

Reactive, semi-permeable interfaces play important roles in key biological processes such as targeted drug delivery, lipid metabolism, and signal transduction. These systems involve coupled surface reactions, transmembrane transport, and interfacial deformation, often triggered by local biochemical signals. The strong mechanochemical couplings complicate the modeling of such interfacial dynamics. We propose a thermodynamically consistent continuum framework that integrates bulk fluid motion, interfacial dynamics, surface chemistry, and selective solute exchange, derived via an energy variation approach to ensure mass conservation and energy dissipation. To efficiently solve the resulting coupled system, we develop a finite element scheme within an Arbitrary Lagrangian-Eulerian (ALE) framework, incorporating the Barrett-Garcke-Nurnberg (BGN) strategy to maintain mesh regularity and preserve conservation laws. Numerical experiments verify the convergence and conservation properties of the scheme and demonstrate its ability in capturing complex interfacial dynamics. Two biologically inspired examples showcase the model's versatility: cholesterol efflux via the ABCG1 pathway, involving multistage interfacial reactions and HDL uptake; and a self-propelled droplet system with reaction-activated permeability, mimicking drug release in pathological environments. This work provides a unified computational platform for studying strongly coupled biochemical and mechanical interactions at interfaces, offering new insights into reactive transport processes in both biological and industrial contexts.

math.NA↗

arXiv2025

Hybrid Polynomial Zonotopes: A Set Representation for Reachability Analysis in Hybrid Nonaffine Systems

Reachability analysis for hybrid nonaffine systems remains computationally challenging, as existing set representations--including constrained, polynomial, and hybrid zonotopes--either lose tightness under high-order nonaffine maps or suffer exponential blow-up after discrete jumps. This paper introduces Hybrid Polynomial Zonotope (HPZ), a novel set representation that combines the mode-dependent generator structure of hybrid zonotopes with the algebraic expressiveness of polynomial zonotopes. HPZs compactly encode non-convex reachable states across modes by attaching polynomial exponents to each hybrid generator, enabling precise capture of high-order state-input couplings without vertex enumeration. We develop a comprehensive library of HPZ operations, including Minkowski sum, linear transformation, and intersection. Theoretical analysis and computational experiments demonstrate that HPZs achieve superior tightness preservation and computational efficiency compared to existing approaches for hybrid system reachability analysis.

eess.SY↗

arXiv2025

A Variance-Reduced Cubic-Regularized Newton for Policy Optimization

In this paper, we study a second-order approach to policy optimization in reinforcement learning. Existing second-order methods often suffer from suboptimal sample complexity or rely on unrealistic assumptions about importance sampling. To overcome these limitations, we propose VR-CR-PN, a variance-reduced cubic-regularized policy Newton algorithm. To the best of our knowledge, this is the first algorithm that integrates Hessian-aided variance reduction with second-order policy optimization, effectively addressing the distribution shift problem and achieving best-known sample complexity under general nonconvex conditions but without the need for importance sampling. We theoretically establish that VR-CR-PN achieves a sample complexity of $\tilde{\mathcal{O}}(ε^{-3})$ to reach an $ε$-second-order stationary point, significantly improving upon the previous best result of $\tilde{\mathcal{O}}(ε^{-3.5})$ under comparable assumptions. As an additional contribution, we introduce a novel Hessian estimator for the expected return function, which admits a uniform upper bound independent of the horizon length $H$, allowing the algorithm to achieve horizon-independent sample complexity.

cs.LG↗

arXiv2025

Hybrid Advertising in the Sponsored Search

Online advertisements are a primary revenue source for e-commerce platforms. Traditional advertising models are store-centric, selecting winning stores through auction mechanisms. Recently, a new approach known as joint advertising has emerged, which presents sponsored bundles combining one store and one brand in ad slots. Unlike traditional models, joint advertising allows platforms to collect payments from both brands and stores. However, each of these two advertising models appeals to distinct user groups, leading to low click-through rates when users encounter an undesirable advertising model. To address this limitation and enhance generality, we propose a novel advertising model called ''Hybrid Advertising''. In this model, each ad slot can be allocated to either an independent store or a bundle. To find the optimal auction mechanisms in hybrid advertising, while ensuring nearly dominant strategy incentive compatibility and individual rationality, we introduce the Hybrid Regret Network (HRegNet), a neural network architecture designed for this purpose. Extensive experiments on both synthetic and real-world data demonstrate that the mechanisms generated by HRegNet significantly improve platform revenue compared to established baseline methods.

cs.GT↗

arXiv2025

Efficient Learning of A Unified Policy For Whole-body Manipulation and Locomotion Skills

Equipping quadruped robots with manipulators provides unique loco-manipulation capabilities, enabling diverse practical applications. This integration creates a more complex system that has increased difficulties in modeling and control. Reinforcement learning (RL) offers a promising solution to address these challenges by learning optimal control policies through interaction. Nevertheless, RL methods often struggle with local optima when exploring large solution spaces for motion and manipulation tasks. To overcome these limitations, we propose a novel approach that integrates an explicit kinematic model of the manipulator into the RL framework. This integration provides feedback on the mapping of the body postures to the manipulator's workspace, guiding the RL exploration process and effectively mitigating the local optima issue. Our algorithm has been successfully deployed on a DeepRobotics X20 quadruped robot equipped with a Unitree Z1 manipulator, and extensive experimental results demonstrate the superior performance of this approach.

cs.RO↗

arXiv2025

Minimax Optimal Two-Stage Algorithm For Moment Estimation Under Covariate Shift

Covariate shift occurs when the distribution of input features differs between the training and testing phases. In covariate shift, estimating an unknown function's moment is a classical problem that remains under-explored, despite its common occurrence in real-world scenarios. In this paper, we investigate the minimax lower bound of the problem when the source and target distributions are known. To achieve the minimax optimal bound (up to a logarithmic factor), we propose a two-stage algorithm. Specifically, it first trains an optimal estimator for the function under the source distribution, and then uses a likelihood ratio reweighting procedure to calibrate the moment estimator. In practice, the source and target distributions are typically unknown, and estimating the likelihood ratio may be unstable. To solve this problem, we propose a truncated version of the estimator that ensures double robustness and provide the corresponding upper bound. Extensive numerical studies on synthetic examples confirm our theoretical findings and further illustrate the effectiveness of our proposed method.

stat.ML↗

arXiv2025

Quark: Implementing Convolutional Neural Networks Entirely on Programmable Data Plane

The rapid development of programmable network devices and the widespread use of machine learning (ML) in networking have facilitated efficient research into intelligent data plane (IDP). Offloading ML to programmable data plane (PDP) enables quick analysis and responses to network traffic dynamics, and efficient management of network links. However, PDP hardware pipeline has significant resource limitations. For instance, Intel Tofino ASIC has only 10Mb SRAM in each stage, and lacks support for multiplication, division and floating-point operations. These constraints significantly hinder the development of IDP. This paper presents \quark, a framework that fully offloads convolutional neural network (CNN) inference onto PDP. \quark employs model pruning to simplify the CNN model, and uses quantization to support floating-point operations. Additionally, \quark divides the CNN into smaller units to improve resource utilization on the PDP. We have implemented a testbed prototype of \quark on both P4 hardware switch (Intel Tofino ASIC) and software switch (i.e., BMv2). Extensive evaluation results demonstrate that \quark achieves 97.3\% accuracy in anomaly detection task while using only 22.7\% of the SRAM resources on the Intel Tofino ASIC switch, completing inference tasks at line rate with an average latency of 42.66$μs$.

cs.NI↗

arXiv2025

Phase-space excluded-volume approach for light clusters in nuclear medium

A phase-space excluded-volume approach is developed to investigate the in-medium properties of light clusters in nuclear matter. In this approach, light clusters can exist only if the total nucleon phase-space occupation of the surrounding nuclear medium -- including explicit contributions from light clusters -- is sufficiently low. The distribution functions of nucleons and light clusters are determined self-consistently by accounting for the interplay between in-medium effects and thermodynamic properties. By employing standard Skyrme energy-density functionals to model the nuclear mean-field potential, the approach enables the evaluation of the Mott momentum and the fraction of light clusters in nuclear matter. Furthermore, it can be readily integrated into dynamical models, to study in-medium effects on light clusters, based on measured yields in heavy-ion collisions.

nucl-th↗

arXiv2025

Symmetry energy and neutron matter equation of state at $ρ_0/3$ from the electric dipole polarizability in $^{48}$Ca, $^{68}$Ni and $^{208}$Pb

Based on the quasiparticle random phase approximation implemented via the finite amplitude method, we employ a set of representative relativistic mean-field models to investigate the sensitivity of the inverse electric dipole polarizability $1/α_{\mathrm{D}}$ in $^{48}\mathrm{Ca}$, $^{68}\mathrm{Ni}$, and $^{208}\mathrm{Pb}$ to the symmetry energy $E_{\rm{sym}}(ρ)$ and the neutron matter equation of state $E_{\rm{PNM}}(ρ)$ at a subsaturation density of $ρ= ρ_0/3$. Combined with predictions from nonrelativistic Skyrme energy density functionals (EDFs), our results reveal strong linear correlations between $1/α_{\mathrm{D}}$ and both $E_{\rm{sym}}(ρ_0/3)$ and $E_{\rm{PNM}}(ρ_0/3)$. In particular, the $1/α_{\mathrm{D}}$--$E_{\rm{PNM}}(ρ_0/3)$ correlation for $^{208}\mathrm{Pb}$ is found to be nearly model-independent. A Bayesian analysis of the measured values of $α_{\rm{D}}$ in $^{48}\mathrm{Ca}$, $^{68}\mathrm{Ni}$, and $^{208}\mathrm{Pb}$ yields quantitative constraints of $E_{\mathrm{sym}}(ρ_0/3) = 17.8^{+1.1(1.8)}_{-0.9(1.6)}~\mathrm{MeV}$ and $E_{\mathrm{PNM}}(ρ_0/3) = 9.1^{+0.8(1.4)}_{-0.9(1.4)}~\mathrm{MeV}$ at the 68\% (90\%) confidence level, respectively. The extracted value of $E_{\mathrm{PNM}}(ρ_0/3)$ exceeds most predictions from microscopic many-body theories, suggesting a mild tension between nuclear EDF-based constraints derived from $α_{\mathrm{D}}$ data and results from \textit{ab initio} calculations.

nucl-th↗

arXiv2025

Experimental demonstration of attosecond hard X-ray pulses

We present the first direct experimental confirmation of attosecond pulse generation in the hard X-ray regime with a free-electron laser. Our experiment is based on measurements of a nonlinear optical phenomenon known as amplified spontaneous emission (ASE) from 3d transition metals. By analyzing the yield of the collective X-ray fluorescence induced by ultrashort pulses at the Linac Coherent Light Source, we identify the generation of attosecond pulses and shot-to-shot fluctuations in their duration, ranging from 100 as to 400 as. The observed product of bandwidth and pulse duration for 100 as pulses is approximately 2 fs$\cdot$eV, indicating the generation of nearly transform-limited pulses. Our results extend the photon energy reach of attosecond techniques by one order of magnitude, providing the ability to simultaneously probe matter on the time-scales of electronic phenomena and with atomic spatial resolution. Furthermore, attosecond hard X-ray pulses can outrun the fastest radiation damage processes, paving the way to single-shot damage-free X-ray measurements.

physics.optics↗

arXiv2025

Exploring Training and Inference Scaling Laws in Generative Retrieval

Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models (LLMs) generate target documents directly from a query. As a novel paradigm, the mechanisms that underpin its performance and scalability remain largely unexplored. We systematically investigate training and inference scaling laws in generative retrieval, exploring how model size, training data scale, and inference-time compute jointly influence performance. We propose a novel evaluation metric inspired by contrastive entropy and generation loss, providing a continuous performance signal that enables robust comparisons across diverse generative retrieval methods. Our experiments show that n-gram-based methods align strongly with training and inference scaling laws. We find that increasing model size, training data scale, and inference-time compute all contribute to improved performance, highlighting the complementary roles of these factors in enhancing generative retrieval. Across these settings, LLaMA models consistently outperform T5 models, suggesting a particular advantage for larger decoder-only models in generative retrieval. Our findings underscore that model sizes, data availability, and inference computation interact to unlock the full potential of generative retrieval, offering new insights for designing and optimizing future systems.

cs.IR↗

arXiv2025

Multi-Instrument Search for Gamma-Ray Counterpart of X-ray Transients detected by EP/WXT

As a soft X-ray imager with unprecedentedly large field of view, EP/WXT has detected many (fast) X-ray transients, whose nature is very intriguing. Whether there is gamma-ray counterpart for the X-ray transient provides important implications for its origin. Some of them have been reported to be associated with GRB, however, a systematic study on the gamma-ray emission of these X-ray transients is lacking. In this work, we implemented a comprehensive targeted search for gamma-ray counterparts to 63 X-ray transients reported by EP/WXT during its first year of operation, using the dedicated multiple-instrument search pipeline, ETJASMIN, with GECAM-B, GECAM-C, Fermi/GBM, and \textit{Insight}-HXMT data. We find that 14 out of 63 (22\%) EP/WXT X-ray transients have gamma-ray counterparts. For other transients, ETJASMIN pipeline provided upper limit of gamma-ray emission, which is more stringent than that given by individual instrument. Moreover, we investigated the properties of the X-ray transients and their gamma-ray counterparts, and explored the relation between the x-ray transient and gamma-ray counterpart.

astro-ph.HE↗

arXiv2025

Single-pulse-based interstellar scintillation studies of RRATs

The nature of irregularly spaced pulses of rotating radio transients (RRATs) complicates interstellar scintillation studies. In this letter, we report the primary scintillation parameters of a sample of RRATs using pairwise correlations of pulse spectra. Moreover, from the measured scintillation velocities, we constrain their transverse velocities. We also find a reduced modulation index, $\rm{m=0.13\pm0.01}$, for RRAT~J1538+2345. Several possible explanations are discussed. Furthermore, the single-pulse-based interstellar scintillation technique is applicable to other pulsar populations, including nulling pulsars and those with short scintillation timescales, and fast radio bursts.

astro-ph.HE↗