AmadeusGPT: a natural language interface for interactive animal behavioral analysis

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As urban populations continue to expand and vehicle density increases, the need for effective traffic monitoring and management becomes increasingly critical. Urban areas face significant challenges such as growing congestion, safety concerns, and parking ...

PSI and North Carolina State University are developing a high-resolution multi-physics core solver for Pressurized Water Reactor (PWR) analysis in Cartesian geometry, using the neutron transport code nTRACER and two Machine Learning (ML) models providing t ...

This paper focuses on source-free domain adaptation for object detection in computer vision. This task is challenging and of great practical interest, due to the cost of obtaining annotated data sets for every new domain. Recent research has proposed vario ...

This chapter aims to provide a gentle introduction to Causal Learning (CL), and motivation for its application to medical image analysis, seeking for more robustness against data and domain drifts, and a reliable tool to answer counterfactual questions and ...

Quantum extreme learning machines (QELMs) have emerged as a promising framework for quantum machine learning. Their appeal lies in the rich feature map induced by the dynamics of a quantum substrate-the quantum reservoir-and the efficient post-measurement ...

Pronunciation is a critical yet challenging aspect of language acquisition, especially for children. In this work, we propose a new teachable agent system designed to support children's pronunciation learning through a Learning-by-Teaching paradigm to addr ...

Visual grouping is the ability to combine parts of objects to form whole objects, and it is crucial for general and robust vision. Despite this, a comprehensive model of how the process works in humans remains elusive. In my thesis, I investigate how well ...

Attention-based transformers have achieved tremendous success across a variety of disciplines including natural languages. To deepen our understanding of their sequential modeling capabilities, there is a growing interest in using Markov input processes to ...

During fear learning, associations between a sensory cue (conditioned stimulus, CS) and an aversive stimulus (unconditioned stimulus, US) are formed in specific brain circuits. The lateral amygdala (LA) is involved in CS-US integration; however, US pathway ...

This paper presents STEEL-3dPointClouds, a dataset of deformed steel beam-columns obtained using high-fidelity physics-based numerical simulations. These simulations trace the inelastic deformations of hot-rolled wide-flange steel beam-columns under differ ...

Protein engineering has recently seen tremendous transformation due to machine learning (ML) tools that predict structure from sequence at unprecedented precision. Predicting catalytic activity, however, remains challenging, restricting our capabilities to ...

Discovering principles underlying the control of animal behavior requires a tight dialogue between experiments and neuromechanical models. Such models have primarily been used to investigate motor control with less emphasis on how the brain and motor syste ...

Deep neural networks (DNNs) provide state-of-the-art accuracy for vision tasks, but they require significant resources for training. Thus, they are trained on cloud servers far from the edge devices that acquire the data. This issue increases communication ...

While Machine Learning (ML) applications have shown impressive achievements in tasks such as computer vision, NLP, and control problems, such achievements were possible, first and foremost, in the best-case-scenario setting. Unfortunately, settings where M ...

Graph neural network (GNN) models have presented astonishing achievements in various application fields. However, they are shown to be vulnerable to adversarial attacks on graph structure and unnoticeable perturbations on the graph structure can cause sign ...

Machine Learning (ML) and linear System Identification (SI) have been historically developed independently. In this paper, we leverage well-established ML tools - especially the automatic differentiation framework - to introduce SIMBa, a family of discrete ...

We present the development of a new algorithm which combines state-of-the-art energy-dispersive x-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spec ...

We study the impact of the batch

n_b

on the iteration time

T

of training two-layer neural networks with one-pass stochastic gradient descent (SGD) on multi-index target functions of isotropic covariates. We characterize the optimal batch size minimizin ...

The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samp ...

Aging populations face significant cognitive challenges, particularly in working memory (WM). Transcranial alternating current stimulation (tACS) offer promising avenues for cognitive enhancement, especially when inspired by brain physiology. This study (N ...

AmadeusGPT: a natural language interface for interactive animal behavioral analysis

Chattez avec Graph Search

Drone-Based Urban Traffic Monitoring and User Behavior Prediction

High-resolution predictions of the coolant properties for the 3D PWR core with artificial neural networks based on CTF

Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-training Strategies and Performance Insights

Causality: fundamental principles and tools

On fundamental aspects of quantum extreme learning machines

Learning by Teaching: Designing Teachable Agents to Support Children's Pronunciation Skill Learning

Differences in Visual Perception in Humans and Deep Neural Networks

Attention with Markov: A Curious Case of Single-layer Transformers

A posterior insula to lateral amygdala pathway transmits US-offset information with a limited role in fear learning

STEEL-3dPointClouds: dataset supporting quantification of residual life and reusability of steel beam-columns

Data-driven protease engineering by DNA-recording and epistasis-aware machine learning

NeuroMechFly v2: simulating embodied sensorimotor control in adult Drosophila

Hierarchical Training of Deep Neural Networks Using Early Exiting

Adversarial Evasion on LLMs

A Joint Graph Signal and Laplacian Denoising Network

Stable Linear Subspace Identification: A Machine Learning Approach

From STEM-EDXS data to phase separation and quantification using physics-guided NMF

Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs

Euclid preparation XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning

Physiology-inspired bifocal fronto-parietal tACS for working memory enhancement