[Re] Meta learning with differentiable closed-form solvers

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Animals have to navigate complex environments and perform intricate swimming maneuvers in the real world. To conquer these challenges, animals evolved a variety of motion control strategies. While it is known that many factors contribute to motion control, ...

FDTD/FEM simulations, strengthened by machine learning, achieve 95.44% optical absorption efficiency in TiO 2 /TiN plasmonic nanocavities for solar energy harvesting. ...

Real-time bioaerosol monitoring is improving the quality of life for people affected by allergies, but it often relies on deep learning models which pose challenges for widespread adoption. These models are typically trained in a supervised fashion and req ...

Atomic force microscopy (AFM), a member of the scanning probe microscopy (SPM) family, holds a unique position as a nano-characterization instrument in the fields of physics, chemistry, and biology. Its ability to provide atomic resolution and operate in v ...

Defending aligned Large Language Models (LLMs) against jailbreaking attacks is a challenging problem, with existing approaches requiring multiple requests or even queries to auxiliary LLMs, making them computationally heavy. Instead, we focus on detecting ...

Neurons in the human amygdala and hippocampus are classically thought to encode a person’s identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semanti ...

Cloud computing environments increasingly provision FPGAs because of their fine-grained, highly parallel, and flexible hardware architecture. The availability of FPGAs in the cloud fueled research on the security risks associated with exposing FPGA fabric ...

Understanding the concept of dynamic aperture provides essential insights into nonlinear beam dynamics, beam losses, and the beam lifetime in circular particle accelerators. This comprehension is crucial for the functioning of modern hadron synchrotrons li ...

Augmented reality in additive fabrication is predominantly applied to the manufacturing of structures from regularly shaped materials. Our work however embraces natural heterogeneity, by focusing on the assembly of irregularly shaped elements such as miner ...

Studies of cybersickness (CS) often require a significant invest- ment in creating the primary VR environment and other experiment- related features. In addition, minor differences in VR content in independent studies may lead to opposite results. This mot ...

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

Forecasting is a capability inherent in humans when navigating. Humans routinely plan their paths, considering the potential future movements of those around them. Similarly, to achieve comparable sophistication and safety, autonomous systems must embrace ...

Background: 3D reconstruction of Wilms’ tumor provides several advantages but are not systematically performed because manual segmentation is extremely time-consuming. The objective of our study was to develop an artificial intelligence tool to automate th ...

The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we p ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

Energy efficiency is crucial for the sustainable operation of all industrial and urban sectors. However, practicing engineers have seldom access to open-source tools that can readily evaluate and compare scenarios in terms of energy consumption, cost, and ...

Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs have enabled new avenues, specifically the inclusion of spatial data (envir ...

Prediction of ground motion triggered by earthquakes is a prime concern for both the seismology community and geotechnical earthquake engineering one. The subfield occupied with such a problem is termed site response analysis (SRA), its one-dimensional fla ...

Purpose The aim of this study was to develop a novel approach for predicting the expression status of Epidermal Growth Factor Receptor (EGFR) and its subtypes in patients with Non-Small Cell Lung Cancer (NSCLC) using a Three-Dimensional Convolutional Neura ...

Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based medical image segmentation, the robust performance on various sizes of lesi ...

[Re] Meta learning with differentiable closed-form solvers

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Using deep reinforcement learning to investigate stretch feedback during swimming of the lamprey

Machine-learning-empowered FDTD/FEM simulations for predictive solar energy absorption in plasmonic metamaterial nanocavity arrays

Self-supervised and few-shot learning for robust bioaerosol monitoring

Advancing atomic force microscopy through open source instrumentation

Single-pass Detection of Jailbreaking Input in Large Language Models

Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus

Practical Implementations of Remote Power Side-Channel and Fault-Injection Attacks on Multitenant FPGAs

Assessing the Performance of Deep Learning Predictions for Dynamic Aperture of a Hadron Circular Particle Accelerator

Projector-based augmented stacking framework for irregularly shaped objects

Cybersickness assessment framework(CSAF): An Open Source Repository for Standardized Cybersickness Experiments

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (1/6)

Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

Automation of Wilms’ tumor segmentation by artificial intelligence

Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

ROSMOSE: A Web-based Optimization Tool to Aid Decision-making for the Design and Operation of Industrial and Urban Energy Systems

Multi-Scale and Multimodal Species Distribution Modeling

Data-driven 1D wave propagation for site response analysis

Advancing EGFR mutation subtypes prediction in NSCLC by combining 3D pretrained ConvNeXt, radiomics, and clinical features

Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation