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Found 47 papers, 6 papers with code
Streamlining Biomedical Research with Specialized LLMs
In this paper, we propose a novel system that integrates state-of-the-art, domain-specific large language models with advanced information retrieval techniques to deliver comprehensive and context-aware responses.
Insights into dendritic growth mechanisms in batteries: A combined machine learning and computational study
In this study, we investigated dendrite growth mechanisms in batteries using a combined machine learning approach, specifically a two-dimensional artificial convolutional neural network (CNN) model, along with computational methods.
Battery State of Health Estimation and Incremental Capacity Analysis for Charging with General Current Profiles Using Neural Networks
Second, two related convolutional neural networks (CNNs), called U-Net and Conv-Net, are proposed to construct virtual IC/DV curves and estimate the state of health (SOH) from general charging profiles across any state-of-charge (SOC) ranges that satisfy some constraints.
Physics-Driven Self-Supervised Deep Learning for Free-Surface Multiple Elimination
Instead of relying on traditional supervised learning, in the context of free-surface multiple elimination, we propose a method in which the DL model learns to effectively parameterize the free-surface multiple-free wavefield from the full wavefield by incorporating the underlying physics into the loss computation.
Physics-Trained Neural Network as Inverse Problem Solver for Potential Fields: An Example of Downward Continuation between Arbitrary Surfaces
Downward continuation is a critical task in potential field processing, including gravity and magnetic fields, which aims to transfer data from one observation surface to another that is closer to the source of the field.
Aligning First, Then Fusing: A Novel Weakly Supervised Multimodal Violence Detection Method
Weakly supervised violence detection refers to the technique of training models to identify violent segments in videos using only video-level labels.
Anomaly Detection In Surveillance Videos
Multiple Instance Learning
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Super-Resolution for Remote Sensing Imagery via the Coupling of a Variational Model and Deep Learning
Image super-resolution (SR) is an effective way to enhance the spatial resolution and detail information of remote sensing images, to obtain a superior visual quality.
A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method
In this paper, we propose a novel two-step image super resolution method concatenating multi-frame super-resolution (MFSR) with single-frame super-resolution (SFSR), to progressively upsample images to the desired resolution.
The Fusion of Large Language Models and Formal Methods for Trustworthy AI Agents: A Roadmap
Finally, we show that unifying these two computation paradigms -- integrating the flexibility and intelligence of LLMs with the rigorous reasoning abilities of FMs -- has transformative potential for the development of trustworthy AI software systems.
MaCTG: Multi-Agent Collaborative Thought Graph for Automatic Programming
With the rapid advancement of Large Language Models (LLMs), LLM-based approaches have demonstrated strong problem-solving capabilities across various domains.
Nonlinear Magnetics Model for Permanent Magnet Synchronous Machines Capturing Saturation and Temperature Effects
This paper proposes a nonlinear magnetics model for Permanent Magnet Synchronous Machines (PMSMs) that accurately captures the effects of magnetic saturation in the machine iron and variations in rotor temperature on the permanent magnet excitation.
FTSmartAudit: A Knowledge Distillation-Enhanced Framework for Automated Smart Contract Auditing Using Fine-Tuned LLMs
Our experimental results demonstrate that smaller models can surpass state-of-the-art commercial models and tools in detecting vulnerabilities in smart contracts.
Using Convolutional Neural Networks for Denoising and Deblending of Marine Seismic Data
Deblending in common channel domain with the use of a CNN yields relatively good results and is an improvement compared to shot domain.
Deep learning-based shot-domain seismic deblending
To streamline fast-track processing of large data volumes, we have developed a deep learning approach to deblend seismic data in the shot domain based on a practical strategy for generating high-quality training data along with a list of data conditioning techniques to improve performance of the data-driven model.
A convolutional neural network approach to deblending seismic data
The network is also demonstrated to be robust and adaptive by using the trained model to firstly deblend a new data set from a different geological area with a slightly different delay time setting, and secondly deblend shots with blending noise in the top part of the data.
MSDiagnosis: A Benchmark for Evaluating Large Language Models in Multi-Step Clinical Diagnosis
In this paper, we propose a Chinese clinical diagnostic benchmark, called MSDiagnosis.
DMSD-CDFSAR: Distillation from Mixed-Source Domain for Cross-Domain Few-shot Action Recognition
In the first branch, a Domain Temporal Encoder is employed to capture temporal features for both the source and target domains.
PharmaGPT: Domain-Specific Large Language Models for Bio-Pharmaceutical and Chemistry
Large language models (LLMs) have revolutionized Natural Language Processing (NLP) by minimizing the need for complex feature engineering.
PatentGPT: A Large Language Model for Intellectual Property
In recent years, large language models(LLMs) have attracted significant attention due to their exceptional performance across a multitude of natural language process tasks, and have been widely applied in various fields.
Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation
Our proposed Behavior-Contextualized Item Preference Network discerns and learns users' specific item preferences within each behavior.
Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs).
State of Health Estimation for Battery Modules with Parallel-Connected Cells Under Cell-to-Cell Variations
State of health (SOH) estimation for lithium-ion battery modules with cells connected in parallel is a challenging problem, especially with cell-to-cell variations.
Robust Model Predictive Control for Enhanced Fast Charging on Electric Vehicles through Integrated Power and Thermal Management
This paper explores the synergies between integrated power and thermal management (iPTM) and battery charging in an electric vehicle (EV).
Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition
The second module (MLT) focuses on the Multiple-level feature of the support prototype and query sample to mine more information for the alignment, which operates on different level features.
MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation
In this paper, we propose MB-HGCN, a novel multi-behavior recommendation model that uses a hierarchical graph convolutional network to learn user and item embeddings from coarse-grained on the global level to fine-grained on the behavior-specific level.
Introduction of an intriguing approach for eletric current transformer on-site examining repairing
However, due to the frequent occurrence of full current passing through the current transformer during use, its secondary winding has relatively more turns.
Decision-making with Speculative Opponent Models
To address this issue, we introduce Distributional Opponent-aided Multi-agent Actor-Critic (DOMAC), the first speculative opponent modelling algorithm that relies solely on local information (i. e., the controlled agent's observations, actions, and rewards).
Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation
In particular, we design a cascading residual graph convolutional network structure, which enables our model to learn user preferences by continuously refining user embeddings across different types of behaviors.
Control Co-design of a Hydrokinetic Turbine with Open-loop Optimal Control
The optimization formulation incorporates a coupled dynamic-hydrodynamic model to maximize the rotor power efficiency for various time-variant flow profiles.
Energy-optimal Three-dimensional Path-following Control of Autonomous Underwater Vehicles under Ocean Currents
In the first stage, the surge velocity, heave velocity, and pitch angle setpoints are optimized by minimizing the required vehicle propulsion energy under currents, and the line-of-sight (LOS) guidance law is used to generate the yaw angle setpoint that ensures path following.
Eco-Coasting Strategies Using Road Grade Preview: Evaluation and Online Implementation Based on Mixed Integer Model Predictive Control
However, the comprehensive comparison between different coasting strategies and online performance of the eco-coasting strategy using road grade preview are still unclear because of the oversimplification and the integer variable in the optimal control problems.
Artificial Neural Network and its Application Research Progress in Distillation
Artificial neural networks learn various rules and algorithms to form different ways of processing information, and have been widely used in various chemical processes.
An Empirical Study on End-to-End Singing Voice Synthesis with Encoder-Decoder Architectures
With the rapid development of neural network architectures and speech processing models, singing voice synthesis with neural networks is becoming the cutting-edge technique of digital music production.
Finite difference method for inhomogeneous fractional Dirichlet problem
Based on this splitting, we respectively discretize the one- and two-dimensional integral fractional Laplacian with the inhomogeneous Dirichlet boundary condition and give the corresponding truncation errors with the help of the interpolation estimate.
Numerical Analysis Numerical Analysis
A Unified Joint Maximum Mean Discrepancy for Domain Adaptation
From the revealed unified JMMD, we illustrate that JMMD degrades the feature-label dependence (discriminability) that benefits to classification, and it is sensitive to the label distribution shift when the label kernel is the weighted class conditional one.
Robust State of Health Estimation of Lithium-ion Batteries Using Convolutional Neural Network and Random Forest
The challenge lies in the fact that partial discharge truncates the data available for SOH estimation, thereby leading to the loss or distortion of common SOH indicators.
Sparsely-Labeled Source Assisted Domain Adaptation
However, there are usually a large number of unlabeled data but only a few labeled data in the source domain, and how to transfer knowledge from this sparsely-labeled source domain to the target domain is still a challenge, which greatly limits their application in the wild.
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture
First, the efficiency of the Bayesian optimization is improved, where multiple input locations are evaluated massively parallel in an asynchronous manner to accelerate the optimization convergence with respect to physical runtime.
Importance Filtered Cross-Domain Adaptation
Firstly, an importance filtered mechanism is devised to generate filtered soft labels to mitigate negative transfer desirably.
Question Answering based Clinical Text Structuring Using Pre-trained Language Model
Clinical text structuring is a critical and fundamental task for clinical research.
Trainable back-propagated functional transfer matrices
In experiments, it is demonstrated that the revised rules can be used to train a range of functional connections: 20 different functions are applied to neural networks with up to 10 hidden layers, and most of them gain high test accuracies on the MNIST database.
The Impact of Road Configuration in V2V-based Cooperative Localization: Mathematical Analysis and Real-world Evaluation
It has been shown, in our previous work, that the GNSS error can be reduced from several meters to sub-meter level by matching the biased GNSS positioning to a digital map with road constraints.
Systems and Control
Optimization of Vehicle Connections in V2V-based Cooperative Localization
Cooperative map matching (CMM) uses the Global Navigation Satellite System (GNSS) positioning of a group of vehicles to improve the standalone localization accuracy.
Systems and Control
Improving Localization Accuracy in Connected Vehicle Networks Using Rao-Blackwellized Particle Filters: Theory, Simulations, and Experiments
A Rao-Blackwellized particle filter (RBPF) is used to jointly estimate the common biases of the pseudo-ranges and the vehicle positions.
Systems and Control
