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Found 44 papers, 15 papers with code
Leveraging Seq2seq Language Generation for Multi-level Product Issue Identification
To harness such information to better serve customers, in this paper, we created a machine learning approach to automatically identify product issues and uncover root causes from the customer feedback text.
Multi-Physics Simulations via Coupled Fourier Neural Operator
To overcome these challenges, we introduce a novel coupled multi-physics neural operator learning (COMPOL) framework that extends the capabilities of Fourier operator layers to model interactions among multiple physical processes.
ITCFN: Incomplete Triple-Modal Co-Attention Fusion Network for Mild Cognitive Impairment Conversion Prediction
To address these challenges, we propose an innovative multimodal approach for predicting MCI conversion, focusing specifically on the issues of missing positron emission tomography (PET) data and integrating diverse medical information.
Neural Reflectance Fields for Radio-Frequency Ray Tracing
We achieve this by translating the neural reflectance field from optics to RF domain by modelling both the amplitude and phase of RF signals to account for the multipath effects.
An Experimental Study of Passive UAV Tracking with Digital Arrays and Cellular Downlink Signals
Given the prospects of the low-altitude economy (LAE) and the popularity of unmanned aerial vehicles (UAVs), there are increasing demands on monitoring flying objects at low altitude in wide urban areas.
BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models
This dataset includes 818 pairs of high-resolution CXR and dual-energy subtraction soft tissue images collected from a partner hospital.
Predicting Human Brain States with Transformer
The human brain is a complex and highly dynamic system, and our current knowledge of its functional mechanism is still very limited.
Receiver-Centric Generative Semantic Communications
To solve this problem, this paper presents a receiver-centric generative semantic communication system, where each transmission is initialized by the receiver.
Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets
Drawing from these findings, we propose Manipulation Centric Representation (MCR), a foundation representation learning framework capturing both visual features and the dynamics information such as actions and proprioceptions of manipulation tasks to improve manipulation centricity.
ED-ViT: Splitting Vision Transformer for Distributed Inference on Edge Devices
We conduct extensive experiments on five datasets with three model structures, demonstrating that our approach significantly reduces inference latency on edge devices and achieves a model size reduction of up to 28. 9 times and 34. 1 times, respectively, while maintaining test accuracy comparable to the original Vision Transformer.
GeoTransformer: Enhancing Urban Forecasting with Dependency Retrieval and Geospatial Attention
Recent advances in urban forecasting have leveraged high-dimensional spatial data through two primary approaches: graph-based methods that rely on predefined spatial structures and region-based methods that use satellite imagery for local features.
TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scaling
Moreover, the dynamic clipping strategy in TC-KANRecon adjusts the cropping interval according to the sampling steps, thereby mitigating image detail loss typicalching the visual features of the images.
Chromosomal Structural Abnormality Diagnosis by Homologous Similarity
Pathogenic chromosome abnormalities are very common among the general population.
EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations
Content-based recommendation systems play a crucial role in delivering personalized content to users in the digital world.
Goal-oriented compression for $L_p$-norm-type goal functions: Application to power consumption scheduling
Motivated by applications such as the smart grid, this paper focuses on a goal function which is of $L_p$-norm-type.
Exploring Correlations of Self-Supervised Tasks for Graphs
Specifically, we evaluate the performance of the representations trained by one specific task on other tasks and define correlation values to quantify task correlations.
System and Method to Determine ME/CFS and Long COVID Disease Severity Using a Wearable Sensor
Objective: We present a simple parameter, calculated from a single wearable sensor, that can be used to objectively measure disease severity in people with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) or Long COVID.
Robust deep labeling of radiological emphysema subtypes using squeeze and excitation convolutional neural networks: The MESA Lung and SPIROMICS Studies
Pulmonary emphysema, the progressive, irreversible loss of lung tissue, is conventionally categorized into three subtypes identifiable on pathology and on lung computed tomography (CT) images.
Unsupervised Airway Tree Clustering with Deep Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study
High-resolution full lung CT scans now enable the detailed segmentation of airway trees up to the 6th branching generation.
SPAR: Personalized Content-Based Recommendation via Long Engagement Attention
Leveraging users' long engagement histories is essential for personalized content recommendations.
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface.
Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases
To address these issues, this paper proposes an innovative method of leukocyte detection: the Multi-level Feature Fusion and Deformable Self-attention DETR (MFDS-DETR).
Generalizable Visual Reinforcement Learning with Segment Anything Model
Learning policies that can generalize to unseen environments is a fundamental challenge in visual reinforcement learning (RL).
Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns
In this paper, we identify the fundamental cause of structural divergence as the discrepancy of generative patterns between the pre-training and downstream graphs.
Ranked #1 on
Graph Classification
on PROTEINS
(Accuracy (10 fold) metric)
CGS-Mask: Making Time Series Predictions Intuitive for All
Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time.
BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images
As a remedial measure, bone suppression techniques have been introduced.
Dynamic Uploading Scheduling in mmWave-Based Sensor Networks via Mobile Blocker Detection
As a result, the AoI degradation arising from link blockage can be forecast and mitigated.
SST: A Simplified Swin Transformer-based Model for Taxi Destination Prediction based on Existing Trajectory
Accurately predicting the destination of taxi trajectories can have various benefits for intelligent location-based services.
Predictive Resource Allocation in mmWave Systems with Rotation Detection
Millimeter wave (MmWave) has been regarded as a promising technology to support high-capacity communications in 5G era.
mmAlert: mmWave Link Blockage Prediction via Passive Sensing
It is demonstrated via experiments that the mmAlert system can always detect the motions of the walking person close to the LoS path, and predict 90\% of the LoS blockage with sensing time of 1. 4 seconds.
Hybrid Censored Quantile Regression Forest to Assess the Heterogeneous Effects
We propose a variable importance decomposition to measure the impact of a variable on the treatment effect function.
Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network
We therefore propose a novel Structure-aware Path Aggregation Graph Neural Network (PathNet) aiming to generalize GNNs for both homophily and heterophily graphs.
Ranked #1 on
Node Classification
on Citeseer
(Accuracy (%) metric)
An Indoor Environment Sensing and Localization System via mmWave Phased Array
At the end, we show that the reconstructed room layout can be utilized to locate a mobile device according to its AoA spectrum, even with single access point.
Energy-efficient transmission policies for the linear quadratic control of scalar systems
This paper considers controlled scalar systems relying on a lossy wireless feedback channel.
Passive Motion Detection via mmWave Communication System
Relying on the passive sensing system, a dataset of received signals, where three types of hand gestures are sensed, is collected by using Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) paths as the reference channel respectively.
Maskin Meets Abreu and Matsushima
The theory of full implementation has been criticized for using integer/modulo games which admit no equilibrium (Jackson (1992)).
Improving Factual Consistency of Abstractive Summarization on Customer Feedback
E-commerce stores collect customer feedback to let sellers learn about customer concerns and enhance customer order experience.
Direct Implementation with Evidence
In a richer model of evidence due to <cite>KT2012</cite>, we establish pure-strategy implementation with two or more agents in a direct revelation mechanism.
Universal phase dynamics in VO2 switches revealed by ultrafast operando diffraction
Strongly correlated materials that exhibit an insulator-metal transition are key candidates in the search for new computing platforms.
Materials Science Mesoscale and Nanoscale Physics
A New Approach of Data Pre-processing for Data Compression in Smart Grids
The conventional approach to pre-process data for compression is to apply transforms such as the Fourier, the Karhunen-Lo\`{e}ve, or wavelet transforms.
ROC-Guided Survival Trees and Forests
Tree-based methods are popular nonparametric tools in studying time-to-event outcomes.
Methodology
Simplex Search Based Brain Storm Optimization
Through modeling human's brainstorming process, the brain storm optimization (BSO) algorithm has become a promising population-based evolutionary algorithm.
Dynamic scaling for the growth of non-equilibrium fluctuations during thermophoretic diffusion in microgravity
Both experiments and simulations show that during the onset the fluctuations exhibit scale invariance at large wave vectors.
Soft Condensed Matter
Slowing-down of non-equilibrium concentration fluctuations in confinement
Fluctuations in a fluid are strongly affected by the presence of a macroscopic gradient making them long-ranged and enhancing their amplitude.
Soft Condensed Matter Mesoscale and Nanoscale Physics
