Grouping patients meaningfully can give insights about the different types of patients, their needs, and the priorities. Finding groups that are meaningful is however very challenging as background knowledge is often required to determine what a useful grouping is. In this paper we propose an approach that is able to find groups of patients based on a small sample of positive examples given by a domain expert. Because of that, the approach relies on very limited efforts by the domain experts. The approach groups based on the activities and diagnostic/billing codes within health pathways of patients. To define such a grouping based on the sample of patients efficiently, frequent patterns of activities are discovered and used to measure the similarity between the care pathways of other patients to the patients in the sample group. This approach results in an insightful definition of the group. The proposed approach is evaluated using several datasets obtained from a large university medical center. The evaluation shows F1-scores of around 0.7 for grouping kidney injury and around 0.6 for diabetes.

We propose a new topic modeling procedure that takes advantage of the fact that the There are many extensions of LDA, including a nonparametric Latent Dirichlet Allocation (LDA) log likelihood extension based on the Dirichlet process function is asymptotically equivalent called Hierarchical Dirichlet Process (Teh et al., to the logarithm of the volume of the topic 2005), a correlated topic extension based on the logistic simplex. This allows topic modeling to be normal prior on the topic proportions (Lafferty reformulated as finding the probability simplex and Blei, 2006), and a time-varying topic modeling that minimizes its volume and encloses extension (Blei and Lafferty, 2006). There are the documents that are represented as distributions two main approaches for estimation of the parameters over words. A convex relaxation of probabilistic topic models: the variational of the minimum volume topic model optimization approximation popularized by Blei et al. (2003) and is proposed, and it is shown that the sampling based approach studied by Pritchard the relaxed problem has the same global et al. (2000).

Based on the tree architecture, the objective of this paper is to design deep neural networks with two or more hidden layers (called deep nets) for realization of radial functions so as to enable rotational invariance for near-optimal function approximation in an arbitrarily high dimensional Euclidian space. It is shown that deep nets have much better performance than shallow nets (with only one hidden layer) in terms of approximation accuracy and learning capabilities. In particular, for learning radial functions, it is shown that near-optimal rate can be achieved by deep nets but not by shallow nets. Our results illustrate the necessity of depth in neural network design for realization of rotation-invariance target functions.

Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main steps, subgraph selection and fact selection, the research community has developed sophisticated approaches. However, the importance of subgraph ranking and leveraging the subject--relation dependency of a KB fact have not been sufficiently explored. Motivated by this, we present a unified framework to describe and analyze existing approaches. Using this framework as a starting point, we focus on two aspects: improving subgraph selection through a novel ranking method and leveraging the subject--relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores. Our methods achieve a new state of the art (85.44% in accuracy) on the SimpleQuestions dataset.

Classification of jets with deep learning has gained significant attention in recent times. However, the performance of deep neural networks is often achieved at the cost of interpretability. Here we propose an interpretable network trained on the jet spectrum $S_{2}(R)$ which is a two-point correlation function of the jet constituents. The spectrum can be derived from a functional Taylor series of an arbitrary jet classifier function of energy flows. An interpretable network can be obtained by truncating the series. The intermediate feature of the network is an infrared and collinear safe C-correlator which allows us to estimate the importance of a $S_{2}(R)$ deposit at an angular scale R in the classification. The performance of the architecture is comparable to that of a convolutional neural network (CNN) trained on jet images, although the number of inputs and complexity of architecture is significantly simpler than the CNN classifier. We consider two examples: one is the classification of two-prong jets which differ in color charge of the mother particle, and the other is a comparison between Pythia 8 and Herwig 7 generated jets.

Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to boost the efficacy of such datasets to improve learning-based models. We propose D$^2$-City, a large-scale comprehensive collection of dashcam videos collected by vehicles on DiDi's platform. D$^2$-City contains more than 10000 video clips which deeply reflect the diversity and complexity of real-world traffic scenarios in China. We also provide bounding boxes and tracking annotations of 12 classes of objects in all frames of 1000 videos and detection annotations on keyframes for the remainder of the videos. Compared with existing datasets, D$^2$-City features data in varying weather, road, and traffic conditions and a huge amount of elaborate detection and tracking annotations. By bringing a diverse set of challenging cases to the community, we expect the D$^2$-City dataset will advance the perception and related areas of intelligent driving.

Advertisements (ads) often contain strong affective content to capture viewer attention and convey an effective message to the audience. However, most computational affect recognition (AR) approaches examine ads via the text modality, and only limited work has been devoted to decoding ad emotions from audiovisual or user cues. This work (1) compiles an affective ad dataset capable of evoking coherent emotions across users; (2) explores the efficacy of content-centric convolutional neural network (CNN) features for AR vis-\~a-vis handcrafted audio-visual descriptors; (3) examines user-centric ad AR from Electroencephalogram (EEG) responses acquired during ad-viewing, and (4) demonstrates how better affect predictions facilitate effective computational advertising as determined by a study involving 18 users. Experiments reveal that (a) CNN features outperform audiovisual descriptors for content-centric AR; (b) EEG features are able to encode ad-induced emotions better than content-based features; (c) Multi-task learning performs best among a slew of classification algorithms to achieve optimal AR, and (d) Pursuant to (b), EEG features also enable optimized ad insertion onto streamed video, as compared to content-based or manual insertion techniques in terms of ad memorability and overall user experience.

A group of Google employees have called for the removal of a rightwing thinktank leader from the company's new artificial intelligence council, citing her anti-LGBT and anti-immigrant record. Employees published a letter on Monday criticizing the appointment of Kay Coles James, the president of the Heritage Foundation, to Google's newly formed advisory council for "the responsible development of AI". James has a history of fighting trans rights and LGBT protections, and has advocated for Donald Trump's proposed border wall. "In selecting James, Google is making clear that its version of'ethics' values proximity to power over the wellbeing of trans people, other LGBTQ people and immigrants," the employees wrote in the letter, which was published online and shared internally at the company. "Such a position directly contravenes Google's stated values."

It can't dribble, let alone slam dunk, but Toyota's basketball robot hardly ever misses a free throw or a 3-pointer. The 207-centimeter-tall (6 feet 10-inches) machine made five of eight 3-point shots in a demonstration in a Tokyo suburb Monday, a ratio its engineers say is worse than usual. Toyota Motor Corp.'s robot, called Cue 3, computes a three-dimensional image where the basket is, using sensors on its torso, and adjusts motors inside its arm and knees to give the shot the right angle and propulsion for a swish. Efforts in developing human-shaped robots underline a global shift in robotics use from pre-programmed mechanical arms in limited situations like factories to functioning in the real world with people. The 2017 version of the robot was designed to make free throws.

A recently discovered vulnerability baked inside Intel hardware allows hackers to intercept important data inside one's computer, say security experts. The flaw, discovered by the security firm, Positive Technologies, was revealed late last month at a Black Hat conference in Singapore and shows how bad actors can exploit a feature called the The Visualization of Internal Signals Architecture (VISA) to scrape a computer's memory for sensitive data. By gaining access to VISA, which is used to test chips before they're released to the public, hackers can leverage the system to monitor information happening inside a machine's controller hub -- a set of computer chips that controls aspects of the CPU and more -- say analysts at Positive Technologies. According to security experts, an Intel chipset flaw allows hackers to scrape your computers most important information. 'We found out that it is possible to access Intel VISA on ordinary motherboards, with no specific equipment needed,' said Positive Technologies expert, Maxim Goryachy, in a statement.