arXiv subjects

Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 433 records · Page 24Linked to original sources

arXiv2025

Mining Large Independent Sets on Massive Graphs

The Maximum Independent Set problem is fundamental for extracting conflict-free structure from large graphs, with applications in scheduling, recommendation, and network analysis. However, existing heuristics can stagnate when search schedules are fixed and information from past solutions is underused, leading to wasted effort in low-quality regions of the search space. We present ARCIS, an efficient algorithm for mining large independent sets on massive graphs. ARCIS couples two main components. The first is an adaptive restart policy that refreshes exploration when progress slows. The second is Consensus-Guided Vertex Fixing, which restricts the search to the non-consensus region of the graph by fixing vertices consistently observed within a round. The consensus is maintained as a running intersection within each round, and because it is recomputed at every restart, the fixing is reversible. Vertices that later lose support are automatically unfixed and their neighborhoods re-enter the working graph, which corrects occasional mistakes while preserving progress. Experiments on 222 graphs from four benchmark suites show that ARCIS attains the best or tied-best solution quality in most instances while delivering competitive runtime and low variability. Ablation studies isolate the impact of each component, indicating that ARCIS is a practical and robust method for large-scale graph mining.

cs.AI↗

arXiv2025

MoPFormer: Motion-Primitive Transformer for Wearable-Sensor Activity Recognition

Human Activity Recognition (HAR) with wearable sensors is challenged by limited interpretability, which significantly impacts cross-dataset generalization. To address this challenge, we propose Motion-Primitive Transformer (MoPFormer), a novel self-supervised framework that enhances interpretability by tokenizing inertial measurement unit signals into semantically meaningful motion primitives and leverages a Transformer architecture to learn rich temporal representations. MoPFormer comprises two stages. The first stage is to partition multi-channel sensor streams into short segments and quantize them into discrete ``motion primitive'' codewords, while the second stage enriches those tokenized sequences through a context-aware embedding module and then processes them with a Transformer encoder. The proposed MoPFormer can be pre-trained using a masked motion-modeling objective that reconstructs missing primitives, enabling it to develop robust representations across diverse sensor configurations. Experiments on six HAR benchmarks demonstrate that MoPFormer not only outperforms state-of-the-art methods but also successfully generalizes across multiple datasets. More importantly, the learned motion primitives significantly enhance both interpretability and cross-dataset performance by capturing fundamental movement patterns that remain consistent across similar activities, regardless of dataset origin.

cs.CV↗

arXiv2025

SN 2024iss: A Double-peaked Type IIb Supernova with Evidence of Circumstellar Interaction

We present optical, ultraviolet, and X-ray observations of supernova (SN) 2024iss, a Type IIb SN that shows a prominent double-peaked light curve. We modeled the first peak with a semianalytical shock-cooling model and the X-ray emission with a free-free model. We compare the envelope radius and mass-loss rate with other Type IIb SNe to explore the relationships between the progenitor envelope and the circumstellar material (CSM). The shock-cooling peak in the $V$-band light curve reached $M_V = -17.33\pm 0.26$mag, while the $^{56}$Ni-powered second peak attained $M_V = -17.43\pm 0.26$mag. Early spectra show an photospheric velocity of $\sim19,400\,km\,s^{-1}$ at 3.82days from the H$α$ P~Cygni profile. The Balmer lines persist at least +87 days after the explosion, characterizing hydrogen-rich ejecta. Modeling the first light-curve peak suggests an extended envelope with a mass of $0.11\pm0.04\,M_{\odot}$ and a radius of $244\pm43~R_{\odot}$. Fitting the second light-curve peak with an Arnett-like model indicates a typical $^{56}$Ni mass of $ 0.117\pm0.013~M_{\odot}$ and a relatively low ejecta mass of $1.272\pm0.343\,M_{\odot}$. X-ray observations reveal bright thermal bremsstrahlung emission and indicate a mass-loss rate of $1.6\times10^{-5}\ M_{\odot} \ \rm{yr}^{-1}$. SN 2024iss occupies a transitional position between the two subclasses of extended (eIIb) and compact (cIIb) Type IIb SNe. Its envelope radius and pre-explosion mass-loss rate appear to be correlated as theoretically predicted. The observational properties of SN 2024iss are compatible with a binary interaction scenario being the dominant mechanism for envelope stripping. Furthermore, the low column density of neutral hydrogen suggests a compact CSM with an outer radius of $\lesssim1.3\times10^{14}$ cm, indicating that the progenitor star experienced eruptive mass loss within $\sim4\,yr$ of its terminal explosion.

astro-ph.HE↗

arXiv2025

ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data

The advent of single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) offers an innovative perspective for deciphering regulatory mechanisms by assembling a vast repository of single-cell chromatin accessibility data. While foundation models have achieved significant success in single-cell transcriptomics, there is currently no foundation model for scATAC-seq that supports zero-shot high-quality cell identification and comprehensive multi-omics analysis simultaneously. Key challenges lie in the high dimensionality and sparsity of scATAC-seq data, as well as the lack of a standardized schema for representing open chromatin regions (OCRs). Here, we present ChromFound, a foundation model tailored for scATAC-seq. ChromFound utilizes a hybrid architecture and genome-aware tokenization to effectively capture genome-wide long contexts and regulatory signals from dynamic chromatin landscapes. Pretrained on 1.97 million cells from 30 tissues and 6 disease conditions, ChromFound demonstrates broad applicability across 6 diverse tasks. Notably, it achieves robust zero-shot performance in generating universal cell representations and exhibits excellent transferability in cell type annotation and cross-omics prediction. By uncovering enhancer-gene links undetected by existing computational methods, ChromFound offers a promising framework for understanding disease risk variants in the noncoding genome.

q-bio.GN↗

arXiv2025

Harnessing Intrinsic Noise for Quantum Simulation of Open Quantum Systems

Simulating open quantum systems on quantum computers presents a fundamental challenge: open quantum dynamics are intrinsically nonunitary, whereas quantum computers operate through unitary evolution. Conventional approaches overcome this mismatch by encoding nonunitary processes into unitary circuits, but such methods incur substantial overhead in both qubits and gates. Here, we propose an alternative perspective. Quantum processors are themselves open systems, inherently subject to noise. Instead of correcting all errors and then encoding nonunitary dynamics with unitary logical qubits and gates, we show how noise can be harnessed as a computational resource. We develop a noise-assisted quantum algorithm that selectively preserves physical noise to emulate nonunitary channels, enabling efficient simulation of open quantum dynamics with minimal qubit requirements. Our approach applies both to noisy intermediate-scale quantum (NISQ) devices and future fault-tolerant architectures. By leveraging intrinsic noise, this method circumvents the need to encode nonunitary dynamics into unitary gates and relaxes fidelity requirements on physical qubits, thereby reducing the overhead of quantum error correction. This framework reframes noise from a limitation into a resource, opening new directions for practical quantum simulation of open systems

quant-ph↗

arXiv2025

PLAN: Proactive Low-Rank Allocation for Continual Learning

Continual learning (CL) requires models to continuously adapt to new tasks without forgetting past knowledge. In this work, we propose \underline{P}roactive \underline{L}ow-rank \underline{A}llocatio\underline{N} (PLAN), a framework that extends Low-Rank Adaptation (LoRA) to enable efficient and interference-aware fine-tuning of large pre-trained models in CL settings. PLAN proactively manages the allocation of task-specific subspaces by introducing orthogonal basis vectors for each task and optimizing them through a perturbation-based strategy that minimizes conflicts with previously learned parameters. Furthermore, PLAN incorporates a novel selection mechanism that identifies and assigns basis vectors with minimal sensitivity to interference, reducing the risk of degrading past knowledge while maintaining efficient adaptation to new tasks. Empirical results on standard CL benchmarks demonstrate that PLAN consistently outperforms existing methods, establishing a new state-of-the-art for continual learning with foundation models.

cs.LG↗

arXiv2025

Domain wall induced topological Hall effect in the chiral-lattice ferromagnet Fe$_x$TaS$_2$

Magnetic topology and its associated emergent phenomena are central to realizing intriguing quantum states and spintronics functionalities. Designing spin textures to achieve strong and distinct electrical responses remains a significant challenge. Layered transition metal dichalcogenides offer a versatile platform for tailoring structural and magnetic properties, enabling access to a wide spectrum of topological magnetic states. Here, we report a domain-wall-driven, large, and tunable topological Hall effect (THE) in a non-centrosymmetric intercalated transition metal dichalcogenides series Fe$_x$TaS$_2$. By systematically varying the Fe intercalation level, we exert precise control over the magnetic ground states, allowing manipulation of the topological Hall effect. Real-space magnetic force microscopy (MFM) provides direct evidence of periodic magnetic stripe domain formation, confirming the microscopic origin of the observed topological transport phenomena. Our findings establish a promising way for tuning the topology of domains to generate substantial electromagnetic responses in layered magnetic materials.

cond-mat.mtrl-sci↗

arXiv2025

EmbodiedBrain: Expanding Performance Boundaries of Task Planning for Embodied Intelligence

The realization of Artificial General Intelligence (AGI) necessitates Embodied AI agents capable of robust spatial perception, effective task planning, and adaptive execution in physical environments. However, current large language models (LLMs) and multimodal LLMs (MLLMs) for embodied tasks suffer from key limitations, including a significant gap between model design and agent requirements, an unavoidable trade-off between real-time latency and performance, and the use of unauthentic, offline evaluation metrics. To address these challenges, we propose EmbodiedBrain, a novel vision-language foundation model available in both 7B and 32B parameter sizes. Our framework features an agent-aligned data structure and employs a powerful training methodology that integrates large-scale Supervised Fine-Tuning (SFT) with Step-Augumented Group Relative Policy Optimization (Step-GRPO), which boosts long-horizon task success by integrating preceding steps as Guided Precursors. Furthermore, we incorporate a comprehensive reward system, including a Generative Reward Model (GRM) accelerated at the infrastructure level, to improve training efficiency. For enable thorough validation, we establish a three-part evaluation system encompassing General, Planning, and End-to-End Simulation Benchmarks, highlighted by the proposal and open-sourcing of a novel, challenging simulation environment. Experimental results demonstrate that EmbodiedBrain achieves superior performance across all metrics, establishing a new state-of-the-art for embodied foundation models. Towards paving the way for the next generation of generalist embodied agents, we open-source all of our data, model weight, and evaluating methods, which are available at https://zterobot.github.io/EmbodiedBrain.github.io.

cs.CV↗

arXiv2025

Provably Efficient Reward Transfer in Reinforcement Learning with Discrete Markov Decision Processes

In this paper, we propose a new solution to reward adaptation (RA) in reinforcement learning, where the agent adapts to a target reward function based on one or more existing source behaviors learned a priori under the same domain dynamics but different reward functions. While learning the target behavior from scratch is possible, it is often inefficient given the available source behaviors. Our work introduces a new approach to RA through the manipulation of Q-functions. Assuming the target reward function is a known function of the source reward functions, we compute bounds on the Q-function and present an iterative process (akin to value iteration) to tighten these bounds. Such bounds enable action pruning in the target domain before learning even starts. We refer to this method as "Q-Manipulation" (Q-M). The iteration process assumes access to a lite-model, which is easy to provide or learn. We formally prove that Q-M, under discrete domains, does not affect the optimality of the returned policy and show that it is provably efficient in terms of sample complexity in a probabilistic sense. Q-M is evaluated in a variety of synthetic and simulation domains to demonstrate its effectiveness, generalizability, and practicality.

cs.LG↗

arXiv2025

StarWhisper Telescope: An AI framework for automating end-to-end astronomical observations

The exponential growth of large-scale telescope arrays has boosted time-domain astronomy development but introduced operational bottlenecks, including labor-intensive observation planning, data processing, and real-time decision-making. Here we present the StarWhisper Telescope system, an AI agent framework automating end-to-end astronomical observations for surveys like the Nearby Galaxy Supernovae Survey. By integrating large language models with specialized function calls and modular workflows, StarWhisper Telescope autonomously generates site-specific observation lists, executes real-time image analysis via pipelines, and dynamically triggers follow-up proposals upon transient detection. The system reduces human intervention through automated observation planning, telescope controlling and data processing, while enabling seamless collaboration between amateur and professional astronomers. Deployed across Nearby Galaxy Supernovae Survey's network of 10 amateur telescopes, the StarWhisper Telescope has detected transients with promising response times relative to existing surveys. Furthermore, StarWhisper Telescope's scalable agent architecture provides a blueprint for future facilities like the Global Open Transient Telescope Array, where AI-driven autonomy will be critical for managing 60 telescopes.

astro-ph.IM↗

arXiv2025

Temporal-order-driven asymmetric quantum interference and temporal coherence enhancement in spontaneous six-wave mixing

Narrow-band multiphoton entanglement sources serve as a core enabling resource for advanced quantum information technologies. Recently, researchers have directly generated energy-time entangled triphoton W states in a hot atomic medium via spontaneous six-wave mixing for the first time. However, a rigorous theoretical framework for this process remains lacking to date, confining our understanding to a mere extension of the biphoton model. Here, we analytically investigate the generation mechanism of energy-time entangled triphotons and their classically controllable optical properties in an electromagnetically induced transparency-assisted five-level cold atomic system. Notably, triphoton generation follows strict temporal ordering, resulting in asymmetric quantum interference in triple coincidence counts--unreplicable and unexplainable by the inherently symmetric biphoton model. These results establish a rigorous physical framework for spontaneous six-wave mixing-generated triphotons, clarify their distinctions from states produced via cascaded nonlinear models, and substantially advance their utility in quantum information protocols.

quant-ph↗

arXiv2025

Copy-Augmented Representation for Structure Invariant Template-Free Retrosynthesis

Retrosynthesis prediction is fundamental to drug discovery and chemical synthesis, requiring the identification of reactants that can produce a target molecule. Current template-free methods struggle to capture the structural invariance inherent in chemical reactions, where substantial molecular scaffolds remain unchanged, leading to unnecessarily large search spaces and reduced prediction accuracy. We introduce C-SMILES, a novel molecular representation that decomposes traditional SMILES into element-token pairs with five special tokens, effectively minimizing editing distance between reactants and products. Building upon this representation, we incorporate a copy-augmented mechanism that dynamically determines whether to generate new tokens or preserve unchanged molecular fragments from the product. Our approach integrates SMILES alignment guidance to enhance attention consistency with ground-truth atom mappings, enabling more chemically coherent predictions. Comprehensive evaluation on USPTO-50K and large-scale USPTO-FULL datasets demonstrates significant improvements: 67.2% top-1 accuracy on USPTO-50K and 50.8% on USPTO-FULL, with 99.9% validity in generated molecules. This work establishes a new paradigm for structure-aware molecular generation with direct applications in computational drug discovery.

cs.LG↗

arXiv2025

MRSAudio: A Large-Scale Multimodal Recorded Spatial Audio Dataset with Refined Annotations

Humans rely on multisensory integration to perceive spatial environments, where auditory cues enable sound source localization in three-dimensional space. Despite the critical role of spatial audio in immersive technologies such as VR/AR, most existing multimodal datasets provide only monaural audio, which limits the development of spatial audio generation and understanding. To address these challenges, we introduce MRSAudio, a large-scale multimodal spatial audio dataset designed to advance research in spatial audio understanding and generation. MRSAudio spans four distinct components: MRSLife, MRSSpeech, MRSMusic, and MRSSing, covering diverse real-world scenarios. The dataset includes synchronized binaural and ambisonic audio, exocentric and egocentric video, motion trajectories, and fine-grained annotations such as transcripts, phoneme boundaries, lyrics, scores, and prompts. To demonstrate the utility and versatility of MRSAudio, we establish five foundational tasks: audio spatialization, and spatial text to speech, spatial singing voice synthesis, spatial music generation and sound event localization and detection. Results show that MRSAudio enables high-quality spatial modeling and supports a broad range of spatial audio research. Demos and dataset access are available at https://mrsaudio.github.io.

cs.SD↗

arXiv2025

DCMIL: A Progressive Representation Learning of Whole Slide Images for Cancer Prognosis Analysis

The burgeoning discipline of computational pathology shows promise in harnessing whole slide images (WSIs) to quantify morphological heterogeneity and develop objective prognostic modes for human cancers. However, progress is impeded by the computational bottleneck of gigapixel-size inputs and the scarcity of dense manual annotations. Current methods often overlook fine-grained information across multi-magnification WSIs and variations in tumor microenvironments. Here, we propose an easy-to-hard progressive representation learning, termed dual-curriculum contrastive multi-instance learning (DCMIL), to efficiently process WSIs for cancer prognosis. The model does not rely on dense annotations and enables the direct transformation of gigapixel-size WSIs into outcome predictions. Extensive experiments on twelve cancer types (5,954 patients, 12.54 million tiles) demonstrate that DCMIL outperforms standard WSI-based prognostic models. Additionally, DCMIL identifies fine-grained prognosis-salient regions, provides robust instance uncertainty estimation, and captures morphological differences between normal and tumor tissues, with the potential to generate new biological insights. All codes have been made publicly accessible at https://github.com/tuuuc/DCMIL.

cs.CV↗

arXiv2025

Symmetry-forbidden intraband transitions leading to ultralow Gilbert damping in van der Waals ferromagnets

Based upon first-principles calculations, we report ultralow Gilbert damping in two-dimensional (2D) van derWaals (vdW) ferromagnets. The low damping occurs at weak scattering because mirror symmetry prohibits intraband transitions. The monotonic dependence on the electronic scattering rate suggests the absent lower limit, in contrast to conventional ferromagnetic materials. Breaking mirror symmetry through magnetization rotation, layer stacking, or structural phase transition significantly increases damping by enabling intraband transitions. Topological nodal lines, also protected by mirror symmetry, contribute substantially to interband-transition-mediated damping, which can be tuned by adjusting the Fermi level. Our findings elucidate the unique characteristics of Gilbert damping in 2D vdW ferromagnets, providing valuable insights for designing low-dimensional spintronic devices with high energy efficiency.

cond-mat.mtrl-sci↗

arXiv2025

Synergistic Integration and Discrepancy Resolution of Contextualized Knowledge for Personalized Recommendation

The integration of large language models (LLMs) into recommendation systems has revealed promising potential through their capacity to extract world knowledge for enhanced reasoning capabilities. However, current methodologies that adopt static schema-based prompting mechanisms encounter significant limitations: (1) they employ universal template structures that neglect the multi-faceted nature of user preference diversity; (2) they implement superficial alignment between semantic knowledge representations and behavioral feature spaces without achieving comprehensive latent space integration. To address these challenges, we introduce CoCo, an end-to-end framework that dynamically constructs user-specific contextual knowledge embeddings through a dual-mechanism approach. Our method realizes profound integration of semantic and behavioral latent dimensions via adaptive knowledge fusion and contradiction resolution modules. Experimental evaluations across diverse benchmark datasets and an enterprise-level e-commerce platform demonstrate CoCo's superiority, achieving a maximum 8.58% improvement over seven cutting-edge methods in recommendation accuracy. The framework's deployment on a production advertising system resulted in a 1.91% sales growth, validating its practical effectiveness. With its modular design and model-agnostic architecture, CoCo provides a versatile solution for next-generation recommendation systems requiring both knowledge-enhanced reasoning and personalized adaptation.

cs.IR↗

arXiv2025

FedLoRA-Optimizer: Federated LoRA Fine-Tuning with Global and Local Optimization in Heterogeneous Data Scenarios

Federated efficient fine-tuning has emerged as an approach that leverages distributed data and computational resources across nodes to address the challenges of large-scale fine-tuning and privacy preservation. The Low-Rank Adaptation (LoRA) enables efficient fine-tuning of large-scale pre-trained models by introducing trainable low-rank matrices into weight updates.However, in heterogeneous data scenarios, client drift weakens the generalization of the global model, and local models often fail to meet the personalized needs of individual clients.Moreover, existing federated LoRA efficient fine-tuning techniques overlook fine-grained analysis of the tuning matrices. To address this, we conducted preliminary experiments and found that different LoRA matrices exhibit different sensitivity to changes in the direction and magnitude of their vectors.We thus propose a fine-grained federated LoRA tuning method. By fine-tuning the more sensitive directional vectors in the A matrix, which encode shared knowledge, our method learns shared features more effectively across clients and enhances global generalization. Simultaneously, by fine-tuning the more sensitive magnitude vectors in the B matrix, which encode personalized knowledge, our method better captures personalized knowledge, enabling detailed adaptation to local data. The method uses a pipeline combining global and local optimizers. Global optimization further improves local models, achieving collaborative optimization between global and local levels. This improves both the generalization ability of the global model and the personalized adaptation of local models under heterogeneous data scenarios. Experiments on Databricks-Dolly-15k and Natural Instructions with LLaMA2-7B and Deepseek-7B confirm that our method improves global performance by 0.39% and local performance by 0.59%.

cs.LG↗

arXiv2025

Student-AI Interaction in an LLM-Empowered Learning Environment: A Cluster Analysis of Engagement Profiles

Integrating Large Language Models (LLMs) into educational practice enables personalized learning by accommodating diverse learner behaviors. This study explored diverse learner profiles within a multi-agent, LLM-empowered learning environment. Data was collected from 312 undergraduate students at a university in China as they participated in a six-module course. Based on hierarchical cluster analyses of system profiles and student-AI interactive dialogues, we found that students exhibit varied behavioral, cognitive, and emotional engagement tendencies. This analysis allowed us to identify two types of dropouts (early dropouts and stagnating interactors) and three completer profiles (active questioners, responsive navigators, and lurkers). The results showed that high levels of interaction do not always equate to productive learning and vice versa. Prior knowledge significantly influenced interaction patterns and short-term learning benefits. Further analysis of the human-AI dialogues revealed that some students actively engaged in knowledge construction, while others displayed a high frequency of regulatory behaviors. Notably, both groups of students achieved comparable learning gains, demonstrating the effectiveness of the multi-agent learning environment in supporting personalized learning. These results underscore the complex and multifaceted nature of engagement in human-AI collaborative learning and provide practical implications for the design of adaptive educational systems.

cs.CY↗