arXiv subjects

Wei Dai

Publications and source records attributed to Wei Dai.

At least 127 records · Page 7Linked to original sources

arXiv2025

Flexible Exoskeleton Control Based on Binding Alignment Strategy and Full-arm Coordination Mechanism

In rehabilitation, powered, and teleoperation exoskeletons, connecting the human body to the exoskeleton through binding attachments is a common configuration. However, the uncertainty of the tightness and the donning deviation of the binding attachments will affect the flexibility and comfort of the exoskeletons, especially during high-speed movement. To address this challenge, this paper presents a flexible exoskeleton control approach with binding alignment and full-arm coordination. Firstly, the sources of the force interaction caused by donning offsets are analyzed, based on which the interactive force data is classified into the major, assistant, coordination, and redundant component categories. Then, a binding alignment strategy (BAS) is proposed to reduce the donning disturbances by combining different force data. Furthermore, we propose a full-arm coordination mechanism (FCM) that focuses on two modes of arm movement intent, joint-oriented and target-oriented, to improve the flexible performance of the whole exoskeleton control during high-speed motion. In this method, we propose an algorithm to distinguish the two intentions to resolve the conflict issue of the force component. Finally, a series of experiments covering various aspects of exoskeleton performance (flexibility, adaptability, accuracy, speed, and fatigue) were conducted to demonstrate the benefits of our control framework in our full-arm exoskeleton.

cs.RO↗

arXiv2025

HI-GVF: Shared Control based on Human-Influenced Guiding Vector Fields for Human-multi-robot Cooperation

Human-multi-robot shared control leverages human decision-making and robotic autonomy to enhance human-robot collaboration. While widely studied, existing systems often adopt a leader-follower model, limiting robot autonomy to some extent. Besides, a human is required to directly participate in the motion control of robots through teleoperation, which significantly burdens the operator. To alleviate these two issues, we propose a layered shared control computing framework using human-influenced guiding vector fields (HI-GVF) for human-robot collaboration. HI-GVF guides the multi-robot system along a desired path specified by the human. Then, an intention field is designed to merge the human and robot intentions, accelerating the propagation of the human intention within the multi-robot system. Moreover, we give the stability analysis of the proposed model and use collision avoidance based on safety barrier certificates to fine-tune the velocity. Eventually, considering the firefighting task as an example scenario, we conduct simulations and experiments using multiple human-robot interfaces (brain-computer interface, myoelectric wristband, eye-tracking), and the results demonstrate that our proposed approach boosts the effectiveness and performance of the task.

cs.RO↗

arXiv2025

A Novel Approach to for Multimodal Emotion Recognition : Multimodal semantic information fusion

With the advancement of artificial intelligence and computer vision technologies, multimodal emotion recognition has become a prominent research topic. However, existing methods face challenges such as heterogeneous data fusion and the effective utilization of modality correlations. This paper proposes a novel multimodal emotion recognition approach, DeepMSI-MER, based on the integration of contrastive learning and visual sequence compression. The proposed method enhances cross-modal feature fusion through contrastive learning and reduces redundancy in the visual modality by leveraging visual sequence compression. Experimental results on two public datasets, IEMOCAP and MELD, demonstrate that DeepMSI-MER significantly improves the accuracy and robustness of emotion recognition, validating the effectiveness of multimodal feature fusion and the proposed approach.

cs.CV↗

arXiv2025

Bayesian Beamforming for Integrated Sensing and Communication Systems

The uncertainty of the sensing target brings great challenge to the beamforming design of the integrated sensing and communication (ISAC) system. To address this issue, we model the scattering coefficient and azimuth angle of the target as random variables and introduce a novel metric, expected detection probability (EPd), to quantify the average detection performance from a Bayesian perspective. Furthermore, we design a Bayesian beamforming scheme to optimize the expected detection probability under the limited power budget and communication performance constraints. A successive convex approximation and semidefinite relaxation-based (SCA-SDR) algorithm is developed for the complicated non-convex optimization problem corresponding to the beamforming scheme. Simulation results show that the proposed scheme outperforms other benchmarks and exhibits robust detection performance when parameters of the target are unknown and random.

eess.SP↗

arXiv2025

Radial symmetry and sharp asymptotic behaviors of nonnegative solutions to weighted doubly $D^{1,p}$-critical quasi-linear nonlocal elliptic equations with Hardy potential

In this paper, we mainly consider nonnegative weak solutions $u\in D^{1,p}(\R^{N})$ to the doubly $D^{1,p}(\R^{N})$-critical nonlocal quasi-linear Schrödinger-Hartree equation: \begin{align*} -Δ_p u- μ\frac{u^{p-1}}{|x|^p}=\left(|x|^{-2p}\ast |u|^{p}\right)|u|^{p-2}u \qquad &\mbox{in} \,\, \mathbb{R}^N, \end{align*} where $N\geq3$, $0\leqμ< \barμ:=\left( (N-p)/p \right)^p$ and $1 0$, due to appearance of the Hardy potential, the equation has singularity at $0\in\mathbb{R}^{N}$ and hence is not translation invariant, so sharp asymptotic estimates near the origin must be involved. First, we establish regularity and the sharp estimates on asymptotic behaviors near the origin and the infinity for any positive solution $u\in D^{1,p}(\R^{N})$ (and $|\nabla u|$) to more general equation $-\triangle_p u - μ\frac{1}{|x|^p}u^{p-1}=V(x)\frac{1}{|x|^s}u^{p-1}$ with $N\geq2$, $0\leqμ< \barμ$, $1<p<N$, $0\leq s < p$ and $0\leq V(x)\in L^\frac{N}{p-s}(\R^N)$. Then, as a consequence, we can apply the method of moving planes to prove that all the nontrivial nonnegative solutions in $D^{1,p}(\R^{N})$ are radially symmetric and strictly radially decreasing about the origin $0\in\mathbb{R}^{N}$. The sharp asymptotic estimates and radial symmetry for more general weighted doubly $D^{1,p}$-critical nonlocal quasi-linear equations were also derived. Our results extend the results in \cite{DLL} from the special case $μ=0$ to general cases $0\leqμ<\barμ$.

math.AP↗

arXiv2025

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount

In object detection, the instance count is typically used to define whether a dataset exhibits a long-tail distribution, implicitly assuming that models will underperform on categories with fewer instances. This assumption has led to extensive research on category bias in datasets with imbalanced instance counts. However, models still exhibit category bias even in datasets where instance counts are relatively balanced, clearly indicating that instance count alone cannot explain this phenomenon. In this work, we first introduce the concept and measurement of category information amount. We observe a significant negative correlation between category information amount and accuracy, suggesting that category information amount more accurately reflects the learning difficulty of a category. Based on this observation, we propose Information Amount-Guided Angular Margin (IGAM) Loss. The core idea of IGAM is to dynamically adjust the decision space of each category based on its information amount, thereby reducing category bias in long-tail datasets. IGAM Loss not only performs well on long-tailed benchmark datasets such as LVIS v1.0 and COCO-LT but also shows significant improvement for underrepresented categories in the non-long-tailed dataset Pascal VOC. Comprehensive experiments demonstrate the potential of category information amount as a tool and the generality of our proposed method.

cs.CV↗

arXiv2025

Stationary and Sparse Denoising Approach for Corticomuscular Causality Estimation

Objective: Cortico-muscular communication patterns are instrumental in understanding movement control. Estimating significant causal relationships between motor cortex electroencephalogram (EEG) and surface electromyogram (sEMG) from concurrently active muscles presents a formidable challenge since the relevant processes underlying muscle control are typically weak in comparison to measurement noise and background activities. Methodology: In this paper, a novel framework is proposed to simultaneously estimate the order of the autoregressive model of cortico-muscular interactions along with the parameters while enforcing stationarity condition in a convex program to ensure global optimality. The proposed method is further extended to a non-convex program to account for the presence of measurement noise in the recorded signals by introducing a wavelet sparsity assumption on the excitation noise in the model. Results: The proposed methodology is validated using both simulated data and neurophysiological signals. In case of simulated data, the performance of the proposed methods has been compared with the benchmark approaches in terms of order identification, computational efficiency, and goodness of fit in relation to various noise levels. In case of physiological signals our proposed methods are compared against the state-of-the-art approaches in terms of the ability to detect Granger causality. Significance: The proposed methods are shown to be effective in handling stationarity and measurement noise assumptions, revealing significant causal interactions from brain to muscles and vice versa.

eess.SP↗

arXiv2025

Anisotropic Finsler $N$-Laplacian Liouville equation in convex cones

We consider the anisotropic Finsler $N$-Laplacian Liouville equation \[-Δ^{H}_{N}u=e^u \qquad {\rm{in}}\,\, \mathcal{C},\] where $N\geq2$, $\mathcal{C}\subseteq\mathbb{R}^{N}$ is an open convex cone including $\mathbb{R}^{N}$, the half space $\mathbb{R}^{N}_{+}$ and $\frac{1}{2^{m}}$-space $\mathbb{R}^{N}_{2^{-m}}:=\{x\in\mathbb{R}^{N}\mid x_{1},\cdots,x_{m}>0\}$ ($m=1,\cdots,N$), and the anisotropic Finsler $N$-Laplacian $Δ^{H}_{N}$ is induced by a positively homogeneous function $H(x)$ of degree $1$. All solutions to the Finsler $N$-Laplacian Liouville equation with finite mass are completely classified. In particular, if $H(ξ)=|ξ|$, then the Finsler $N$-Laplacian $Δ^{H}_{N}$ reduces to the regular $N$-Laplacian $Δ_N$. Our result is a counterpart in the limiting case $p=N$ of the classification results in \cite{CFR} for the critical anisotropic $p$-Laplacian equations with $1<p<N$ in convex cones, and also extends the classification results in \cite{CK,CL,CW,CL2,E} for Liouville equation in the whole space $\mathbb{R}^{N}$ to general convex cones. In our proof, besides exploiting the anisotropic isoperimetric inequality inside convex cones, we have also proved and applied the radial Poincaré type inequality (Lemma \ref{A1}), which are key ingredients in the proof and of their own importance and interests.

math.AP↗

arXiv2025

High Transmission in 120-degree Sharp Bends of Inversion-symmetric and Inversion-asymmetric Photonic Crystal Waveguides

Bending loss is one of the serious problems for constructing nanophotonic integrated circuits. Recently, many works reported that valley photonic crystals (VPhCs) enable significantly high transmission via 120-degree sharp bends. However, it is unclear whether the high bend-transmission results directly from the valley-photonic effects, which are based on the breaking of inversion symmetry. In this study, we conduct a series of comparative numerical and experimental investigations of bend-transmission in various triangular PhCs with and without inversion symmetry and reveal that the high bend-transmission is solely determined by the domain-wall configuration and independent of the existence of the inversion symmetry. Preliminary analysis of the polarization distribution indicates that high bend-transmissions are closely related to the appearance of local topological polarization singularities near the bending section. Our work demonstrates that high transmission can be achieved in a much wider family of PhC waveguides, which may provide novel designs for low-loss nanophotonic integrated circuits with enhanced flexibility and a new understanding of the nature of valley-photonics

physics.optics↗

arXiv2025

High-frequency readout free from transmon multi-excitation resonances

Quantum computation will rely on quantum error correction to counteract decoherence. Successfully implementing an error correction protocol requires the fidelity of qubit operations to be well-above error correction thresholds. In superconducting quantum computers, measurement of the qubit state remains the lowest-fidelity operation. For the transmon, a prototypical superconducting qubit, measurement is carried out by scattering a microwave tone off the qubit. Conventionally, the frequency of this tone is of the same order as the transmon frequency. The measurement fidelity in this approach is limited by multi-excitation resonances in the transmon spectrum which are activated at high readout power. These resonances excite the qubit outside of the computational basis, violating the desired quantum non-demolition character of the measurement. Here, we find that strongly detuning the readout frequency from that of the transmon exponentially suppresses the strength of spurious multi-excitation resonances. By increasing the readout frequency up to twelve times the transmon frequency, we achieve a quantum non-demolition measurement fidelity of 99.93% with a residual probability of leakage to non-computational states of only 0.02%.

quant-ph↗

arXiv2025

Exposing the dead-cone effect of jet quenching in QCD medium

When an energetic parton traverses the hot QCD medium it may suffer multiple scattering and lose its energy. The medium-induced gluon radiation for a massive quark will be suppressed relative to that of a light quark due to the dead-cone effect. The development of new declustering techniques of jet evolution makes a direct study of the dead-cone effect in the QCD medium possible for the first time. In this work, we compute the emission angle distribution of the charm-quark initiated splittings in $\rm D^0$ meson tagged jet and that of the light parton initiated splittings in an inclusive jet in p+p and Pb+Pb at $\rm 5.02$~TeV by utilizing the declustering techniques of jet evolution. The heavy quark propagation and indued energy loss in the QCD medium are simulated with the SHELL model based on the Langevin equation. By directly comparing the emission angle distributions of charm-quark-initiated splittings with those of light parton-initiated splittings at the same energy intervals of the initial parton, we provide insights into the fundamental splitting structure in A+A collisions, thereby exploring the possible observation of the dead-cone effect in medium-induced radiation. We further investigate the case of the emission angle distributions normalized to the number of splittings and find the dead-cone effect will broaden the emission angle of the splitting and reduce the possibility to occur such splitting, therefore leading the massive parton to lose less energy. We also find the collisional energy loss mechanism has a negligible impact on the medium modification to the emission angle distribution of the charm-quark initiated splittings for $\rm D^0$-meson tagged jets.

hep-ph↗

arXiv2024

Analyzing Mass School Shootings in the United States from 1999 to 2024 with Game Theory, Probability Analysis, and Machine Learning

Public safety is vital to every country, especially school safety. In the United States, students and educators are concerned about school shootings. There are critical needs to understand the patterns of school shootings. Without this understanding, we cannot take action to prevent school shootings. Existing research that includes statistical analysis usually focuses on public mass shootings or just shooting incidents that have occurred in the past and there are hardly any articles focusing on mass school shootings. Here we firstly define mathematic models through gam theory. Then, we evaluate shootings events in schools for recently 26-year (1999-2024). Compared with the number of mass school shootings in COVID-19 period, we predict the number of mass school shooting events in the US will be reduced through four machine learning models. We also identify that mass school shootings usually take average 31 minutes with four periods. The annual probability of mass school shootings is 1.23 E-5 (or one in 81,604) per school. The shootings mostly occur inside buildings, especially classrooms and hallways. By interpreting these data and conducting various statistical analysis, this will ultimately help the law enforcement and schools to reduce the future school shootings. The research data sets could be downloaded via the website: https://publicsafetyinfo.com

cs.CY↗

arXiv2024

Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation

Unsupervised anomaly detection (UAD) plays an important role in modern data analytics and it is crucial to provide simple yet effective and guaranteed UAD algorithms for real applications. In this paper, we present a novel UAD method for tabular data by evaluating how much noise is in the data. Specifically, we propose to learn a deep neural network from the clean (normal) training dataset and a noisy dataset, where the latter is generated by adding highly diverse noises to the clean data. The neural network can learn a reliable decision boundary between normal data and anomalous data when the diversity of the generated noisy data is sufficiently high so that the hard abnormal samples lie in the noisy region. Importantly, we provide theoretical guarantees, proving that the proposed method can detect anomalous data successfully, although the method does not utilize any real anomalous data in the training stage. Extensive experiments through more than 60 benchmark datasets demonstrate the effectiveness of the proposed method in comparison to 12 baselines of UAD. Our method obtains a 92.27\% AUC score and a 1.68 ranking score on average. Moreover, compared to the state-of-the-art UAD methods, our method is easier to implement.

cs.LG↗

arXiv2024

Fully and partially distributed Quantum Generalized Benders Decomposition for Unit Commitment Problems

A series of hybrid quantum-classical generalized Benders decomposition (GBD) algorithms are proposed to address unit commitment (UC) problems under centralized, distributed, and partially distributed frameworks. In the centralized approach, the quantum GBD transforms the master problem (MP) into a quadratic unconstrained binary optimization form suitable for quantum computing. For distributed systems, the distributed consensus quantum GBD employs an average consensus strategy to reformulate subproblems into local subproblems. By leveraging the dual information, local cutting planes are constructed to decompose the MP into local master problems (LMPs). This approach reduces the qubit overhead and addresses the partitioning requirements. The consensus-inspired quantum GBD (CIQGBD) and its partially distributed variant, D-CIQGBD are proposed based on optimizing the allocation of relaxation variables directly, the algorithms construct more rational cutting planes, thereby enhancing the minimum eigenenergy gap of the system Hamiltonian during quantum annealing and improving the computational efficiency. Extensive experiments under various UC scenarios validate the performance of the above-mentioned hybrid algorithms. Compared to the classical solver Gurobi, D-CIQGBD demonstrates a speed advantage in solving the security-constrained UC problem on the IEEE-RTS 24-bus system. These results provide new perspectives on leveraging quantum computing for the distributed optimization of power systems.

quant-ph↗

arXiv2024

MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples

Aligning Large Language Models (LLMs) with human feedback is crucial for their development. Existing preference optimization methods such as DPO and KTO, while improved based on Reinforcement Learning from Human Feedback (RLHF), are inherently derived from PPO, requiring a reference model that adds GPU memory resources and relies heavily on abundant preference data. Meanwhile, current preference optimization research mainly targets single-question scenarios with two replies, neglecting optimization with multiple replies, which leads to a waste of data in the application. This study introduces the MPPO algorithm, which leverages the average likelihood of model responses to fit the reward function and maximizes the utilization of preference data. Through a comparison of Point-wise, Pair-wise, and List-wise implementations, we found that the Pair-wise approach achieves the best performance, significantly enhancing the quality of model responses. Experimental results demonstrate MPPO's outstanding performance across various benchmarks. On MT-Bench, MPPO outperforms DPO, ORPO, and SimPO. Notably, on Arena-Hard, MPPO surpasses DPO and ORPO by substantial margins. These achievements underscore the remarkable advantages of MPPO in preference optimization tasks.

cs.CL↗

arXiv2024

A Multi-agent Simulation for the Mass School Shootings

The increasing frequency of mass school shootings in the United States has been raised as a critical concern. Active shooters kill innocent students and educators in schools. These tragic events highlight the urgent need for effective strategies to minimize casualties. This study aims to address the challenge of simulating and assessing potential mitigation measures by developing a multi-agent simulation model. Our model is designed to estimate casualty rates and evacuation efficiency during active shooter scenarios within school buildings. The simulation evaluates the impact of a gun detection system on safety outcomes. By simulating school shooting incidents with and without this system, we observe a significant improvement in evacuation rates, which increased from 16.6% to 66.6%. Furthermore, the Gun Detection System reduced the average casualty rate from 24.0% to 12.2% within a period of six minutes, based on a simulated environment with 100 students. We conducted a total of 48 simulations across three different floor layouts, varying the number of students and time intervals to assess the system's adaptability. We anticipate that the research will provide a starting point for demonstrating that a gunshot detection system can significantly improve both evacuation rates and casualty reduction.

cs.CY↗

arXiv2024

sEMG-based Gesture-Free Hand Intention Recognition: System, Dataset, Toolbox, and Benchmark Results

In sensitive scenarios, such as meetings, negotiations, and team sports, messages must be conveyed without detection by non-collaborators. Previous methods, such as encrypting messages, eye contact, and micro-gestures, had problems with either inaccurate information transmission or leakage of interaction intentions. To this end, a novel gesture-free hand intention recognition scheme was proposed, that adopted surface electromyography(sEMG) and isometric contraction theory to recognize different hand intentions without any gesture. Specifically, this work includes four parts: (1) the experimental system, consisting of the upper computer software, self-conducted myoelectric watch, and sports platform, is built to get sEMG signals and simulate multiple usage scenarios; (2) the paradigm is designed to standard prompt and collect the gesture-free sEMG datasets. Eight-channel signals of ten subjects were recorded twice per subject at about 5-10 days intervals; (3) the toolbox integrates preprocessing methods (data segmentation, filter, normalization, etc.), commonly used sEMG signal decoding methods, and various plotting functions, to facilitate the research of the dataset; (4) the benchmark results of widely used methods are provided. The results involve single-day, cross-day, and cross-subject experiments of 6-class and 12-class gesture-free hand intention when subjects with different sports motions. To help future research, all data, hardware, software, and methods are open-sourced on the following website: click here.

cs.HC↗

arXiv2024

EEG-DIF: Early Warning of Epileptic Seizures through Generative Diffusion Model-based Multi-channel EEG Signals Forecasting

Multi-channel EEG signals are commonly used for the diagnosis and assessment of diseases such as epilepsy. Currently, various EEG diagnostic algorithms based on deep learning have been developed. However, most research efforts focus solely on diagnosing and classifying current signal data but do not consider the prediction of future trends for early warning. Additionally, since multi-channel EEG can be essentially regarded as the spatio-temporal signal data received by detectors at different locations in the brain, how to construct spatio-temporal information representations of EEG signals to facilitate future trend prediction for multi-channel EEG becomes an important problem. This study proposes a multi-signal prediction algorithm based on generative diffusion models (EEG-DIF), which transforms the multi-signal forecasting task into an image completion task, allowing for comprehensive representation and learning of the spatio-temporal correlations and future developmental patterns of multi-channel EEG signals. Here, we employ a publicly available epilepsy EEG dataset to construct and validate the EEG-DIF. The results demonstrate that our method can accurately predict future trends for multi-channel EEG signals simultaneously. Furthermore, the early warning accuracy for epilepsy seizures based on the generated EEG data reaches 0.89. In general, EEG-DIF provides a novel approach for characterizing multi-channel EEG signals and an innovative early warning algorithm for epilepsy seizures, aiding in optimizing and enhancing the clinical diagnosis process. The code is available at https://github.com/JZK00/EEG-DIF.

eess.SP↗