arXiv subjects

Siyuan Zhang

Publications and source records attributed to Siyuan Zhang.

At least 55 records · Page 3Linked to original sources

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↗

arXiv2025

Using MRNet to Predict Lunar Rock Categories Detected by Chang'e 5 Probe

China's Chang'e 5 mission has been a remarkable success, with the chang'e 5 lander traveling on the Oceanus Procellarum to collect images of the lunar surface. Over the past half century, people have brought back some lunar rock samples, but its quantity does not meet the need for research. Under current circumstances, people still mainly rely on the analysis of rocks on the lunar surface through the detection of lunar rover. The Oceanus Procellarum, chosen by Chang'e 5 mission, contains various kind of rock species. Therefore, we first applied to the National Astronomical Observatories of the China under the Chinese Academy of Sciences for the Navigation and Terrain Camera (NaTeCam) of the lunar surface image, and established a lunar surface rock image data set CE5ROCK. The data set contains 100 images, which randomly divided into training, validation and test set. Experimental results show that the identification accuracy testing on convolutional neural network (CNN) models like AlexNet or MobileNet is about to 40.0%. In order to make full use of the global information in Moon images, this paper proposes the MRNet (MoonRockNet) network architecture. The encoding structure of the network uses VGG16 for feature extraction, and the decoding part adds dilated convolution and commonly used U-Net structure on the original VGG16 decoding structure, which is more conducive to identify more refined but more sparsely distributed types of lunar rocks. We have conducted extensive experiments on the established CE5ROCK data set, and the experimental results show that MRNet can achieve more accurate rock type identification, and outperform other existing mainstream algorithms in the identification performance.

cs.CV↗

arXiv2024

Parametric $ρ$-Norm Scaling Calibration

Output uncertainty indicates whether the probabilistic properties reflect objective characteristics of the model output. Unlike most loss functions and metrics in machine learning, uncertainty pertains to individual samples, but validating it on individual samples is unfeasible. When validated collectively, it cannot fully represent individual sample properties, posing a challenge in calibrating model confidence in a limited data set. Hence, it is crucial to consider confidence calibration characteristics. To counter the adverse effects of the gradual amplification of the classifier output amplitude in supervised learning, we introduce a post-processing parametric calibration method, $ρ$-Norm Scaling, which expands the calibrator expression and mitigates overconfidence due to excessive amplitude while preserving accuracy. Moreover, bin-level objective-based calibrator optimization often results in the loss of significant instance-level information. Therefore, we include probability distribution regularization, which incorporates specific priori information that the instance-level uncertainty distribution after calibration should resemble the distribution before calibration. Experimental results demonstrate the substantial enhancement in the post-processing calibrator for uncertainty calibration with our proposed method.

cs.LG↗

arXiv2024

Color-Kinematics Duality and Dual Conformal Symmetry for A Four-loop Form Factor in N=4 SYM

We obtain the full-color four-loop three-point form factor of the stress-tensor supermultiplet in N=4 SYM, based on the color-kinematics (CK) duality and generalized unitarity method. The CK-dual solution, while manifesting all dual Jacobi relations and satisfying the minimal power-counting of loop momenta, lies in a 133-dimensional solution space. We also show that the planar form factor integrand satisfies precisely a directional dual conformal symmetry in the lightlike limit of the operator momentum, which is supported by explicit four-loop calculations.

hep-th↗

arXiv2024

Evaluating Fairness in Black-box Algorithmic Markets: A Case Study of Ride Sharing in Chicago

This study examines fairness within the rideshare industry, focusing on both drivers' wages and riders' trip fares. Through quantitative analysis, we found that drivers' hourly wages are significantly influenced by factors such as race/ethnicity, health insurance status, tenure to the platform, and working hours. Despite platforms' policies not intentionally embedding biases, disparities persist based on these characteristics. For ride fares, we propose a method to audit the pricing policy of a proprietary algorithm by replicating it; we conduct a hypothesis test to determine if the predicted rideshare fare is greater than the taxi fare, taking into account the approximation error in the replicated model. Challenges in accessing data and transparency hinder our ability to isolate discrimination from other factors, underscoring the need for collaboration with rideshare platforms and drivers to enhance fairness in algorithmic wage determination and pricing.

cs.HC↗

arXiv2024

MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition

Multimodal emotion recognition is an important research topic in artificial intelligence. Over the past few decades, researchers have made remarkable progress by increasing the dataset size and building more effective algorithms. However, due to problems such as complex environments and inaccurate annotations, current systems are hard to meet the demands of practical applications. Therefore, we organize the MER series of competitions to promote the development of this field. Last year, we launched MER2023, focusing on three interesting topics: multi-label learning, noise robustness, and semi-supervised learning. In this year's MER2024, besides expanding the dataset size, we further introduce a new track around open-vocabulary emotion recognition. The main purpose of this track is that existing datasets usually fix the label space and use majority voting to enhance the annotator consistency. However, this process may lead to inaccurate annotations, such as ignoring non-majority or non-candidate labels. In this track, we encourage participants to generate any number of labels in any category, aiming to describe emotional states as accurately as possible. Our baseline code relies on MERTools and is available at: https://github.com/zeroQiaoba/MERTools/tree/master/MER2024.

cs.LG↗

arXiv2024

Grain boundaries control lithiation of solid solution substrates in lithium metal batteries

The development of sustainable transportation and communication systems requires an increase in both energy density and capacity retention of Li-batteries. Using substrates forming a solid solution with body centered cubic Li enhances the cycle stability of anode-less batteries. However, it remains unclear how the substrate microstructure affects the lithiation behavior. Here, we deploy a correlative, near-atomic scale probing approach through combined ion- and electron-microscopy to examine the distribution of Li in Li-Ag diffusion couples as model system. We reveal that Li regions with over 93.8% at.% nucleate within Ag at random high angle grain boundaries, whereas grain interiors are not lithiated. We evidence the role of kinetics and mechanical constraint from the microstructure over equilibrium thermodynamics in dictating the lithiation process. The findings suggest that grain size and grain boundary character are critical to enhance the electrochemical performance of interlayers/electrodes, particularly for improving lithiation kinetics and hence reducing dendrite formation.

cond-mat.mtrl-sci↗

arXiv2024

Explainable Multimodal Emotion Recognition

Multimodal emotion recognition is an important research topic in artificial intelligence, whose main goal is to integrate multimodal clues to identify human emotional states. Current works generally assume accurate labels for benchmark datasets and focus on developing more effective architectures. However, emotion annotation relies on subjective judgment. To obtain more reliable labels, existing datasets usually restrict the label space to some basic categories, then hire plenty of annotators and use majority voting to select the most likely label. However, this process may result in some correct but non-candidate or non-majority labels being ignored. To ensure reliability without ignoring subtle emotions, we propose a new task called ``Explainable Multimodal Emotion Recognition (EMER)''. Unlike traditional emotion recognition, EMER takes a step further by providing explanations for these predictions. Through this task, we can extract relatively reliable labels since each label has a certain basis. Meanwhile, we borrow large language models (LLMs) to disambiguate unimodal clues and generate more complete multimodal explanations. From them, we can extract richer emotions in an open-vocabulary manner. This paper presents our initial attempt at this task, including introducing a new dataset, establishing baselines, and defining evaluation metrics. In addition, EMER can serve as a benchmark task to evaluate the audio-video-text understanding performance of multimodal LLMs.

cs.MM↗

arXiv2024

Operando Insights on the Degradation Mechanisms of Rhenium-doped and Undoped Molybdenum Disulfide Nanocatalysts for Electrolyzer Applications

MoS2 nanostructures are promising catalysts for proton-exchange-membrane (PEM) electrolyzers to replace expensive noble metals. Their broadscale application demands high activity for the hydrogen evolution reaction (HER) as well as robust durability. Doping is commonly applied to enhance the HER activity of MoS2-based nanocatalysts, but the effect of dopants in the electrochemical and structural stability is yet to be discussed. Herein, we correlate operando electrochemical measurements to the structural evolution of the materials down to the nanometric scale by identical location electron microscopy and spectroscopy. The range of stable operation for MoS2 nanocatalysts with and without rhenium doping is experimentally defined. The responsible degradation mechanisms at first electrolyte contact, open circuit stabilization and HER conditions are experimentally identified and confirmed with the calculated Pourbaix diagram of Re-doped MoS2. Doping MoS2-based nanocatalysts is validated as a promising strategy for the continuous improvement of high performance and durable PEM electrolyzers.

physics.app-ph↗