arXiv subjects

Siyuan Zhang

Publications and source records attributed to Siyuan Zhang.

At least 55 records · Page 3Linked to original sources

arXiv2025

Search for Light Dark Matter with 259-day data in PandaX-4T

We present a search for light dark matter particles through their interactions with atomic electrons and nucleons, utilizing PandaX-4T data with an effective exposure of 1.04 tonne$\cdot$year for ionization-only data and 1.20 tonne$\cdot$year for paired data. Our analysis focuses on the energy range (efficiency$>$0.01) of approximately 0.33 to 3 keV for nuclear recoils, and from 0.04 to 0.39 keV for electronic recoils. We establish the most stringent constraints on spin-independent dark matter-nucleon interactions within a mass range of 2.5 to 5.0 GeV/$c^2$, spin-dependent neutron-only interactions within 2.0 to 5.3 GeV/$c^2$, and spin-dependent proton-only interactions within 2.0 to 3.8 GeV/$c^2$. Their corresponding limits at 3\,GeV/$c^2$ are $1.1 \times 10^{-43}$, $1.6 \times 10^{-38}$, and $5.6 \times 10^{-37}$\,cm$^2$, respectively. Additionally, our results improve the upper limits on the dark matter-electron scattering cross-section by a factor of 1.5 and 9.3 for heavy and light mediator scenarios respectively within 50 MeV/$c^2$ to 10 GeV/$c^2$, compared with previous best results.

hep-ex↗

arXiv2025

Tomographic beta-gamma spectroscopy of nuclear beta decay

Nuclear $β$ decay, a sensitive probe of nuclear structure and weak interactions, has become a precision test bed for physics beyond the Standard Model, driven by recent advances in spectrometric techniques. Here we introduce tomographic $β$-$γ$ spectroscopy (TBGS) of nuclear $β$ decay, a method that detects the energies of $β$, $γ$, and internal conversion electrons while simultaneously reconstructing the energy deposition vertices. Using the PandaX-4T detector operated as a TBGS, we obtain a precise and unbiased decay scheme of $^{214}$Pb, a key background isotope in searches for dark matter and Majorana neutrinos. For the first time, transitions of $^{214}$Pb to both the ground and excited states of $^{214}$Bi are measured concurrently, revealing discrepancies in branching ratios of up to 4.7$σ$ relative to previous evaluations. Combined with state-of-the-art theoretical spectral-shape calculations, these results establish a new benchmark for background modelling in rare-event searches and highlight the potential of TBGS as a versatile tool for fundamental physics and nuclear applications.

nucl-ex↗

arXiv2025

REOBench: Benchmarking Robustness of Earth Observation Foundation Models

Earth observation foundation models have shown strong generalization across multiple Earth observation tasks, but their robustness under real-world perturbations remains underexplored. To bridge this gap, we introduce REOBench, the first comprehensive benchmark for evaluating the robustness of Earth observation foundation models across six tasks and twelve types of image corruptions, including both appearance-based and geometric perturbations. To ensure realistic and fine-grained evaluation, our benchmark focuses on high-resolution optical remote sensing images, which are widely used in critical applications such as urban planning and disaster response. We conduct a systematic evaluation of a broad range of models trained using masked image modeling, contrastive learning, and vision-language pre-training paradigms. Our results reveal that (1) existing Earth observation foundation models experience significant performance degradation when exposed to input corruptions. (2) The severity of degradation varies across tasks, model architectures, backbone sizes, and types of corruption, with performance drop varying from less than 1% to over 20%. (3) Vision-language models show enhanced robustness, particularly in multimodal tasks. REOBench underscores the vulnerability of current Earth observation foundation models to real-world corruptions and provides actionable insights for developing more robust and reliable models. Code and data are publicly available at https://github.com/lx709/REOBench.

cs.CV↗

arXiv2025

Chemically tailored planar defect phases in the Ta-Fe μ-phase

Intermetallics often exhibit complex crystal structures, which give rise to intricate defect structures that critically influence their mechanical and functional properties. Despite studies on individual defect types, a comprehensive understanding of the defect landscape in μ-phases, a class of topologically close-packed phases, remains elusive. In this study, we investigated the planar defect structures in the Ta-Fe μ-phase across a compositional range of 46 to 58 at.% Ta using electron microscopy and density functional theory calculations. Electron backscatter diffraction and high-resolution scanning transmission electron microscopy reveal a transition from basal twin boundaries and planar faults containing C14 TaFe2 Laves phase layers at a low Ta content to pyramidal {1\bar{1}02} twins at a higher Ta content. Density functional theory calculations of defect formation energies confirm a chemical potential-driven stabilisation of Laves phase lamellae. The prevalence of pyramidal twins in Ta-rich μ-phase samples is attributed to the competitive nature of different planar defects during solidification. A defect landscape for μ-phases is proposed, illustrating the interplay between site occupancy, dislocation types and planar faults across the chemical potential space. These findings provide fundamental insights into defect engineering in structurally complex intermetallics and open pathways for optimising material properties through chemical tuning.

cond-mat.mtrl-sci↗

arXiv2025

Exploring the Generalizability of Factual Hallucination Mitigation via Enhancing Precise Knowledge Utilization

Large Language Models (LLMs) often struggle to align their responses with objective facts, resulting in the issue of factual hallucinations, which can be difficult to detect and mislead users without relevant knowledge. Although post-training techniques have been employed to mitigate the issue, existing methods usually suffer from poor generalization and trade-offs in other different capabilities. In this paper, we propose to address these by directly augmenting LLM's fundamental ability to precisely leverage its knowledge and introduce PKUE (Precise Knowledge Utilization Enhancement), which fine-tunes the model on self-generated responses to precise and simple factual questions through preference optimization. Furthermore, we construct FactualBench, a comprehensive and precise factual QA dataset containing 181k Chinese data spanning 21 domains, to facilitate both evaluation and training. Extensive experiments demonstrate that PKUE significantly improves LLM overall performance, with consistent enhancement across factual tasks of various forms, general tasks beyond factuality, and tasks in different language.

cs.CL↗

arXiv2025

Configuration Requirements for 21-cm Forest Background Quasar Searches with the Moon-based Interferometer

The 21-cm forest offers a powerful cosmological probe of the thermal history and small-scale structure of the intergalactic medium during the Epoch of Reionization (EoR). Its success, however, critically depends on the availability of high-redshift radio-loud quasars (HzRLQs) as background sources. In this work, we investigate the configuration requirements for a Moon-based low-frequency radio interferometer aimed at maximizing the detection of HzRLQs for future 21-cm forest studies. Building upon a previously developed quasar luminosity function (QLF), we forecast HzRLQ abundances under various array configurations. Assuming a total survey area of $10^4\,\mathrm{deg}^2$ and 1 year of observation, we compare continuum surveys with 10 MHz bandwidth and 21-cm forest surveys with 5 kHz resolution. Our results show that a minimum collecting area of $\sim$6 500 m$^2$ enables detection at $z \sim 6$, while SKA-like arrays ($N_{\mathrm{st}} = 512$) extend the detection limit to $z \sim 10$ for 21-cm forest survey and $z \sim 16$ for continuum survey. Larger arrays with $N_{\mathrm{st}} = 2048$ can reach $z \sim 11$ in 21-cm forest mode. We also explore configurations that maintain fixed collecting areas while increasing the number to enhance survey efficiency. This boosts source detection but significantly increases the data volume and computational demands. These results underscore the importance of optimizing array design for different survey goals and balancing sensitivity, spectral resolution, and data management. A well-designed Moon-based array could open a new observational window on reionization and early cosmic structure formation.

astro-ph.CO↗

arXiv2025

Electrochemically-driven formation of Intermetallic Cu3ZnLi2 alters Li-transport in nanostructured bimetallic battery anode

The role of Li-based batteries in the electrification of society cannot be understated, however their operational lifetime is often limited by the formation of dendrites, i.e. the localised deposition of Li that can cause shorts between the two electrodes leading to the failure of the battery. Nanocrystalline bimetallic current collectors can be used for anode-free Li-metal batteries, with improved Li plating and limited or suppressed formation of dendrites. Here, we demonstrate that the microstructure of an alpha-Brass current collector, Cu 63% Zn 37%, used in an anode-free Li-metal battery evolves during cycling. It initially had a nanocrystalline deformation layer approximately 80 nm in thickness after polishing. After 100 cycles, the initial deformed brass layer was partially converted to a ternary Laves phase Cu3ZnLi2 within a nanocrystalline brass matrix that grew to 200 - 250 nm in thickness. Upon Li stripping, the phase partially decomposes electrochemically, but what remains can sequester Li thus forming "dead Li" thereby contributing to capacity loss. We propose a mechanism for the microstructural evolution including dynamic recrystallization and phase formation. Since this ternary Laves phase emerges during electrochemical cycling alone, binary alloy current collectors must be assessed for metastable ternary phase formation under different cycling conditions to either stabilize and exploit such phases or electrochemically fully strip them.

cond-mat.mtrl-sci↗

arXiv2025

Precise Measurement of $^{216}$Po Half-life with Exact Parent-daughter Pairing in PandaX-4T

We report a precise measurement of $^{216}\rm Po$ half-life using the PandaX-4T liquid xenon time projection chamber (TPC). $^{220}\rm Rn $, emanating from a $^{228}\rm Th $ calibration source, is injected to the detector and undergoes successive $α$ decays, first to $^{216}\rm Po$ and then to $^{212}\rm Pb$. PandaX-4T detector measures the 5-dimensional (5D) information of each decay, including time, energy, and 3-dimensional positions. Therefore, we can identify the $^{220}\rm Rn $ and $^{216}\rm Po$ decay events and pair them exactly to extract the lifetime of each $^{216}\rm Po$. With a large data set and high-precision $^{220}\rm $Rn-$^{216}\rm $Po pairing technique, we measure the $^{216}\rm Po$ half-life to be $143.7\pm0.5$ ms, which is the most precise result to date and agrees with previously published values. The leading precision of this measurement demonstrates the power of 5D calorimeter and the potential of exact parent-daughter pairing in the xenon TPC.

nucl-ex↗

arXiv2025

STAIR: Improving Safety Alignment with Introspective Reasoning

Ensuring the safety and harmlessness of Large Language Models (LLMs) has become equally critical as their performance in applications. However, existing safety alignment methods typically suffer from safety-performance trade-offs and the susceptibility to jailbreak attacks, primarily due to their reliance on direct refusals for malicious queries. In this paper, we propose STAIR, a novel framework that integrates SafeTy Alignment with Itrospective Reasoning. We enable LLMs to identify safety risks through step-by-step analysis by self-improving chain-of-thought (CoT) reasoning with safety awareness. STAIR first equips the model with a structured reasoning capability and then advances safety alignment via iterative preference optimization on step-level reasoning data generated using our newly proposed Safety-Informed Monte Carlo Tree Search (SI-MCTS). We further train a process reward model on this data to guide test-time searches for improved responses. Extensive experiments show that STAIR effectively mitigates harmful outputs while better preserving helpfulness, compared to instinctive alignment strategies. With test-time scaling, STAIR achieves a safety performance comparable to Claude-3.5 against popular jailbreak attacks. Relevant resources in this work are available at https://github.com/thu-ml/STAIR.

cs.CL↗

arXiv2025

Universal Quantum Computational Spectroscopy on a Quantum Chip

Spectroscopy underpins modern scientific discovery across diverse disciplines. While experimental spectroscopy probes material properties through scattering or radiation measurements, computational spectroscopy combines theoretical models with experimental data to predict spectral properties, essential for advancements in physics, chemistry, and materials science. However, quantum systems present unique challenges for computational spectroscopy due to their inherent complexity, and current quantum algorithms remain largely limited to static and closed quantum systems. Here, we present and demonstrate a universal quantum computational spectroscopy framework that lifts these limitations. Through leveraging coherently controlled quantum dynamics, our method efficiently reconstructs the spectral information for both closed and open systems, furtherly for time-dependent driven systems. We experimentally validate this approach using a programmable silicon-photonic quantum processing chip, capable of high-fidelity time-evolution simulations. The versatility of our framework is demonstrated through spectroscopic computations for diverse quantum systems -- including spin systems, non-Hermitian systems, and quantum Floquet systems -- revealing novel phenomena such as parity-time symmetry breaking and topological holonomy that are inaccessible to conventional spectroscopy or quantum eigenstate algorithms. {Furthermore, systematic benchmarking of UQCS against existing quantum algorithms is numerically performed to demonstrate its unprecedented capabilities and superior performance. This work establishes a noise-robust and transformative paradigm for quantum spectral analysis.

quant-ph↗

arXiv2025

From Structural Design to Dynamics Modeling: Control-Oriented Development of a 3-RRR Parallel Ankle Rehabilitation Robot

This paper presents the development of a wearable ankle rehabilitation robot based on a 3-RRR spherical parallel mechanism (SPM) to support multi-DOF recovery through pitch, roll, and yaw motions. The system features a compact, ergonomic structure designed for comfort, safety, and compatibility with ankle biomechanics. A complete design-to-dynamics pipeline has been implemented, including structural design, kinematic modeling for motion planning, and Lagrangian-based dynamic modeling for torque estimation and simulation analysis. Preliminary simulations verify stable joint coordination and smooth motion tracking under representative rehabilitation trajectories. The control framework is currently being developed to enhance responsiveness across the workspace. Future work will focus on integrating personalized modeling and adaptive strategies to address kinematic singularities through model based control. This work establishes a foundational platform for intelligent, personalized ankle rehabilitation, enabling both static training and potential extension to gait-phase-timed assistance.

cs.RO↗

arXiv2025

The High Voltage Splitter board for the JUNO SPMT system

The Jiangmen Underground Neutrino Observatory (JUNO) in southern China is designed to study neutrinos from nuclear reactors and natural sources to address fundamental questions in neutrino physics. Achieving its goals requires continuous operation over a 20-year period. The small photomultiplier tube (small PMT or SPMT) system is a subsystem within the experiment composed of 25600 3-inch PMTs and their associated readout electronics. The High Voltage Splitter (HVS) is the first board on the readout chain of the SPMT system and services the PMTs by providing high voltage for biasing and by decoupling the generated physics signal from the high-voltage bias for readout, which is then fed to the front-end board. The necessity to handle high voltage, manage a large channel count, and operate stably for 20 years imposes significant constraints on the physical design of the HVS. This paper serves as a comprehensive documentation of the HVS board: its role in the SPMT readout system, the challenges in its design, performance and reliability metrics, and the methods employed for production and quality control.

physics.ins-det↗

arXiv2025

OV-MER: Towards Open-Vocabulary Multimodal Emotion Recognition

Multimodal Emotion Recognition (MER) is a critical research area that seeks to decode human emotions from diverse data modalities. However, existing machine learning methods predominantly rely on predefined emotion taxonomies, which fail to capture the inherent complexity, subtlety, and multi-appraisal nature of human emotional experiences, as demonstrated by studies in psychology and cognitive science. To overcome this limitation, we advocate for introducing the concept of open vocabulary into MER. This paradigm shift aims to enable models to predict emotions beyond a fixed label space, accommodating a flexible set of categories to better reflect the nuanced spectrum of human emotions. To achieve this, we propose a novel paradigm: Open-Vocabulary MER (OV-MER), which enables emotion prediction without being confined to predefined spaces. However, constructing a dataset that encompasses the full range of emotions for OV-MER is practically infeasible; hence, we present a comprehensive solution including a newly curated database, novel evaluation metrics, and a preliminary benchmark. By advancing MER from basic emotions to more nuanced and diverse emotional states, we hope this work can inspire the next generation of MER, enhancing its generalizability and applicability in real-world scenarios. Code and dataset are available at: https://github.com/zeroQiaoba/AffectGPT.

cs.HC↗

arXiv2025

Topological $π/2$ modes in photonic waveguide arrays

Periodic driving is a powerful tool to generate exotic topological phases without static counterparts, such as the anomalous chiral edge modes from bulk bands with zero Chern number and topological $π$ modes exhibiting period-doubled dynamics. Recently, a new class of Floquet topological mode, namely the $π/2$ mode, which carries four-period periodicity and has potential applications in quantum computing, was proposed based on a square-root method and realized in an acoustic system. Here we propose a laser-written waveguide array lattice to realize topological $π/2$ modes in photonics. Our photonic model simulates a square-root periodically driven Su-Schrieffer-Heeger model and has a rich phase diagram allowing for the co-existence of conventional zero, $π$ modes, and the new $π/2$ modes. Through numerical simulations of the wave equation, we uncover the unique four-period evolution feature of the $π/2$ modes. Our model, which only contains four waveguides per unit cell and two driving steps, is easy to implement with current fabrication techniques and may find applications in quantum optics.

physics.optics↗

arXiv2025

Kinetics and direct imaging of electrochemically formed palladium hydride for efficient hydrogen evolution reaction

Active and reliable electrocatalysts are fundamental to renewable energy technologies. PdCoO2 has recently been recognized as a promising catalyst template for the hydrogen evolution reaction (HER) in acidic media thanks to the formation of active PdHx. In this article, we monitor the transformation of single PdCoO2 particles during HER, and confirm their almost complete transformation to PdHx with sub-millimeter depths and cracks throughout the particles. Using operando mass spectrometry, Co dissolution is observed under reductive potentials, leading to PdHx formation, whereas the dissolution partial current is found to be 0.1 % of the HER current. The formation of PdHx is confirmed through secondary ion mass spectrometry and quantitatively analyzed by atom probe tomography, enabled by isotope labelling of hydrogen using heavy water. Despite dry storage and high vacuum during sample preparations, an overall composition of PdD0.28 is measured for the PdHx sample, with separation between alpha- (D-poor) and beta- (D-rich) PdHx phases. The PdHx phase formed on PdCoO2 particles is stable for a wide electrochemical potential window, until Pd dissolution is observed at open circuit potentials. Our findings highlight the critical role of a templated growth method in obtaining stabilized PdHx, enabling efficient HER without the commonly slow activation processes observed in Pd. This offers insights into the design of more efficient electrocatalysts for renewable energy technologies.

physics.chem-ph↗

arXiv2025

Feature-Based Dual Visual Feature Extraction Model for Compound Multimodal Emotion Recognition

This article presents our results for the eighth Affective Behavior Analysis in-the-wild (ABAW) competition.Multimodal emotion recognition (ER) has important applications in affective computing and human-computer interaction. However, in the real world, compound emotion recognition faces greater issues of uncertainty and modal conflicts. For the Compound Expression (CE) Recognition Challenge,this paper proposes a multimodal emotion recognition method that fuses the features of Vision Transformer (ViT) and Residual Network (ResNet). We conducted experiments on the C-EXPR-DB and MELD datasets. The results show that in scenarios with complex visual and audio cues (such as C-EXPR-DB), the model that fuses the features of ViT and ResNet exhibits superior performance.Our code are avalible on https://github.com/MyGitHub-ax/8th_ABAW

cs.CV↗

arXiv2025

Log Optimization Simplification Method for Predicting Remaining Time

Information systems generate a large volume of event log data during business operations, much of which consists of low-value and redundant information. When performance predictions are made directly from these logs, the accuracy of the predictions can be compromised. Researchers have explored methods to simplify and compress these data while preserving their valuable components. Most existing approaches focus on reducing the dimensionality of the data by eliminating redundant and irrelevant features. However, there has been limited investigation into the efficiency of execution both before and after event log simplification. In this paper, we present a prediction point selection algorithm designed to avoid the simplification of all points that function similarly. We select sequences or self-loop structures to form a simplifiable segment, and we optimize the deviation between the actual simplifiable value and the original data prediction value to prevent over-simplification. Experiments indicate that the simplified event log retains its predictive performance and, in some cases, enhances its predictive accuracy compared to the original event log.

cs.LG↗

arXiv2025

Environment-Aware Dynamic Pruning for Pipelined Edge Inference

IoT and edge-based inference systems require unique solutions to overcome resource limitations and unpredictable environments. In this paper, we propose an environment-aware dynamic pruning system that handles the unpredictability of edge inference pipelines. While traditional pruning approaches can reduce model footprint and compute requirements, they are often performed only once, offline, and are not designed to react to transient or post-deployment device conditions. Similarly, existing pipeline placement strategies may incur high overhead if reconfigured at runtime, limiting their responsiveness. Our approach allows slices of a model, already placed on a distributed pipeline, to be ad-hoc pruned as a means of load-balancing. To support this capability, we introduce two key components: (1) novel training strategies that endow models with robustness to post-deployment pruning, and (2) an adaptive algorithm that determines the optimal pruning level for each node based on monitored bottlenecks. In real-world experiments on a Raspberry Pi 4B cluster running camera-trap workloads, our method achieves a 1.5x speedup and a 3x improvement in service-level objective (SLO) attainment, all while maintaining high accuracy.

cs.DC↗