arXiv subjects

Jian Wang

Publications and source records attributed to Jian Wang.

At least 199 records · Page 11Linked to original sources

arXiv2026

A localized coupling approach to interacting continuous-state branching processes

We introduce a class of continuous-state branching processes with immigration, predation and competition, which can be viewed as a combination of the classical Lotka-Volterra model and continuous-state branching processes with competition that were introduced by Berestycki, Fittipaldi, and Fontbona (Probab. Theory Relat. Fields, 2018). This model can be constructed as a unique strong solution to a class of two-dimensional stochastic differential equations with jumps. We establish sharp conditions for the uniform ergodicity in the total variation of this model. Our proof relies on a novel, localized Markovian coupling approach, which is of its own interest in the ergodicity theory of Markov processes with interactions.

math.PR↗

arXiv2026

DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval

Retrieval-augmented generation has achieved strong performance on knowledge-intensive tasks where query-document relevance can be identified through direct lexical or semantic matches. However, many real-world queries involve abstract reasoning, analogical thinking, or multi-step inference, which existing retrievers often struggle to capture. To address this challenge, we present DIVER, a retrieval pipeline designed for reasoning-intensive information retrieval. It consists of four components. The document preprocessing stage enhances readability and preserves content by cleaning noisy texts and segmenting long documents. The query expansion stage leverages large language models to iteratively refine user queries with explicit reasoning and evidence from retrieved documents. The retrieval stage employs a model fine-tuned on synthetic data spanning medical and mathematical domains, along with hard negatives, enabling effective handling of reasoning-intensive queries. Finally, the reranking stage combines pointwise and listwise strategies to produce both fine-grained and globally consistent rankings. On the BRIGHT benchmark, DIVER achieves state-of-the-art nDCG@10 scores of 46.8 overall and 31.9 on original queries, consistently outperforming competitive reasoning-aware models. These results demonstrate the effectiveness of reasoning-aware retrieval strategies in complex real-world tasks.

cs.IR↗

arXiv2026

Evaluation of gNB Monostatic Sensing for UAV Use Case

3GPP Release 19 has initiated the standardization of integrated sensing and communications (ISAC), including a channel model for monostatic sensing, evaluation scenarios, and performance assessment methodologies. These common assumptions provide an important basis for ISAC evaluation, but reproducible end-to-end studies still require a transparent sensing implementation. This paper evaluates 5G New Radio (NR) base station (gNB)-based monostatic sensing for the Unmanned Aerial Vehicle (UAV) use case using a 5G NR downlink Cyclic Prefix-Orthogonal Frequency Division Multiplexing (CP-OFDM) waveform and positioning reference signals (PRS), following 3GPP Urban Macro-Aerial Vehicle (UMa-AV) scenario assumptions. We present an end-to-end processing chain for multi-target detection and 3D localization, achieving more than 70% detection probability with less than 5% false alarm rate, in the considered scenario. For correctly detected targets, localization errors are on the order of a few meters, with a 90th-percentile error of 4m and 6m in the vertical and horizontal directions, respectively. To support reproducible baseline studies and further research, we release the simulator 5GNRad, which reproduces our evaluation

eess.SP↗

arXiv2026

Aligning Multimodal Sequential Recommendations via Robust Direct Preference Optimization with Sparse MoE

Preference-based alignment objectives have been widely adopted, from RLHF-style pairwise learning in large language models to emerging applications in recommender systems. Yet, existing work rarely examines how Direct Preference Optimization (DPO) behaves under implicit feedback, where unobserved items are not reliable negatives. We conduct systematic experiments on multimodal sequential recommendation to compare common negative-selection strategies and their interaction with DPO training. Our central finding is that a simple modification, replacing deterministic hard negatives with stochastic sampling from a dynamic top-K candidate pool, consistently improves ranking performance. We attribute its effectiveness to two factors: (1) reducing erroneous suppressive gradients caused by false negatives, and (2) retaining informative hard signals while smoothing optimization via controlled stochasticity. With an optional sparse Mixture-of-Experts encoder for efficient capacity scaling, RoDPO achieves up to 5.25% NDCG@5 on three Amazon benchmarks, with nearly unchanged inference cost.

cs.IR↗

arXiv2026

HandX: Scaling Bimanual Motion and Interaction Generation

Synthesizing human motion has advanced rapidly, yet realistic hand motion and bimanual interaction remain underexplored. Whole-body models often miss the fine-grained cues that drive dexterous behavior, finger articulation, contact timing, and inter-hand coordination, and existing resources lack high-fidelity bimanual sequences that capture nuanced finger dynamics and collaboration. To fill this gap, we present HandX, a unified foundation spanning data, annotation, and evaluation. We consolidate and filter existing datasets for quality, and collect a new motion-capture dataset targeting underrepresented bimanual interactions with detailed finger dynamics. For scalable annotation, we introduce a decoupled strategy that extracts representative motion features, e.g., contact events and finger flexion, and then leverages reasoning from large language models to produce fine-grained, semantically rich descriptions aligned with these features. Building on the resulting data and annotations, we benchmark diffusion and autoregressive models with versatile conditioning modes. Experiments demonstrate high-quality dexterous motion generation, supported by our newly proposed hand-focused metrics. We further observe clear scaling trends: larger models trained on larger, higher-quality datasets produce more semantically coherent bimanual motion. Our dataset is released to support future research.

cs.CV↗

arXiv2026

FatigueFormer: Static-Temporal Feature Fusion for Robust sEMG-Based Muscle Fatigue Recognition

We present FatigueFormer, a semi-end-to-end framework that deliberately combines saliency-guided feature separation with deep temporal modeling to learn interpretable and generalizable muscle fatigue dynamics from surface electromyography (sEMG). Unlike prior approaches that struggle to maintain robustness across varying Maximum Voluntary Contraction (MVC) levels due to signal variability and low SNR, FatigueFormer employs parallel Transformer-based sequence encoders to separately capture static and temporal feature dynamics, fusing their complementary representations to improve performance stability across low- and high-MVC conditions. Evaluated on a self-collected dataset spanning 30 participants across four MVC levels (20-80%), it achieves state-of-the-art accuracy and strong generalization under mild-fatigue conditions. Beyond performance, FatigueFormer enables attention-based visualization of fatigue dynamics, revealing how feature groups and time windows contribute differently across varying MVC levels, offering interpretable insight into fatigue progression.

cs.LG↗

arXiv2026

Achieving Optimal Sample Complexity for a Broader Class of Signals in Sparse Phase Retrieval

Sparse phase retrieval aims to recover a $k$-sparse signal from $m$ phaseless measurements. While the theoretically optimal sample complexity for successful recovery is $Ω(k \log n)$, existing algorithms can only achieve this bound for signals with specific structural assumptions, leading to a notable gap between theory and practice. To bridge this gap, we introduce an efficient initialization algorithm, termed generalized Exponential Spectral Pursuit (gESP). We prove that gESP can significantly expand the family of signals that are guaranteed to be recovered with the optimal sample complexity, thereby extending the scope of theoretical optimality to a much broader class of signals. Extensive simulations validate our theoretical findings and demonstrate that gESP consistently outperforms the state-of-the-art methods across diverse signal types.

cs.IT↗

arXiv2026

Engineering energy-time entanglement from resonance fluorescence

Resonance fluorescence from a coherently driven two-level emitter is a minimal quantum optical field that combines phase coherence with single-photon-level nonlinearity. Here we show that it can be engineered, using only passive linear interferometry, into energy-time entanglement. By injecting resonance fluorescence from a single quantum dot into an asymmetric Mach--Zehnder interferometer operated near destructive interference of the single-photon component, we generate an output field whose coincidence statistics are dominated by the simultaneous two-photon contribution |2> and the temporally separated photon-pair contribution |11>. In a Franson geometry, these two sectors are resolved on the coincidence-delay axis, and both exhibit high-visibility nonlocal interference fringes and violate the Clauser--Horne--Shimony--Holt Bell inequality. Our results reveal a general route for engineering entanglement from resonance fluorescence using passive optics.

quant-ph↗

arXiv2026

Unleashing Guidance Without Classifiers for Human-Object Interaction Animation

Generating realistic human-object interaction (HOI) animations remains challenging because it requires jointly modeling dynamic human actions and diverse object geometries. Prior diffusion-based approaches often rely on hand-crafted contact priors or human-imposed kinematic constraints to improve contact quality. We propose LIGHT, a data-driven alternative in which guidance emerges from the denoising pace itself, reducing dependence on manually designed priors. Building on diffusion forcing, we factor the representation into modality-specific components and assign individualized noise levels with asynchronous denoising schedules. In this paradigm, cleaner components guide noisier ones through cross-attention, yielding guidance without auxiliary classifiers. We find that this data-driven guidance is inherently contact-aware, and can be enhanced when training is augmented with a broad spectrum of synthetic object geometries, encouraging invariance of contact semantics to geometric diversity. Extensive experiments show that pace-induced guidance more effectively mirrors the benefits of contact priors than conventional classifier-free guidance, while achieving higher contact fidelity, more realistic HOI generation, and stronger generalization to unseen objects and tasks.

cs.CV↗

arXiv2026

Chalcogen Doping Effect on the Insulator-to-Metal Transition in GdPS

Topological semimetals offer a rich platform for exploring massless fermion physics and realizing exotic properties with potential technological applications. GdPS, a magnetic semiconductor derived from the nodal-line semimetal ZrSiS family, exhibits a field-induced insulator-to-metal transition driven by exchange splitting. This transition is accompanied by an unusual, isotropic, and gigantic negative magnetoresistance, attributed to negligible magnetic anisotropy resulting from the weak spin-orbit coupling of half-filled Gd3+ 4f orbitals and light S atoms. In this work, we investigate Se substitution, which is expected to enhance spin-orbit coupling. Indeed, we observe slightly increased magnetic anisotropy in magnetotransport. Moreover, Se substitution suppresses the field-induced insulator-to-metal transition, likely due to an enlarged band gap that demands a higher exchange splitting to close. These findings provide deeper insights into the interplay between spin-orbit coupling, magnetic anisotropy, and transport behavior in GdPS, offering guidance for future materials design for desired functionalities.

cond-mat.mtrl-sci↗

arXiv2026

Aesthetic Assessment of Chinese Handwritings Based on Vision Language Models

The handwriting of Chinese characters is a fundamental aspect of learning the Chinese language. Previous automated assessment methods often framed scoring as a regression problem. However, this score-only feedback lacks actionable guidance, which limits its effectiveness in helping learners improve their handwriting skills. In this paper, we leverage vision-language models (VLMs) to analyze the quality of handwritten Chinese characters and generate multi-level feedback. Specifically, we investigate two feedback generation tasks: simple grade feedback (Task 1) and enriched, descriptive feedback (Task 2). We explore both low-rank adaptation (LoRA)-based fine-tuning strategies and in-context learning methods to integrate aesthetic assessment knowledge into VLMs. Experimental results show that our approach achieves state-of-the-art performances across multiple evaluation tracks in the CCL 2025 workshop on evaluation of handwritten Chinese character quality.

cs.CV↗

arXiv2026

A possible superconducting gap signature with filling temperature around 40 K in hexagonal iron telluride islands

Superconductivity in the iron-chalcogenide series FeSe-Fe(Te, Se)-FeTe has been restricted to the near neighbor of iron selenide (FeSe), with a general consensus that iron telluride (FeTe) is not superconducting. In this study, we report the method to grow FeTe islands with atomically flat surface and hexagonal lattice on SrTiO3 (001) substrates, in which a gap structure with a gap-filling temperature close to 40 K is detected by scanning tunneling spectroscopy. Such signature is examined under various conditions and reminiscent of a superconducting gap structure. This work might offer a potential platform to explore new superconductors at ambient pressure.

cond-mat.supr-con↗

arXiv2026

Semantic One-Dimensional Tokenizer for Image Reconstruction and Generation

Visual generative models based on latent space have achieved great success, underscoring the significance of visual tokenization. Mapping images to latents boosts efficiency and enables multimodal alignment for scaling up in downstream tasks. Existing visual tokenizers primarily map images into fixed 2D spatial grids and focus on pixel-level restoration, which hinders the capture of representations with compact global semantics. To address these issues, we propose \textbf{SemTok}, a semantic one-dimensional tokenizer that compresses 2D images into 1D discrete tokens with high-level semantics. SemTok sets a new state-of-the-art in image reconstruction, achieving superior fidelity with a remarkably compact token representation. This is achieved via a synergistic framework with three key innovations: a 2D-to-1D tokenization scheme, a semantic alignment constraint, and a two-stage generative training strategy. Building on SemTok, we construct a masked autoregressive generation framework, which yields notable improvements in downstream image generation tasks. Experiments confirm the effectiveness of our semantic 1D tokenization. Our code will be open-sourced.

cs.CV↗

arXiv2026

Snapmoji: Instant Generation of Animatable Dual-Stylized Avatars

Despite the increasing popularity of avatar systems such as Snapchat Bitmojis, existing production avatar platforms face several limitations, such as a limited number of predefined assets, tedious customization processes, and inefficient rendering requirements. Addressing these shortcomings, we introduce Snapmoji, an avatar generation system that instantly creates 3D avatars, and enables customization in a process we call dual-stylization. Snapmoji first maps a selfie of a user to a primary avatar (e.g., Bitmoji style) using a new technique we name Gaussian Domain Adaptation (GDA), then applies a secondary style (e.g., skeleton, yarn, toy) to the primary avatar, all while preserving the user's identity. The generated 3D avatars can then be rendered an animated on mobile devices at 30-40 FPS.

cs.GR↗

arXiv2026

Time-reversal symmetry breaking superconductivity with electronic glass in nickelate (La, Pr, Sm)3Ni2O7 films

The discovery of Ruddlesden-Popper (R-P) nickelate superconductors under high pressure heralds a new chapter of high-transition temperature (high-Tc) superconductivity. Recently, ambient-pressure superconductivity is achieved in R-P bilayer nickelate thin films through epitaxial compressive strain, unlocking the potential for understanding the nature of the unconventional superconductivity. Here, through electrical transport study, we report the discovery of time-reversal symmetry (TRS) breaking superconductivity with electronic glass in bilayer nickelate (La, Pr, Sm)3Ni2O7 films. It emerges in the lower-temperature regime of superconducting transition to the zero-resistance state, and is captured by three remarkable characteristics: 1. Unconventional magnetoresistance hysteresis, the direct evidence of TRS breaking, which is robust under different magnetic field orientations and differs fundamentally from trapped vortices or long-range-ordered magnetism. Successive oxygen reductions simultaneously weaken both the superconductivity and hysteresis, revealing their mutual connections to selective electronic orbitals. 2. Magnetic field history-dependence and zero-field non-reciprocity in the current-voltage responses, further substantiating the intrinsic and spontaneous TRS breaking. 3. Logarithmically slow resistance relaxations upon the removal of magnetic field, the hallmarks of glassy dynamics. Distinguished by the striking magnetic field history- and time-dependent properties, our findings uncover an unprecedented superconducting state in the nickelate superconductors, providing phenomenological and conceptual advances for future research on high-Tc superconductivity.

cond-mat.supr-con↗

arXiv2026

Data-Augmented Deep Learning for Downhole Depth Sensing and Validation

Accurate downhole depth measurement is essential for oil and gas well operations, directly influencing reservoir contact, production efficiency, and operational safety. Collar correlation using a casing collar locator (CCL) is fundamental for precise depth calibration. While neural network has achieved significant progress in collar recognition, preprocessing methods for such applications remain underdeveloped. Moreover, the limited availability of real well data poses substantial challenges for training neural network models that require extensive datasets. This paper presents a system integrated into a downhole toolstring for CCL log acquisition to facilitate dataset construction. Comprehensive preprocessing methods for data augmentation are proposed, and their effectiveness is evaluated using baseline neural network models. Through systematic experimentation across diverse configurations, the contribution of each augmentation method is analyzed. Results demonstrate that standardization, label distribution smoothing, and random cropping are fundamental prerequisites for model training, while label smoothing regularization, time scaling, and multiple sampling significantly enhance model generalization capabilities. Incorporating the proposed augmentation methods into the two baseline models results in maximum F1 score improvements of 0.027 and 0.024 for the TAN and MAN models, respectively. Furthermore, applying these techniques yields F1 score gains of up to 0.045 for the TAN model and 0.057 for the MAN model compared to prior studies. Performance evaluation on real CCL waveforms confirms the effectiveness and practical applicability of our approach. This work addresses the existing gaps in data augmentation methodologies for training casing collar recognition models under CCL data-limited conditions, and provides a technical foundation for the future automation of downhole operations.

cs.LG↗

arXiv2026

Velocity Disambiguation for Video Frame Interpolation

Existing video frame interpolation (VFI) methods blindly predict where each object is at a specific timestep t ("time indexing"), which struggles to predict precise object movements. Given two images of a baseball, there are infinitely many possible trajectories: accelerating or decelerating, straight or curved. This often results in blurry frames as the method averages out these possibilities. Instead of forcing the network to learn this complicated time-to-location mapping implicitly, we provide the network with an explicit hint on how far the object has traveled between start and end frames, a novel approach termed "distance indexing". This method offers a clearer learning goal for models, reducing the uncertainty tied to object speeds. Moreover, even with this extra guidance, objects can still be blurry especially when they are equally far from both input frames, due to the directional ambiguity in long-range motion. To solve this, we propose an iterative reference-based estimation strategy that breaks down a long-range prediction into several short-range steps. When integrating our plug-and-play strategies into state-of-the-art learning-based models, they exhibit markedly superior perceptual quality in arbitrary time interpolations, using a uniform distance indexing map in the same format as time indexing without requiring extra computation. Furthermore, we demonstrate that if additional latency is acceptable, a continuous map estimator can be employed to compute a pixel-wise dense distance indexing using multiple nearby frames. Combined with efficient multi-frame refinement, this extension can further disambiguate complex motion, thus enhancing performance both qualitatively and quantitatively. Additionally, the ability to manually specify distance indexing allows for independent temporal manipulation of each object, providing a novel tool for video editing tasks such as re-timing.

cs.CV↗

arXiv2026

Emergent quantum phenomena via phase-coherence engineering in infinite-layer nickelate superconductors

Dimensionality of a physical system, conventionally an invariant geometric characteristic, fundamentally governs the universality class of phase transitions and the landscape of emergent collective phenomena. In low-dimensional or layered high-temperature superconductors, the macroscopic phase coherence of superconducting orders is typically confined in two dimensions, underscoring the critical role of phase fluctuations in determining the overall phase diagrams. Here, we strategically enhance the phase fluctuations by fabricating periodically arranged nano-holes in the infinite-layer nickelate superconducting films, effectively constructing Josephson junction arrays. In the nano-patterned films, the weakening of macroscopic phase coherence drives a two-stage superconducting transition towards an anomalous metallic ground state with saturated resistance. The emergence of charge-2e quantum oscillations manifests the coherence across the array, while an anomalous zero-field magnetoresistance peak signifies the extreme quantum phase fluctuations persisting to ultralow temperatures. Remarkably, with quantum fluctuations enhanced synergistically by nano-patterning and magnetic fields, an anomalous reversal of superconducting anisotropy is observed in Nd-nickelates, where in-plane critical fields fall below out-of-plane values. The evolution of anisotropy may unmask an internal exchange-Zeeman field coupled to the collective electronic states. Our results unveil how superconductivity evolves in response to phase fluctuations, establishing nano-patterning as a powerful paradigm to uncover hidden intertwined orders in strongly correlated systems.

cond-mat.supr-con↗