State-of-the-art web services for de novo protein structure prediction

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Atom probe tomography (APT) is a burgeoning characterization technique that provides compositional mapping of materials in three-dimensions at near-atomic scale. Since its significant expansion in the past 30 years, we estimate that one million APT dataset ...

While convolution and self-attention mechanisms have dominated architectural design in deep learning, this survey examines a fundamental yet understudied primitive: the Hadamard product. Despite its widespread implementation across various applications, th ...

While stability analysis is a mainstay for control science, especially computing regions of attraction of equilibrium points, until recently most stability analysis tools always required explicit knowledge of the model or a high-fidelity simulator represen ...

The rise of high-throughput first-principles calculations of materials since the turn of the millennium has gifted the fields of physics, quantum chemistry, and materials science with a mountainous and ever-growing pile of data ready to be mined for hidden ...

This work investigates adversarial training in the context of margin-based linear classifiers in the high-dimensional regime where the dimension d and the number of data points n diverge with a fixed ratio α = n/d. We introduce a tractable mathematical mod ...

The Songdo Vision dataset provides high-resolution (4K, 3840×2160 pixels) RGB images annotated with categorized axis-aligned bounding boxes (BBs) for vehicle detection from a high-altitude bird’s-eye view (BeV) perspective. Captured over Songdo Internation ...

The uncanny ability of over-parameterised neural networks to generalise well has been explained using various ‘simplicity biases’. These theories postulate that neural networks avoid overfitting by first fitting simple, linear classifiers before learning m ...

We introduce a new task, novel view synthesis for LiDAR sensors. While traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel views, they fall short of producing accurate and realistic LiDAR patterns bec ...

Planning for diverse real-world robotic tasks necessitates to know and write all constraints. However, instances exist where these constraints are either unknown or challenging to specify accurately. A possible solution is to infer the unknown constraints ...

Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate recommendations. ...

Metal additive manufacturing is a recent breakthrough technology that promises automated production of complex geometric shapes at low operating costs. However, its potential is not yet fully exploited due to the low reproducibility of quality in mass prod ...

The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens ...

Constrained Markov decision processes (CMDPs) are a common way to model safety constraints in reinforcement learning. State-of-the-art methods for efficiently solving CMDPs are based on primal-dual algorithms. For these algorithms, all currently known regr ...

The rapid advancement of artificial intelligence (AI) has greatly influenced numerous research areas, leading to significant breakthroughs in human face-related technologies, particularly in face recognition and deepfake detection. While offering substanti ...

In these days of information overload and high-throughput analysis, it is easy to lose focus on the study of individual proteins. It is our conjecture that such investigations are still crucially important and offer uniquely penetrative insights. We thus p ...

State-of-the-art web services for de novo protein structure prediction

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Machine learning enhanced atom probe tomography analysis

Hadamard product in deep learning: Introduction, Advances and Challenges

Robustly Learning Regions of Attraction From Fixed Data

Representing and machine learning complex electronic-structure properties of materials

A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs

Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City

Neural networks trained with SGD learn distributions of increasing complexity

LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields

Positive-Unlabeled Constraint Learning for Inferring Nonlinear Continuous Constraints Functions From Expert Demonstrations

Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention

Unsupervised quality monitoring of metal additive manufacturing using Bayesian adaptive resonance

Assessment of fine-tuned large language models for real-world chemistry and material science applications

Truly No-Regret Learning in Constrained MDPs

Enhancing Explainability and Performance in AI-Driven Face Recognition and Deepfake Detection

Let's get biophysical - How to get your favorite protein's digits☆