Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem

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Elucidating the functional effects of missense variants is crucial yet challenging. To investigate their impact, we fine-tuned protein language models, including ESM2 and ProtT5, to classify 20 protein features at amino acid resolution. In addition, we tra ...

Accurate human trajectory prediction is one of the most crucial tasks for autonomous driving, ensuring its safety. Yet, existing models often fail to fully leverage the visual cues that humans subconsciously communicate when navigating the space. In this w ...

Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challengi ...

Sports helmets provide incomplete protection against brain injuries. Here we aim to improve helmet liner efficiency by employing a novel approach that optimizes their properties. By exploiting a finite element model that simulates head impacts, we develope ...

Modeling spatial-temporal interactions among neighboring agents is at the heart of multi-agent problems such as motion forecasting and crowd navigation. Despite notable progress, it remains unclear to which extent modern representations can capture the cau ...

Adversarial robustness has been studied extensively in image classification, especially for the ℓ∞-threat model, but significantly less so for related tasks such as object detection and semantic segmentation, where attacks turn out to be a much harder opti ...

The Songdo Traffic dataset delivers precisely georeferenced vehicle trajectories captured through high-altitude bird's-eye view (BeV) drone footage over Songdo International Business District, South Korea. Comprising approximately 700,000 unique trajectori ...

Density-functional theory with extended Hubbard functionals (DFT + U + V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to ...

We present a method to control a text-to-image generative model to produce training data useful for supervised learning. Unlike previous works that employ an open-loop approach via pre-defined prompts to generate new data using either a language model or h ...

In this work, we investigate Speech Foundation Models (SFMs) for Parkinson's Disease (PD) detection. We explore two main approaches: (1) using SFMs as frozen feature extractors and, (2) fine-tuning/adapting SFMs for PD detection. We propose a cross-validat ...

Recent advancements in natural language processing, driven by the success of large language models, have led to the scaling up of models to unprecedented sizes. While these models have achieved remarkable performance across various tasks, their computation ...

Cell tracking is a ubiquitous image analysis task in live-cell microscopy. Unlike multiple object tracking (MOT) for natural images, cell tracking typically involves hundreds of similar-looking objects that can divide in each frame, making it a particularl ...

With the advent of surveys like Euclid and Vera C. Rubin, astrophysicists will have access to both deep, high-resolution images and multiband images. However, these two types are not simultaneously available in any single dataset. It is therefore vital to ...

There is growing excitement about the potential of leveraging artificial intelligence (AI) to tackle some of the outstanding barriers to the full deployment of robots in daily lives. However, action and sensing in the physical world pose greater and differ ...

Snow height measurements are still the backbone of any snow cover monitoring, whether based on modeling or remote sensing. These ground-based measurements are often realized using ultrasonic or laser technologies. In challenging environments, such as high ...

Large language models (LLMs) have gained widespread interest owing to their ability to process human language and perform tasks on which they have not been explicitly trained. However, we possess only a limited systematic understanding of the chemical capa ...

Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘pre ...

Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes ...

© The Author(s) 2024.Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, h ...

Many technologically important material properties are underpinned by disorder and short-range structural correlations; therefore, elucidating structure-property relationships in functional materials requires understanding both the average and the local st ...

Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem

Chat with Graph Search

Fine-tuning protein language models to understand the functional impact of missense variants

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches

Helmet material design for mitigating traumatic axonal injuries through AI-driven constitutive law enhancement

Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations

Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models

Songdo Traffic: High Accuracy Georeferenced Vehicle Trajectories from a Large-Scale Study in a Smart City

Machine learning Hubbard parameters with equivariant neural networks

Controlled Training Data Generation with Diffusion Models

Automatic Parkinson's disease detection from speech: Layer selection vs adaptation of foundation models

Parameter-efficient Methods for Training and Inference of Transformer-based Models

TRACKASTRA: Transformer-Based Cell Tracking for Live-Cell Microscopy

Joint Multiband Deconvolution for Euclid and Vera C. Rubin Images

A Roadmap for Ai in Robotics

Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2)

A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists

A prediction rigidity formalism for low-cost uncertainties in trained neural networks

Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process

A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting

Unravelling the components of diffuse scattering using deep learning