Water has puzzled scientists for decades. For the last 30 years or so, they have theorized that when cooled down to a very low temperature like -100C, water might be able to separate into two liquid phases of different densities. Like oil and water, these phases don't mix and may help explain some of water's other strange behavior, like how it becomes less dense as it cools. It's almost impossible to study this phenomenon in a lab, though, because water crystallizes into ice so quickly at such low temperatures. Now, new research from the Georgia Institute of Technology uses machine learning models to better understand water's phase changes, opening more avenues for a better theoretical understanding of various substances. With this technique, the researchers found strong computational evidence in support of water's liquid-liquid transition that can be applied to real-world systems that use water to operate.

A smart city is a city that uses technology to provide services and solve city problems. The main goals of a smart city are to improve policy efficiency, reduce waste and inconvenience, improve social and economic quality, and maximize social inclusion. Due to the breadth of technologies that have been implemented under the smart city label, it is difficult to distill a precise definition of a smart city. As the world's population continues to urbanize – by 2050, 66% of the world's population is expected to be urban – there is a global trend toward the creation of smart cities. This tendency not only causes many physical, social, behavioural, economic, and infrastructure issues, but it also creates many opportunities.

This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

In recent years, researchers have been developing increasingly advanced computational techniques, such as deep learning algorithms, to complete a variety of tasks. One task that they have been trying to address is known as "sketch-based image retrieval" (SBIR). SBIR tasks entail retrieving images of a particular object or visual concept among a wide collection or database based on sketches made by human users. To automate this task, researchers have been trying to develop tools that can analyze human sketches and identify images that are related to the sketch or contain the same object. Despite the promising results achieved by some of these tools, developing techniques that perform consistently well on SBIR tasks has so far proved challenging.

Sports concussions, Parkinson's disease, and hormone therapy for cancer - all can have memory loss as symptoms. But do the biochemical processes of each type of memory loss have anything to reveal about the memory loss that is part of Alzheimer's disease? Rong Xu, PhD, recently received a total of $5 million for two projects that will use big data methods for a comprehensive look at a range of factors that may inform the mechanism of Alzheimer's and related dementia. "Vast amounts of data from seemingly unrelated sources present opportunities to researchers who aim to extract information that would help develop drugs or treatments," Says Xu, "This is especially true for diseases and conditions that may involve multiple genetic variations and that also have social or environmental influences." Xu is an associate professor at the Case Western Reserve University School of Medicine's Department of Population and Quantitative Health Sciences.

Despite the increase in the number of epilepsy medications available, as many as one-third of patients are refractory, or non-responders, to the medication. Uncontrolled epilepsy has many dangers associated with seizures, including injury from falls, breathing problems, and even sudden death. Debilitating seizures from epilepsy also greatly reduce quality of life, as normal activities are impaired. Epilepsy surgery is often recommended to patients who do not respond to medications. Many patients are hesitant to undergo brain surgery, in part, due to fear of operative risks and the fact that only about two-thirds of patients are seizure-free one year after surgery.

Medical University of South Carolina (MUSC) neurologists have developed a new method based on artificial intelligence that may eventually help both patients and doctors weigh the pros and cons of using brain surgery to treat debilitating seizures caused by epilepsy. This study, which focused on mesial temporal lobe epilepsy (TLE), was published in the September 2018 issue of Epilepsia. Beyond the clinical implications of incorporating this analytical method into clinicians' decision making processes, this work also highlights how artificial intelligence is driving change in the medical field. Despite the increase in the number of epilepsy medications available, as many as one-third of patients are refractory, or non-responders, to the medication. Uncontrolled epilepsy has many dangers associated with seizures, including injury from falls, breathing problems, and even sudden death.

IMAGE: The figure shows a personalized structural connectome; the strength of each connection between all possible brain regions is used to train a deep neural network to predict one of two... view more Medical University of South Carolina (MUSC) neurologists have developed a new method based on artificial intelligence that may eventually help both patients and doctors weigh the pros and cons of using brain surgery to treat debilitating seizures caused by epilepsy. This study, which focused on mesial temporal lobe epilepsy (TLE), was published in the September 2018 issue of Epilepsia. Beyond the clinical implications of incorporating this analytical method into clinicians' decision making processes, this work also highlights how artificial intelligence is driving change in the medical field. Despite the increase in the number of epilepsy medications available, as many as one-third of patients are refractory, or non-responders, to the medication. Uncontrolled epilepsy has many dangers associated with seizures, including injury from falls, breathing problems, and even sudden death.

Medical University of South Carolina (MUSC) neurologists have developed a new method based on artificial intelligence that may eventually help both patients and doctors weigh the pros and cons of using brain surgery to treat debilitating seizures caused by epilepsy. This study, which focused on mesial temporal lobe epilepsy (TLE), was published in the September 2018 issue of Epilepsia. Beyond the clinical implications of incorporating this analytical method into clinicians' decision making processes, this work also highlights how artificial intelligence is driving change in the medical field. Despite the increase in the number of epilepsy medications available, as many as one-third of patients are refractory, or non-responders, to the medication. Uncontrolled epilepsy has many dangers associated with seizures, including injury from falls, breathing problems, and even sudden death.

Medical University of South Carolina (MUSC) neurologists have developed a new method based on artificial intelligence that may eventually help both patients and doctors weigh the pros and cons of using brain surgery to treat debilitating seizures caused by epilepsy. This study, which focused on mesial temporal lobe epilepsy (TLE), was published in the September 2018 issue of Epilepsia. Beyond the clinical implications of incorporating this analytical method into clinicians' decision making processes, this work also highlights how artificial intelligence is driving change in the medical field. Despite the increase in the number of epilepsy medications available, as many as one-third of patients are refractory, or non-responders, to the medication. Uncontrolled epilepsy has many dangers associated with seizures, including injury from falls, breathing problems, and even sudden death.