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Found 54 papers, 21 papers with code
Sensing Security Oriented OFDM-ISAC Against Multi-Intercept Threats
Building on this analysis, we formulate a new ISAC design problem that explicitly considers sensing security, and we develop a low-complexity, efficient optimization approach to solve it.
Taming the Untamed: Graph-Based Knowledge Retrieval and Reasoning for MLLMs to Conquer the Unknown
The real value of knowledge lies not just in its accumulation, but in its potential to be harnessed effectively to conquer the unknown.
E-InMeMo: Enhanced Prompting for Visual In-Context Learning
Large-scale models trained on extensive datasets have become the standard due to their strong generalizability across diverse tasks.
Probing then Editing Response Personality of Large Language Models
We conduct probing experiments on 11 open-source LLMs over the PersonalityEdit benchmark and find that LLMs predominantly encode personality for responding in their middle and upper layers, with instruction-tuned models demonstrating a slightly clearer separation of personality traits.
Kimi-VL Technical Report
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities - all while activating only 2. 8B parameters in its language decoder (Kimi-VL-A3B).
Mirror Online Conformal Prediction with Intermittent Feedback
Online conformal prediction enables the runtime calibration of a pre-trained artificial intelligence model using feedback on its performance.
GS-SDF: LiDAR-Augmented Gaussian Splatting and Neural SDF for Geometrically Consistent Rendering and Reconstruction
The accurate LiDAR point clouds enable a trained neural signed distance field to offer a manifold geometry field, This motivates us to offer an SDF-based Gaussian initialization for physically grounded primitive placement and a comprehensive geometric regularization for geometrically consistent rendering and reconstruction.
Investigating the Adaptive Robustness with Knowledge Conflicts in LLM-based Multi-Agent Systems
Recent advances in Large Language Models (LLMs) have upgraded them from sophisticated text generators to autonomous agents capable of corporation and tool use in multi-agent systems (MASs).
Synergizing Covert Transmission and mmWave ISAC for Secure IoT Systems
We design the transmit beamforming to guarantee information transmission security, communication quality-of-service (QoS) and sensing accuracy.
SP-SLAM: Neural Real-Time Dense SLAM With Scene Priors
In this paper, we introduce SP-SLAM, a novel neural RGB-D SLAM system that performs tracking and mapping in real-time.
Simultaneous Localization and Mapping
Surface Reconstruction
RSGaussian:3D Gaussian Splatting with LiDAR for Aerial Remote Sensing Novel View Synthesis
This study presents RSGaussian, an innovative novel view synthesis (NVS) method for aerial remote sensing scenes that incorporate LiDAR point cloud as constraints into the 3D Gaussian Splatting method, which ensures that Gaussians grow and split along geometric benchmarks, addressing the overgrowth and floaters issues occurs.
ReLayout: Towards Real-World Document Understanding via Layout-enhanced Pre-training
Recent approaches for visually-rich document understanding (VrDU) uses manually annotated semantic groups, where a semantic group encompasses all semantically relevant but not obviously grouped words.
Neural Surface Reconstruction and Rendering for LiDAR-Visual Systems
We unify the training of the NDF and NeRF using a spatial-varying scale SDF-to-density transformation for levels of detail for both structure and appearance.
Putting People in LLMs' Shoes: Generating Better Answers via Question Rewriter
By enhancing the intelligibility of human questions for black-box LLMs, our question rewriter improves the quality of generated answers.
DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models
Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain.
Revolutionizing Urban Safety Perception Assessments: Integrating Multimodal Large Language Models with Street View Images
However, achieving this goal often requires extensive human annotation to train safety ranking models, and the architectural differences between cities hinder the transferability of these models.
Exploiting Target Location Distribution in MIMO Radar: PCRB vs. PSBP for Waveform Design
This paper investigates the issue of how to exploit prior target location distribution for multiple input multiple output (MIMO) radar waveform design.
Cooperative Integrated Sensing and Communication Networks: Analysis and Distributed Design
Second, to show the benefits of Co-ISACNet, we propose to jointly design the HBF to maximize the network communication capacity while satisfying the constraint of beampattern similarity for radar sensing, which results in a highly dimensional and non-convex problem.
Distributed Optimization
Integrated sensing and communication
Explainable Image Recognition via Enhanced Slot-attention Based Classifier
A novel loss function specifically for ESCOUTER is designed to fine-tune the model's behavior, enabling it to toggle between positive and negative explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
CTBench: A Comprehensive Benchmark for Evaluating Language Model Capabilities in Clinical Trial Design
Given study-specific metadata, CTBench evaluates AI models' ability to determine the baseline features of a clinical trial (CT), which include demographic and relevant features collected at the trial's start from all participants.
SFANet: Spatial-Frequency Attention Network for Weather Forecasting
Weather forecasting plays a critical role in various sectors, driving decision-making and risk management.
Massive MIMO-ISAC System With 1-Bit ADCs/DACs
This paper investigates a hardware-efficient massive multiple-input multiple-output integrated sensing and communication (MIMO-ISAC) system with 1-bit analog-to-digital converters (ADCs)/digital-to-analog converters (DACs).
Task-Oriented Hybrid Beamforming for OFDM-DFRC Systems with Flexibly Controlled Space-Frequency Spectra
This paper investigates the issues of the hybrid beamforming design for the orthogonal frequency division multiplexing dual-function radar-communication (DFRC) system in multiple task scenarios involving the radar scanning and detection task and the target tracking task.
Enhancing Physical Layer Security in Dual-Function Radar-Communication Systems with Hybrid Beamforming Architecture
Additionally, the location of the radar target is also imperfectly known by the BS.
APAR: LLMs Can Do Auto-Parallel Auto-Regressive Decoding
The massive adoption of large language models (LLMs) demands efficient deployment strategies.
Towards Robust and Accurate Visual Prompting
Visual prompting, an efficient method for transfer learning, has shown its potential in vision tasks.
Instruct Me More! Random Prompting for Visual In-Context Learning
Our findings suggest that InMeMo offers a versatile and efficient way to enhance the performance of visual ICL with lightweight training.
Concatenated Masked Autoencoders as Spatial-Temporal Learner
Learning representations from videos requires understanding continuous motion and visual correspondences between frames.
MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension
The large language models have achieved superior performance on various natural language tasks.
TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction
Our summarization compression can reduce 65% of the retrieval token size with further 0. 3% improvement on the accuracy; semantic compression provides a more flexible way to trade-off the token size with performance, for which we can reduce the token size by 20% with only 1. 6% of accuracy drop.
AgentTuning: Enabling Generalized Agent Abilities for LLMs
Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of LLMs themselves without compromising their general abilities.
Improving Facade Parsing with Vision Transformers and Line Integration
We introduce a new pipeline known as Revision-based Transformer Facade Parsing (RTFP).
IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuning
This approach is different from the pruning method as it is not limited by the initial number of training parameters, and each parameter matrix has a higher rank upper bound for the same training overhead.
CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care
In an effort to minimize the reliance on human resources for performance evaluation, we offer off-the-shelf judgment models for automatically assessing the LF output of LLMs given benchmark questions.
Causal Intervention for Measuring Confidence in Drug-Target Interaction Prediction
Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development.
Cramer-Rao Lower Bound Analysis for OTFS and OFDM Modulation Systems
The orthogonal time frequency space (OTFS) modulation as a promising signal representation attracts growingcinterest for integrated sensing and communication (ISAC), yet its merits over orthogonal frequency division multiplexing (OFDM) remain controversial.
Learning Bottleneck Concepts in Image Classification
Using some image classification tasks as our testbed, we demonstrate BotCL's potential to rebuild neural networks for better interpretability.
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction
Reversely, the model Reconstructs a more robust equilibrium state prediction by transforming edge-level predictions to node-level with a sphere-fitting algorithm.
Graph Neural Network
Initial Structure to Relaxed Energy (IS2RE), Direct
+1
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning
Offline safe RL is of great practical relevance for deploying agents in real-world applications.
Relative Entropy-Based Constant-Envelope Beamforming for Target Detection in Large-Scale MIMO Radar With Low-Resoultion ADCs
In the second stage, an efficient iterative algorithm based on majorization-minimization is presented to obtain the constant-envelope beamformer according to the attained transmit power.
A Dual-Function Radar-Communication System Empowered by Beyond Diagonal Reconfigurable Intelligent Surface
This work focuses on the use of reconfigurable intelligent surface (RIS) in dual-function radar-communication (DFRC) systems to improve communication capacity and sensing precision, and enhance coverage for both functions.
Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction
Graph neural networks (GNNs) have drawn more and more attention from material scientists and demonstrated a high capacity to establish connections between the structure and properties.
Dynamic Hybrid Beamforming Design for Dual-Function Radar-Communication Systems
This paper investigates dynamic hybrid beamforming (HBF) for a dual-function radar-communication (DFRC) system, where the DFRC base station (BS) simultaneously serves multiple single-antenna users and senses a target in the presence of multiple clutters.
Hybrid Beamforming in mmWave Dual-Function Radar-Communication Systems: Models, Technologies, and Challenges
As a promising technology in beyond-5G (B5G) and 6G, dual-function radar-communication (DFRC) aims to ensure both radar sensing and communication on a single integrated platform with unified signaling schemes.
LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction
Precise congestion prediction from a placement solution plays a crucial role in circuit placement.
Double-Phase-Shifter based Hybrid Beamforming for mmWave DFRC in the Presence of Extended Target and Clutters
In millimeter-wave (mmWave) dual-function radar-communication (DFRC) systems, hybrid beamforming (HBF) is recognized as a promising technique utilizing a limited number of radio frequency chains.
Panoptic-aware Image-to-Image Translation
The panoptic perception (i. e., foreground instances and background semantics of the image scene) is extracted to achieve alignment between object content codes of the input domain and panoptic-level style codes sampled from the target style space, then refined by a proposed feature masking module for sharping object boundaries.
Manifold Optimization Methods for Hybrid beamforming in mmWave Dual-Function Radar-Communication System
This paper considers two hybrid beamforming architectures, i. e. the partially-connected and fully-connected structures, for mmWave dual-function radar communication (DFRC) system, where the transmitter communicates with the downlink users and detects radar targets simultaneously.
The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods
For an autonomous robotic system, monitoring surgeon actions and assisting the main surgeon during a procedure can be very challenging.
Match Them Up: Visually Explainable Few-shot Image Classification
Few-shot learning (FSL) approaches are usually based on an assumption that the pre-trained knowledge can be obtained from base (seen) categories and can be well transferred to novel (unseen) categories.
Ranked #36 on
Few-Shot Image Classification
on CIFAR-FS 5-way (5-shot)
Automated Grading System of Retinal Arterio-venous Crossing Patterns: A Deep Learning Approach Replicating Ophthalmologist's Diagnostic Process of Arteriolosclerosis
Our severity grading method was able to validate crossing points with precision and recall of 96. 3% and 96. 3%, respectively.
Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentation
Semantic video segmentation is a key challenge for various applications.
SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition
Explainable artificial intelligence has been gaining attention in the past few years.
