arXiv subjects

Hui Wang

Publications and source records attributed to Hui Wang.

At least 163 records · Page 9Linked to original sources

arXiv2025

High-Power Dual-Channel Field Chamber for High-Frequency Magnetic Neuromodulation

Several novel methods, including magnetogenetics and magnetoelectric stimulation, use high frequency alternating magnetic fields to precisely manipulate neural activity. To quantify the behavioral effects of such interventions in a freely moving mouse, we developed a dual-channel magnetic chamber, specifically designed for rate-sensitive magnetothermal-genetic stimulation, and adaptable for other uses of alternating magnetic fields. Through an optimized coil design, the system allows independent control of two spatially orthogonal uniform magnetic fields delivered at different frequencies within a 10 cm x 10 cm x 6 cm chamber. The two channels have nominal frequencies of 50 and 550 kHz with peak magnetic field strengths of 88 and 12.5 mT, achieved with resonant coil drives having peak voltages of 1.6 and 1.8 kV and currents of 1.0 and 0.26 kA, respectively. Additionally, a liquid cooling system enables magnetic field generation for second-level duration, and an observation port and camera allow video capture of the animal's behavior within the chamber. The system generates high-amplitude magnetic fields across two widely separated frequency channels with negligible interference (< 1%). Relatively uniform magnetic field distribution (+/-10% across 94% of the chamber volume) is maintained throughout the chamber, and temperature increase of the inner side of the coil enclosure during the operation is limited to < 0.35 °C/s to ensure in vivo safety. Using cobalt-doped and undoped iron oxide nanoparticles, we demonstrate channel-specific heating rates of 3.5 °C/s and 1.5 °C/s, respectively, validating frequency-selectivity. Both channels can run continuously for four seconds stably.

eess.SY↗

arXiv2025

Ultra-Fast Wireless Power Hacking

The rapid growth of electric vehicles (EVs) has driven the development of roadway wireless charging technology, effectively extending EV driving range. However, wireless charging introduces significant cybersecurity challenges. Any receiver within the magnetic field can potentially extract energy, and previous research demonstrated that a hacker could detect the operating frequency and steal substantial power. However, our approach required time to track new frequencies or precise adjustments of inductance and capacitance, which would be less effective against potential rapid transmitter frequency changes or capacitance drift. As a solution, we enhanced the interceptor and enabled it to intrude as well as steal energy within just three cycles of the high-frequency signal. Moreover, it can work without any circuit parameters or look-up tables. The key innovation is synchronizing the receiver current with the phase of the magnetic sensor voltage. Through MATLAB / Simulink simulations, finite-element analysis, and experimental validation, we demonstrated that our improved method can steal over 76% of the power received by a fully resonant receiver under identical conditions. This attack demonstrates that simple frequency-changing power encryption offers limited protection against such threats.

cs.CR↗

arXiv2025

Controlling the recovery time of the superconducting nanowire single-photon detector with a voltage-controlled cryogenic tunable resistor

Superconducting nanowire single-photon detectors (SNSPD), owing to their unique performance, are currently the standard detector in most demanding single-photon experiments. One important metric for any single-photon detector is the deadtime (or recovery time), defined as the minimum temporal separation between consecutive detection events. In SNSPDs, the recovery time is more subtle, as the detection efficiency does not abruptly drop to zero when the temporal separation between detection events gets smaller, instead, it increases gradually as the SNSPD current recovers. SNSPD's recovery time is dominated by its kinetic inductance, the readout impedance, and the degree of saturation of internal efficiency. Decreasing the kinetic inductance or increasing the readout impedance can accelerate the recovery process. Significant reduction of the SNSPD recovery time, by, for example, adding a series resistor in the readout circuitry, is possible but can lead to detector latching which hinders further detector operation or enforces underbiasing and hence a reduction in detection efficiency. Previous research has demonstrated passive resistive networks for the reduction of recovery time that rely on trial and error to find the appropriate resistance values. Here we show, using a novel, cryogenically compatible, and tunable resistor technology, one can find the optimized impedance values, delivering fast SNSPD recovery time, while maintaining maximum internal detection efficiency. Here we show an increase of more than 2 folds in both maximum achievable detection rates and the achievable detection efficiency at high photon fluxes, demonstrating detection rates as high as 120 Mcps with no loss of internal detection efficiency.

physics.ins-det↗

arXiv2025

TC-GS: A Faster Gaussian Splatting Module Utilizing Tensor Cores

3D Gaussian Splatting (3DGS) renders pixels by rasterizing Gaussian primitives, where conditional alpha-blending dominates the computational cost in the rendering pipeline. This paper proposes TC-GS, an algorithm-independent universal module that expands the applicability of Tensor Core (TCU) for 3DGS, leading to substantial speedups and seamless integration into existing 3DGS optimization frameworks. The key innovation lies in mapping alpha computation to matrix multiplication, fully utilizing otherwise idle TCUs in existing 3DGS implementations. TC-GS provides plug-and-play acceleration for existing top-tier acceleration algorithms and integrates seamlessly with rendering pipeline designs, such as Gaussian compression and redundancy elimination algorithms. Additionally, we introduce a global-to-local coordinate transformation to mitigate rounding errors from quadratic terms of pixel coordinates caused by Tensor Core half-precision computation. Extensive experiments demonstrate that our method maintains rendering quality while providing an additional 2.18x speedup over existing Gaussian acceleration algorithms, thereby achieving a total acceleration of up to 5.6x.

cs.GR↗

arXiv2025

Fast Energy-Theft Attack on Frequency-Varying Wireless Power without Additional Sensors

With the popularity of wireless charging, energy access protection and cybersecurity are gaining importance, especially in public places. Currently, the most common energy encryption method uses frequency and associated impedance variation. However, we have proven that this method is not reliable, since a hacker can detect the changing frequency and adjust the compensation. However, the previously presented system needed time to follow the updated frequency, while encryption systems may vary the frequency faster to avoid energy theft. Furthermore, the previous system required an additional sensor coil. To solve these problems, we optimized the attack and the associated system, which can intrude and steal energy within 0.2 ms. The key is the elimination of the time-consuming maximum receiver current regulation. Also, we use the main receiving coil rather than any additional sensor antenna to detect the magnetic field. Thus, the new hardware is even simpler. A simulation model and experimental results demonstrate the fast response speed of the attack on encrypted wireless power and steal 65% of the power. Overall, the applicability of the attack is highly improved and leaves less room for hardening the encryption. The results demonstrate that energy access protection needs to be given great attention.

cs.CR↗

arXiv2025

Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature

As Artificial Intelligence (AI) increasingly becomes an active collaborator in co-creation, understanding the distribution and dynamic of agency is paramount. The Human-Computer Interaction (HCI) perspective is crucial for this analysis, as it uniquely reveals the interaction dynamics and specific control mechanisms that dictate how agency manifests in practice. Despite this importance, a systematic synthesis mapping agency configurations and control mechanisms within the HCI/CSCW literature is lacking. Addressing this gap, we reviewed 134 papers from top-tier HCI/CSCW venues (e.g., CHI, UIST, CSCW) over the past 20 years. This review yields four primary contributions: (1) an integrated theoretical framework structuring agency patterns, control mechanisms, and interaction contexts, (2) a comprehensive operational catalog of control mechanisms detailing how agency is implemented; (3) an actionable cross-context map linking agency configurations to diverse co-creative practices; and (4) grounded implications and guidance for future CSCW research and the design of co-creative systems, addressing aspects like trust and ethics.

cs.HC↗

arXiv2025

Aerial-Ground Image Feature Matching via 3D Gaussian Splatting-based Intermediate View Rendering

The integration of aerial and ground images has been a promising solution in 3D modeling of complex scenes, which is seriously restricted by finding reliable correspondences. The primary contribution of this study is a feature matching algorithm for aerial and ground images, whose core idea is to generate intermediate views to alleviate perspective distortions caused by the extensive viewpoint changes. First, by using aerial images only, sparse models are reconstructed through an incremental SfM (Structure from Motion) engine due to their large scene coverage. Second, 3D Gaussian Splatting is then adopted for scene rendering by taking as inputs sparse points and oriented images. For accurate view rendering, a render viewpoint determination algorithm is designed by using the oriented camera poses of aerial images, which is used to generate high-quality intermediate images that can bridge the gap between aerial and ground images. Third, with the aid of intermediate images, reliable feature matching is conducted for match pairs from render-aerial and render-ground images, and final matches can be generated by transmitting correspondences through intermediate views. By using real aerial and ground datasets, the validation of the proposed solution has been verified in terms of feature matching and scene rendering and compared comprehensively with widely used methods. The experimental results demonstrate that the proposed solution can provide reliable feature matches for aerial and ground images with an obvious increase in the number of initial and refined matches, and it can provide enough matches to achieve accurate ISfM reconstruction and complete 3DGS-based scene rendering.

cs.CV↗

arXiv2025

Multimodal Representation-disentangled Information Bottleneck for Multimodal Recommendation

Multimodal data has significantly advanced recommendation systems by integrating diverse information sources to model user preferences and item characteristics. However, these systems often struggle with redundant and irrelevant information, which can degrade performance. Most existing methods either fuse multimodal information directly or use rigid architectural separation for disentanglement, failing to adequately filter noise and model the complex interplay between modalities. To address these challenges, we propose a novel framework, the Multimodal Representation-disentangled Information Bottleneck (MRdIB). Concretely, we first employ a Multimodal Information Bottleneck to compress the input representations, effectively filtering out task-irrelevant noise while preserving rich semantic information. Then, we decompose the information based on its relationship with the recommendation target into unique, redundant, and synergistic components. We achieve this decomposition with a series of constraints: a unique information learning objective to preserve modality-unique signals, a redundant information learning objective to minimize overlap, and a synergistic information learning objective to capture emergent information. By optimizing these objectives, MRdIB guides a model to learn more powerful and disentangled representations. Extensive experiments on several competitive models and three benchmark datasets demonstrate the effectiveness and versatility of our MRdIB in enhancing multimodal recommendation.

cs.IR↗

arXiv2025

MECap-R1: Emotion-aware Policy with Reinforcement Learning for Multimodal Emotion Captioning

Speech Emotion Captioning (SEC) has emerged as a notable research direction. The inherent complexity of emotional content in human speech makes it challenging for traditional discrete classification methods to provide an adequate representation. Consequently, utilizing natural language to describe speech emotions presents a novel avenue for more effectively capturing and expressing affect. In this paper, we propose MECap-R1, a pioneering emotion-aware policy with reinforcement learning for multimodal emotion captioning. By employing Group Relative Policy Optimization with emotion-aware reward (Emo-GRPO), the framework precisely captures the emotion and semantic features, thereby addressing the shortcomings of rigid rules in handling the dynamic and flexible nature of captions. Experimental results on the EmotionTalk dataset demonstrate that MECap-R1 performs well in generating emotion descriptions and achieves substantial gains in both accuracy and diversity.

cs.SD↗

arXiv2025

Image Super-Resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features

The Swin Transformer image super-resolution (SR) reconstruction network primarily depends on the long-range relationship of the window and shifted window attention to explore features. However, this approach focuses only on global features, ignoring local ones, and considers only spatial interactions, disregarding channel and spatial-channel feature interactions, limiting its nonlinear mapping capability. Therefore, this study proposes an enhanced Swin Transformer network (ESTN) that alternately aggregates local and global features. During local feature aggregation, shift convolution facilitates the interaction between local spatial and channel information. During global feature aggregation, a block sparse global perception module is introduced, wherein spatial information is reorganized and the recombined features are then processed by a dense layer to achieve global perception. Additionally, multiscale self-attention and low-parameter residual channel attention modules are introduced to aggregate information across different scales. Finally, the effectiveness of ESTN on five public datasets and a local attribution map (LAM) are analyzed. Experimental results demonstrate that the proposed ESTN achieves higher average PSNR, surpassing SRCNN, ELAN-light, SwinIR-light, and SMFANER+ models by 2.17dB, 0.13dB, 0.12dB, and 0.1dB, respectively, with LAM further confirming its larger receptive field. ESTN delivers improved quality of SR images. The source code can be found at https://github.com/huangyuming2021/ESTN.

cs.CV↗

arXiv2025

Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio question answering

With the rapid progress of large audio-language models (LALMs), audio question answering (AQA) has emerged as a challenging task requiring both fine-grained audio understanding and complex reasoning. While current methods mainly rely on constructing new datasets via captioning or reasoning traces, existing high-quality AQA data remains underutilized. To address this, we propose Omni-CLST, an error-aware Curriculum Learning framework with guided Selective Chain-of-Thought. The framework efficiently leverages existing high-quality dataset through two key strategies: an error-aware curriculum that organizes samples by difficulty, and a guided thought dropout mechanism that focuses reasoning on challenging cases. Experiments show that Omni-CLST achieves 73.80% on MMAU-mini and a new state of the art of 64.30% on MMAR, demonstrating robust generalization in multimodal audio-language understanding.

cs.SD↗

arXiv2025

FELLE: Autoregressive Speech Synthesis with Token-Wise Coarse-to-Fine Flow Matching

To advance continuous-valued token modeling and temporal-coherence enforcement, we propose FELLE, an autoregressive model that integrates language modeling with token-wise flow matching. By leveraging the autoregressive nature of language models and the generative efficacy of flow matching, FELLE effectively predicts continuous-valued tokens (mel-spectrograms). For each continuous-valued token, FELLE modifies the general prior distribution in flow matching by incorporating information from the previous step, improving coherence and stability. Furthermore, to enhance synthesis quality, FELLE introduces a coarse-to-fine flow-matching mechanism, generating continuous-valued tokens hierarchically, conditioned on the language model's output. Experimental results demonstrate the potential of incorporating flow-matching techniques in autoregressive mel-spectrogram modeling, leading to significant improvements in TTS generation quality, as shown in https://aka.ms/felle.

cs.CL↗

arXiv2025

TTA-Bench: A Comprehensive Benchmark for Evaluating Text-to-Audio Models

Text-to-Audio (TTA) generation has made rapid progress, but current evaluation methods remain narrow, focusing mainly on perceptual quality while overlooking robustness, generalization, and ethical concerns. We present TTA-Bench, a comprehensive benchmark for evaluating TTA models across functional performance, reliability, and social responsibility. It covers seven dimensions including accuracy, robustness, fairness, and toxicity, and includes 2,999 diverse prompts generated through automated and manual methods. We introduce a unified evaluation protocol that combines objective metrics with over 118,000 human annotations from both experts and general users. Ten state-of-the-art models are benchmarked under this framework, offering detailed insights into their strengths and limitations. TTA-Bench establishes a new standard for holistic and responsible evaluation of TTA systems. The dataset and evaluation tools are open-sourced at https://nku-hlt.github.io/tta-bench/.

cs.SD↗

arXiv2025

The AudioMOS Challenge 2025

This is the summary paper for the AudioMOS Challenge 2025, the very first challenge for automatic subjective quality prediction for synthetic audio. The challenge consists of three tracks. The first track aims to assess text-to-music samples in terms of overall quality and textual alignment. The second track is based on the four evaluation dimensions of Meta Audiobox Aesthetics, and the test set consists of text-to-speech, text-to-audio, and text-to-music samples. The third track focuses on synthetic speech quality assessment in different sampling rates. The challenge attracted 24 unique teams from both academia and industry, and improvements over the baselines were confirmed. The outcome of this challenge is expected to facilitate development and progress in the field of automatic evaluation for audio generation systems.

cs.SD↗

arXiv2025

Realization of an untrusted intermediate relay architecture using a quantum dot single-photon source

To fully exploit the potential of quantum technologies, quantum networks are needed to link different systems, significantly enhancing applications in computing, cryptography, and metrology. Central to these networks are quantum relays that can facilitate long-distance entanglement distribution and quantum communication. In this work, we present a modular and scalable quantum relay architecture using a high-quality single-photon source. The proposed network incorporates three untrusted intermediate nodes and is capable of a repetition rate of 304.52 MHz. We use a measurement-device-independent protocol to demonstrate secure key establishment over fibers covering up to 300 kilometers. This study highlights the potential of single-photon sources in quantum relays to enhance information transmission, expand network coverage, and improve deployment flexibility, with promising applications in future quantum networks.

quant-ph↗

arXiv2025

Intrinsic nonlinear valley Nernst effect

We investigate the intrinsic nonlinear valley Nernst effect, which induces a transverse valley current via a second-order thermoelectric response to a longitudinal temperature gradient. The effect arises from the Berry connection polarizability dipole of valley electrons and is permissible in both inversion-symmetric and inversion-asymmetric materials. We demonstrate that the response tensor is connected to the intrinsic nonlinear valley Hall conductivity through a generalized Mott relation, with the two being directly proportional at low temperatures, scaled by the Lorenz number. We elucidate the symmetry constraints governing this effect and develop a theory for its nonlocal measurement, revealing a nonlocal second-harmonic signal with a distinct $ρ^2$ scaling. This signal comprises two scaling terms, with their ratio corresponding to the square of the thermopower normalized by the Lorenz number. Key characteristics are demonstrated using a tilted Dirac model and first-principles calculations on bilayer WTe$_2$. Possible extrinsic contributions and alternative experimental detection methods, e.g., by valley pumping and by nonreciprocal directional dichroism, are discussed. These findings underscore the significance of band quantum geometry on electron dynamics and establish a theoretical foundation for nonlinear valley caloritronics.

cond-mat.mes-hall↗

arXiv2025

Robust quantum computational advantage with programmable 3050-photon Gaussian boson sampling

The creation of large-scale, high-fidelity quantum computers is not only a fundamental scientific endeavour in itself, but also provides increasingly robust proofs of quantum computational advantage (QCA) in the presence of unavoidable noise and the dynamic competition with classical algorithm improvements. To overcome the biggest challenge of photon-based QCA experiments, photon loss, we report new Gaussian boson sampling (GBS) experiments with 1024 high-efficiency squeezed states injected into a hybrid spatial-temporal encoded, 8176-mode, programmable photonic quantum processor, Jiuzhang 4.0, which produces up to 3050 photon detection events. Our experimental results outperform all classical spoofing algorithms, particularly the matrix product state (MPS) method, which was recently proposed to utilise photon loss to reduce the classical simulation complexity of GBS. Using the state-of-the-art MPS algorithm on the most powerful supercomputer EI Capitan, it would take > $10^{42}$ years to construct the required tensor network for simulation, while our Jiuzhang 4.0 quantum computer takes 25.6 $μ$s to produce a sample. This work establishes a new frontier of QCA and paves the way to fault-tolerant photonic quantum computing hardware.

quant-ph↗

arXiv2025

LGE-Guided Cross-Modality Contrastive Learning for Gadolinium-Free Cardiomyopathy Screening in Cine CMR

Cardiomyopathy, a principal contributor to heart failure and sudden cardiac mortality, demands precise early screening. Cardiac Magnetic Resonance (CMR), recognized as the diagnostic 'gold standard' through multiparametric protocols, holds the potential to serve as an accurate screening tool. However, its reliance on gadolinium contrast and labor-intensive interpretation hinders population-scale deployment. We propose CC-CMR, a Contrastive Learning and Cross-Modal alignment framework for gadolinium-free cardiomyopathy screening using cine CMR sequences. By aligning the latent spaces of cine CMR and Late Gadolinium Enhancement (LGE) sequences, our model encodes fibrosis-specific pathology into cine CMR embeddings. A Feature Interaction Module concurrently optimizes diagnostic precision and cross-modal feature congruence, augmented by an uncertainty-guided adaptive training mechanism that dynamically calibrates task-specific objectives to ensure model generalizability. Evaluated on multi-center data from 231 subjects, CC-CMR achieves accuracy of 0.943 (95% CI: 0.886-0.986), outperforming state-of-the-art cine-CMR-only models by 4.3% while eliminating gadolinium dependency, demonstrating its clinical viability for wide range of populations and healthcare environments.

cs.CV↗