Mitigating the substantial undesirable impact of transportation systems on the environment is paramount. Thus, predicting Greenhouse Gas (GHG) emissions is one of the profound topics, especially with the emergence of intelligent transportation systems (ITS). We develop a deep learning framework to predict link-level GHG emission rate (ER) (in CO2eq gram/second) based on the most representative predictors, such as speed, density, and the GHG ER of previous time steps. In particular, various specifications of the long-short term memory (LSTM) networks with exogenous variables are examined and compared with clustering and the autoregressive integrated moving average (ARIMA) model with exogenous variables. The downtown Toronto road network is used as the case study and highly detailed data are synthesized using a calibrated traffic microsimulation and MOVES. It is found that LSTM specification with speed, density, GHG ER, and in-links speed from three previous minutes performs the best while adopting 2 hidden layers and when the hyper-parameters are systematically tuned. Adopting a 30 second updating interval improves slightly the correlation between true and predicted GHG ERs, but contributes negatively to the prediction accuracy as reflected on the increased root mean square error (RMSE) value. Efficiently predicting GHG emissions at a higher frequency with lower data requirements will pave the way to non-myopic eco-routing on large-scale road networks {to alleviate the adverse impact on the global warming

For Dr Mary-Anne Hartley, a medical doctor and researcher in EPFL's intelligent Global Health group (iGH), 2020 has been relentless. "It's not a relaxing time to study infectious diseases," she explained. Since the beginning of the COVID-19 pandemic, Dr Hartley's research team has been working non-stop with nearby Swiss university hospitals on two major projects. Using artificial intelligence (AI), they have developed new algorithms that, with data from ultrasound images and auscultation (chest/lung) sounds, can accurately diagnose the novel coronavirus in patients and predict how ill they are likely to become. "We've named the new deep learning algorithms DeepChest – using lung ultrasound images – and DeepBreath – using breath sounds from a digital stethoscope. This AI is helping us to better understand complex patterns in these fundamental clinical exams. So far, results are highly promising," said Professor Jaggi.

Artificial intelligence (AI) has solved one of biology's grand challenges: predicting how proteins fold from a chain of amino acids into 3D shapes that carry out life's tasks. This week, organizers of a protein-folding competition announced the achievement by researchers at DeepMind, a U.K.-based AI company. They say the DeepMind method will have far-reaching effects, among them dramatically speeding the creation of new medications. “What the DeepMind team has managed to achieve is fantastic and will change the future of structural biology and protein research,” says Janet Thornton, director emeritus of the European Bioinformatics Institute. “This is a 50-year-old problem,” adds John Moult, a structural biologist at the University of Maryland, Shady Grove, and co-founder of the competition, Critical Assessment of Protein Structure Prediction (CASP). “I never thought I'd see this in my lifetime.” The body uses tens of thousands of different proteins, each a string of dozens to hundreds of amino acids. The order of the amino acids dictates how the myriad pushes and pulls between them give rise to proteins' complex 3D shapes, which, in turn, determine how they function. Knowing those shapes helps researchers devise drugs that can lodge in proteins' crevices. And being able to synthesize proteins with a desired structure could speed development of enzymes to make biofuels and degrade waste plastic. ![Figure][1] CREDITS: (GRAPH) C. BICKEL/ SCIENCE ; (DATA) CASP For decades, researchers deciphered proteins' structures using experimental techniques such as x-ray crystallography or cryo–electron microscopy (cryo-EM). But such methods can take years and don't always work. Structures have been solved for only about 170,000 of the more than 200 million proteins discovered across life forms. In the 1960s, researchers realized if they could work out all interactions within a protein's sequence, they could predict its shape. But the amino acids in any given sequence could interact in so many different ways that the number of possible structures was astronomical. Computational scientists jumped on the problem, but progress was slow. In 1994, Moult and colleagues launched CASP, which takes place every 2 years. Entrants get amino acid sequences for about 100 proteins whose structures are not known. Some groups compute a structure for each sequence, while others determine it experimentally. The organizers then compare the computational predictions with the lab results and give the predictions a global distance test (GDT) score. Scores above 90 on the 100-point scale are considered on par with experimental methods, Moult says. Even in 1994, predicted structures for small, simple proteins could match experimental results. But for larger, challenging proteins, computations' GDT scores were about 20, “a complete catastrophe,” says Andrei Lupas, a CASP judge and evolutionary biologist at the Max Planck Institute for Developmental Biology. By 2016, competing groups had reached scores of about 40 for the hardest proteins, mostly by drawing insights from known structures of proteins that were closely related to the CASP targets. When DeepMind first competed, in 2018, its algorithm, called AlphaFold, relied on this comparative strategy. But AlphaFold also incorporated a computational approach called deep learning, in which the software is trained on vast data troves—in this case, the sequences and structures of known proteins—and learns to spot patterns. DeepMind won handily, beating the competition by an average of 15% on each structure, and winning GDT scores of up to about 60 for the hardest targets. But the predictions were still too coarse, says John Jumper, who heads AlphaFold's development at DeepMind. “We knew how far we were from biological relevance.” So the team combined deep learning with an “attention algorithm” that mimics the way a person might assemble a jigsaw puzzle: connecting pieces in clumps—in this case clusters of amino acids—and then searching for ways to join the clumps in a larger whole. Working with a computer network built around 128 machine learning processors, they trained the algorithm on all 170,000 or so known protein structures. And it worked. In this year's CASP, AlphaFold achieved a median GDT score of 92.4. For the most challenging proteins, AlphaFold scored a median of 87, 25 points above the next best predictions. It even excelled at solving structures of proteins that sit wedged in cell membranes, which are central to many human diseases but notoriously difficult to solve with x-ray crystallography. Venki Ramakrishnan, a structural biologist at the Medical Research Council Laboratory of Molecular Biology, calls the result “a stunning advance on the protein folding problem.” All groups in this year's competition improved, Moult says. But with AlphaFold, Lupas says, “The game has changed.” The organizers even worried DeepMind may have cheated somehow. So Lupas set a special challenge: a membrane protein from a species of archaea, an ancient group of microbes. For 10 years, his team had tried to get its x-ray crystal structure. “We couldn't solve it.” But AlphaFold had no trouble. It returned a detailed image of a three-part protein with two helical arms in the middle. The model enabled Lupas and his team to make sense of their x-ray data; within half an hour, they had fit their experimental results to AlphaFold's predicted structure. “It's almost perfect,” Lupas says. “They could not possibly have cheated on this. I don't know how they do it.” As a condition of entering CASP, DeepMind—like all groups—agreed to reveal sufficient details about its method for other groups to re-create it. That will be a boon for experimentalists, who will be able to use structure predictions to make sense of opaque x-ray and cryo-EM data. It could also enable drug designers to work out the structure of every protein in new and dangerous pathogens like SARS-CoV-2, a key step in the hunt for molecules to block them, Moult says. Still, AlphaFold doesn't do everything well. In CASP, it faltered on one protein, an amalgam of 52 small repeating segments, which distort each others' positions as they assemble. Jumper says the team now wants to train AlphaFold to solve such structures, as well as those of complexes of proteins that work together to carry out key functions in the cell. Even though one grand challenge has fallen, others will undoubtedly emerge. “This isn't the end of something,” Thornton says. “It's the beginning of many new things.” [1]: pending:yes

For Dr. Mary-Anne Hartley, a medical doctor and researcher in EPFL's intelligent Global Health group (iGH), 2020 has been relentless. "It's not a relaxing time to study infectious diseases," she explained. Since the beginning of the COVID-19 pandemic, Dr. Hartley's research team has been working non-stop with nearby Swiss university hospitals on two major projects. Using artificial intelligence (AI), they have developed new algorithms that, with data from ultrasound images and auscultation (chest/lung) sounds, can accurately diagnose the novel coronavirus in patients and predict how ill they are likely to become. "We've named the new deep learning algorithms DeepChest--using lung ultrasound images--and DeepBreath--using breath sounds from a digital stethoscope. This AI is helping us to better understand complex patterns in these fundamental clinical exams. So far, results are highly promising," said Professor Jaggi.

A "deep learning" software program from Google-owned lab DeepMind showed great progress in solving one of biology's greatest challenges – understanding protein folding. Protein folding is the process by which a protein takes its shape from a string of building blocks to its final three-dimensional structure, which determines its function. By better predicting how proteins take their structure, or "fold," scientists can more quickly develop drugs that, for example, block the action of crucial viral proteins. Solving what biologists call "the protein-folding problem" is a big deal. Proteins are the workhorses of cells and are present in all living organisms.

We first entered CASP13 in 2018 with our initial version of AlphaFold, which achieved the highest accuracy among participants. Afterwards, we published a paper on our CASP13 methods in Nature with associated code, which has gone on to inspire other work and community-developed open source implementations. Now, new deep learning architectures we've developed have driven changes in our methods for CASP14, enabling us to achieve unparalleled levels of accuracy. These methods draw inspiration from the fields of biology, physics, and machine learning, as well as of course the work of many scientists in the protein folding field over the past half-century. A folded protein can be thought of as a "spatial graph", where residues are the nodes and edges connect the residues in close proximity.

The grand challenge of protein folding hit the news this week when it was announced that the latest version of DeepMind's AlphaFold system had predicted protein structures with very high accuracy in CASP's 2020 experiment. Proteins are large, complex molecules, and the shape of a particular protein is closely linked to the function it performs. The ability to accurately predict protein structures would enable scientists to gain a greater understanding of how they work and what they do. This new version of AlphaFold builds on the initial system, which you can read about in this paper. The associated code is available here.

MONTREAL, CANADA and HSINCHU, TAIWAN – December 3, 2020 – The push for low-power and low-latency deep learning models, computing hardware, and systems for artificial intelligence (AI) inference on edge devices continues to create exciting new opportunities. There has been unprecedented interest from industry stakeholders in the development of hardware and software solutions for on-device deep learning, also called Edge AI. This has already begun to yield progress on hallmark applications such as keyword spotting in audio classification, anomaly detection and, in this case, person detection in computer vision applications. Specifically, tinyML, the branch of machine learning tailored to ultra-low power systems, holds tremendous promise. The efficiency of proposed solutions (milliwatt or even microwatt power consumption) and vast applicability and deployment of such devices in real-world settings will lead to over 100 billion IoT sensors and devices expected to ship over the next 5 years 1.

The components of time-series can be as complex and sophisticated as the data itself. With every passing second, the data obtained multiplies and modelling becomes tricky. For instance, social media platforms, the data handling chores get worse with their increasing popularity. Twitter stores 1.5 petabytes of logical time series data and handles 25K query requests per minute. There are more critical applications of time series modelling, such as IoT and on various edge devices.

In a neural network, the activation function is responsible for transforming the summed weighted input from the node into the activation of the node or output for that input. The rectified linear activation function or ReLU for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero. It has become the default activation function for many types of neural networks because a model that uses it is easier to train and often achieves better performance. In this tutorial, you will discover the rectified linear activation function for deep learning neural networks. A Gentle Introduction to the Rectified Linear Activation Function for Deep Learning Neural Networks Photo by Bureau of Land Management, some rights reserved. A neural network is comprised of layers of nodes and learns to map examples of inputs to outputs. For a given node, the inputs are multiplied by the weights in a node and summed together.