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Found 56 papers, 8 papers with code
A Unified Alternating Optimization Framework for Joint Sensor and Actuator Configuration in LQG Systems
This paper fills a gap in the literature by considering a joint sensor and actuator configuration problem under the linear quadratic Gaussian (LQG) performance without assuming a predefined set of candidate components.
Breaking the Loop: Detecting and Mitigating Denial-of-Service Vulnerabilities in Large Language Models
Large Language Models (LLMs) have significantly advanced text understanding and generation, becoming integral to applications across education, software development, healthcare, entertainment, and legal services.
ProBench: Benchmarking Large Language Models in Competitive Programming
To bridge the gap for high-level code reasoning assessment, we propose ProBench to benchmark LLMs in competitive programming, drawing inspiration from the International Collegiate Programming Contest.
Detecting LLM Fact-conflicting Hallucinations Enhanced by Temporal-logic-based Reasoning
Large language models (LLMs) face the challenge of hallucinations -- outputs that seem coherent but are actually incorrect.
Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning
With the increasing prevalence of autonomous vehicles (AVs), their vulnerability to various types of attacks has grown, presenting significant security challenges.
Learning-based Detection of GPS Spoofing Attack for Quadrotors
Safety-critical cyber-physical systems (CPS), such as quadrotor UAVs, are particularly prone to cyber attacks, which can result in significant consequences if not detected promptly and accurately.
Large Language Model Safety: A Holistic Survey
The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural language understanding and generation.
Model-Editing-Based Jailbreak against Safety-aligned Large Language Models
Large Language Models (LLMs) have transformed numerous fields by enabling advanced natural language interactions but remain susceptible to critical vulnerabilities, particularly jailbreak attacks.
A Secure Estimator with Gaussian Bernoulli Mixture Model
The implementation of cyber-physical systems in real-world applications is challenged by safety requirements in the presence of sensor threats.
Material Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning
Machine learning has become a crucial tool for predicting the properties of crystalline materials.
Efficient and Effective Universal Adversarial Attack against Vision-Language Pre-training Models
Vision-language pre-training (VLP) models, trained on large-scale image-text pairs, have become widely used across a variety of downstream vision-and-language (V+L) tasks.
Bias-VarianceTrade-off in Kalman Filter-Based Disturbance Observers
In contrast, the Kalman filter-based disturbance observer (KF-DOB) achieves minimum mean-square error estimation when the disturbance model is fully specified.
Efficient Detection of Toxic Prompts in Large Language Models
ToxicDetector achieves high accuracy, efficiency, and scalability, making it a practical method for toxic prompt detection in LLMs.
On the Effects of Modeling Errors on Distributed Continuous-time Filtering
This paper offers a comprehensive performance analysis of the distributed continuous-time filtering in the presence of modeling errors.
Designing Consensus-Based Distributed Filtering over Directed Graphs
The transient performance is also analyzed with the fusion step tending to infinity.
Performance Analysis of Distributed Filtering under Mismatched Noise Covariances
Additionally, we prove that the estimation error covariance of the consensus-based distributed filter under mismatched noise covariances can be bounded by the Frobenius norms of the noise covariance deviations and the trace of the nominal performance evaluation index.
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data
To mitigate this challenge, we present FuxiTranyu, an open-source multilingual LLM, which is designed to satisfy the need of the research community for balanced and high-performing multilingual capabilities.
GlitchProber: Advancing Effective Detection and Mitigation of Glitch Tokens in Large Language Models
In this work, we aim to enhance the understanding of glitch tokens and propose techniques for their detection and mitigation.
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy
This paper investigates the use of the cubic-regularized Newton method within a federated learning framework while addressing two major concerns that commonly arise in federated learning: privacy leakage and communication bottleneck.
Recent Advances in Data-driven Intelligent Control for Wireless Communication: A Comprehensive Survey
As the volume of data continues to escalate, the integration of data-driven methods has become indispensable for enabling adaptive and intelligent control mechanisms in future wireless communication systems.
NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing
In this work, we implement NeuSemSlice, a novel framework that introduces the semantic slicing technique to effectively identify critical neuron-level semantic components in DNN models for semantic-aware model maintenance tasks.
Continuous Embedding Attacks via Clipped Inputs in Jailbreaking Large Language Models
Security concerns for large language models (LLMs) have recently escalated, focusing on thwarting jailbreaking attempts in discrete prompts.
Self and Cross-Model Distillation for LLMs: Effective Methods for Refusal Pattern Alignment
Our results show that these methods significantly improve refusal rates and reduce unsafe content, with cross-model distilling achieving refusal rates close to Claude3's 94. 51%.
CRiskEval: A Chinese Multi-Level Risk Evaluation Benchmark Dataset for Large Language Models
Each question is accompanied with 4 answer choices that state opinions or behavioral tendencies corresponding to the question.
Lockpicking LLMs: A Logit-Based Jailbreak Using Token-level Manipulation
Large language models (LLMs) have transformed the field of natural language processing, but they remain susceptible to jailbreaking attacks that exploit their capabilities to generate unintended and potentially harmful content.
Communication-efficient and Differentially-private Distributed Nash Equilibrium Seeking with Linear Convergence
Due to the nature of distributed systems, privacy and communication efficiency are two critical concerns.
Compression-based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games
To jointly address these issues, we propose an algorithm that uses stochastic compression to save communication resources and conceal information through random errors induced by compression.
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.
FineMath: A Fine-Grained Mathematical Evaluation Benchmark for Chinese Large Language Models
We conduct extensive experiments on a wide range of LLMs on FineMath and find that there is still considerable room for improvements in terms of mathematical reasoning capability 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 Regression: Robustness, Optimality, and Application on Magnetometer Calibration
This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression.
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.
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.
