Here's a quick story you've probably heard before, followed by one you probably haven't. In 1979 a young Steve Jobs paid a visit to Xerox PARC, the legendary R&D lab in Palo Alto, California, and witnessed a demonstration of something now called the graphical user interface. An engineer from PARC used a prototype mouse to navigate a computer screen studded with icons, drop-down menus, and "windows" that overlapped each other like sheets of paper on a desktop. It was unlike anything Jobs had seen before, and he was beside himself. "Within 10 minutes," he would later say, "it was so obvious that every computer would work this way someday." As legend has it, Jobs raced back to Apple and commanded a team to set about replicating and improving on what he had just seen at PARC. And with that, personal computing sprinted off in the direction it has been traveling for the past 40 years, from the first Macintosh all the way up to the iPhone.

It's been a rocky week, at home and abroad. Bomb threats were made against Jewish community centers and schools. More controversy over Russia's role in the 2016 elections engulfed Congress and the White House. Russia and China vetoed new sanctions on Syria. Deadly tornadoes ripped across the Midwest.

Early and accurate identification of parkinsonian syndromes (PS) involving presynaptic degeneration from non-degenerative variants such as Scans Without Evidence of Dopaminergic Deficit (SWEDD) and tremor disorders, is important for effective patient management as the course, therapy and prognosis differ substantially between the two groups. In this study, we use Single Photon Emission Computed Tomography (SPECT) images from healthy normal, early PD and SWEDD subjects, as obtained from the Parkinson's Progression Markers Initiative (PPMI) database, and process them to compute shape- and surface fitting-based features for the three groups. We use these features to develop and compare various classification models that can discriminate between scans showing dopaminergic deficit, as in PD, from scans without the deficit, as in healthy normal or SWEDD. Along with it, we also compare these features with Striatal Binding Ratio (SBR)-based features, which are well-established and clinically used, by computing a feature importance score using Random forests technique. We observe that the Support Vector Machine (SVM) classifier gave the best performance with an accuracy of 97.29%. These features also showed higher importance than the SBR-based features. We infer from the study that shape analysis and surface fitting are useful and promising methods for extracting discriminatory features that can be used to develop diagnostic models that might have the potential to help clinicians in the diagnostic process.

Topic models are one of the most popular methods for learning representations of text, but a major challenge is that any change to the topic model requires mathematically deriving a new inference algorithm. A promising approach to address this problem is autoencoding variational Bayes (AEVB), but it has proven diffi- cult to apply to topic models in practice. We present what is to our knowledge the first effective AEVB based inference method for latent Dirichlet allocation (LDA), which we call Autoencoded Variational Inference For Topic Model (AVITM). This model tackles the problems caused for AEVB by the Dirichlet prior and by component collapsing. We find that AVITM matches traditional methods in accuracy with much better inference time. Indeed, because of the inference network, we find that it is unnecessary to pay the computational cost of running variational optimization on test data. Because AVITM is black box, it is readily applied to new topic models. As a dramatic illustration of this, we present a new topic model called ProdLDA, that replaces the mixture model in LDA with a product of experts. By changing only one line of code from LDA, we find that ProdLDA yields much more interpretable topics, even if LDA is trained via collapsed Gibbs sampling.

Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution. There are many variants of Poisson factorization methods who show state-of-the-art performance on real-world recommendation tasks. However, most of them do not explicitly take into account the temporal behavior and the recurrent activities of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model and brings to the table a rich family of time-sensitive factorization models. To elaborate, we instantiate several variants of RPF who are capable of handling dynamic user preferences and item specification (DRPF), modeling the social-aspect of product adoption (SRPF), and capturing the consumption heterogeneity among users and items (HRPF). We also develop a variational algorithm for approximate posterior inference that scales up to massive data sets. Furthermore, we demonstrate RPF's superior performance over many state-of-the-art methods on synthetic dataset, and large scale real-world datasets on music streaming logs, and user-item interactions in M-Commerce platforms.

March 3, 2017 --On March 6, NASA's MAVEN Mars orbiter is expected to cross paths with the Red Planet's moon Phobos. At first, computer models showed the two satellites missing each other by just seven seconds. Mission controllers decided that was too great a risk for the $671 million spacecraft, whose name stands for Mars Atmosphere and Volatile EvolutioN. So, on Tuesday, they fired its rocket engine enough to increase its velocity by 0.4 meters per second. Controllers say that small correction will yield a safe 2.5 minutes between MAVEN and Phobos on the 6th.

Ride hailing company Uber Technologies has for years used a secret tool to deceive the authorities in markets where its service faced resistance by law enforcement or was banned, the New York Times reported, citing sources. An Uber tool called Greyball used data collected from the Uber app and other methods to find and circumvent officials, the NYT reported on Friday. The service was able to show them a fake app populated with'ghost' cars and cancel their rides. Uber has for years used a secret tool to deceive the authorities in markets where its service faced resistance by law enforcement or was banned, the New York Times has claimed. In one example, Uber was able to create a'fake' service to fool investigators.

Hey, game developers: William Gibson called. He wants his dystopian sci-fi future back. Walking among the flashy, flickering and noisy booths of the GDC show floor and its surrounding events, the pattern becomes clear -- a significant portion of these games have a strong sci-fi vibe, many of them dealing with the idea of futuristic corporate overreach and gritty technological espionage. Take the ID@Xbox showcase for example. Of the 20 games on display, at least half are set in sci-fi worlds or feature dystopian themes (or both), including Tacoma, Tokyo 42, Tower 57, Songbringer and Aven Colony. However, two titles in particular encapsulate the raw, gritty future that's a staple of the cyberpunk genre: Ruiner by Polish studio Reikon and observer_ by Bloober Team.

By 2020, an average internet user will use 1.5GB of traffic a day, and daily video traffic will reach 1PB, Intel predicts. A huge amount of data will be generated by autonomous vehicles, mobile devices, and internet-of-things devices. Every day, more information is being collected and sent to faster servers in mega data centers, which analyze and make sense of it. That analysis has helped improved image and speech recognition and is making autonomous cars a reality. Emerging superfast data networks like 5G -- a melting pot of wireless technologies -- will dispatch even more gathered information, which could stress data centers.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Enjoy today's videos, and let us know if you have suggestions for next week. This is one of the best things I have ever seen. In this video we present a new safety module for robots to ensure safety for different tools in collaborative tasks. This module, filled with air pressure during the robot motion, covers mounted tools and carried workpieces.