arXiv subjects

Jie Li

Publications and source records attributed to Jie Li.

At least 217 records · Page 12Linked to original sources

arXiv2025

Bridging the Gap between Continuous and Informative Discrete Representations by Random Product Quantization

Self-supervised learning (SSL) has become a core technique in speech processing, but the high dimensionality of its representations makes discretization essential for improving efficiency. However, existing discretization methods still suffer from significant information loss, resulting in a notable performance gap compared to continuous representations. To overcome these limitations, we propose two quantization-based discretization methods: Product Quantization (PQ) and Random Product Quantization (RPQ). PQ partitions the original feature space into multiple subspaces and independently quantizes each sub-vector, producing a fused set of discrete units that retain diverse information from different subspaces, thereby mitigating the loss associated with single-cluster quantization. RPQ further enhances representation diversity by randomly sampling a fixed proportion of feature dimensions multiple times to construct sub-vectors, thereby better capturing the variability in the data distribution. Theoretical analysis shows that RPQ reduces the correlation coefficient rho (where 0 <= rho <= 1) between sub-quantizers. Its quantization error is lower-bounded by the product of rho and epsilon-kms, where epsilon-kms denotes the quantization error of a single K-means quantizer. Experimental results on a combined dataset built from LibriSpeech and ML-SUPERB show that PQ and RPQ outperform standard K-means discretization, achieving relative improvements of 21.8 percent and 20.0 percent in WER on LibriSpeech, and 24.1 percent and 19.6 percent in CER on ML-SUPERB, respectively. Moreover, their performance is competitive with, and in some cases even surpasses, that of continuous SSL representations.

eess.AS↗

arXiv2025

BoRe-Depth: Self-supervised Monocular Depth Estimation with Boundary Refinement for Embedded Systems

Depth estimation is one of the key technologies for realizing 3D perception in unmanned systems. Monocular depth estimation has been widely researched because of its low-cost advantage, but the existing methods face the challenges of poor depth estimation performance and blurred object boundaries on embedded systems. In this paper, we propose a novel monocular depth estimation model, BoRe-Depth, which contains only 8.7M parameters. It can accurately estimate depth maps on embedded systems and significantly improves boundary quality. Firstly, we design an Enhanced Feature Adaptive Fusion Module (EFAF) which adaptively fuses depth features to enhance boundary detail representation. Secondly, we integrate semantic knowledge into the encoder to improve the object recognition and boundary perception capabilities. Finally, BoRe-Depth is deployed on NVIDIA Jetson Orin, and runs efficiently at 50.7 FPS. We demonstrate that the proposed model significantly outperforms previous lightweight models on multiple challenging datasets, and we provide detailed ablation studies for the proposed methods. The code is available at https://github.com/liangxiansheng093/BoRe-Depth.

cs.CV↗

arXiv2025

CueBench: Advancing Unified Understanding of Context-Aware Video Anomalies in Real-World

How far are deep models from real-world video anomaly understanding (VAU)? Current works typically emphasize on detecting unexpected occurrences deviated from normal patterns or comprehending anomalous events with interpretable descriptions. However, they exhibit only a superficial comprehension of real-world anomalies, with limited breadth in complex principles and subtle context that distinguish the anomalies from normalities, e.g., climbing cliffs with safety gear vs. without it. To this end, we introduce CueBench, the first of its kind Benchmark, devoted to Context-aware video anomalies within a Unified Evaluation framework. We comprehensively establish an event-centric hierarchical taxonomy that anchors two core event types: 14 conditional and 18 absolute anomaly events, defined by their refined semantics from diverse contexts across 174 scenes and 198 attributes. Based on this, we propose to unify and benchmark context-aware VAU with various challenging tasks across recognition, temporal grounding, detection, and anticipation. This also serves as a rigorous and fair probing evaluation suite for generative-discriminative as well as generalized-specialized vision-language models (VLMs). To address the challenges underlying CueBench, we further develop Cue-R1 based on R1-style reinforcement fine-tuning with verifiable, task-aligned, and hierarchy-refined rewards in a unified generative manner. Extensive results on CueBench reveal that, existing VLMs are still far from satisfactory real-world anomaly understanding, while our Cue-R1 surpasses these state-of-the-art approaches by over 24% on average.

cs.CV↗

arXiv2025

Efficient Repair of (k+2, k) Degraded Read Friendly MDS Array Codes With Sub-packetization 2

In this paper, we present two constructions of degraded read friendly (DRF) MDS array codes with two parity nodes and a sub-packetization level of 2 over small finite fields, applicable for any arbitrary code length. The first construction achieves the smallest repair bandwidth among all existing constructions with the same parameters, and is asymptotically optimal with respect to the lower bound on the average repair bandwidth characterized by Zhang et al. The second construction supports two repair mechanisms, depending on whether computation within the helper nodes is permitted or not during the node repair process, thereby optimizing either the repair bandwidth or the rebuilding access.

cs.IT↗

arXiv2025

A Vision for Access Control in LLM-based Agent Systems

The autonomy and contextual complexity of LLM-based agents render traditional access control (AC) mechanisms insufficient. Static, rule-based systems designed for predictable environments are fundamentally ill-equipped to manage the dynamic information flows inherent in agentic interactions. This position paper argues for a paradigm shift from binary access control to a more sophisticated model of information governance, positing that the core challenge is not merely about permission, but about governing the flow of information. We introduce Agent Access Control (AAC), a novel framework that reframes AC as a dynamic, context-aware process of information flow governance. AAC operates on two core modules: (1) multi-dimensional contextual evaluation, which assesses not just identity but also relationships, scenarios, and norms; and (2) adaptive response formulation, which moves beyond simple allow/deny decisions to shape information through redaction, summarization, and paraphrasing. This vision, powered by a dedicated AC reasoning engine, aims to bridge the gap between human-like nuanced judgment and scalable Al safety, proposing a new conceptual lens for future research in trustworthy agent design.

cs.MA↗

arXiv2025

DPTrack:Directional Kernel-Guided Prompt Learning for Robust Nighttime Aerial Tracking

Existing nighttime aerial trackers based on prompt learning rely solely on spatial localization supervision, which fails to provide fine-grained cues that point to target features and inevitably produces vague prompts. This limitation impairs the tracker's ability to accurately focus on the object features and results in trackers still performing poorly. To address this issue, we propose DPTrack, a prompt-based aerial tracker designed for nighttime scenarios by encoding the given object's attribute features into the directional kernel enriched with fine-grained cues to generate precise prompts. Specifically, drawing inspiration from visual bionics, DPTrack first hierarchically captures the object's topological structure, leveraging topological attributes to enrich the feature representation. Subsequently, an encoder condenses these topology-aware features into the directional kernel, which serves as the core guidance signal that explicitly encapsulates the object's fine-grained attribute cues. Finally, a kernel-guided prompt module built on channel-category correspondence attributes propagates the kernel across the features of the search region to pinpoint the positions of target features and convert them into precise prompts, integrating spatial gating for robust nighttime tracking. Extensive evaluations on established benchmarks demonstrate DPTrack's superior performance. Our code will be available at https://github.com/zzq-vipsl/DPTrack.

cs.CV↗

arXiv2025

Less is More: Denoising Knowledge Graphs For Retrieval Augmented Generation

Retrieval-Augmented Generation (RAG) systems enable large language models (LLMs) instant access to relevant information for the generative process, demonstrating their superior performance in addressing common LLM challenges such as hallucination, factual inaccuracy, and the knowledge cutoff. Graph-based RAG further extends this paradigm by incorporating knowledge graphs (KGs) to leverage rich, structured connections for more precise and inferential responses. A critical challenge, however, is that most Graph-based RAG systems rely on LLMs for automated KG construction, often yielding noisy KGs with redundant entities and unreliable relationships. This noise degrades retrieval and generation performance while also increasing computational cost. Crucially, current research does not comprehensively address the denoising problem for LLM-generated KGs. In this paper, we introduce DEnoised knowledge Graphs for Retrieval Augmented Generation (DEG-RAG), a framework that addresses these challenges through: (1) entity resolution, which eliminates redundant entities, and (2) triple reflection, which removes erroneous relations. Together, these techniques yield more compact, higher-quality KGs that significantly outperform their unprocessed counterparts. Beyond the methods, we conduct a systematic evaluation of entity resolution for LLM-generated KGs, examining different blocking strategies, embedding choices, similarity metrics, and entity merging techniques. To the best of our knowledge, this is the first comprehensive exploration of entity resolution in LLM-generated KGs. Our experiments demonstrate that this straightforward approach not only drastically reduces graph size but also consistently improves question answering performance across diverse popular Graph-based RAG variants.

cs.CL↗

arXiv2025

Cognitive-Aligned Spatio-Temporal Large Language Models For Next Point-of-Interest Prediction

The next point-of-interest (POI) recommendation task aims to predict the users' immediate next destinations based on their preferences and historical check-ins, holding significant value in location-based services. Recently, large language models (LLMs) have shown great potential in recommender systems, which treat the next POI prediction in a generative manner. However, these LLMs, pretrained primarily on vast corpora of unstructured text, lack the native understanding of structured geographical entities and sequential mobility patterns required for next POI prediction tasks. Moreover, in industrial-scale POI prediction applications, incorporating world knowledge and alignment of human cognition, such as seasons, weather conditions, holidays, and users' profiles (such as habits, occupation, and preferences), can enhance the user experience while improving recommendation performance. To address these issues, we propose CoAST (Cognitive-Aligned Spatial-Temporal LLMs), a framework employing natural language as an interface, allowing for the incorporation of world knowledge, spatio-temporal trajectory patterns, profiles, and situational information. Specifically, CoAST mainly comprises of 2 stages: (1) Recommendation Knowledge Acquisition through continued pretraining on the enriched spatial-temporal trajectory data of the desensitized users; (2) Cognitive Alignment to align cognitive judgments with human preferences using enriched training data through Supervised Fine-Tuning (SFT) and a subsequent Reinforcement Learning (RL) phase. Extensive offline experiments on various real-world datasets and online experiments deployed in "Guess Where You Go" of AMAP App homepage demonstrate the effectiveness of CoAST.

cs.AI↗

arXiv2025

Construction of Kondo Chains by Engineering Porphyrin π-Radicals on Au(111)

Quantum manipulation of molecular radical spins provides a crucial platform for exploring emergent phenomena in many-body systems. Here, we combine surface-confined synthesis with scanning tunneling microscopy(STM)tip-induced dehydrogenation to achieve atom-precise engineering of quasi-one-dimensional porphyrin-based Kondo chains (1-7 units) on Au(111). High-resolution STS measurements and low-energy effective modeling collectively demonstrate that π-radicals at each fused-porphyrin unit form Kondo singlets screened by conduction electrons. Adjacent singlets develop direct coherent coupling via quantum-state-overlap-enabled electron tunneling. Crucially, chiral symmetry in the effective model governs zero-mode distribution-present in odd-length chains yet absent in even-length chains-which dictates pronounced odd-even quantum effects in STS spectra of finite chains. Furthermore, the number of parallel porphyrin chains non-monotonically tunes the competition between the Kondo effect and spin exchange, showing opposing trends in strength and demonstrating that both wave-function overlap and the SOMO-LUMO gap collectively govern these interactions. This work simultaneously resolves the dimensional dependence of many-body correlations in confined quantum systems and pioneers approaches for quantum-critical manipulation in molecular spin architectures.

cond-mat.mes-hall↗

arXiv2025

Improving Hierarchical Representations of Vectorized HD Maps with Perspective Clues

The construction of vectorized High-Definition (HD) maps from onboard surround-view cameras has become a significant focus in autonomous driving. However, current map vector estimation pipelines face two key limitations: input-agnostic queries struggle to capture complex map structures, and the view transformation leads to information loss. These issues often result in inaccurate shape restoration or missing instances in map predictions. To address this concern, we propose a novel approach, namely \textbf{PerCMap}, which explicitly exploits clues from perspective-view features at both instance and point level. Specifically, at instance level, we propose Cross-view Instance Activation (CIA) to activate instance queries across surround-view images, thereby helping the model recover the instance attributes of map vectors. At point level, we design Dual-view Point Embedding (DPE), which fuses features from both views to generate input-aware positional embeddings and improve the accuracy of point coordinate estimation. Extensive experiments on \textit{nuScenes} and \textit{Argoverse 2} demonstrate that PerCMap achieves strong and consistent performance across benchmarks, reaching 67.1 and 70.5 mAP, respectively.

cs.CV↗

arXiv2025

Surface band-selective moiré effect induces flat band in mixed-dimensional heterostructures

In this work, we reveal a curious type of moiré effect that selectively modifies the surface states of bulk crystal. We synthesize mixed-dimensional heterostructures consisting of a noble gas monolayer grow on the surface of bulk Bi(111), and determine the electronic structure of the heterostructures using angle-resolved photoemission spectroscopy. We directly observe moiré replicas of the Bi(111) surface states, while the bulk states remain barely changed. Meanwhile, we achieve control over the moiré period in the range of 25 Å to 80 Å by selecting monolayers of different noble gases and adjusting the annealing temperature. At large moiré periods, we observe hybridization between the surface band replicas, which leads to the formation of a correlated flat band. Our results serve as a bridge for understanding the moiré modulation effect from 2D to 3D systems, and provide a feasible approach for the realization of correlated phenomena through the engineering of surface states via moiré effects.

cond-mat.str-el↗

arXiv2025

Three-dimensional optical characterization of magnetostrictive deformation in magnomechanical systems

Magnomechanical systems with YIG spheres have been proven to be an ideal system for studying magnomechanically induced transparency, dynamical backaction, and rich nonlinear effects, such as the magnon-phonon cross-Kerr effect. Accurate characterization of the magnetostriction induced deformation displacement is important as it can be used for, e.g., estimating the magnon excitation number and the strength of the dynamical backaction. Here we propose an optical approach for detecting the magnetostrictive deformation of a YIG sphere in three dimensions (3Ds) with high precision. It is based on the deformation induced spatial high-order modes of the scattered field, postselection, and balanced homodyne detection. With feasible parameters, we show that the measurement precision of the deformation in $x$, $y$, and $z$ directions can reach the picometer level. We further reveal the advantages of our scheme using a higher-order probe beam and balanced homodyne detection by means of quantum and classical Fisher information. The real-time and high-precision measurement of the YIG sphere's deformation in 3Ds can be used to determinate specific mechanical modes, characterize the magnomechanical dynamical backaction and the 3D cooling of the mechanical vibration, and thus finds a wide range of applications in magnomechanics.

quant-ph↗

arXiv2025

Optomagnonic continuous-variable quantum teleportation enhanced by non-Gaussian distillation

The capability of magnons to coherently couple with various quantum systems makes them an ideal candidate to build hybrid quantum systems. The optomagnonic coupling is essential for constructing a hybrid magnonic quantum network, as the transmission of quantum information among remote quantum nodes must be accomplished using light rather than microwave field. Here we provide an optomagnonic continuous-variable quantum teleportation protocol, which enables the transfer of an input optical state to a remote magnon mode. To overcome the currently relatively weak coupling in the experiment, we introduce non-Gaussian distillation operations to enhance the optomagnonic entanglement and thus the fidelity of the teleportation. An auxiliary microwave cavity is adopted to realize the non-Gaussian and displacement operations on magnons. We show that a series of optical states, such as coherent, single-photon, squeezed and cat states, can be teleported to the magnon mode. The work provides guidance for the experimental realization of magnonic quantum repeaters and quantum networks and a new route to prepare diverse magnonic quantum states exploiting the photon-to-magnon quantum teleportation.

quant-ph↗

arXiv2025

Uncertainty-Guided Model Selection for Tabular Foundation Models in Biomolecule Efficacy Prediction

In-context learners like TabPFN are promising for biomolecule efficacy prediction, where established molecular feature sets and relevant experimental results can serve as powerful contextual examples. However, their performance is highly sensitive to the provided context, making strategies like post-hoc ensembling of models trained on different data subsets a viable approach. An open question is how to select the best models for the ensemble without access to ground truth labels. In this study, we investigate an uncertainty-guided strategy for model selection. We demonstrate on an siRNA knockdown efficacy task that a TabPFN model using straightforward sequence-based features can surpass specialized state-of-the-art predictors. We also show that the model's predicted inter-quantile range (IQR), a measure of its uncertainty, has a negative correlation with true prediction error. We developed the OligoICP method, which selects and averages an ensemble of models with the lowest mean IQR for siRNA efficacy prediction, achieving superior performance compared to naive ensembling or using a single model trained on all available data. This finding highlights model uncertainty as a powerful, label-free heuristic for optimizing biomolecule efficacy predictions.

cs.LG↗

arXiv2025

Disentangling Multiplex Spatial-Temporal Transition Graph Representation Learning for Socially Enhanced POI Recommendation

Next Point-of-Interest (POI) recommendation is a research hotspot in business intelligence, where users' spatial-temporal transitions and social relationships play key roles. However, most existing works model spatial and temporal transitions separately, leading to misaligned representations of the same spatial-temporal key nodes. This misalignment introduces redundant information during fusion, increasing model uncertainty and reducing interpretability. To address this issue, we propose DiMuST, a socially enhanced POI recommendation model based on disentangled representation learning over multiplex spatial-temporal transition graphs. The model employs a novel Disentangled variational multiplex graph Auto-Encoder (DAE), which first disentangles shared and private distributions using a multiplex spatial-temporal graph strategy. It then fuses the shared features via a Product of Experts (PoE) mechanism and denoises the private features through contrastive constraints. The model effectively captures the spatial-temporal transition representations of POIs while preserving the intrinsic correlation of their spatial-temporal relationships. Experiments on two challenging datasets demonstrate that our DiMuST significantly outperforms existing methods across multiple metrics.

cs.AI↗

arXiv2025

Mesoscopic Helices of Polar Domains in a Quadruple Perovskite

A significant effort in condensed matter physics is dedicated to the search for exotic arrangements of electric dipoles in crystals. Non-collinear dipolar arrangements mimicking magnetic spin textures, such as polar vortices and skyrmions, have been realized, but others, like helices of dipoles, have remained elusive. While earlier work claimed the presence of a helical dipole modulation in ferroelectric BiCu$_{x}$Mn$_{7-x}$O$_{12}$, our results rule out such a structure, motivating a renewed search for helical textures. Using atomic-resolution imaging, we report a novel form of helical order in which polar domains self-organize into a mesoscopic helical pattern: from domain to domain, polarization rotation follows a consistent handedness. Both right- and left-handed chirality emerging from this helical ordering are observed. This discovery establishes mesoscopic ordering as novel mechanism for inducing helical textures, and hence chirality, in ferroelectric crystals, paralleling the efforts in liquid crystals and supramolecular assemblies.

cond-mat.mes-hall↗

arXiv2025

StPR: Spatiotemporal Preservation and Routing for Exemplar-Free Video Class-Incremental Learning

Video Class-Incremental Learning (VCIL) seeks to develop models that continuously learn new action categories over time without forgetting previously acquired knowledge. Unlike traditional Class-Incremental Learning (CIL), VCIL introduces the added complexity of spatiotemporal structures, making it particularly challenging to mitigate catastrophic forgetting while effectively capturing both frame-shared semantics and temporal dynamics. Existing approaches either rely on exemplar rehearsal, raising concerns over memory and privacy, or adapt static image-based methods that neglect temporal modeling. To address these limitations, we propose Spatiotemporal Preservation and Routing (StPR), a unified and exemplar-free VCIL framework that explicitly disentangles and preserves spatiotemporal information. First, we introduce Frame-Shared Semantics Distillation (FSSD), which identifies semantically stable and meaningful channels by jointly considering semantic sensitivity and classification contribution. These important semantic channels are selectively regularized to maintain prior knowledge while allowing for adaptation. Second, we design a Temporal Decomposition-based Mixture-of-Experts (TD-MoE), which dynamically routes task-specific experts based on their temporal dynamics, enabling inference without task ID or stored exemplars. Together, StPR effectively leverages spatial semantics and temporal dynamics, achieving a unified, exemplar-free VCIL framework. Extensive experiments on UCF101, HMDB51, and Kinetics400 show that our method outperforms existing baselines while offering improved interpretability and efficiency in VCIL. Code is available in the supplementary materials.

cs.CV↗

arXiv2025

Machine Learning Powered Feasible Path Framework with Adaptive Sampling for Black-box Optimization

Black-box optimization (BBO) involves functions that are unknown, inexact and/or expensive-to-evaluate. Existing BBO algorithms face several challenges, including high computational cost from extensive evaluations, difficulty in handling complex constraints, lacking theoretical convergence guarantees and/or instability due to large solution quality variation. In this work, a machine learning-powered feasible path optimization framework (MLFP) is proposed for general BBO problems including complex constraints. An adaptive sampling strategy is first proposed to explore optimal regions and pre-filter potentially infeasible points to reduce evaluations. Machine learning algorithms are leveraged to develop surrogates of black-boxes. The feasible path algorithm is employed to accelerate theoretical convergence by updating independent variables rather than all. Computational studies demonstrate MLFP can rapidly and robustly converge around the KKT point, even training surrogates with small datasets. MLFP is superior to the state-of-the-art BBO algorithms, as it stably obtains the same or better solutions with fewer evaluations for benchmark examples.

math.OC↗