arXiv subjects

Qi Wang

Publications and source records attributed to Qi Wang.

At least 307 records · Page 17Linked to original sources

arXiv2025

Hierarchical Count Echo State Network Models with Application to Graduate Student Enrollments

Poisson autoregressive count models have evolved into a time series staple for correlated count data. This paper proposes an alternative to Poisson autoregressions: count echo state networks. Echo state networks can be statistically analyzed in frequentist manners via optimizing penalized likelihoods, or in Bayesian manners via MCMC sampling. This paper develops Poisson echo state techniques for count data and applies them to a massive count data set containing the number of graduate students from 1,758 United States universities during the years 1972-2021 inclusive. Negative binomial models are also implemented to better handle overdispersion in the counts. Performance of the proposed models are compared via their forecasting performance as judged by several methods. In the end, a hierarchical negative binomial based echo state network is judged as the superior model.

stat.ME↗

arXiv2025

"We do use it, but not how hearing people think": How the Deaf and Hard of Hearing Community Uses Large Language Model Tools

Generative AI tools, particularly those utilizing large language models (LLMs), are increasingly used in everyday contexts. While these tools enhance productivity and accessibility, little is known about how Deaf and Hard of Hearing (DHH) individuals engage with them or the challenges they face when using them. This paper presents a mixed-method study exploring how the DHH community uses Text AI tools like ChatGPT to reduce communication barriers and enhance information access. We surveyed 80 DHH participants and conducted interviews with 11 participants. Our findings reveal important benefits, such as eased communication and bridging Deaf and hearing cultures, alongside challenges like lack of American Sign Language (ASL) support and Deaf cultural understanding. We highlight unique usage patterns, propose inclusive design recommendations, and outline future research directions to improve Text AI accessibility for the DHH community.

cs.HC↗

arXiv2025

EMO2: End-Effector Guided Audio-Driven Avatar Video Generation

In this paper, we propose a novel audio-driven talking head method capable of simultaneously generating highly expressive facial expressions and hand gestures. Unlike existing methods that focus on generating full-body or half-body poses, we investigate the challenges of co-speech gesture generation and identify the weak correspondence between audio features and full-body gestures as a key limitation. To address this, we redefine the task as a two-stage process. In the first stage, we generate hand poses directly from audio input, leveraging the strong correlation between audio signals and hand movements. In the second stage, we employ a diffusion model to synthesize video frames, incorporating the hand poses generated in the first stage to produce realistic facial expressions and body movements. Our experimental results demonstrate that the proposed method outperforms state-of-the-art approaches, such as CyberHost and Vlogger, in terms of both visual quality and synchronization accuracy. This work provides a new perspective on audio-driven gesture generation and a robust framework for creating expressive and natural talking head animations.

cs.CV↗

arXiv2025

MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography Segmentation

Ultrasound imaging frequently encounters challenges, such as those related to elevated noise levels, diminished spatiotemporal resolution, and the complexity of anatomical structures. These factors significantly hinder the model's ability to accurately capture and analyze structural relationships and dynamic patterns across various regions of the heart. Mamba, an emerging model, is one of the most cutting-edge approaches that is widely applied to diverse vision and language tasks. To this end, this paper introduces a U-shaped deep learning model incorporating a large-window Mamba scale (LMS) module and a hierarchical feature fusion approach for echocardiographic segmentation. First, a cascaded residual block serves as an encoder and is employed to incrementally extract multiscale detailed features. Second, a large-window multiscale mamba module is integrated into the decoder to capture global dependencies across regions and enhance the segmentation capability for complex anatomical structures. Furthermore, our model introduces auxiliary losses at each decoder layer and employs a dual attention mechanism to fuse multilayer features both spatially and across channels. This approach enhances segmentation performance and accuracy in delineating complex anatomical structures. Finally, the experimental results using the EchoNet-Dynamic and CAMUS datasets demonstrate that the model outperforms other methods in terms of both accuracy and robustness. For the segmentation of the left ventricular endocardium (${LV}_{endo}$), the model achieved optimal values of 95.01 and 93.36, respectively, while for the left ventricular epicardium (${LV}_{epi}$), values of 87.35 and 87.80, respectively, were achieved. This represents an improvement ranging between 0.54 and 1.11 compared with the best-performing model.

eess.IV↗

arXiv2025

Multiple-Satellite Cooperative Information Communication and Location Sensing in LEO Satellite Constellations

Integrated sensing and communication (ISAC) and ubiquitous connectivity are two usage scenarios of sixth generation (6G) networks. In this context, low earth orbit (LEO) satellite constellations, as an important component of 6G networks, is expected to provide ISAC services across the globe. In this paper, we propose a novel dual-function LEO satellite constellation framework that realizes information communication for multiple user equipments (UEs) and location sensing for interested target simultaneously with the same hardware and spectrum. In order to improve both information transmission rate and location sensing accuracy within limited wireless resources under dynamic environment, we design a multiple-satellite cooperative information communication and location sensing algorithm by jointly optimizing communication beamforming and sensing waveform according to the characteristics of LEO satellite constellation. Finally, extensive simulation results are presented to demonstrate the competitive performance of the proposed algorithms.

cs.IT↗

arXiv2025

Deep Unfolding Network with Spatial Alignment for multi-modal MRI reconstruction

Multi-modal Magnetic Resonance Imaging (MRI) offers complementary diagnostic information, but some modalities are limited by the long scanning time. To accelerate the whole acquisition process, MRI reconstruction of one modality from highly undersampled k-space data with another fully-sampled reference modality is an efficient solution. However, the misalignment between modalities, which is common in clinic practice, can negatively affect reconstruction quality. Existing deep learning-based methods that account for inter-modality misalignment perform better, but still share two main common limitations: (1) The spatial alignment task is not adaptively integrated with the reconstruction process, resulting in insufficient complementarity between the two tasks; (2) the entire framework has weak interpretability. In this paper, we construct a novel Deep Unfolding Network with Spatial Alignment, termed DUN-SA, to appropriately embed the spatial alignment task into the reconstruction process. Concretely, we derive a novel joint alignment-reconstruction model with a specially designed cross-modal spatial alignment term. By relaxing the model into cross-modal spatial alignment and multi-modal reconstruction tasks, we propose an effective algorithm to solve this model alternatively. Then, we unfold the iterative steps of the proposed algorithm and design corresponding network modules to build DUN-SA with interpretability. Through end-to-end training, we effectively compensate for spatial misalignment using only reconstruction loss, and utilize the progressively aligned reference modality to provide inter-modality prior to improve the reconstruction of the target modality. Comprehensive experiments on three real datasets demonstrate that our method exhibits superior reconstruction performance compared to state-of-the-art methods.

eess.IV↗

arXiv2025

KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models

Large Language Models (LLMs) have shown impressive performance in various tasks, including knowledge graph completion (KGC). However, current studies mostly apply LLMs to classification tasks, like identifying missing triplets, rather than ranking-based tasks, where the model ranks candidate entities based on plausibility. This focus limits the practical use of LLMs in KGC, as real-world applications prioritize highly plausible triplets. Additionally, while graph paths can help infer the existence of missing triplets and improve completion accuracy, they often contain redundant information. To address these issues, we propose KG-CF, a framework tailored for ranking-based KGC tasks. KG-CF leverages LLMs' reasoning abilities to filter out irrelevant contexts, achieving superior results on real-world datasets. The code and datasets are available at \url{https://anonymous.4open.science/r/KG-CF}.

cs.AI↗

arXiv2025

Bridge the Inference Gaps of Neural Processes via Expectation Maximization

The neural process (NP) is a family of computationally efficient models for learning distributions over functions. However, it suffers from under-fitting and shows suboptimal performance in practice. Researchers have primarily focused on incorporating diverse structural inductive biases, \textit{e.g.} attention or convolution, in modeling. The topic of inference suboptimality and an analysis of the NP from the optimization objective perspective has hardly been studied in earlier work. To fix this issue, we propose a surrogate objective of the target log-likelihood of the meta dataset within the expectation maximization framework. The resulting model, referred to as the Self-normalized Importance weighted Neural Process (SI-NP), can learn a more accurate functional prior and has an improvement guarantee concerning the target log-likelihood. Experimental results show the competitive performance of SI-NP over other NPs objectives and illustrate that structural inductive biases, such as attention modules, can also augment our method to achieve SOTA performance. Our code is available at \url{https://github.com/hhq123gogogo/SI_NPs}.

cs.LG↗

arXiv2024

A versatile method for nano-fabrication on diamond film: flexible diamond metasurfaces as a demonstration

Diamond exhibits superb performance across a wide range of applications due to its enormous outstanding properties in electronic, photonic and quantum fields. Yet heterogeneous integration of diamond for on-chip functionalities, like 2D materials, remains challenging due to the hard acquisition of scalable, transferable and ultrathin diamond samples. Recently, the edge-exposed exfoliation has been demonstrated as an effective way to produce wafer-scale, freestanding and ultrathin diamond films. However, the incompatibility of the newly developed diamond film with conventional nano-fabrication methods makes it difficult to fabricate diamond film into practical devices. Herein, we demonstrate the mask-transferring by sugar as a versatile method for pattern-definition on diamond films, which shows excellent geometrical resolution and accuracy comparing to conventional approaches. Additionally, based on this method, the flexible all-diamond metasurfaces functioning as structural colors have been achieved, which indicates its huge potential for fabricating more diamond-related devices.

physics.optics↗

arXiv2024

An Incremental Clustering Baseline for Event Detection on Twitter

Event detection in text streams is a crucial task for the analysis of online media and social networks. One of the current challenges in this field is establishing a performance standard while maintaining an acceptable level of computational complexity. In our study, we use an incremental clustering algorithm combined with recent advancements in sentence embeddings. Our objective is to compare our findings with previous studies, specifically those by Cao et al. (2024) and Mazoyer et al. (2020). Our results demonstrate significant improvements and could serve as a relevant baseline for future research in this area.

cs.CL↗

arXiv2024

Unimodal and Multimodal Static Facial Expression Recognition for Virtual Reality Users with EmoHeVRDB

In this study, we explored the potential of utilizing Facial Expression Activations (FEAs) captured via the Meta Quest Pro Virtual Reality (VR) headset for Facial Expression Recognition (FER) in VR settings. Leveraging the EmojiHeroVR Database (EmoHeVRDB), we compared several unimodal approaches and achieved up to 73.02% accuracy for the static FER task with seven emotion categories. Furthermore, we integrated FEA and image data in multimodal approaches, observing significant improvements in recognition accuracy. An intermediate fusion approach achieved the highest accuracy of 80.42%, significantly surpassing the baseline evaluation result of 69.84% reported for EmoHeVRDB's image data. Our study is the first to utilize EmoHeVRDB's unique FEA data for unimodal and multimodal static FER, establishing new benchmarks for FER in VR settings. Our findings highlight the potential of fusing complementary modalities to enhance FER accuracy in VR settings, where conventional image-based methods are severely limited by the occlusion caused by Head-Mounted Displays (HMDs).

cs.CV↗

arXiv2024

GTDE: Grouped Training with Decentralized Execution for Multi-agent Actor-Critic

The rapid advancement of multi-agent reinforcement learning (MARL) has given rise to diverse training paradigms to learn the policies of each agent in the multi-agent system. The paradigms of decentralized training and execution (DTDE) and centralized training with decentralized execution (CTDE) have been proposed and widely applied. However, as the number of agents increases, the inherent limitations of these frameworks significantly degrade the performance metrics, such as win rate, total reward, etc. To reduce the influence of the increasing number of agents on the performance metrics, we propose a novel training paradigm of grouped training decentralized execution (GTDE). This framework eliminates the need for a centralized module and relies solely on local information, effectively meeting the training requirements of large-scale multi-agent systems. Specifically, we first introduce an adaptive grouping module, which divides each agent into different groups based on their observation history. To implement end-to-end training, GTDE uses Gumbel-Sigmoid for efficient point-to-point sampling on the grouping distribution while ensuring gradient backpropagation. To adapt to the uncertainty in the number of members in a group, two methods are used to implement a group information aggregation module that merges member information within the group. Empirical results show that in a cooperative environment with 495 agents, GTDE increased the total reward by an average of 382\% compared to the baseline. In a competitive environment with 64 agents, GTDE achieved a 100\% win rate against the baseline.

cs.MA↗

arXiv2024

YIG/CoFeB bilayer magnonic diode

We demonstrate a magnonic diode based on a bilayer structure of Yttrium Iron Garnet (YIG) and Cobalt Iron Boron (CoFeB). The bilayer exhibits pronounced non-reciprocal spin-wave propagation, enabled by dipolar coupling and the magnetic properties of the two layers. The YIG layer provides low damping and efficient spin-wave propagation, while the CoFeB layer introduces strong magnetic anisotropy, critical for achieving diode functionality. Experimental results, supported by numerical simulations, show unidirectional propagation of Magnetostatic Surface Spin Waves (MSSW), significantly suppressing backscattered waves. This behavior was confirmed through wavevector-resolved and micro-focused Brillouin Light Scattering measurements and is supported by numerical simulations. The proposed YIG/SiO$_2$/CoFeB bilayer magnonic diode demonstrates the feasibility of leveraging non-reciprocal spin-wave dynamics for functional magnonic devices, paving the way for energy-efficient, wave-based signal processing technologies.

physics.app-ph↗

arXiv2024

Optimisation and Loss Analyses of Pulsed Field Magnetisation in a Superconducting Motor with Cryocooled Iron Cores

A 2D electromagnetic-thermal coupled numerical model has been developed using the finite element method and validated against experimental data to investigate a superconducting machine featuring high-temperature superconducting (HTS) tape stacks and cryocooled iron cores. The HTS stacks are transformed into trapped field stacks (TFSs) through pulsed field magnetisation (PFM), generating rotor fields. After PFM, the superconducting motor operates on the same principle as permanent magnet synchronous motors. This study explores the behaviour of HTS stacks by altering the stack's layer number from one to nine and adjusting the pulsed current amplitude from 250 A to 1000 A. The primary objective of this paper is to identify the optimal combination of pulsed current amplitudes and TFS layer numbers for achieving maximum magnetisation fields. The secondary objective is to evaluate the overall losses in both superconducting and non-superconducting parts of the machine during magnetisation, including heat generated in various layers of the TFS, and losses in the motor's active materials (copper windings and iron cores). Two motor configurations were proposed, and two calculation methods using linear interpolation of iron losses and steel grades were introduced to estimate the iron losses for the studied iron material, M270-35A. This pioneering study is expected to serve as a valuable reference for loss analyses and structural design considerations in developing superconducting machines.

cond-mat.supr-con↗

arXiv2024

The Hausdorff measure and uniform fibre conditions for Barański carpet

For a self-affine carpet $K$ of Barański, we establish a dichotomy: $ \text{either }\quad 0<\mathcal{H}^{\dim_{\text{H}} K}(K)<+\infty \quad\text{ or } \quad\mathcal{H}^{\dim_{\text{H}} K}(K)=+\infty. $ We introduce four types of uniform fibre condition for $K$: Hausdorff ($\textbf{u.f.H}$), Box ($\textbf{u.f.B}$), Assouad ($\textbf{u.f.A}$), and Lower ($\textbf{u.f.L}$), which are progressively stronger, with $ \textbf{u.f.L} \Longrightarrow \textbf{u.f.A} \Longrightarrow \textbf{u.f.B} \Longrightarrow \textbf{u.f.H}, $ and each implication is strict. The condition $\textbf{u.f.H}$ serves as a criterion for the dichotomy. The remaining three conditions provide an equivalent characterization for the coincidence of any two distinct dimensions. The condition $\textbf{u.f.L}$ is also equivalent to the Ahlfors regularity of $K$. As a corollary, $\dim_{\text{H}} K=\dim_{\text{B}} K$ is sufficient but not necessary for $0<\mathcal{H}^{\dim_{\text{H}} K}(K)<+\infty$.

math.CA↗

arXiv2024

Realization of inverse-design magnonic logic gates

Magnonic logic gates represent a crucial step toward realizing fully magnonic data processing systems without reliance on conventional electronic or photonic elements. Recently, a universal and reconfigurable inverse-design device has been developed, featuring a 7$\times$7 array of independent current loops that create local inhomogeneous magnetic fields to scatter spin waves in a Yttrium-Iron-Garnet film. While initially used for linear RF components, we now demonstrate key non-linear logic gates, NOT, OR, NOR, AND, NAND, and a half-adder, sufficient for building a full processor. In this system, binary data ("0" and "1") are encoded in the spin-wave amplitude. The contrast ratio, representing the difference between logic states, achieved values of 34, 53.9, 11.8, 19.7, 17, and 9.8 dB for these gates, respectively.

cond-mat.other↗

arXiv2024

Assouad and lower dimensions of graph-directed Bedford-McMullen carpets

We calculate the Assouad and lower dimensions of graph-directed Bedford-McMullen carpets, which reflect the extreme local scaling laws of the sets, in contrasting with known results on Hausdorff and box dimensions. We also investigate the relationship between distinct dimensions. In particular, we identify an equivalent condition when the box and Assouad dimension coincide, and show that under this condition, the Hausdorff dimension attains the same value.

math.CA↗

arXiv2024

Novel Superconducting Ternary Hydrides under High Pressure

The abundant chemical compositions in ternary hydrides bring much more possibility to explore high temperature superconductors under lower pressure. Here we constructed 115 ternary hydrides on the basis of the elements substitution using 16 metal elements within 5 reported prototype structures. We conducted a three-step approach to screen and study these candidate structures in the aspects of dynamical stability, formation energy and relative enthalpy, respectively. Based on this approach, we found three meta-stable compounds with hydrogen clathrate cages in the space group of P-3m1, including Y2CdH18, Y2InH18 and Ca2SnH18. All of the structures are superconductive under high pressure with Tc above 110 K, which is larger than the superconductive temperature of liquid nitrogen. Our study enriches the database of novel ternary hydrides under high pressure, and provides insight for future theoretical and experimental researches.

cond-mat.supr-con↗