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Data centers like this Google facility in Iowa use copious amounts of electricity

Lexical borrowing, the transfer of words from one language to another, is one of the most frequent processes in language evolution. In order to detect borrowings, linguists make use of various strategies, combining evidence from various sources. Despite the increasing popularity of computational...

Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges. Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies have been proposed as an...

Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy’s utility. Recently, it has been shown that artificial intelligence (AI) can transform one...

Materials discovery is rapidly revolutionizing all aspects of our lives. However, the design and fabrication of materials are often unsustainable and resource-intensive. Hence, we need a paradigm shift towards designing sustainable materials in silico. Machine learning, a subfield of artificial...

Data is the bread and butter of a Data Scientist, so knowing many approaches to loading data for analysis is crucial. Here, five Python techniques to bring in your data are reviewed with code examples for you to follow.

A new deep-learning algorithm could provide advanced notice when systems — from satellites to data centers — are falling out of whack.

Researchers show that deep reinforcement learning can be used to design more efficient nuclear reactors.

Earth system models predict that increases in atmospheric and soil dryness will reduce photosynthesis in the Amazon rainforest, with large implications for the global carbon cycle. Using in situ observations, solar-induced fluorescence, and nonlinear machine learning techniques, we show that, in...

Knowledge of spatiotemporal distribution and likelihood of (re)occurrence of salt-affected soils is crucial to our understanding of land degradation and for planning effective remediation strategies in face of future climatic uncertainties. However, conventional methods used for tracking the...

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on...

Regularization techniques are crucial for preventing your models from overfitting and enables them perform better on your validation and test sets. This guide provides a thorough overview with code of four key approaches you can use for regularization in TensorFlow.

A smart thermostat quickly learns to optimize building microclimates for both energy consumption and user preference.

Physical unclonable function (PUF) is a cornerstone of anticounterfeiting. However, conventional PUF key-based secure tags encounter several bottlenecks, such as complicated manufacturing, specialized and tedious readout, long authentication time, and insufficient stability. Here, we utilize various...

Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained...

Deep learning is a powerful approach for distinguishing classes of images, and there is a growing interest in applying these methods to delimit species, particularly in the identification of mosquito vectors. Visual identification of mosquito species is the foundation of mosquito-borne disease...

(This article originally appeared on KDNuggets.com here. For more, visit https://www.kdnuggets.com/) 

Having recently spotlighted a similar resource from Microsoft, the Natural Language Processing Best Practices & Examples repository, today we bring to you its computer vision counterpart.

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The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally...

Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which has been hampered by our inability to monitor...

In this article, we will focus on various machine learning, deep learning models, and applications of AI which can pave the way for a new data-centric era of discovery in healthcare.