Computers may be taught to identify features in nerve cells that have not been stained or undergone other damaging treatments for microscope use, an approach with the potential to revolutionize the way researchers study neurodegenerative diseases such as Parkinson's. "Researchers are now generating extraordinary amounts of data. For neuroscientists, this means that training machines to help analyze this information can help speed up our understanding of how the cells of the brain are put together and in applications related to drug development," Margaret Sutherland, PhD, said in a press release. Sutherland is program director at the National Institute of Neurological Disorders and Stroke (NINDS), which helps fund the research. The study "In silico labeling: Predicting fluorescent labels in unlabeled images" was published in the journal Cell.

An Indian student in the US has created a space-detecting algorithm that can help tackle the problem of finding a parking spot by using big data analytics and save a person's time and money. Sai Nikhil Reddy Mettupally, who is studying at The University of Alabama in Huntsville (UAH), has also won second prize at the 2018 Science and Technology Open House competition for his creation. According to a university press release, Sai's creation relies on big data analytics and deep-learning techniques to lead drivers directly to an empty parking spot. Big data analytics is a complex process of examining large and varied data sets to uncover information including hidden patterns, unknown correlations, market trends and customer preferences. Sai conceived the idea shortly after the university transitioned to zone parking last fall. "The data show that, on a typical day, there is a high chance that students or faculty members will have difficulty getting a parking spot between 11 am and 1 pm, leading to the wastage of time and fuel, and adding to the pollution" He says.

These devices, particularly the smart mobile phones have transformed over a period of time from merely communication tools to smart and highly personal devices enabling to assist the users in their variety of day-to-day situations in their daily life. In the real word, users' interest on "Mobile Phones" is more and more than other platforms like "Desktop Computer" or "Tablet Computer" over time [36]. People use mobile phones not only for voice communication between individuals but also for various activities such as applications (mobile apps) using, Internet browsing, emailing, using online social network, instant messaging etc [28]. Recent advances in the sensing capabilities of smart mobile phones make them enable to collect the rich contextual information and users' various activity records with mobile phones through the device logs. These historical mobile phone data are simply as the collection of the past contexts and user's activities with the mobile phones for these past contexts. These are phone call logs [39] having phone call activities, app usages logs [45] having various mobile application usages, mobile phone notification logs [22] having the responses with various notifications from different applications, web logs [13] having Internet browsing activities of the mobile phone users. The main characteristic of such kind of phone log data is that it contains the actual diverse activities of the users in different contexts in their real world life. Modeling smartphone user behaviors by developing various computational machine learning methods (rule-based learning) in order to analyze different behavioral patterns in different contexts, and eventually predict the next behaviors or detect strange behaviors utilizing such mobile phone data, can be used for build- 2 Iqbal H. Sarker*

Sound source separation has attracted attention from Music Information Retrieval(MIR) researchers, since it is related to many MIR tasks such as automatic lyric transcription, singer identification, and voice conversion. In this paper, we propose an intuitive spectrogram-based model for source separation by adapting U-Net. We call it Spectrogram-Channels U-Net, which means each channel of the output corresponds to the spectrogram of separated source itself. The proposed model can be used for not only singing voice separation but also multi-instrument separation by changing only the number of output channels. In addition, we propose a loss function that balances volumes between different sources. Finally, we yield performance that is state-of-the-art on both separation tasks.

Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.

To improve customer experiences, brands are increasingly turning to data to give them insights and direction. Some of the benefits include helping brands personalize the customer experience. This creates a more enjoyable and memorable moment for customers. In return, they can become repeat customers. Plus, they may tell others about their (hopefully) great experience.

This letter presents a physical human-robot interaction scenario in which a robot guides and performs the role of a teacher within a defined dance training framework. A combined cognitive and physical feedback of performance is proposed for assisting the skill learning process. Direct contact cooperation has been designed through an adaptive impedance-based controller that adjusts according to the partner's performance in the task. In measuring performance, a scoring system has been designed using the concept of progressive teaching (PT). The system adjusts the difficulty based on the user's number of practices and performance history. Using the proposed method and a baseline constant controller, comparative experiments have shown that the PT presents better performance in the initial stage of skill learning. An analysis of the subjects' perception of comfort, peace of mind, and robot performance have shown a significant difference at the p < .01 level, favoring the PT algorithm.

There is huge demand for robots to work alongside humans in heterogeneous teams. To achieve a high degree of fluidity, robots must be able to (1) recognize their human co-worker's intent, and (2) adapt to this intent accordingly, providing useful aid as a teammate. The literature to date has made great progress in these two areas -- recognition and adaptation -- but largely as separate research activities. In this work, we present a unified approach to these two problems, in which recognition and adaptation occur concurrently and holistically within the same framework. We introduce Pike, an executive for human-robot teams, that allows the robot to continuously and concurrently reason about what a human is doing as execution proceeds, as well as adapt appropriately. The result is a mixed-initiative execution where humans and robots interact fluidly to complete task goals.Key to our approach is our task model: a contingent, temporally-flexible team-plan with explicit choices for both the human and robot. This allows a single set of algorithms to find implicit constraints between sets of choices for the human and robot (as determined via causal link analysis and temporal reasoning), narrowing the possible decisions a rational human would take (hence achieving intent recognition) as well as the possible actions a robot could consistently take (hence achieving adaptation). Pike makes choices based on the preconditions of actions in the plan, temporal constraints, unanticipated disturbances, and choices made previously (by either agent).Innovations of this work include (1) a framework for concurrent intent recognition and adaptation for contingent, temporally-flexible plans, (2) the generalization of causal links for contingent, temporally-flexible plans along with related extraction algorithms, and (3) extensions to a state-of-the-art dynamic execution system to utilize these causal links for decision making.

Richard K. Morgan has spent most of the past decade working on his fantasy trilogy A Land Fit For Heroes. The books were popular with readers, but Morgan has received a steady stream of emails urging him to write more science fiction in the vein of his 2002 debut Altered Carbon. His new novel Thin Air definitely fits the bill, delivering more of Morgan's signature blend of mystery, sci-fi, sex, and violence. "I'm coming back to science fiction, and what I really wanted to do was have some fun with it," Morgan says in Episode 332 of the Geek's Guide to the Galaxy podcast. "And go back to that noir vibe, and really pick up on the pulse that the Kovacs books had."

Vertex centrality measures are a multi-purpose analysis tool, commonly used in many application environments to retrieve information and unveil knowledge from the graphs and network structural properties. However, the algorithms of such metrics are expensive in terms of computational resources when running real-time applications or massive real world networks. Thus, approximation techniques have been developed and used to compute the measures in such scenarios. In this paper, we demonstrate and analyze the use of neural network learning algorithms to tackle such task and compare their performance in terms of solution quality and computation time with other techniques from the literature. Our work offers several contributions. We highlight both the pros and cons of approximating centralities though neural learning. By empirical means and statistics, we then show that the regression model generated with a feedforward neural networks trained by the Levenberg-Marquardt algorithm is not only the best option considering computational resources, but also achieves the best solution quality for relevant applications and large-scale networks. Keywords: Vertex Centrality Measures, Neural Networks, Complex Network Models, Machine Learning, Regression Model