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High school students receive an introduction to neuroscience and neural engineering, neuroethics, scientific communication and the latest developments in brain-computer interfaces. The goal of YSP and YSP-REACH is to provide students with exposure to the field of neural engineering and provide basic preparation for college studies in STEM ...
A large collection of Question to SPARQL specially design for Open Domain Neural Question Answering over DBpedia Knowledgebase. This dataset contains a large collection of Open Neural SPARQL Templates and instances for training Neural SPARQL Machines; it was pre-processed by semi-automatic annotation tools as well as by three SPARQL experts ...
Several results in situ SEM and TEM were demonstrated for thin films by his group [7] including a stage for simultaneous electrical and mechanical testing, although this set-up used external actuation and sensing. [8] A major breakthrough in MEMS-electronic integration was made by Horacio D. Espinosa and his group at Northwestern University.
Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).
Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, or enhance neural systems. Neural engineers are uniquely qualified to solve design problems at the interface of living neural tissue and non-living constructs.
The CMERI-CoEFM was established as Mechanical Engineering Research & Development Organization (MERADO) at Ludhiana as an extension center of CMERI to concentrate on the technology development and expertise needs of around 65,000 small & medium scale industries, concentrated in and around Ludhiana, Punjab. In the past, a major component of R&D ...
Hochreiter developed the long short-term memory (LSTM) neural network architecture in his diploma thesis in 1991 leading to the main publication in 1997. [3] [4] LSTM overcomes the problem of numerical instability in training recurrent neural networks (RNNs) that prevents them from learning from long sequences (vanishing or exploding gradient).
A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs. It was developed in 2015 for image recognition , and won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) of that year.