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Found 53 papers, 12 papers with code
The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters
Theory-of-Mind (ToM) is a fundamental psychological capability that allows humans to understand and interpret the mental states of others.
Is Segment Anything Model 2 All You Need for Surgery Video Segmentation? A Systematic Evaluation
Surgery video segmentation is an important topic in the surgical AI field.
An Immersive Multi-Elevation Multi-Seasonal Dataset for 3D Reconstruction and Visualization
Significant progress has been made in photo-realistic scene reconstruction over recent years.
Machine Learning Co-pilot for Screening of Organic Molecular Additives for Perovskite Solar Cells
Machine learning (ML) has been extensively employed in planar perovskite photovoltaics to screen effective organic molecular additives, while encountering predictive biases for novel materials due to small datasets and reliance on predefined descriptors.
Can OpenAI o1 outperform humans in higher-order cognitive thinking?
For scientific reasoning, it achieved near-perfect performance (mean = 0. 99, SD = 0. 12) on the TOSLS,, exceeding the highest human scores of 0. 85, SD = 0. 13 (z = 1. 78).
QueEn: A Large Language Model for Quechua-English Translation
Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics.
Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research
Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity.
Trajectory Attention for Fine-grained Video Motion Control
This paper introduces trajectory attention, a novel approach that performs attention along available pixel trajectories for fine-grained camera motion control.
Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation
Despite significant improvements, we observe that semantic ambiguity persists when generating multiple similar subjects, as the guidance provided by overlap loss is not explicit enough.
OracleSage: Towards Unified Visual-Linguistic Understanding of Oracle Bone Scripts through Cross-Modal Knowledge Fusion
Oracle bone script (OBS), as China's earliest mature writing system, present significant challenges in automatic recognition due to their complex pictographic structures and divergence from modern Chinese characters.
Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translation
Our comparison with the zero-shot performance of GPT-4o and LLaMA 3. 1 405B, highlights the significant challenges these models face when translating into low-resource languages.
Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios
Artificial Intelligence (AI) has become essential in modern healthcare, with large language models (LLMs) offering promising advances in clinical decision-making.
Using Generative AI and Multi-Agents to Provide Automatic Feedback
This study investigates the use of generative AI and multi-agent systems to provide automatic feedback in educational contexts, particularly for student constructed responses in science assessments.
MvDrag3D: Drag-based Creative 3D Editing via Multi-view Generation-Reconstruction Priors
To overcome these limitations, we introduce MVDrag3D, a novel framework for more flexible and creative drag-based 3D editing that leverages multi-view generation and reconstruction priors.
Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents
This paper presents a novel application of large language models (LLMs) to enhance user comprehension of privacy policies through an interactive dialogue agent.
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis
Neuromorphic computing has emerged as a promising energy-efficient alternative to traditional artificial intelligence, predominantly utilizing spiking neural networks (SNNs) implemented on neuromorphic hardware.
A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Education
This study evaluates OpenAI o1-preview's ability to perform higher-order cognitive tasks across 14 dimensions, including critical thinking, systems thinking, computational thinking, design thinking, metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning.
Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law
To address this issue, we propose an efficient law article recommendation approach utilizing a Knowledge Graph (KG) and a Large Language Model (LLM).
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
-Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.
Trustworthy Conceptual Explanations for Neural Networks in Robot Decision-Making
Black box neural networks are an indispensable part of modern robots.
Quantum-Powered Personalized Learning
This paper explores the transformative potential of quantum computing in the realm of personalized learning.
Rebalancing Multi-Label Class-Incremental Learning
To address the issue above, we propose a Rebalance framework for both the Loss and Label levels (RebLL), which integrates two key modules: asymmetric knowledge distillation (AKD) and online relabeling (OR).
Enhancing Fine-grained Object Detection in Aerial Images via Orthogonal Mapping
This letter introduces Orthogonal Mapping (OM), a simple yet effective method aimed at addressing the challenge of semantic confusion inherent in FGOD.
Physics-Informed AI Inverter
This letter devises an AI-Inverter that pilots the use of a physics-informed neural network (PINN) to enable AI-based electromagnetic transient simulations (EMT) of grid-forming inverters.
Transparent Object Depth Completion
The perception of transparent objects for grasp and manipulation remains a major challenge, because existing robotic grasp methods which heavily rely on depth maps are not suitable for transparent objects due to their unique visual properties.
Video Diffusion Models are Training-free Motion Interpreter and Controller
MOFT provides a distinct set of benefits, including the ability to encode comprehensive motion information with clear interpretability, extraction without the need for training, and generalizability across diverse architectures.
Delta Tensor: Efficient Vector and Tensor Storage in Delta Lake
The exponential growth of artificial intelligence (AI) and machine learning (ML) applications has necessitated the development of efficient storage solutions for vector and tensor data.
Dense Vision Transformer Compression with Few Samples
In particular, the issue of sparse compression exists in traditional CNN few-shot methods, which can only produce very few compressed models of different model sizes.
Confidence Self-Calibration for Multi-Label Class-Incremental Learning
The partial label challenge in Multi-Label Class-Incremental Learning (MLCIL) arises when only the new classes are labeled during training, while past and future labels remain unavailable.
FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation
In this paper, we introduce FRESCO, intra-frame correspondence alongside inter-frame correspondence to establish a more robust spatial-temporal constraint.
AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation
However, simply adopting models derived from DFKD for real-world applications suffers significant performance degradation, due to the discrepancy between teachers' training data and real-world scenarios (student domain).
BAGS: Blur Agnostic Gaussian Splatting through Multi-Scale Kernel Modeling
Recent efforts in using 3D Gaussians for scene reconstruction and novel view synthesis can achieve impressive results on curated benchmarks; however, images captured in real life are often blurry.
Image Denoising with Machine Learning: A Novel Approach to Improve Quantum Image Processing Quality and Reliability
Quantum Image Processing (QIP) is a field that aims to utilize the benefits of quantum computing for manipulating and analyzing images.
Task Success is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors
Large-scale generative models are shown to be useful for sampling meaningful candidate solutions, yet they often overlook task constraints and user preferences.
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction
This paper presents a unique solution to challenges in medical image processing by incorporating an adaptive curve grey wolf optimization (ACGWO) algorithm into neural network backpropagation.
Revolutionizing Finance with LLMs: An Overview of Applications and Insights
Additionally, we conducted holistic tests on multiple financial tasks through the combination of natural language instructions.
DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing
Our key idea is to capture the semantics of the two images by fitting two LoRAs to them respectively, and interpolate between both the LoRA parameters and the latent noises to ensure a smooth semantic transition, where correspondence automatically emerges without the need for annotation.
Software-Defined Virtual Synchronous Condenser
Synchronous condensers (SCs) play important roles in integrating wind energy into relatively weak power grids.
Physics-Informed Induction Machine Modelling
This rapid communication devises a Neural Induction Machine (NeuIM) model, which pilots the use of physics-informed machine learning to enable AI-based electromagnetic transient simulations.
Scalable Neural Dynamic Equivalence for Power Systems
Traditional grid analytics are model-based, relying strongly on accurate models of power systems, especially the dynamic models of generators, controllers, loads and other dynamic components.
Physics-Aware Neural Dynamic Equivalence of Power Systems
The contributions are threefold: (1) an ODE-Net-enabled NeuDyE formulation to enable a continuous-time, data-driven dynamic equivalence of power systems; (2) a physics-informed NeuDyE learning method (PI-NeuDyE) to actively control the closed-loop accuracy of NeuDyE without an additional verification module; (3) a physics-guided NeuDyE (PG-NeuDyE) to enhance the method's applicability even in the absence of analytical physics models.
Implementation of The Future of Drug Discovery: QuantumBased Machine Learning Simulation (QMLS)
This paper is based on our first paper, where we pitched the concept of machine learning combined with quantum simulations.
Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation
The framework includes two parts: key frame translation and full video translation.
Neuro-Dynamic State Estimation for Networked Microgrids
We devise neuro-dynamic state estimation (Neuro-DSE), a learning-based dynamic state estimation (DSE) algorithm for networked microgrids (NMs) under unknown subsystems.
PanoFlow: Learning 360° Optical Flow for Surrounding Temporal Understanding
PanoFlow achieves state-of-the-art performance on the public OmniFlowNet and the established FlowScape benchmarks.
CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Driving
In this paper, we propose a new deep network architecture for optical flow estimation in autonomous driving--CSFlow, which consists of two novel modules: Cross Strip Correlation module (CSC) and Correlation Regression Initialization module (CRI).
Explainable Sentence-Level Sentiment Analysis for Amazon Product Reviews
For the study of interpretability, we consider the attention weights distribution of single sentence and the attention weights of main aspect terms.
Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems
Transient stability assessment (TSA) is a cornerstone for resilient operations of today's interconnected power grids.
Friends and Foes in Learning from Noisy Labels
Learning from examples with noisy labels has attracted increasing attention recently.
Neuro-Reachability of Networked Microgrids
A neural ordinary differential equations network (ODE-Net)-enabled reachability method (Neuro-Reachability) is devised for the dynamic verification of networked microgrids (NMs) with unidentified subsystems and heterogeneous uncertainties.
Indications for very high metallicity and absence of methane for the eccentric exo-Saturn WASP-117b
Using a 1D atmosphere model with isothermal temperature, uniform cloud deck and equilibrium chemistry, the Bayesian evidence of a retrieval analysis of the transmission spectrum indicates a preference for a high atmospheric metallicity ${\rm [Fe/H]}=2. 58^{+0. 26}_{-0. 37}$ and clear skies.
Earth and Planetary Astrophysics Solar and Stellar Astrophysics
Reliability Validation of Learning Enabled Vehicle Tracking
However, it is unclear if and how the adversarial examples over learning components can affect the overall system-level reliability.
Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)
This paper proposes an approach to detect moving objects in Wide Area Motion Imagery (WAMI), in which the objects are both small and well separated.
