arXiv subjects

Yu Wang

Publications and source records attributed to Yu Wang.

At least 649 records · Page 36Linked to original sources

arXiv2025

Towards LifeSpan Cognitive Systems

Building a human-like system that continuously interacts with complex environments -- whether simulated digital worlds or human society -- presents several key challenges. Central to this is enabling continuous, high-frequency interactions, where the interactions are termed experiences. We refer to this envisioned system as the LifeSpan Cognitive System (LSCS). A critical feature of LSCS is its ability to engage in incremental and rapid updates while retaining and accurately recalling past experiences. In this paper we focus on the domain of Large Language Models (LLMs), where we identify two major challenges: (1) Abstraction and Experience Merging, and (2) Long-term Retention with Accurate Recall. These properties are essential for storing new experiences, organizing past experiences, and responding to the environment in ways that leverage relevant historical data. Unlike language models with continual learning, which typically rely on large corpora for fine-tuning and focus on improving performance within specific domains or tasks, LSCS must rapidly and incrementally update with new information from its environment at a high frequency. Existing technologies with the potential of solving the above two major challenges can be classified into four classes based on a conceptual metric called Storage Complexity, which measures the relative space required to store past experiences. Each of these four classes of technologies has its own strengths and limitations while we argue none of them alone can achieve LSCS alone. To this end, we propose a potential instantiation for LSCS that can integrate all four classes of technologies. The new instantiation, serving as a conjecture, operates through two core processes: Absorbing Experiences and Generating Responses.

cs.CL↗

arXiv2025

Observation of condensed moire exciton polaritons in twisted photonic lattices at room temperature

Moire lattices attract significant attention in double-layer graphene and TMD layer heterostructures as well as in photonic crystals due to the interesting exotic physics that emerges within these structures. However, direct measurement of the moiré ground, excited states and Bloch bands in twisted photonic lattices is still illusive. In this work we report strong coupling between excitons in CsPbBr3 microplates and moire photonic modes at room temperature. Depending on the coupling strength between the nearest potential sites, we observe staggered moire polariton ground states, excited states and moire polariton bands. Phase locked moire zero (in-phase) states and moire pi (antiphase) states with different spatial distributions are measured. The moire polariton distribution can be tuned into the shape of a parallelogram by controlling the depth and width of the potential in one photonic lattice with another superimposed one fixed. In addition, moire polaritons in twisted 2D honeycomb lattices are also observed. Increasing the pumping density, we realize exciton polariton condensation in the moire potential sites of the 1D/2D twisted lattices with the coherence time of around 1.4 ps. Our work lays the foundation to study coherent moire polariton condensation in twisted photonic lattices at room temperature.

physics.optics↗

arXiv2025

Beyond-Hubbard pairing in a cuprate ladder

The Hubbard model is believed to capture the essential physics of cuprate superconductors. However, recent theoretical studies suggest that it fails to reproduce a robust and homogeneous superconducting ground state. Here, using resonant inelastic x-ray scattering and density matrix renormalization group calculations, we show that magnetic excitations in the prototypical cuprate ladder Sr$_{14}$Cu$_{24}$O$_{41}$ are inconsistent with those of a simple Hubbard model. The magnetic response of hole carriers, contributing to an emergent branch of spin excitations, is strongly suppressed. This effect is the consequence of d-wave-like pairing, enhanced by nearly an order of magnitude through a large nearest-neighbor attractive interaction. The similarity between cuprate ladders and the two-dimensional compounds suggests that such an enhanced hole pairing may be a universal feature of superconducting cuprates.

cond-mat.str-el↗

arXiv2025

Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap

Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations. Due to the diversification of internet platforms and the exponential growth of users and items, the importance of cold-start recommendation (CSR) is becoming increasingly evident. At the same time, large language models (LLMs) have achieved tremendous success and possess strong capabilities in modeling user and item information, providing new potential for cold-start recommendations. However, the research community on CSR still lacks a comprehensive review and reflection in this field. Based on this, in this paper, we stand in the context of the era of large language models and provide a comprehensive review and discussion on the roadmap, related literature, and future directions of CSR. Specifically, we have conducted an exploration of the development path of how existing CSR utilizes information, from content features, graph relations, and domain information, to the world knowledge possessed by large language models, aiming to provide new insights for both the research and industrial communities on CSR. Related resources of cold-start recommendations are collected and continuously updated for the community in https://github.com/YuanchenBei/Awesome-Cold-Start-Recommendation.

cs.IR↗

arXiv2025

Dynamic Sub-graph Distillation for Robust Semi-supervised Continual Learning

Continual learning (CL) has shown promising results and comparable performance to learning at once in a fully supervised manner. However, CL strategies typically require a large number of labeled samples, making their real-life deployment challenging. In this work, we focus on semi-supervised continual learning (SSCL), where the model progressively learns from partially labeled data with unknown categories. We provide a comprehensive analysis of SSCL and demonstrate that unreliable distributions of unlabeled data lead to unstable training and refinement of the progressing stages. This problem severely impacts the performance of SSCL. To address the limitations, we propose a novel approach called Dynamic Sub-Graph Distillation (DSGD) for semi-supervised continual learning, which leverages both semantic and structural information to achieve more stable knowledge distillation on unlabeled data and exhibit robustness against distribution bias. Firstly, we formalize a general model of structural distillation and design a dynamic graph construction for the continual learning progress. Next, we define a structure distillation vector and design a dynamic sub-graph distillation algorithm, which enables end-to-end training and adaptability to scale up tasks. The entire proposed method is adaptable to various CL methods and supervision settings. Finally, experiments conducted on three datasets CIFAR10, CIFAR100, and ImageNet-100, with varying supervision ratios, demonstrate the effectiveness of our proposed approach in mitigating the catastrophic forgetting problem in semi-supervised continual learning scenarios.

cs.LG↗

arXiv2025

Study the quantum resolution sizes and atomic bonding states of two-dimensional tin monoxide

Understanding the interatomic bonding and electronic properties of two-dimensional (2D) materials is crucial for preparing high-performance 2D semiconductor materials. We have calculated the band structure, electronic properties, and bonding characteristics of SnO in 2D materials by using density functional theory (DFT) and combining bond energy and bond charge models. Atomic bonding analysis enables us to deeply and meticulously analyze the interatomic bonding and charge transfer in the layered structure of SnO. This study greatly enhances our understanding of the local bonding state on the surface of 2D structural materials. In addition, we use the renormalization method to operate energy to determine the wave function at different quantum resolutions. This is of great significance for describing the size and phase transition of nanomaterials.

cond-mat.mtrl-sci↗

arXiv2025

PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit

Whole Slide Image (WSI) analysis plays a crucial role in modern digital pathology, enabling large-scale feature extraction from tissue samples. However, traditional feature extraction pipelines based on tools like CellProfiler often involve lengthy workflows, requiring WSI segmentation into patches, feature extraction at the patch level, and subsequent mapping back to the original WSI. To address these challenges, we present PySpatial, a high-speed pathomics toolkit specifically designed for WSI-level analysis. PySpatial streamlines the conventional pipeline by directly operating on computational regions of interest, reducing redundant processing steps. Utilizing rtree-based spatial indexing and matrix-based computation, PySpatial efficiently maps and processes computational regions, significantly accelerating feature extraction while maintaining high accuracy. Our experiments on two datasets-Perivascular Epithelioid Cell (PEC) and data from the Kidney Precision Medicine Project (KPMP)-demonstrate substantial performance improvements. For smaller and sparse objects in PEC datasets, PySpatial achieves nearly a 10-fold speedup compared to standard CellProfiler pipelines. For larger objects, such as glomeruli and arteries in KPMP datasets, PySpatial achieves a 2-fold speedup. These results highlight PySpatial's potential to handle large-scale WSI analysis with enhanced efficiency and accuracy, paving the way for broader applications in digital pathology.

eess.IV↗

arXiv2025

BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Biomedical data is inherently multimodal, comprising physical measurements and natural language narratives. A generalist biomedical AI model needs to simultaneously process different modalities of data, including text and images. Therefore, training an effective generalist biomedical model requires high-quality multimodal data, such as parallel image-text pairs. Here, we present PMC-15M, a novel dataset that is two orders of magnitude larger than existing biomedical multimodal datasets such as MIMIC-CXR, and spans a diverse range of biomedical image types. PMC-15M contains 15 million biomedical image-text pairs collected from 4.4 million scientific articles. Based on PMC-15M, we have pretrained BiomedCLIP, a multimodal foundation model, with domain-specific adaptations tailored to biomedical vision-language processing. We conducted extensive experiments and ablation studies on standard biomedical imaging tasks from retrieval to classification to visual question-answering (VQA). BiomedCLIP achieved new state-of-the-art results in a wide range of standard datasets, substantially outperforming prior approaches. Intriguingly, by large-scale pretraining on diverse biomedical image types, BiomedCLIP even outperforms state-of-the-art radiology-specific models such as BioViL in radiology-specific tasks such as RSNA pneumonia detection. In summary, BiomedCLIP is a fully open-access foundation model that achieves state-of-the-art performance on various biomedical tasks, paving the way for transformative multimodal biomedical discovery and applications. We release our models at https://aka.ms/biomedclip to facilitate future research in multimodal biomedical AI.

cs.CV↗

arXiv2025

Retrieval-Augmented Generation with Graphs (GraphRAG)

Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from external sources. Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information, making it a golden resource for RAG in tremendous real-world applications. As a result, we have recently witnessed increasing attention on equipping RAG with Graph, i.e., GraphRAG. However, unlike conventional RAG, where the retriever, generator, and external data sources can be uniformly designed in the neural-embedding space, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains. Given the broad applicability, the associated design challenges, and the recent surge in GraphRAG, a systematic and up-to-date survey of its key concepts and techniques is urgently desired. Following this motivation, we present a comprehensive and up-to-date survey on GraphRAG. Our survey first proposes a holistic GraphRAG framework by defining its key components, including query processor, retriever, organizer, generator, and data source. Furthermore, recognizing that graphs in different domains exhibit distinct relational patterns and require dedicated designs, we review GraphRAG techniques uniquely tailored to each domain. Finally, we discuss research challenges and brainstorm directions to inspire cross-disciplinary opportunities. Our survey repository is publicly maintained at https://github.com/Graph-RAG/GraphRAG/.

cs.IR↗

arXiv2025

Navigating Chemical-Linguistic Sharing Space with Heterogeneous Molecular Encoding

Chemical language models (CLMs) are prominent for their effectiveness in exploring chemical space and enabling molecular engineering. However, while exploring chemical-linguistic space, CLMs suffer from the gap between natural language and molecular representations. This challenge is primarily due to the inherent modeling differences between molecules and texts: molecules operate unified modeling to learn chemical space, while natural language sequentially models the semantic space. Additionally, the limited availability of high-quality text-to-molecule datasets further exacerbates this challenge. To address the problem, we first verified the information bias in molecular representations from different perspectives. We then developed the Heterogeneous Molecular Encoding (HME) framework, a unified molecular encoder compressing the molecular features from fragment sequence, topology, and conformation with Q-learning. To better model chemical-linguistic space, we further constructed the MCMoD dataset, which contains over one million molecules with various conditions, including properties, fragments, and descriptions. Experimentally, HME promotes CLMs to achieve chemical-linguistic sharing space exploration: (1) chemical space exploration with linguistic guidance, where HME achieves significant improvements (+8.9\% FCD) for molecular design in multiple constraints, even in zero-shot scenarios; (2) linguistic space exploration with molecular guidance, where HME generates textual descriptions with high qualities (+11.6\% BLEU) for molecules. These results highlight the precision of HME in handling multi-objective and cross-domain tasks, as well as its remarkable generalization capability on unseen task combinations. HME offers a new perspective on navigating chemical-linguistic sharing space, advancing the potential of CLMs in both fundamental research and practical applications in chemistry.

cs.CE↗

arXiv2025

GRB 190114C: Fireball Energy Budget and Radiative Efficiency Revisited

The jet composition of gamma-ray bursts (GRBs), as well as how efficiently the jet converts its energy to radiation, are long-standing problems in GRB physics. Here, we reported a comprehensive temporal and spectral analysis of the TeV-emitting bright GRB 190114C. Its high fluence ($\sim$ 4.4$\times$10$^{-4}$ erg cm$^{-2}$) allows us to conduct the time-resolved spectral analysis in great detail and study their variations down to a very short time-scale ($\sim$0.1 s) while preserving a high significance. Its prompt emission consists of three well-separated pulses. The first two main pulses ($P_1$ and $P_2$) exhibit independently strong thermal components, starting from the third pulse ($P_3$) and extending to the entire afterglow, the spectra are all nonthermal, the synchrotron plus Compton upscattering model well interprets the observation. By combining the thermal ($P_1$ and $P_2$) and the nonthermal ($P_3$) observations based on two different scenarios (global and pulse properties) and following the method described in Zhang et al., we measure the fireball parameters and GRB radiative efficiency with little uncertainties for this GRB. A relevantly high GRB radiative efficiency is obtained based on both the global and pulse properties, suggesting that if GRBs are powered by fireballs, the efficiency can sometimes be high. More interestingly, though the observed parameters are individually different (e.g., the amount of mass loading $M$), the radiative efficiency obtained from $P_1$ ($η_γ=36.0\pm6.5\%$) and $P_2$ ($η_γ=41.1\pm1.9\%$) is roughly the same, which implies that the central engine of the same GRB has some common properties.

astro-ph.HE↗

arXiv2025

PoVF: Empowering Decentralized Blockchain Systems with Verifiable Function Consensus

Consensus mechanism is the core technology for blockchain to ensure that transactions are executed in sequence. It also determines the decentralization, security, and efficiency of blockchain. Existing mechanisms all have certain centralization issues and fail to ensure the decentralization of blockchain networks. A decentralized and efficient mechanism is required to improve blockchain systems. This paper proposes a fair consensus mechanism called Proof of Verifiable Functions (PoVF), based on the verifiability and unpredictability of verifiable functions. PoVF provides a sufficiently fair mechanism, ensuring that all nodes in blockchain network have equal opportunity to participate in consensus. In addition, a structure called "Delay buffer" is proposed to ensure transactions are executed sequentially. It delay the selection of blocks to avoid blockchain forks caused by broadcasting and transaction execution confusion. According to our security analysis, PoVF is provably secure and has the ability to resist potential adversaries. According to the experiments, PoVF-based blockchain can process up to 4000 transactions per second with nodes configured with only 4-core CPUs. This paper uses the Gini coefficient to measure the decentralization of blockchains, and the PoVF-based blockchain achieves the lowest Gini coefficient of 0.39 among all sampled blockchains. PoVF has been shown to provide sufficient efficiency while ensuring decentralization and security through experiments.

cs.CR↗

arXiv2024

Quantum-Assisted Online Task Offloading and Resource Allocation in MEC-Enabled Satellite-Aerial-Terrestrial Integrated Networks

In the era of Internet of Things (IoT), multi-access edge computing (MEC)-enabled satellite-aerial-terrestrial integrated network (SATIN) has emerged as a promising technology to provide massive IoT devices with seamless and reliable communication and computation services. This paper investigates the cooperation of low Earth orbit (LEO) satellites, high altitude platforms (HAPs), and terrestrial base stations (BSs) to provide relaying and computation services for vastly distributed IoT devices. Considering the uncertainty in dynamic SATIN systems, we formulate a stochastic optimization problem to minimize the time-average expected service delay by jointly optimizing resource allocation and task offloading while satisfying the energy constraints. To solve the formulated problem, we first develop a Lyapunov-based online control algorithm to decompose it into multiple one-slot problems. Since each one-slot problem is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computers, we further propose novel hybrid quantum-classical generalized Benders' decomposition (HQCGBD) algorithms to solve the problem efficiently by leveraging quantum advantages in parallel computing. Numerical results validate the effectiveness of the proposed MEC-enabled SATIN schemes.

cs.NI↗

arXiv2024

E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling

Recent advances in autoregressive (AR) models with continuous tokens for image generation show promising results by eliminating the need for discrete tokenization. However, these models face efficiency challenges due to their sequential token generation nature and reliance on computationally intensive diffusion-based sampling. We present ECAR (Efficient Continuous Auto-Regressive Image Generation via Multistage Modeling), an approach that addresses these limitations through two intertwined innovations: (1) a stage-wise continuous token generation strategy that reduces computational complexity and provides progressively refined token maps as hierarchical conditions, and (2) a multistage flow-based distribution modeling method that transforms only partial-denoised distributions at each stage comparing to complete denoising in normal diffusion models. Holistically, ECAR operates by generating tokens at increasing resolutions while simultaneously denoising the image at each stage. This design not only reduces token-to-image transformation cost by a factor of the stage number but also enables parallel processing at the token level. Our approach not only enhances computational efficiency but also aligns naturally with image generation principles by operating in continuous token space and following a hierarchical generation process from coarse to fine details. Experimental results demonstrate that ECAR achieves comparable image quality to DiT Peebles & Xie [2023] while requiring 10$\times$ FLOPs reduction and 5$\times$ speedup to generate a 256$\times$256 image.

cs.CV↗

arXiv2024

Drawing the Line: Enhancing Trustworthiness of MLLMs Through the Power of Refusal

Multimodal large language models (MLLMs) excel at multimodal perception and understanding, yet their tendency to generate hallucinated or inaccurate responses undermines their trustworthiness. Existing methods have largely overlooked the importance of refusal responses as a means of enhancing MLLMs reliability. To bridge this gap, we present the Information Boundary-aware Learning Framework (InBoL), a novel approach that empowers MLLMs to refuse to answer user queries when encountering insufficient information. To the best of our knowledge, InBoL is the first framework that systematically defines the conditions under which refusal is appropriate for MLLMs using the concept of information boundaries proposed in our paper. This framework introduces a comprehensive data generation pipeline and tailored training strategies to improve the model's ability to deliver appropriate refusal responses. To evaluate the trustworthiness of MLLMs, we further propose a user-centric alignment goal along with corresponding metrics. Experimental results demonstrate a significant improvement in refusal accuracy without noticeably compromising the model's helpfulness, establishing InBoL as a pivotal advancement in building more trustworthy MLLMs.

cs.CL↗

arXiv2024

Ultrafast Spin Rotation of Relativistic Lepton Beams via Terahertz Wave in a Dielectric-Lined Waveguide

Spin rotation is central for the spin-manipulation of lepton beams which, in turn, plays an important role in investigation of the properties of spin-polarized lepton beams and the examination of spin-dependent interactions. However, realization of compact and ultrafast spin rotation of lepton beams, between longitudinal and transverse polarizations, still faces significant challenges. Here, we put forward a novel method for ultrafast (picosecond-timescale) spin rotation of a relativistic lepton beam via employing a moderate-intensity terahertz (THz) wave in a dielectric-lined waveguide (DLW). The lepton beam undergoes spin precession induced by the THz magnetic field. We find that optimizing the lepton velocity and THz phase velocity in the DLW can mitigate the impact of transverse Lorentz forces on the lepton beam and increase the precession frequency, thereby maintaining the beam quality and enhancing the efficiency of transverse-to-longitudinal spin rotation. The final polarization degree of the lepton beam exceeds $98\%$, and the energy spread can be improved significantly. Flexibility in adjusting the electromagnetic modes within the DLW adds further potential for spin-manipulation, and holds promise for advancing the development of spin-polarized particle beams, which have broad applications in materials science and atomic, nuclear, and high-energy physics.

physics.plasm-ph↗

arXiv2024

Spectral Convolutional Neural Network Chip for In-sensor Edge Computing of Incoherent Natural Light

Convolutional neural networks (CNNs) are representative models of artificial neural networks (ANNs). However, the considerable power consumption and limited computing speed of electrical computing platforms restrict further CNN development on edge devices. Optical neural networks are considered next-generation physical implementations of ANNs, but their capabilities are limited by on-chip integration scale and requirement for coherent light sources. This study proposes a spectral convolutional neural network (SCNN) of incoherent natural light by an optical convolutional layer (OCL) and a reconfigurable electrical backend. The OCL is implemented by integrating very large-scale, pixel-aligned spectral filters on a CMOS image sensor on a 12-inch wafer, facilitating highly parallel spectral vector-inner products of incident light. It accepts broadband incoherent natural light containing two spatial and one spectral dimension directly as input with the function of matter meta-imaging. This unique optoelectronic framework empowers in-sensor optical analog computing at extremely high energy efficiency because the OCL is driven by the energy of the information carrier, i. e. natural light. To the best of our knowledge, this is the first integrated optical computing utilizing natural light. We employ the same SCNN chip for completely different real-world complex tasks,and achieve accuracies of over 96% for pathological diagnosis and almost 100% for face anti-spoofing at video rates. The SCNN framework has an unprecedented new function of substance identification, provides a feasible optoelectronic and integrated optical CNN implementation for edge devices or cellphones, providing them with practical and powerful edge computing abilities and facilitating diverse applications, such as intelligent robotics, industrial automation, medical diagnosis, and remote sensing.

physics.optics↗

arXiv2024

A Novel Method of Estimating GRB Peak Energies Beyond the \emph{Swift}/BAT Limit

The \emph{Swift} Burst Alert Telescope (BAT), operating in the 15--150 keV energy band, struggles to detect the peak energy ($E_{\rm p}$) of gamma-ray bursts (GRBs), as most GRBs have $E_{\rm p}$ values typically distributed between 200-300 keV, exceeding BAT's upper limit. To address this, we develop an innovative method to robustly estimate the lower limit of $E_{\rm p}$ for GRBs with $E_{\rm p}>150$ keV. This approach relies on the intrinsic curvature of GRB spectra, which is already evident within the BAT energy range for such GRBs. By fitting BAT spectra with a cutoff power-law model and extrapolating the spectral curvature beyond BAT's range, we, therefore, can estimate the cutoff energy ($E^{'}_{\rm c}$) beyond 150 keV and the corresponding peak energy ($E^{'}_{\rm p}$). We applied this method to 17 GRBs, categorizing them into two main groups. Group I (10 bursts) maintains $α$ within a typical range (from $\sim$ -0.8 to $\sim$ -1.20) with increasing $E_{\rm c}$; Group II (2 bursts) maintains $E_{\rm c}$ within a typical range (300-500 keV) but with varying $α$. Our results show that for $E_{\rm c}\lesssim $1000 keV, the estimated $E^{'}_{\rm c}$ aligns well with observed values. Moreover, the reliability of $E^{'}_{\rm c}$ also depends on $α$: bursts with harder $α$ (e.g., $α\gtrsim -2/3$) show reduced accuracy, while bursts with softer $α$ (e.g., $α\lesssim -2/3$) yield more precise estimates. In conclusion, this method is well-suited for GRB spectra with moderately observed $E_{\rm c}$ ($E_{\rm p}$) values and $α$ indices that are not too hard.

astro-ph.HE↗