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A key concept underlying the specific functionalities of metasurfaces is the use of constituent components to shape the wavefront of the light on demand. Metasurfaces are versatile, novel platforms for manipulating the scattering, color, phase, or intensity of light. Currently, one of the typical...

In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the...

While deepfakes threaten to destroy our perception of reality, the tech giants are throwing down the gauntlet and working to enhance the state of the art in combating doctored videos and images.

MIT economist Daron Acemoglu’s new research puts a number on the job costs of automation.

Trees are used by animals, humans and machines to classify information and make decisions. Natural tree structures displayed by synapses of the brain involves potentiation and depression capable of branching and is essential for survival and learning. Demonstration of such features in synthetic...

Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this paper, we demonstrate a crucial phenomenon: Deep learning typically yields unstable methods for image reconstruction...

The identification of light sources represents a task of utmost importance for the development of multiple photonic technologies. Over the last decades, the identification of light sources as diverse as sunlight, laser radiation, and molecule fluorescence has relied on the collection of photon...

Data labeling is so hot right now… but could this rapidly emerging market face disruption from a small team at Stanford and the Snorkel open source project, which enables highly efficient programmatic labeling that is 10 to 1,000x as efficient as hand labeling?

An MIT system uses wireless signals to measure in-home appliance usage to better understand health tendencies.

Study finds manufacturing companies that are quick to automate can thrive, but overall employment drops.

Neural-network quantum states have been successfully used to study a variety of lattice and continuous-space problems. Despite a great deal of general methodological developments, representing fermionic matter is however still early research activity. Here we present an extension of neural-network...

Background

CT is the most common imaging modality in traumatic brain injury (TBI). However, its conventional use requires expert clinical interpretation and does not provide detailed quantitative outputs, which may have prognostic importance. We aimed to use deep learning to reliably and...

(Frankle & Carbin, 2019) shows that there exist winning tickets (small but critical subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve comparable accuracies to the latter in a similar number of iterations. However, the identification of these winning tickets...

Job-replacing tech has directly driven the income gap since the late 1980s, economists report.

Engineers develop a rapid screening system to test fracture resistance in billions of potential materials.

Biostatisticians use statistical techniques that your current everyday data scientists have probably never heard of. This is a great example where lack of domain knowledge exposes you as someone that does not know what they are doing and are merely hopping on a trend.

Neural-network quantum states have been successfully used to study a variety of lattice and continuous-space problems. Despite a great deal of general methodological developments, representing fermionic matter is however still early research activity. Here we present an extension of neural-network...

Fracture is a catastrophic process whose understanding is critical for evaluating the integrity and sustainability of engineering materials. Here, we present a machine-learning approach to predict fracture processes connecting molecular simulation into a physics-based data-driven multiscale model...

Naturally occurring arsenic in groundwater affects millions of people worldwide. We created a global prediction map of groundwater arsenic exceeding 10 micrograms per liter using a random forest machine-learning model based on 11 geospatial environmental parameters and more than 50,000 aggregated...

We introduce the FakeHealth, a new data repository for fake health news detection. Following a preliminary analysis to demonstrate its features, we consider additional potential directions for better identifying fake news.