arXiv subjects

Yanbin Wang

Publications and source records attributed to Yanbin Wang.

At least 19 recordsLinked to original sources

arXiv2026

Stitching Molecular Worlds Together with Physics-Coupled Diffusion Models

Complex chemical systems often contain multiple components or large molecules, giving rise to substantial chemical and conformational complexity. Modeling the structures of these systems is crucial for mechanistic understanding of chemical processes and rational design, but remains highly challenging for both conventional theoretical approaches and machine learning. Here, we introduce physical-interaction-coupled diffusion models (PICDiff), a framework that couples independently trained diffusion models for subsystems of a complex system through explicit bonded and nonbonded interactions during inference. PICDiff reduces the difficulty of applying generative models to chemically and conformationally complex systems by decomposing them into smaller subsystems that are less complex and more amenable to machine-learning-based modeling. Using peptide-polymer binding and polymer conformation sampling as examples, we demonstrate that PICDiff can quantitatively sample conformations and model the thermodynamics of complex chemical systems. These results show that PICDiff provides a general and practical approach for modeling complex chemical systems by combining learned models of simpler molecular subsystems through physical interactions.

physics.chem-ph↗

arXiv2026

DeltaDiff: Training-Free, Physics-Guided Machine Learning for Predicting Mutant Protein Structures

Determining mutant protein structures is critical for understanding the mechanistic roles of mutations in biochemical processes. However, experimental characterization and conventional theoretical modeling are often expensive and time-consuming. Recent advances in machine learning provide new opportunities to efficiently predict protein structures from primary sequences. Nevertheless, applying these models to proteins with single-site or few-site mutations remains challenging because mutant sequences are often highly similar to their wild-type counterparts. Here, we introduce DeltaDiff, a physics-guided inference framework for mutant-structure generation that incorporates mutation-aware physical guidance into a baseline diffusion model. We evaluate DeltaDiff on three representative systems: Chignolin T8P, Novispirin G-10, and BBL D162N. All three examples involve nonlocal structural changes, making accurate mutant-structure prediction challenging. DeltaDiff captures key mutation-induced conformational changes without requiring retraining or fine-tuning of the baseline model. These results establish a foundation for efficient mutant-structure prediction at a fraction of the cost of conventional methods, facilitating rational mutant design.

physics.chem-ph↗

arXiv2026

TeachPro: Multi-Label Qualitative Teaching Evaluation via Cross-View Graph Synergy and Semantic Anchored Evidence Encoding

Standardized Student Evaluation of Teaching often suffer from low reliability, restricted response options, and response distortion. Existing machine learning methods that mine open-ended comments usually reduce feedback to binary sentiment, which overlooks concrete concerns such as content clarity, feedback timeliness, and instructor demeanor, and provides limited guidance for instructional improvement.We propose TeachPro, a multi-label learning framework that systematically assesses five key teaching dimensions: professional expertise, instructional behavior, pedagogical efficacy, classroom experience, and other performance metrics. We first propose a Dimension-Anchored Evidence Encoder, which integrates three core components: (i) a pre-trained text encoder that transforms qualitative feedback annotations into contextualized embeddings; (ii) a prompt module that represents five teaching dimensions as learnable semantic anchors; and (iii) a cross-attention mechanism that aligns evidence with pedagogical dimensions within a structured semantic space. We then propose a Cross-View Graph Synergy Network to represent student comments. This network comprises two components: (i) a Syntactic Branch that extracts explicit grammatical dependencies from parse trees, and (ii) a Semantic Branch that models latent conceptual relations derived from BERT-based similarity graphs. BiAffine fusion module aligns syntactic and semantic units, while a differential regularizer disentangles embeddings to encourage complementary representations. Finally, a cross-attention mechanism bridges the dimension-anchored evidence with the multi-view comment representations. We also contribute a novel benchmark dataset featuring expert qualitative annotations and multi-label scores. Extensive experiments demonstrate that TeachPro offers superior diagnostic granularity and robustness across diverse evaluation settings.

cs.CL↗

arXiv2025

IP-Augmented Multi-Modal Malicious URL Detection Via Token-Contrastive Representation Enhancement and Multi-Granularity Fusion

Malicious URL detection remains a critical cybersecurity challenge as adversaries increasingly employ sophisticated evasion techniques including obfuscation, character-level perturbations, and adversarial attacks. Although pre-trained language models (PLMs) like BERT have shown potential for URL analysis tasks, three limitations persist in current implementations: (1) inability to effectively model the non-natural hierarchical structure of URLs, (2) insufficient sensitivity to character-level obfuscation, and (3) lack of mechanisms to incorporate auxiliary network-level signals such as IP addresses-all essential for robust detection. To address these challenges, we propose CURL-IP, an advanced multi-modal detection framework incorporating three key innovations: (1) Token-Contrastive Representation Enhancer, which enhances subword token representations through token-aware contrastive learning to produce more discriminative and isotropic embeddings; (2) Cross-Layer Multi-Scale Aggregator, employing hierarchical aggregation of Transformer outputs via convolutional operations and gated MLPs to capture both local and global semantic patterns across layers; and (3) Blockwise Multi-Modal Coupler that decomposes URL-IP features into localized block units and computes cross-modal attention weights at the block level, enabling fine-grained inter-modal interaction. This architecture enables simultaneous preservation of fine-grained lexical cues, contextual semantics, and integration of network-level signals. Our evaluation on large-scale real-world datasets shows the framework significantly outperforms state-of-the-art baselines across binary and multi-class classification tasks.

cs.CR↗

arXiv2025

ProTDyn: a foundation Protein language model for Thermodynamics and Dynamics generation

Molecular dynamics (MD) simulation has long been the principal computational tool for exploring protein conformational landscapes and dynamics, but its application is limited by high computational cost. We present ProTDyn, a foundation protein language model that unifies conformational ensemble generation and multi-timescale dynamics modeling within a single framework. Unlike prior approaches that treat these tasks separately, ProTDyn allows flexible independent and identically distributed (i.i.d.) ensemble sampling and dynamic trajectory simulation. Across diverse protein systems, ProTDyn yields thermodynamically consistent ensembles, faithfully reproduces dynamical properties over multiple timescales, and generalizes to proteins beyond its training data. It offers a scalable and efficient alternative to conventional MD simulations.

physics.bio-ph↗

arXiv2025

Bridging Source Code and Bytecode for Smart Contract Vulnerability Detection via Dual-Perspective Cross-Modal Distillation

Smart contract vulnerabilities have caused substantial financial losses, yet most deployed contracts are closed-source, forcing detection to operate on bytecode --- which lacks the high-level semantic information available in source code. To compensate, recent cross-modal methods distill knowledge from source-code models into bytecode detectors by aligning the two modalities. However, these methods align source and bytecode only at the graph level through global embedding matching, whereas vulnerability is a property of specific nodes and control-flow regions --- so the student preserves global structure but loses the fine-grained node-level correspondences that decide a contract's safety. We propose ExDoS, a dual-focus cross-modal distillation framework that addresses this limitation from three angles. First, We introduce aligned vulnerability patterns that mark corresponding nodes in both source and bytecode graphs, establishing the missing node-level supervision and cross-modal correspondence; with these correspondences in place, we then propose a dual-attention graph network that applies relation-aware attention and adaptive node weighting so that vulnerable nodes survive into the graph-level embedding rather than being diluted by uniform aggregation; given preserved node-level signals and established correspondences, we also propose a dual-focus distillation objective whose global loss retains whole-graph alignment while its local loss matches expert-paired nodes to enforce region-level consistency. On real-world contracts, ExDoS reaches F1 of 90.86%, 90.23%, and 83.94% for reentrancy, timestamp dependency, and infinite loop --- improving by 2.7--5.1 points over the strongest per-type baseline. Our ablations confirm that the pattern annotations, local alignment, and attentive encoding each address a distinct limitation of prior distillation-based approaches.

cs.CR↗

arXiv2025

URL2Graph++: Unified Semantic-Structural-Character Learning for Malicious URL Detection

Malicious URL detection remains a major challenge in cybersecurity, primarily due to two factors: (1) the exponential growth of the Internet has led to an immense diversity of URLs, making generalized detection increasingly difficult; and (2) attackers are increasingly employing sophisticated obfuscation techniques to evade detection. We advocate that addressing these challenges fundamentally requires: (1) obtaining semantic understanding to improve generalization across vast and diverse URL sets, and (2) accurately modeling contextual relationships within the structural composition of URLs. In this paper, we propose a novel malicious URL detection method combining multi-granularity graph learning with semantic embedding to jointly capture semantic, character-level, and structural features for robust URL analysis. To model internal dependencies within URLs, we first construct dual-granularity URL graphs at both subword and character levels, where nodes represent URL tokens/characters and edges encode co-occurrence relationships. To obtain fine-grained embeddings, we initialize node representations using a character-level convolutional network. The two graphs are then processed through jointly trained Graph Convolutional Networks to learn consistent graph-level representations, enabling the model to capture complementary structural features that reflect co-occurrence patterns and character-level dependencies. Furthermore, we employ BERT to derive semantic representations of URLs for semantically aware understanding. Finally, we introduce a gated dynamic fusion network to combine the semantically enriched BERT representations with the jointly optimized graph vectors, further enhancing detection performance. We extensively evaluate our method across multiple challenging dimensions. Results show our method exceeds SOTA performance, including against large language models.

cs.CR↗

arXiv2025

KGBERT4Eth: A Feature-Complete Transformer Powered by Knowledge Graph for Multi-Task Ethereum Fraud Detection

Ethereum's rapid ecosystem expansion and transaction anonymity have triggered a surge in malicious activity. Detection mechanisms currently bifurcate into three technical strands: expert-defined features, graph embeddings, and sequential transaction patterns, collectively spanning the complete feature sets of Ethereum's native data layer. Yet the absence of cross-paradigm integration mechanisms forces practitioners to choose between sacrificing sequential context awareness, structured fund-flow patterns, or human-curated feature insights in their solutions. To bridge this gap, we propose KGBERT4Eth, a feature-complete pre-training encoder that synergistically combines two key components: (1) a Transaction Semantic Extractor, where we train an enhanced Transaction Language Model (TLM) to learn contextual semantic representations from conceptualized transaction records, and (2) a Transaction Knowledge Graph (TKG) that incorporates expert-curated domain knowledge into graph node embeddings to capture fund flow patterns and human-curated feature insights. We jointly optimize pre-training objectives for both components to fuse these complementary features, generating feature-complete embeddings. To emphasize rare anomalous transactions, we design a biased masking prediction task for TLM to focus on statistical outliers, while the Transaction TKG employs link prediction to learn latent transaction relationships and aggregate knowledge. Furthermore, we propose a mask-invariant attention coordination module to ensure stable dynamic information exchange between TLM and TKG during pre-training. KGBERT4Eth significantly outperforms state-of-the-art baselines in both phishing account detection and de-anonymization tasks, achieving absolute F1-score improvements of 8-16% on three phishing detection benchmarks and 6-26% on four de-anonymization datasets.

cs.CR↗

arXiv2025

LMAE4Eth: Generalizable and Robust Ethereum Fraud Detection by Exploring Transaction Semantics and Masked Graph Embedding

Current Ethereum fraud detection methods rely on context-independent, numerical transaction sequences, failing to capture semantic of account transactions. Furthermore, the pervasive homogeneity in Ethereum transaction records renders it challenging to learn discriminative account embeddings. Moreover, current self-supervised graph learning methods primarily learn node representations through graph reconstruction, resulting in suboptimal performance for node-level tasks like fraud account detection, while these methods also encounter scalability challenges. To tackle these challenges, we propose LMAE4Eth, a multi-view learning framework that fuses transaction semantics, masked graph embedding, and expert knowledge. We first propose a transaction-token contrastive language model (TxCLM) that transforms context-independent numerical transaction records into logically cohesive linguistic representations. To clearly characterize the semantic differences between accounts, we also use a token-aware contrastive learning pre-training objective together with the masked transaction model pre-training objective, learns high-expressive account representations. We then propose a masked account graph autoencoder (MAGAE) using generative self-supervised learning, which achieves superior node-level account detection by focusing on reconstructing account node features. To enable MAGAE to scale for large-scale training, we propose to integrate layer-neighbor sampling into the graph, which reduces the number of sampled vertices by several times without compromising training quality. Finally, using a cross-attention fusion network, we unify the embeddings of TxCLM and MAGAE to leverage the benefits of both. We evaluate our method against 21 baseline approaches on three datasets. Experimental results show that our method outperforms the best baseline by over 10% in F1-score on two of the datasets.

cs.CR↗

arXiv2025

MEMS Vapor Cells-based Rydberg-atom Electrometry Toward Miniaturization and High Sensitivity

Rydberg-atom electrometry, as an emerging cutting-edge technology, features high sensitivity, broad bandwidth, calibration-free operation, and beyond. However, until now the key atomic vapor cells used for confining electric field-sensitive Rydberg atoms nearly made with traditional glass-blown techniques, hindering the miniaturization, integration, and batch manufacturing. Here, we present the wafer-level MEMS atomic vapor cells with glass-silicon-glass sandwiched structure that are batch-manufactured for both frequency stability and electric field measurement. We use specially customized ultra-thick silicon wafers with a resistivity exceeding 10,000 cm, three orders of magnitude higher than that of typical silicon, and a thickness of 6 mm, providing a 4-fold improvement in optical interrogation length. With the as-developed MEMS atomic vapor cell, we configured a high-sensitivity Rydberg-atom electrometry with the minimal detectable microwave field to be 2.8 mV/cm. This combination of miniaturization and sensitivity represents a significant advance in the state-of-the-art field of Rydberg-atom electrometry, paving the way for chip-scale Rydberg-atom electrometry and potentially opening up new applications in a wider variety of fields.

physics.atom-ph↗

arXiv2025

Constructing Generalized Sample Transition Probabilities with Biased Simulations

In molecular dynamics (MD) simulations, accessing transition probabilities between states is crucial for understanding kinetic information, such as reaction paths and rates. However, standard MD simulations are hindered by the capacity to visit the states of interest, prompting the use of enhanced sampling to accelerate the process. Unfortunately, biased simulations alter the inherent probability distributions, making kinetic computations using techniques such as diffusion maps challenging. Here, we use a coarse-grained Markov chain to estimate the intrinsic pairwise transition probabilities between states sampled from a biased distribution. Our method, which we call the generalized sample transition probability (GSTP), can recover transition probabilities without relying on an underlying stochastic process and specifying the form of the kernel function, which is necessary for the diffusion map method. The proposed algorithm is validated on model systems such as a harmonic oscillator, alanine dipeptide in vacuum, and met-enkephalin in solvent. The results demonstrate that GSTP effectively recovers the unbiased eigenvalues and eigenstates from biased data. GSTP provides a general framework for analyzing kinetic information in complex systems, where biased simulations are necessary to access longer timescales.

physics.chem-ph↗

arXiv2025

Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection

With the rapid expansion of web-based applications and cloud services, malicious JavaScript code continues to pose significant threats to user privacy, system integrity, and enterprise security. But, detecting such threats remains challenging due to sophisticated code obfuscation techniques and JavaScript's inherent language characteristics, particularly its nested closure structures and syntactic flexibility. In this work, we propose DeCoda, a hybrid defense framework that combines large language model (LLM)-based deobfuscation with code graph learning: (1) We first construct a sophisticated prompt-learning pipeline with multi-stage refinement, where the LLM progressively reconstructs the original code structure from obfuscated inputs and then generates normalized Abstract Syntax Tree (AST) representations; (2) In JavaScript ASTs, dynamic typing scatters semantically similar nodes while deeply nested functions fracture scope capturing, introducing structural noise and semantic ambiguity. To address these challenges, we then propose to learn hierarchical code graph representations via a Cluster-wise Graph that synergistically integrates graph transformer network, node clustering, and node-to-cluster attention to simultaneously capture both local node-level semantics and global cluster-induced structural relationships from AST graph. Experimental results demonstrate that our method achieves F1-scores of 94.64% and 97.71% on two benchmark datasets, demonstrating absolute improvements of 10.74% and 13.85% over state-of-the-art baselines. In false-positive control evaluation at fixed FPR levels (0.0001, 0.001, 0.01), our approach delivers 4.82, 5.91, and 2.53 higher TPR respectively compared to the best-performing baseline. These results highlight the effectiveness of LLM-based deobfuscation and underscore the importance of modeling cluster-level relationships in detecting malicious code.

cs.CR↗

arXiv2025

COMS-Integrated Atomic Vapor Cells with Ultra-long Optical Access for Highly Sensitive and Scalable Quantum Sensors

The most appealing features of chip-scale quantum sensors are their capability to maintain extreme sensitivity while enabling large-scale batch manufacturing. This necessitates high-level integration and wafer-level fabrication of atomic vapor cells. In this paper, we describe a micromachining paradigm for wafer-level atomic vapor cells functionalized by CMOS-compatible non-magnetic heaters and temperature sensors and demonstrate several innovative applications. Leveraging standard micro-nanofabrication technology, the integrated vapor cells achieved an ultra-long optical access of 5 mm, nearly four time that of previously microfabricated vapor cells. The feasibility of the integrated atomic vapor cells fabrication process was verified by a consecutive 30-day aging test in a harsh environment (operating temperature of 473 K and vacuum of approximately 1 Pa). Benefiting from the ultra-long optical path, we observed several typical quantum effects, including the saturation absorption and spin fluctuations, a regime previously inaccessible with conventional micromachined vapor cells. Finally, a zero-field quantum magnetometry with an ultra-high magnetic sensitivity of 12 fT/Hz1/2 was also demonstrated. Our achievements broaden the potential applications of microfabricated atomic vapor cells and pave the way for scalable manufacturing of ultrasensitive, chip-scale quantum sensors.

quant-ph↗

arXiv2025

DiT-SGCR: Directed Temporal Structural Representation with Global-Cluster Awareness for Ethereum Malicious Account Detection

The detection of malicious accounts on Ethereum - the preeminent DeFi platform - is critical for protecting digital assets and maintaining trust in decentralized finance. Recent advances highlight that temporal transaction evolution reveals more attack signatures than static graphs. However, current methods either fail to model continuous transaction dynamics or incur high computational costs that limit scalability to large-scale transaction networks. Furthermore, current methods fail to consider two higher-order behavioral fingerprints: (1) direction in temporal transaction flows, which encodes money movement trajectories, and (2) account clustering, which reveals coordinated behavior of organized malicious collectives. To address these challenges, we propose DiT-SGCR, an unsupervised graph encoder for malicious account detection. Specifically, DiT-SGCR employs directional temporal aggregation to capture dynamic account interactions, then coupled with differentiable clustering and graph Laplacian regularization to generate high-quality, low-dimensional embeddings. Our approach simultaneously encodes directional temporal dynamics, global topology, and cluster-specific behavioral patterns, thereby enhancing the discriminability and robustness of account representations. Furthermore, DiT-SGCR bypasses conventional graph propagation mechanisms, yielding significant scalability advantages. Extensive experiments on three datasets demonstrate that DiT-SGCR consistently outperforms state-of-the-art methods across all benchmarks, achieving F1-score improvements ranging from 3.62% to 10.83%.

cs.CE↗

arXiv2025

WebGuard++:Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT

URL+HTML feature fusion shows promise for robust malicious URL detection, since attacker artifacts persist in DOM structures. However, prior work suffers from four critical shortcomings: (1) incomplete URL modeling, failing to jointly capture lexical patterns and semantic context; (2) HTML graph sparsity, where threat-indicative nodes (e.g., obfuscated scripts) are isolated amid benign content, causing signal dilution during graph aggregation; (3) unidirectional analysis, ignoring URL-HTML feature bidirectional interaction; and (4) opaque decisions, lacking attribution to malicious DOM components. To address these challenges, we present WebGuard++, a detection framework with 4 novel components: 1) Cross-scale URL Encoder: Hierarchically learns local-to-global and coarse to fine URL features based on Transformer network with dynamic convolution. 2) Subgraph-aware HTML Encoder: Decomposes DOM graphs into interpretable substructures, amplifying sparse threat signals via Hierarchical feature fusion. 3) Bidirectional Coupling Module: Aligns URL and HTML embeddings through cross-modal contrastive learning, optimizing inter-modal consistency and intra-modal specificity. 4) Voting Module: Localizes malicious regions through consensus voting on malicious subgraph predictions. Experiments show WebGuard++ achieves significant improvements over state-of-the-art baselines, achieving 1.1x-7.9x higher TPR at fixed FPR of 0.001 and 0.0001 across both datasets.

cs.CR↗

arXiv2025

LCB-CV-UNet: Enhanced Detector for High Dynamic Range Radar Signals

We propose the LCB-CV-UNet to tackle performance degradation caused by High Dynamic Range (HDR) radar signals. Initially, a hardware-efficient, plug-and-play module named Logarithmic Connect Block (LCB) is proposed as a phase coherence preserving solution to address the inherent challenges in handling HDR features. Then, we propose the Dual Hybrid Dataset Construction method to generate a semi-synthetic dataset, approximating typical HDR signal scenarios with adjustable target distributions. Simulation results show about 1% total detection probability improvement with under 0.9% computational complexity added compared with the baseline. Furthermore, it excels 5% over the baseline at the range in 11-13 dB signal-to-noise ratio typical for urban targets. Finally, the real experiment validates the practicality of our model.

eess.SP↗

arXiv2025

PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image Generative Models

Generating high-quality, multi-layer transparent images from text prompts can unlock a new level of creative control, allowing users to edit each layer as effortlessly as editing text outputs from LLMs. However, the development of multi-layer generative models lags behind that of conventional text-to-image models due to the absence of a large, high-quality corpus of multi-layer transparent data. In this paper, we address this fundamental challenge by: (i) releasing the first open, ultra-high-fidelity PrismLayers (PrismLayersPro) dataset of 200K (20K) multilayer transparent images with accurate alpha mattes, (ii) introducing a trainingfree synthesis pipeline that generates such data on demand using off-the-shelf diffusion models, and (iii) delivering a strong, open-source multi-layer generation model, ART+, which matches the aesthetics of modern text-to-image generation models. The key technical contributions include: LayerFLUX, which excels at generating high-quality single transparent layers with accurate alpha mattes, and MultiLayerFLUX, which composes multiple LayerFLUX outputs into complete images, guided by human-annotated semantic layout. To ensure higher quality, we apply a rigorous filtering stage to remove artifacts and semantic mismatches, followed by human selection. Fine-tuning the state-of-the-art ART model on our synthetic PrismLayersPro yields ART+, which outperforms the original ART in 60% of head-to-head user study comparisons and even matches the visual quality of images generated by the FLUX.1-[dev] model. We anticipate that our work will establish a solid dataset foundation for the multi-layer transparent image generation task, enabling research and applications that require precise, editable, and visually compelling layered imagery.

cs.CV↗

arXiv2025

Transformers in Protein: A Survey

As protein informatics advances rapidly, the demand for enhanced predictive accuracy, structural analysis, and functional understanding has intensified. Transformer models, as powerful deep learning architectures, have demonstrated unprecedented potential in addressing diverse challenges across protein research. However, a comprehensive review of Transformer applications in this field remains lacking. This paper bridges this gap by surveying over 100 studies, offering an in-depth analysis of practical implementations and research progress of Transformers in protein-related tasks. Our review systematically covers critical domains, including protein structure prediction, function prediction, protein-protein interaction analysis, functional annotation, and drug discovery/target identification. To contextualize these advancements across various protein domains, we adopt a domain-oriented classification system. We first introduce foundational concepts: the Transformer architecture and attention mechanisms, categorize Transformer variants tailored for protein science, and summarize essential protein knowledge. For each research domain, we outline its objectives and background, critically evaluate prior methods and their limitations, and highlight transformative contributions enabled by Transformer models. We also curate and summarize pivotal datasets and open-source code resources to facilitate reproducibility and benchmarking. Finally, we discuss persistent challenges in applying Transformers to protein informatics and propose future research directions. This review aims to provide a consolidated foundation for the synergistic integration of Transformer and protein informatics, fostering further innovation and expanded applications in the field.

cs.LG↗