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IMAGE: A comparison summary of deep neural networks (DNNs) and discrete choice models (DCMs) characteristics view more
Credit: Singapore-MIT Alliance for Research and Technology (SMART)
Singapore, 19 April, 2021 - Researchers at the Future Urban Mobility (FM) Interdisciplinary Research Group (IRG) at Singapore-MIT Alliance for Research and Technology (SMART), MIT s research enterprise in Singapore, have created a synthetic framework known as theory-based residual neural network (TB-ResNet), which combines discrete choice models (DCMs) and deep neural networks (DNNs), also known as deep learning, to improve individual decision-making analysis used in travel behaviour research.
In this research paper, Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks, recently published in established transportation science journal
SMART breakthrough to enhance travel behavior research with artificial neural networks eurekalert.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from eurekalert.org Daily Mail and Mail on Sunday newspapers.
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IMAGE: An array of multi-colored LEDs periodically arranged to give off visible light as shown above; a combination of InGaN based red, blue, and green LEDs is essential to cover lighting. view more
Credit: Singapore-MIT Alliance for Research and Technology (SMART)
Singapore, 12 April 2021 - Researchers from the Low Energy Electronic Systems (LEES) Interdisciplinary Research Group (IRG) at Singapore-MIT Alliance for Research and Technology (SMART), MIT s research enterprise in Singapore, together with Massachusetts Institute of Technology (MIT) and National University of Singapore (NUS) have found a method to quantify the distribution of compositional fluctuations in the indium gallium nitride (InGaN) quantum wells (QWs) at different indium concentrations.
Credits: Photo courtesy of SMART. Caption: A closeup of the SMART microfluidic DLD assay chip with a Singapore $1 coin for scale Credits: Photo courtesy of SMART. Caption: Experiment flow on the microfluidic DLD assay chip with sample red dye loaded on a microscope stage Credits: Photo courtesy of SMART.
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Researchers from Critical Analytics for Manufacturing Personalized-Medicine (CAMP), an interdisciplinary research group at the Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, have developed a new label-free immune profiling assay that profiles the rapidly changing host immune response in case of infection, in a departure from existing methods that focus on detecting the pathogens themselves, which can often be at low levels wi
Massachusetts Institute of Technology
According to United Nations estimates, the global population is expected to grow by 2 billion within the next 30 years, giving rise to an expected increase in demand for food and agricultural products. Today, biotic and abiotic environmental stresses such as plant pathogens, sudden fluctuations in temperature, drought, soil salinity, and toxic metal pollution – made worse by climate change – impair crop productivity and lead to significant losses in agriculture yield worldwide.
New work from the Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, and Temasek Life Sciences Laboratory (TLL) highlights the potential of recently developed analytical tools that can provide tissue-cell or organelle-specific information on living plants in real-time and can be used on any plant species.