Search Results for author:
Found 28 papers, 3 papers with code
Glitch Tokens in Large Language Models: Categorization Taxonomy and Effective Detection
We present categorizations of the identified glitch tokens and symptoms exhibited by LLMs when interacting with glitch tokens.
Differentially Private Dual Gradient Tracking for Distributed Resource Allocation
This paper investigates privacy issues in distributed resource allocation over directed networks, where each agent holds a private cost function and optimizes its decision subject to a global coupling constraint through local interaction with other agents.
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety
In addition to these benchmarks, we have implemented a phased public evaluation and benchmark update strategy to ensure that OpenEval is in line with the development of Chinese LLMs or even able to provide cutting-edge benchmark datasets to guide the development of Chinese LLMs.
GMPC: Geometric Model Predictive Control for Wheeled Mobile Robot Trajectory Tracking
The configuration of most robotic systems lies in continuous transformation groups.
Distributed Average Consensus via Noisy and Non-Coherent Over-the-Air Aggregation
Over-the-air aggregation has attracted widespread attention for its potential advantages in task-oriented applications, such as distributed sensing, learning, and consensus.
Direct Shooting Method for Numerical Optimal Control: A Modified Transcription Approach
Direct shooting is an efficient method to solve numerical optimal control.
Consistent and Asymptotically Statistically-Efficient Solution to Camera Motion Estimation
Given 2D point correspondences between an image pair, inferring the camera motion is a fundamental issue in the computer vision community.
Groot: Adversarial Testing for Generative Text-to-Image Models with Tree-based Semantic Transformation
With the prevalence of text-to-image generative models, their safety becomes a critical concern.
High-Performance Distributed Control for Large-Scale Linear Systems: A Partitioned Distributed Observer Approach
In recent years, the distributed-observer-based distributed control law has shown powerful ability to arbitrarily approximate the centralized control performance.
A Cross-Language Investigation into Jailbreak Attacks in Large Language Models
To address this research gap, we conducted an extensive empirical study on Multilingual Jailbreak attacks.
An Efficient Distributed Nash Equilibrium Seeking with Compressed and Event-triggered Communication
We demonstrate that SETC-DNES guarantees linear convergence to the NE while achieving even greater reductions in communication costs compared to ETC-DNES.
Generalized Multi-kernel Maximum Correntropy Kalman Filter for Disturbance Estimation
Disturbance observers have been attracting continuing research efforts and are widely used in many applications.
Over-the-air Federated Policy Gradient
In recent years, over-the-air aggregation has been widely considered in large-scale distributed learning, optimization, and sensing.
Sensor Selection for Remote State Estimation with QoS Requirement Constraints
By utilizing the solution of the relaxed problem, we propose a heuristic sensor selection algorithm which can provide a good suboptimal solution.
On the Effects and Optimal Design of Redundant Sensors in Collaborative State Estimation
The paper presents two main results: a theoretical analysis of the effects of redundant sensors and an engineering-oriented optimal design of redundant sensors.
Distributed Nash Equilibrium Seeking with Stochastic Event-Triggered Mechanism
In this paper, we study the problem of consensus-based distributed Nash equilibrium (NE) seeking where a network of players, abstracted as a directed graph, aim to minimize their own local cost functions non-cooperatively.
Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods
We evaluate the effectiveness of our proposed methods by comparing them with existing algorithms in various situations.
Multi-kernel Correntropy Regression: Robustness, Optimality, and Application on Magnetometer Calibration
This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression.
LQG Control Over SWIPT-enabled Wireless Communication Network
In this paper, we consider using simultaneous wireless information and power transfer (SWIPT) to recharge the sensor in the LQG control, which provides a new approach to prolonging the network lifetime.
Consistent and Asymptotically Efficient Localization from Range-Difference Measurements
Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator.
Towards Efficient Dynamic Uplink Scheduling over Multiple Unknown Channels
To alleviate the challenge, we reformulate the problem as a variant of the restless multi-armed bandit (RMAB) problem and leverage Whittle's index theory to design an index-based scheduling policy algorithm.
Harmonic-Copuled Riccati Equations and its Applications in Distributed Filtering
This paper manages to formulate and investigate a new kind of coupled Riccati equations, called harmonic-coupled Riccati equations (HCRE), from the matrix iterative law of the consensus on information-based distributed filtering (CIDF) algortihm proposed in [1], where the solutions of the equations are coupled with harmonic means.
Stochastic Event-triggered Variational Bayesian Filtering
This paper proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances.
Sensor Scheduling Design for Complex Networks under a Distributed State Estimation Framework
The goal of this paper is to seek an optimal sensor scheduling policy minimizing the overall estimation errors.
Consensus-Based Distributed Filtering with Fusion Step Analysis
For consensus on measurement-based distributed filtering (CMDF), through infinite consensus fusion operations during each sampling interval, each node in the sensor network can achieve optimal filtering performance with centralized filtering.
Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends
In order to improve the speed of B&B algorithms, learning techniques have been introduced in this algorithm recently.
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition
For privacy preservation, we propose a novel state-decomposition based gradient tracking approach (SD-Push-Pull) for distributed optimzation over directed networks that preserves differential privacy, which is a strong notion that protects agents' privacy against an adversary with arbitrary auxiliary information.
Privacy-Preserving Push-sum Average Consensus via State Decomposition
That is to say, only the exchanged substate would be visible to an adversary, preventing the initial state information from leakage.
