arXiv subjects

You Zhou

Publications and source records attributed to You Zhou.

At least 91 records · Page 5Linked to original sources

arXiv2025

Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection

Unsupervised Continuous Anomaly Detection (UCAD) faces significant challenges in multi-task representation learning, with existing methods suffering from incomplete representation and catastrophic forgetting. Unlike supervised models, unsupervised scenarios lack prior information, making it difficult to effectively distinguish redundant and complementary multimodal features. To address this, we propose the Multimodal Task Representation Memory Bank (MTRMB) method through two key technical innovations: A Key-Prompt-Multimodal Knowledge (KPMK) mechanism that uses concise key prompts to guide cross-modal feature interaction between BERT and ViT. Refined Structure-based Contrastive Learning (RSCL) leveraging Grounding DINO and SAM to generate precise segmentation masks, pulling features of the same structural region closer while pushing different structural regions apart. Experiments on MVtec AD and VisA datasets demonstrate MTRMB's superiority, achieving an average detection accuracy of 0.921 at the lowest forgetting rate, significantly outperforming state-of-the-art methods. We plan to open source on GitHub.

cs.CV↗

arXiv2025

Signature of the $α$-clustering structure of Light Nuclei in Relativistic Nuclear Collisions

The "imaging-by-smashing" technique has been developed recently in relativistic nuclear collisions. By smashing heavy nuclei at RHIC and the LHC and analyzing the anisotropic expansion (flow) of the final state produced particles, unique information on the structure of the collided nuclei has been obtained. Existing efforts primarily focus on the colliding mode of heavy nuclei collisions. In contrast, nuclear structure studies with collisions of light nuclei and the fixed target mode, despite their significant impact and broad interest, have not been thoroughly explored. In this Letter, we investigate the $α$-clustering signature of $^{20}$Ne and $^{16}$O in the fixed-target $^{208}$Pb--$^{20}$Ne and $^{208}$Pb--$^{16}$O collisions at $\sqrt{s_{_\mathrm{NN}}}$ = 68.5 GeV, using the parton transport model AMPT. The results of two- and four-particle cumulants of anisotropic flow demonstrate a robust $α$-clustering signature that persists regardless of the complex dynamic evolution of the created systems. This study highlights the significant impact of the LHCb SMOG (SMOG2) project in discovering the $α$-clustering signature of light nuclei at relativistic energies.

nucl-th↗

arXiv2025

An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem

Recent advances in neural models have shown considerable promise in solving Traveling Salesman Problems (TSPs) without relying on much hand-crafted engineering. However, while non-autoregressive (NAR) approaches benefit from faster inference through parallelism, they typically deliver solutions of inferior quality compared to autoregressive ones. To enhance the solution quality while maintaining fast inference, we propose DEITSP, a diffusion model with efficient iterations tailored for TSP that operates in a NAR manner. Firstly, we introduce a one-step diffusion model that integrates the controlled discrete noise addition process with self-consistency enhancement, enabling optimal solution prediction through simultaneous denoising of multiple solutions. Secondly, we design a dual-modality graph transformer to bolster the extraction and fusion of features from node and edge modalities, while further accelerating the inference with fewer layers. Thirdly, we develop an efficient iterative strategy that alternates between adding and removing noise to improve exploration compared to previous diffusion methods. Additionally, we devise a scheduling framework to progressively refine the solution space by adjusting noise levels, facilitating a smooth search for optimal solutions. Extensive experiments on real-world and large-scale TSP instances demonstrate that DEITSP performs favorably against existing neural approaches in terms of solution quality, inference latency, and generalization ability. Our code is available at $\href{https://github.com/DEITSP/DEITSP}{https://github.com/DEITSP/DEITSP}$.

cs.LG↗

arXiv2025

Quantum confining excitons with electrostatic moiré superlattice

Quantum confining excitons has been a persistent challenge in the pursuit of strong exciton interactions and quantum light generation. Unlike electrons, which can be readily controlled via electric fields, imposing strong nanoscale potentials on excitons to enable quantum confinement has proven challenging. In this study, we utilize piezoresponse force microscopy to image the domain structures of twisted hexagonal boron nitride (hBN), revealing evidence of strong in-plane electric fields at the domain boundaries. By placing a monolayer MoSe2 only one to two nanometers away from the twisted hBN interface, we observe energy splitting of neutral excitons and Fermi polarons by several millielectronvolts at the moiré domain boundaries. By directly correlating local structural and optical properties, we attribute such observations to excitons confined in a nanoscale one-dimensional electrostatic potential created by the strong in-plane electric fields at the moiré domain boundaries. Intriguingly, this 1D quantum confinement results in pronounced polarization anisotropy in the excitons' reflection and emission, persistent to temperatures as high as ~80 Kelvins. These findings open new avenues for exploring and controlling strongly interacting excitons for classical and quantum optoelectronics.

cond-mat.mes-hall↗

arXiv2025

A Value Mapping Virtual Staining Framework for Large-scale Histological Imaging

The emergence of virtual staining technology provides a rapid and efficient alternative for researchers in tissue pathology. It enables the utilization of unlabeled microscopic samples to generate virtual replicas of chemically stained histological slices, or facilitate the transformation of one staining type into another. The remarkable performance of generative networks, such as CycleGAN, offers an unsupervised learning approach for virtual coloring, overcoming the limitations of high-quality paired data required in supervised learning. Nevertheless, large-scale color transformation necessitates processing large field-of-view images in patches, often resulting in significant boundary inconsistency and artifacts. Additionally, the transformation between different colorized modalities typically needs further efforts to modify loss functions and tune hyperparameters for independent training of networks. In this study, we introduce a general virtual staining framework that is adaptable to various conditions. We propose a loss function based on the value mapping constraint to ensure the accuracy of virtual coloring between different pathological modalities, termed the Value Mapping Generative Adversarial Network (VM-GAN). Meanwhile, we present a confidence-based tiling method to address the challenge of boundary inconsistency arising from patch-wise processing. Experimental results on diverse data with varying staining protocols demonstrate that our method achieves superior quantitative indicators and improved visual perception.

eess.IV↗

arXiv2025

Communication Efficient Cooperative Edge AI via Event-Triggered Computation Offloading

Rare events, despite their infrequency, often carry critical information and require immediate attentions in mission-critical applications such as autonomous driving, healthcare, and industrial automation. The data-intensive nature of these tasks and their need for prompt responses, combined with designing edge AI (or edge inference), pose significant challenges in systems and techniques. Existing edge inference approaches often suffer from communication bottlenecks due to high-dimensional data transmission and fail to provide timely responses to rare events, limiting their effectiveness for mission-critical applications in the sixth-generation (6G) mobile networks. To overcome these challenges, we propose a channel-adaptive, event-triggered edge-inference framework that prioritizes efficient rare-event processing. Central to this framework is a dual-threshold, multi-exit architecture, which enables early local inference for rare events detected locally while offloading more complex rare events to edge servers for detailed classification. To further enhance the system's performance, we developed a channel-adaptive offloading policy paired with an online algorithm to dynamically determine the optimal confidence thresholds for controlling offloading decisions. The associated optimization problem is solved by reformulating the original non-convex function into an equivalent strongly convex one. Using deep neural network classifiers and real medical datasets, our experiments demonstrate that the proposed framework not only achieves superior rare-event classification accuracy, but also effectively reduces communication overhead, as opposed to existing edge-inference approaches.

cs.LG↗

arXiv2024

Imaging the initial condition of heavy-ion collisions and nuclear structure across the nuclide chart

High-energy nuclear collisions encompass three key stages: the structure of the colliding nuclei informed by low-energy nuclear physics, the initial condition (IC) leading to the formation of quark-gluon plasma (QGP), and the hydrodynamic expansion and hadronization of the QGP leading to final-state hadrons observed experimentally. Recent advances in experimental and theoretical methods have ushered in a precision era, enabling an increasingly accurate understanding of these stages. However, most approaches involve simultaneously determining both QGP properties and initial conditions from a single collision system, creating complexity due to the coupled contributions of various stages to the final-state observables. To avoid this, we propose leveraging known knowledge of low-energy nuclear structure and hydrodynamic observables to constrain the IC independently. By conducting comparative studies of collisions involving isobar-like nuclei - species with similar mass numbers but different structures - we disentangle the initial condition's impacts from the QGP properties. This approach not only refines our understanding of the IC but also turns high-energy experiments into a precision tool for imaging nuclear structures, offering insights that complement traditional low-energy approaches. Opportunities for carrying out such comparative experiments at the LHC and other facilities could significantly advance both high-energy and low-energy nuclear physics. Additionally, this approach has implications for the future EIC. While the possibilities are extensive, we focus on selected proposals that could benefit both the high-energy and low-energy nuclear physics communities. Originally prepared as input for the long-range plan of U.S. nuclear physics, this white paper reflects the status as of September 2022, with a brief update on developments since then.

nucl-ex↗

arXiv2024

Exploring the Nuclear Shape Phase Transition in Ultra-Relativistic $^{129}$Xe+$^{129}$Xe Collisions at the LHC

The shape phase transition for certain isotope or isotone chains, associated with the quantum phase transition of finite nuclei, is an intriguing phenomenon in nuclear physics. A notable case is the Xe isotope chain, where the structure transits from a $γ$-soft rotor to a spherical vibrator, with the second-order shape phase transition occurring in the vicinity of $^{128-130}$Xe. In this letter, we focus on investigating the $γ$-soft deformation of $^{129}$Xe associated with the second-order shape phase transition by constructing novel correlators for ultra-relativistic $^{129}$Xe+$^{129}$Xe collisions. In particular, our iEBE-VISHNU model calculations show that the $v_2^2-[p_T]$ correlation $ρ_{2}$ and the mean transverse momentum fluctuation $Γ_{p_T}$, which were previously interpreted as the evidence for the rigid triaxial deformation of $^{129}$Xe, can also be well explained by the $γ$-soft deformation of $^{129}$Xe. We also propose two novel correlators $ρ_{4,2}$ and $ρ_{2,4}$, which carry non-trivial higher-order correlations and show unique capabilities to distinguish between the $γ$-soft and the rigid triaxial deformation of $^{129}$Xe in $^{129}$Xe+$^{129}$Xe collisions at the LHC. The present study also provides a novel way to explore the second-order shape phase transition of finite nuclei with ultra-relativistic heavy ion collisions.

nucl-th↗

arXiv2024

The Moon-forming Impact as a Constraint for the Inner Solar System's Formation

The solar system planets are benchmarks for the planet formation theory. Yet two paradigms coexist for the four terrestrial planets: the prolonged collisional growth among planetesimals lasting $>100$ million years (Myr) and the fast formation via planetesimals accreting pebbles within 10 Myr. Despite their dramatic difference, we can hardly tell which theory is more relevant to the true history of the terrestrial planets' formation. Here, we show that the Moon's origin puts stringent constraints on the pebble accretion scenario, rendering it less favourable. In the pebble accretion model, the one-off giant impact between proto-Earth and Theia rarely (probability $<$ 1\textperthousand) occurs at the right timing and configuration for the Moon formation. Even if a potential impact happens by chance, giant impact simulations reveal perfect mixing between proto-Earth and Theia, leaving no room for the observed primordial Earth mantle heterogeneity and the compositional difference, though small, between Earth and the Moon. Thus, the Earth-Moon system along other terrestrial planets should preferably form from chaotic collisional growth in the inner solar system.

astro-ph.EP↗

arXiv2024

Chiral Flat-Band Optical Cavity with Atomically Thin Mirrors

A fundamental requirement for photonic technologies is the ability to control the confinement and propagation of light. Widely utilized platforms include two-dimensional (2D) optical microcavities in which electromagnetic waves are confined between either metallic or distributed Bragg reflectors. Recently, transition metal dichalcogenides hosting tightly bound excitons with high optical quality have emerged as promising atomically thin mirrors. In this work, we propose and experimentally demonstrate a sub-wavelength 2D nano-cavity using two atomically thin mirrors with degenerate resonances. Angle-resolved measurements show a flat band, which sets this system apart from conventional photonic cavities. Remarkably, we demonstrate how the excitonic nature of the mirrors enables the formation of chiral and tunable optical modes upon the application of an external magnetic field. Moreover, we show the electrical tunability of the confined mode. Our work demonstrates a mechanism for confining light with high-quality excitonic materials, opening perspectives for spin-photon interfaces, and chiral cavity electrodynamics.

physics.optics↗

arXiv2024

Detecting AI-Generated Texts in Cross-Domains

Existing tools to detect text generated by a large language model (LLM) have met with certain success, but their performance can drop when dealing with texts in new domains. To tackle this issue, we train a ranking classifier called RoBERTa-Ranker, a modified version of RoBERTa, as a baseline model using a dataset we constructed that includes a wider variety of texts written by humans and generated by various LLMs. We then present a method to fine-tune RoBERTa-Ranker that requires only a small amount of labeled data in a new domain. Experiments show that this fine-tuned domain-aware model outperforms the popular DetectGPT and GPTZero on both in-domain and cross-domain texts, where AI-generated texts may either be in a different domain or generated by a different LLM not used to generate the training datasets. This approach makes it feasible and economical to build a single system to detect AI-generated texts across various domains.

cs.CL↗

arXiv2024

Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications. Recently, neural networks have gained popularity in this research area because as shown in the literature, they provide strong heuristic solutions to TSPs. Compared to autoregressive neural approaches, non-autoregressive (NAR) networks exploit the inference parallelism to elevate inference speed but suffer from comparatively low solution quality. In this paper, we propose a novel NAR model named NAR4TSP, which incorporates a specially designed architecture and an enhanced reinforcement learning strategy. To the best of our knowledge, NAR4TSP is the first TSP solver that successfully combines RL and NAR networks. The key lies in the incorporation of NAR network output decoding into the training process. NAR4TSP efficiently represents TSP encoded information as rewards and seamlessly integrates it into reinforcement learning strategies, while maintaining consistent TSP sequence constraints during both training and testing phases. Experimental results on both synthetic and real-world TSPs demonstrate that NAR4TSP outperforms five state-of-the-art models in terms of solution quality, inference speed, and generalization to unseen scenarios.

cs.AI↗

arXiv2024

Quantum subspace verification for error correction codes

Benchmarking the performance of quantum error correction codes in physical systems is crucial for achieving fault-tolerant quantum computing. Current methodologies, such as (shadow) tomography or direct fidelity estimation, fall short in efficiency due to the neglect of possible prior knowledge about quantum states. To address the challenge, we introduce a framework of quantum subspace verification, employing the knowledge of quantum error correction code subspaces to reduce the potential measurement budgets. Specifically, we give the sample complexity to estimate the fidelity to the target subspace under some confidence level. Building on the framework, verification operators are developed, which can be implemented with experiment-friendly local measurements for stabilizer codes and quantum low-density parity-check (QLDPC) codes. Our constructions require $O(n-k)$ local measurement settings for both, and the sample complexity of $O(n-k)$ for stabilizer codes and of $O((n-k)^2)$ for generic QLDPC codes, where $n$ and $k$ are the numbers of physical and logical qubits, respectively. Notably, for certain codes like the notable Calderbank-Shor-Steane codes and QLDPC stabilizer codes, the setting number and sample complexity can be significantly reduced and are even independent of $n$. In addition, by combining the proposed subspace verification and direct fidelity estimation, we construct a protocol to verify the fidelity of general magic logical states with exponentially smaller sample complexity than previous methods. Our finding facilitates efficient and feasible verification of quantum error correction codes and also magical states, advancing the realization in practical quantum platforms.

quant-ph↗

arXiv2024

Intrinsic Evaluation of RAG Systems for Deep-Logic Questions

We introduce the Overall Performance Index (OPI), an intrinsic metric to evaluate retrieval-augmented generation (RAG) mechanisms for applications involving deep-logic queries. OPI is computed as the harmonic mean of two key metrics: the Logical-Relation Correctness Ratio and the average of BERT embedding similarity scores between ground-truth and generated answers. We apply OPI to assess the performance of LangChain, a popular RAG tool, using a logical relations classifier fine-tuned from GPT-4o on the RAG-Dataset-12000 from Hugging Face. Our findings show a strong correlation between BERT embedding similarity scores and extrinsic evaluation scores. Among the commonly used retrievers, the cosine similarity retriever using BERT-based embeddings outperforms others, while the Euclidean distance-based retriever exhibits the weakest performance. Furthermore, we demonstrate that combining multiple retrievers, either algorithmically or by merging retrieved sentences, yields superior performance compared to using any single retriever alone.

cs.AI↗

arXiv2024

Content Significance Distribution of Sub-Text Blocks in Articles and Its Application to Article-Organization Assessment

We explore how to capture the significance of a sub-text block in an article and how it may be used for text mining tasks. A sub-text block is a sub-sequence of sentences in the article. We formulate the notion of content significance distribution (CSD) of sub-text blocks, referred to as CSD of the first kind and denoted by CSD-1. In particular, we leverage Hugging Face's SentenceTransformer to generate contextual sentence embeddings, and use MoverScore over text embeddings to measure how similar a sub-text block is to the entire text. To overcome the exponential blowup on the number of sub-text blocks, we present an approximation algorithm and show that the approximated CSD-1 is almost identical to the exact CSD-1. Under this approximation, we show that the average and median CSD-1's for news, scholarly research, argument, and narrative articles share the same pattern. We also show that under a certain linear transformation, the complement of the cumulative distribution function of the beta distribution with certain values of $α$ and $β$ resembles a CSD-1 curve. We then use CSD-1's to extract linguistic features to train an SVC classifier for assessing how well an article is organized. Through experiments, we show that this method achieves high accuracy for assessing student essays. Moreover, we study CSD of sentence locations, referred to as CSD of the second kind and denoted by CSD-2, and show that average CSD-2's for different types of articles possess distinctive patterns, which either conform common perceptions of article structures or provide rectification with minor deviation.

cs.CV↗

arXiv2024

Knowledge-Based Ultra-Low-Latency Semantic Communications for Robotic Edge Intelligence

The 6G mobile networks will feature the widespread deployment of AI algorithms at the network edge, which provides a platform for supporting robotic edge intelligence systems. In such a system, a large-scale knowledge graph (KG) is operated at an edge server as a "remote brain" to guide remote robots on environmental exploration or task execution. In this paper, we present a new air-interface framework targeting the said systems, called knowledge-based robotic semantic communications (SemCom), which consists of a protocol and relevant transmission techniques. First, the proposed robotic SemCom protocol defines a sequence of system operations for executing a given robotic task. They include identification of all task-relevant knowledge paths (KPs) on the KG, semantic matching between KG and object classifier, and uploading of robot's observations for objects recognition and feasible KP identification. Next, to support ultra-low-latency feature transmission (ULL-FT), we propose a novel transmission approach that exploits classifier's robustness, which is measured by classification margin, to compensate for a high bit error probability (BEP) resulting from ultra-low-latency transmission (e.g., short packet and/or no coding). By utilizing the tractable Gaussian mixture model, we derive the relation between BEP and classification margin, which sheds light on system requirements to support ULL-FT. Furthermore, for the case where the classification margin is insufficient for coping with channel distortion, we enhance the ULL-FT approach by studying retransmission and multi-view classification for enlarging the margin and further quantifying corresponding requirements. Finally, experiments using DNNs as classifier models and real datasets are conducted to demonstrate the effectiveness of ULL-FT in communication latency reduction while providing a guarantee on accurate feasible KP identification.

cs.IT↗

arXiv2024

Circuit optimization of qubit IC-POVMs for shadow estimation

Extracting information from quantum systems is crucial in quantum physics and information processing. Methods based on randomized measurements, like shadow estimation, show advantages in effectively achieving such tasks. However, randomized measurements require the application of random unitary evolution, which unavoidably necessitates frequent adjustments to the experimental setup or circuit parameters, posing challenges for practical implementations. To address these limitations, positive operator-valued measurements (POVMs) have been integrated to realize real-time single-setting shadow estimation. In this work, we advance the POVM-based shadow estimation by reducing the CNOT gate count for the implementation circuits of informationally complete POVMs (IC-POVMs), in particular, the symmetric IC-POVMs (SIC-POVMs), through the dimension dilation framework. We show that any single-qubit minimal IC-POVM can be implemented using at most 2 CNOT gates, while an SIC-POVM can be implemented with only 1 CNOT gate. In particular, we provide a concise form of the compilation circuit of any SIC-POVM along with an efficient algorithm for the determination of gate parameters. Moreover, we apply the optimized circuit compilation to shadow estimation, showcasing its noise-resilient performance and highlighting the flexibility in compiling various SIC-POVMs. Our work paves the way for the practical applications of qubit IC-POVMs on quantum platforms.

quant-ph↗

arXiv2024

Directly Estimating Mixed-State Entanglement with Bell Measurement Assistance

Entanglement plays a fundamental role in quantum physics and information processing. Here, we develop an unbiased estimator for mixed-state entanglement in the few-shot scenario and directly estimate it using random unitary evolution in a photonic system. As a supplement to traditional projective measurements, we incorporate Bell measurements on qubit-pairs, enriching the previous randomized measurement scheme, which is no-go in this task with only local unitary evolution. The scheme is scalable to n-qubits via Bell measurements on qubit-pairs. The estimator can be derived directly from a few consecutive outcomes while exhibiting greater robustness to system errors and noise compared to schemes based on shadow estimation. We find that, under a fixed measurement resource, the way with more versatile measurement settings with fewer repeats per setting is more efficient. Our protocol and demonstration advance the direct characterization of quantum states in practice.

quant-ph↗