Mantle convection plays a fundamental role in the long-term thermal evolution of terrestrial planets like Earth, Mars, Mercury and Venus. Yet, key parameters and initial conditions of the partial differential equations governing mantle convection are poorly constrained. This often requires a large sampling of the parameter space to determine which ...
Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation....
The fast and affordable development of novel materials is needed in order to enable technological advancements in application areas such as clean energy, healthcare, sustainable transport, and climate-friendly consumption. However, the development of novel materials is not a trivial task. One of the biggest challenges in materials design and discov...
A cluster of research in Affective Computing suggests that it is possible to infer some characteristics of users’ affective states by analyzing their electrophysiological activity in real-time. However, it is not clear how to use the information extracted from electrophysiological signals to create visual representations of the affective states of ...
Deep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity a...
High-Level Synthesis (HLS) improves productivity compared to Register-Transfer Level (RTL) hardware descriptions. Despite its rise in popularity, there is still a performance gap compared to RTL design flows. One promising approach to bridge this gap has been the use of Domain-Specific Languages (DSLs), which allow increasing performance by restric...
Due to their great performance and scalability properties, deep neural networks have become ubiquitous building blocks of many applications. With the rise of mobile and IoT devices, these models now are also being increasingly deployed and trained in distributed settings, where data is heterogeneous and separated by limited communication channels a...
Anomaly detection is the problem of identifying unusual patterns in data. This problem is relevant for a wide variety of applications in various domains such as fault and damage detection in manufacturing, fraud detection in finance and insurance, intrusion detection in cybersecurity, disease detection in medical diagnosis, or scientific discovery....
This article presents the multimodal BigEarthNet (BigEarthNet-MM) benchmark archive consisting of 590,326 pairs of Sentinel-1 and Sentinel-2 image patches to support deep learning (DL) studies in multimodal, multilabel remote sensing (RS) image retrieval and classification. Each pair of patches in BigEarthNet-MM is annotated with multilabels provid...
In this paper, we demonstrate an application of the Transformer self-attention mechanism in the context of materials science. Our network, the Compositionally Restricted Attention-Based network (CrabNet), explores the area of structure-agnostic materials property predictions when only a chemical formula is provided. Our results show that CrabNet’s ...
