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Found 56 papers, 24 papers with code
Liquidity Pool Design on Automated Market Makers
Automated market makers are a popular type of decentralized exchange in which users trade assets with each other directly and automatically through a liquidity pool and a fixed pricing function.
WcDT: World-centric Diffusion Transformer for Traffic Scene Generation
To enhance the scene diversity and stochasticity, the historical trajectory data is first preprocessed and encoded into latent space using Denoising Diffusion Probabilistic Models (DDPM) enhanced with Diffusion with Transformer (DiT) blocks.
The Fine Line: Navigating Large Language Model Pretraining with Down-streaming Capability Analysis
To address the aforementioned limitations, this paper undertakes a comprehensive comparison of model capabilities at various pretraining intermediate checkpoints.
Annolid: Annotate, Segment, and Track Anything You Need
Annolid is a deep learning-based software package designed for the segmentation, labeling, and tracking of research targets within video files, focusing primarily on animal behavior analysis.
EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting
Precise camera tracking, high-fidelity 3D tissue reconstruction, and real-time online visualization are critical for intrabody medical imaging devices such as endoscopes and capsule robots.
A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation
Meanwhile, we leverage the LLM recommender as a supplemental component (discarded in deployment) to better capture underlying user preferences from heterogeneous interaction behaviors.
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations
However, existing time series anomaly detection methods fall short in tackling the unique characteristics of astronomical observations where each star is inherently independent but interfered by random concurrent noise, resulting in a high rate of false alarms.
A prediction-based forward-looking vehicle dispatching strategy for dynamic ride-pooling
For on-demand dynamic ride-pooling services, e. g., Uber Pool and Didi Pinche, a well-designed vehicle dispatching strategy is crucial for platform profitability and passenger experience.
GaussianObject: Just Taking Four Images to Get A High-Quality 3D Object with Gaussian Splatting
Then we construct a Gaussian repair model based on diffusion models to supplement the omitted object information, where Gaussians are further refined.
EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction
To adapt 3DGS for endoscopic scenes, we propose two strategies, Holistic Gaussian Initialization (HGI) and Spatio-temporal Gaussian Tracking (SGT), to handle the non-trivial Gaussian initialization and tissue deformation problems, respectively.
A Survey of Resource-efficient LLM and Multimodal Foundation Models
Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine learning lifecycle, from training to deployment.
An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks
To overcome the issues of manually identifying assumptions in DL framework development, we constructed a new and largest dataset (i. e., AssuEval) of assumptions collected from the TensorFlow and Keras repositories on GitHub; explored the performance of seven traditional machine learning models (e. g., Support Vector Machine, Classification and Regression Trees), a popular DL model (i. e., ALBERT), and a large language model (i. e., ChatGPT) of identifying assumptions on the AssuEval dataset.
An Incremental Update Framework for Online Recommenders with Data-Driven Prior
To address the aforementioned issue, we propose an incremental update framework for online recommenders with Data-Driven Prior (DDP), which is composed of Feature Prior (FP) and Model Prior (MP).
Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane
Intraoperative imaging techniques for reconstructing deformable tissues in vivo are pivotal for advanced surgical systems.
A Comprehensive End-to-End Computer Vision Framework for Restoration and Recognition of Low-Quality Engineering Drawings
Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality.
Automated Behavioral Analysis Using Instance Segmentation
To demonstrate the effectiveness of our method, we conducted a series of experiments, revealing that our approach achieves exceptional performance levels, comparable to human capabilities, across a diverse range of animal behavior analysis tasks.
Segment Any 3D Gaussians
Interactive 3D segmentation in radiance fields is an appealing task since its importance in 3D scene understanding and manipulation.
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
This work provides a novel theoretical perspective for addressing the problem of imbalanced node classification in GNNs.
Mobile Foundation Model as Firmware
Concurrently, each app contributes a concise, offline fine-tuned "adapter" tailored to distinct downstream tasks.
NLP-based detection of systematic anomalies among the narratives of consumer complaints
We develop an NLP-based procedure for detecting systematic nonmeritorious consumer complaints, simply called systematic anomalies, among complaint narratives.
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction
We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%.
Non-Concave Utility Maximization with Transaction Costs
This paper studies a finite-horizon portfolio selection problem with non-concave terminal utility and proportional transaction costs.
Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues
Reconstructing deformable tissues from endoscopic stereo videos in robotic surgery is crucial for various clinical applications.
Assessment of Anterior Cruciate Ligament Injury Risk Based on Human Key Points Detection Algorithm
This paper aims to detect the potential injury risk of the anterior cruciate ligament (ACL) by proposing an ACL potential injury risk assessment algorithm based on key points of the human body detected using computer vision technology.
Automatic Design Method of Building Pipeline Layout Based on Deep Reinforcement Learning
The layout design of pipelines is a critical task in the construction industry.
Segment Anything in 3D with Radiance Fields
The Segment Anything Model (SAM) emerges as a powerful vision foundation model to generate high-quality 2D segmentation results.
NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry Scaffolds
We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room.
A Survey of Large Language Models
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
Multifunctional fiber-based optoacoustic emitter for non-genetic bidirectional neural communication
In vivo application of mFOE for successful simultaneous optoacoustic stimulation and electrical recording of brain activities was confirmed in mouse hippocampus in both acute and chronical applications up to 1 month.
NeRF-MS: Neural Radiance Fields with Multi-Sequence
Neural radiance fields (NeRF) achieve impressive performance in novel view synthesis when trained on only single sequence data.
FedPD: Federated Open Set Recognition with Parameter Disentanglement
To this end, we aim to study a novel problem of federated open-set recognition (FedOSR), which learns an open-set recognition (OSR) model under federated paradigm such that it classifies seen classes while at the same time detects unknown classes.
A K-variate Time Series Is Worth K Words: Evolution of the Vanilla Transformer Architecture for Long-term Multivariate Time Series Forecasting
In this study, we point out that the current tokenization strategy in MTSF Transformer architectures ignores the token uniformity inductive bias of Transformers.
Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence
In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging.
SPIDR: SDF-based Neural Point Fields for Illumination and Deformation
To enable more accurate illumination updates after deformation, we use the shadow mapping technique to approximate the light visibility updates caused by geometry editing.
Multi tasks RetinaNet for mitosis detection
However, due to the variability of mitotic cell morphology, it is a highly challenging task to detect mitotic cells in tumor tissues.
Video Interpolation by Event-driven Anisotropic Adjustment of Optical Flow
In this paper, we propose an end-to-end training method A^2OF for video frame interpolation with event-driven Anisotropic Adjustment of Optical Flows.
Modeling Two-Way Selection Preference for Person-Job Fit
To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph.
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures.
Optically-generated focused ultrasound for noninvasive brain stimulation with ultrahigh precision
Here, we report optically-generated focused ultrasound (OFUS) for non-invasive brain stimulation with ultrahigh precision.
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy.
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction.
FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection
We convert a lightweight FCOSR model to TensorRT format, which achieves 73. 93 mAP on DOTA1. 0 at a speed of 10. 68 FPS on Jetson Xavier NX with single scale.
Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning
To address this problem, we propose a personalized retrogress-resilient framework to produce a superior personalized model for each client.
Photoacoustic Silk Scaffolds for Neural stimulation and Regeneration
We also confirmed that photoacoustic neural stimulation promoted neurite outgrowth by impacting the brain-derived neurotrophic factor (BDNF) pathway.
Physical Artificial Intelligence: The Concept Expansion of Next-Generation Artificial Intelligence
In this paper we explore the concept of Physicial Artifical Intelligence and propose two subdomains: Integrated Physicial Artifical Intelligence and Distributed Physicial Artifical Intelligence.
MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation
Existing self-training based UDA approaches assign pseudo labels for target data and treat them as ground truth labels to fully leverage unlabeled target data for model adaptation.
Ranked #30 on
Synthetic-to-Real Translation
on SYNTHIA-to-Cityscapes
Learning Better Sentence Representation with Syntax Information
However, how to add external knowledge to further improve the semantic modeling capability of model is worth probing.
Imaging vibrations of locally gated, electromechanical few layer graphene resonators with a moving vacuum enclosure
With it we characterize the lowest frequency mode of a FLG resonator by measuring its frequency response as a function of position on the membrane.
Mesoscale and Nanoscale Physics
Improving Machine Translation by Searching Skip Connections Efficiently
To improve the efficiency of trying different skip connection architectures, we apply the idea of network morphism to add skip connections as a procedure of fine-tuning.
Non-genetic acoustic stimulation of single neurons by a tapered fiber optoacoustic emitter
As an emerging technology, transcranial focused ultrasound has been demonstrated to successfully evoke motor responses in mice, rabbits, and sensory/motor responses in humans.
An Equilibrium Model for the Cross-Section of Liquidity Premia
We study a risk-sharing economy where an arbitrary number of heterogenous agents trades an arbitrary number of risky assets subject to quadratic transaction costs.
Detecting systematic anomalies affecting systems when inputs are stationary time series
We develop an anomaly-detection method when systematic anomalies, possibly statistically very similar to genuine inputs, are affecting control systems at the input and/or output stages.
Anomaly Detection
Time Series Analysis
Methodology
94A12, 94A13, 62P30, 62P35
CHECKED: Chinese COVID-19 Fake News Dataset
Also, misinformation on COVID-19 is frequently spread on social media.
Req2Lib: A Semantic Neural Model for Software Library Recommendation
In this work, we would like to make recommendations based on requirement descriptions to avoid these problems.
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction
Automatically segmenting sub-regions of gliomas (necrosis, edema and enhancing tumor) and accurately predicting overall survival (OS) time from multimodal MRI sequences have important clinical significance in diagnosis, prognosis and treatment of gliomas.
A resnet-based universal method for speckle reduction in optical coherence tomography images
In this work we propose a ResNet-based universal method for speckle reduction in optical coherence tomography (OCT) images.
