Perinatal stroke (PS) is a serious condition that, if undetected and thus untreated, often leads to life-long disability, in particular Cerebral Palsy (CP). In clinical settings, Prechtl's General Movement Assessment (GMA) can be used to classify infant movements using a Gestalt approach, identifying infants at high risk of developing PS. Training and maintenance of assessment skills are essential and expensive for the correct use of GMA, yet many practitioners lack these skills, preventing larger-scale screening and leading to significant risks of missing opportunities for early detection and intervention for affected infants. We present an automated approach to GMA, based on body-worn accelerometers and a novel sensor data analysis method-Discriminative Pattern Discovery (DPD)-that is designed to cope with scenarios where only coarse annotations of data are available for model training. We demonstrate the effectiveness of our approach in a study with 34 newborns (21 typically developing infants and 13 PS infants with abnormal movements). Our method is able to correctly recognise the trials with abnormal movements with at least the accuracy that is required by newly trained human annotators (75%), which is encouraging towards our ultimate goal of an automated PS screening system that can be used population-wide.

Inter-Cell Interference Coordination (ICIC) is a promising way to improve energy efficiency in wireless networks, especially where small base stations are densely deployed. However, traditional optimization based ICIC schemes suffer from severe performance degradation with complex interference pattern. To address this issue, we propose a Deep Reinforcement Learning with Deterministic Policy and Target (DRL-DPT) framework for ICIC in wireless networks. DRL-DPT overcomes the main obstacles in applying reinforcement learning and deep learning in wireless networks, i.e. continuous state space, continuous action space and convergence. Firstly, a Deep Neural Network (DNN) is involved as the actor to obtain deterministic power control actions in continuous space. Then, to guarantee the convergence, an online training process is presented, which makes use of a dedicated reward function as the target rule and a policy gradient descent algorithm to adjust DNN weights. Experimental results show that the proposed DRL-DPT framework consistently outperforms existing schemes in terms of energy efficiency and throughput under different wireless interference scenarios. More specifically, it improves up to 15% of energy efficiency with faster convergence rate.

The min-max problem, also known as the saddle point problem, is a class of optimization problems in which we minimize and maximize two subsets of variables simultaneously. This class of problems can be used to formulate a wide range of signal processing and communication (SPCOM) problems. Despite its popularity, existing theory for this class has been mainly developed for problems with certain special convex-concave structure. Therefore, it cannot be used to guide the algorithm design for many interesting problems in SPCOM, where some kind of non-convexity often arises. In this work, we consider a general block-wise one-sided non-convex min-max problem, in which the minimization problem consists of multiple blocks and is non-convex, while the maximization problem is (strongly) concave. We propose a class of simple algorithms named Hybrid Block Successive Approximation (HiBSA), which alternatingly performs gradient descent-type steps for the minimization blocks and one gradient ascent-type step for the maximization problem. A key element in the proposed algorithm is the introduction of certain properly designed regularization and penalty terms, which are used to stabilize the algorithm and ensure convergence. For the first time, we show that such simple alternating min-max algorithms converge to first-order stationary solutions, with quantifiable global rates. To validate the efficiency of the proposed algorithms, we conduct numerical tests on a number of information processing and wireless communication problems, including the robust learning problem, the non-convex min-utility maximization problems, and certain wireless jamming problem arising in interfering channels.

Our research is focused on understanding and applying biological memory transfers to new AI systems that can fundamentally improve their performance, throughout their fielded lifetime experience. We leverage current understanding of biological memory transfer to arrive at AI algorithms for memory consolidation and replay. In this paper, we propose the use of generative memory that can be recalled in batch samples to train a multi-task agent in a pseudo-rehearsal manner. We show results motivating the need for task-agnostic separation of latent space for the generative memory to address issues of catastrophic forgetting in lifelong learning.

Hands-on exercises deeply focused on the pre-processing (manipulation/wrangling) and visualizing phase - KNIME We will focus on the most time-consuming part of the machine learning process which is the data exploration consisting from data visualisation and data wrangling which serves for data transformation to get well prepared data. We will use open-source, highly intuitive and effective analytics platform KNIME where we will read the data, transform them and visualise them by using KNIME nodes. What you'll learn Pre-process the data (data wrangling) by using Knime analytics platform Model and transform data in KNIME Visualise the data in charts and plots in KNIME Work with the KNIME nodes focused on data wrangling and visualisation Read data and work with more and different file types at one place Join and merge different data Modify, filter, resort, split, filter data, handle with missing values Group and pivot data Use basic math formulas in KNIME Visualise data by using different plots and charts (box plot, pie chart, scatter plot, line plot, histogram) Handle with KNIME knwf files (create, save, move, rename, delete, export, import) Understand the KNIME environment Who this course is for: data analysts, data scientists and those of you willing to learn new things anyone searching open-source, user-friendly, easily understandable and highly effective SW for data analyzing and machine learning tasks without necessity to have programming skills people working with data (also with big data) Course Info: Title: Introduction to KNIME: Pre-processing and visualizing data Description Course: Hands-on exercises deeply focused on the pre-processing (manipulation/wrangling) and visualizing phase - KNIME Instructor: Barbora Stetinova, MBA Duration: 2.5 hours on-demand video Online Classes Platfrom: Udemy GET Udemy Discount Introduction to KNIME: Pre-processing and visualizing data

Traditional approaches to leadership development no longer meet the needs of organizations or individuals. There are three: (1) Organizations, which pay for leadership development, don't always benefit as much as individual learners do. A growing assortment of online courses, social platforms, and learning tools from both traditional providers and upstarts is helping to close the gaps. The need for leadership development has never been more urgent. Companies of all sorts realize that to survive in today's volatile, uncertain, complex, and ambiguous environment, they need leadership skills and organizational capabilities different from those that helped them succeed in the past. There is also a growing recognition that leadership development should not be restricted to the few who are in or close to the C-suite. With the proliferation of collaborative problem-solving platforms and digital "adhocracies" that emphasize individual initiative, employees across the board are increasingly expected to make consequential decisions that align with corporate strategy and culture.

Artificial intelligence (AI) is a rapidly growing industry that's perpetually impressing people with what's possible. Those advancements wouldn't happen without the people working tirelessly to research innovations. Many of the people pushing artificial intelligence forward are male, and that's evidence of a known gender gap associated with the industry. Concentrated efforts are needed to tackle the problem, but it's a situation that could change. The five women here are among those leading the way in AI research and inspiring everyone by their dedication.

There is an undeniable truth that Artificial Intelligence, which we will refer to simply as AI, is the next frontier for the healthcare industry. Several sources have already pegged the market to be worth $36.1 billion by 2025. For those of you who like simple language; the way AI works is by having it developed through machine learning, natural language processing, and deep learning. This process is controlled by programmers, who in a lot of cases are independent contractors. Regulatory frameworks will soon be created to govern this new boom, with consulting and online training courses becoming the next cash cows milking this industry for profits.

Many educational institutions maintain their own data centers. But to Jeff Olson, chief data officer and senior VP of technology strategy at the College Board, all those humming racks of servers are just plumbing--and he doesn't want to be in the plumbing business. He would rather focus on how the College Board, which administers the PSAT, SAT, and Advanced Placement Tests, can help students reach their educational goals. "We need to minimize the amount of work we do to keep systems up and running, and spend more energy innovating on things that matter to people," he says. That's why the College Board has pulled the plug on much of its IT plumbing in favor of the advanced capabilities offered by the cloud.

Since its conceptualisation in 1983, TED Talks have been the go-to platform for people from all walks of life to share their idea and thoughts. Over the last three decades, the platform has witnessed some of the finest speakers capture the imagination of their audience with absolute exuberance. In this article, Analytics India Magazine takes a look at some of the most interesting talks that revolve around emerging tech like artificial intelligence and machine learning. Published in November 2018, Matt Beane, assistant professor of Technology Management at the University of California, addresses the most common fear associated with AI -- machines taking over human jobs. Beane, however, challenges this notion and says that instead of handling the technology carelessly and letting it be a hindrance for getting newer jobs, the potential of the technology can be used in such a way that machine enhanced mentorship to " take full advantage of AI's amazing capabilities while enhancing our skills at the same time."