The empirical measure with samples from some probability measure (which might be known up to a multiplicative factor) has many applications in Bayesian inference [1, 2] and data assimilation [3]. A class of widely used sampling methods is the Markov Chain Monte Carlo (MCMC) methods, where the trajectory of a particle is given by some constructed Markov chain with the desired distribution invariant. The trajectory of the particle is clearly stochastic, and the Monte Carlo methods take effect slowly for small number of samples. Unlike MCMC, the Stein variational Gradient method (proposed by Liu and Wang in [4]) belongs to particle based variational inference sampling methods (see also [5, 6]). These methods update particles by solving optimization problems, and each iteration is expected to make progress. As a nonparametric variational inference method, SVGD gives a deterministic way to generate points that approximate the desired probability distribution by solving an ODE system.

Evaluating the clinical similarities between pairwise patients is a fundamental problem in healthcare informatics. A proper patient similarity measure enables various downstream applications, such as cohort study and treatment comparative effectiveness research. One major carrier for conducting patient similarity research is Electronic Health Records(EHRs), which are usually heterogeneous, longitudinal, and sparse. Though existing studies on learning patient similarity from EHRs have shown being useful in solving real clinical problems, their applicability is limited due to the lack of medical interpretations. Moreover, most previous methods assume a vector-based representation for patients, which typically requires aggregation of medical events over a certain time period. As a consequence, temporal information will be lost. In this paper, we propose a patient similarity evaluation framework based on the temporal matching of longitudinal patient EHRs. Two efficient methods are presented, unsupervised and supervised, both of which preserve the temporal properties in EHRs. The supervised scheme takes a convolutional neural network architecture and learns an optimal representation of patient clinical records with medical concept embedding. The empirical results on real-world clinical data demonstrate substantial improvement over the baselines. We make our code and sample data available for further study.

Significant parts of the recent learning literature on stochastic optimization algorithms focused on the theoretical and practical behaviour of stochastic first order schemes under different convexity properties. Due to its simplicity, the traditional method of choice for most supervised machine learning problems is the stochastic gradient descent (SGD) method. Many iteration improvements and accelerations have been added to the pure SGD in order to boost its convergence in various (strong) convexity setting. However, the Lipschitz gradient continuity or bounded gradients assumptions are an essential requirement for most existing stochastic first-order schemes. In this paper novel convergence results are presented for the stochastic proximal point algorithm in different settings. In particular, without any strong convexity, smoothness or bounded gradients assumptions, we show that a slightly modified quadratic growth assumption is sufficient to guarantee for the stochastic proximal point $\mathcal{O}\left(\frac{1}{k}\right)$ convergence rate, in terms of the distance to the optimal set. Furthermore, linear convergence is obtained for interpolation setting, when the optimal set of expected cost is included in the optimal sets of each functional component.

Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods.

This paper proposes a generative moment matching network (GMMN)-based post-filter that provides inter-utterance pitch variation for deep neural network (DNN)-based singing voice synthesis. The natural pitch variation of a human singing voice leads to a richer musical experience and is used in double-tracking, a recording method in which two performances of the same phrase are recorded and mixed to create a richer, layered sound. However, singing voices synthesized using conventional DNN-based methods never vary because the synthesis process is deterministic and only one waveform is synthesized from one musical score. To address this problem, we use a GMMN to model the variation of the modulation spectrum of the pitch contour of natural singing voices and add a randomized inter-utterance variation to the pitch contour generated by conventional DNN-based singing voice synthesis. Experimental evaluations suggest that 1) our approach can provide perceptible inter-utterance pitch variation while preserving speech quality. We extend our approach to double-tracking, and the evaluation demonstrates that 2) GMMN-based neural double-tracking is perceptually closer to natural double-tracking than conventional signal processing-based artificial double-tracking is.

Apex Legends, the brand new battle royale game that takes place in the world of Titanfall, has already rocketed past 10 million players. Developers announced that the landmark number had already been reached after only 72 hours. That means the game is even growing faster thant the record-breaking speed of Fortnite – which took a fitting two weeks to reach the same milestone. "This has been a truly incredible journey," developers EA said as they announced the new milestone. "We got to a point where we felt some magic. We knew it would be risky to take the franchise in this direction, to go free to play, and do a surprise launch. But we fell in love with Apex Legends and wanted, needed, other people to play it too. Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph The giant human-like robot bears ...

Apple has told the various companies said to be secretly recording people's activities on their iPhones that they must stop immediately. Developers of some of the world's biggest apps have been rumoured to be using technology to watch people as they use their phones, in contravention of Apple's guidelines. Now Apple has said they must stop the recording, alert users to it or face "immediate action". The statement comes after a Techcrunch investigation alleged that many of the App Stores biggest apps were using analytics tools that watched people as they used the apps and recorded them as they did so, potentially hoovering up sensitive data like credit card information as they did so. "Protecting user privacy is paramount in the Apple ecosystem," an Apple spokesperson said.

The report also describes a more effective form of "digital authoritarianism" that is leading the assault on freedom of speech. China, in particular, is actively exporting its approach to internet censorship and surveillance around the world. In an earlier report, "Freedom on the Net," Freedom House outlined how China is offering training sessions and study trips, as well as advanced equipment that takes advantage of artificial intelligence and facial recognition technologies, to monitor internet activity.

With less than three weeks until Samsung is expected to unveil its Galaxy X foldable phone, a new set of rendered images have given the clearest idea yet of what the next-generation smartphone may look like. Based on previous leaks, together with Samsung's official announcements about the phone, the images by Dutch publication LetsGoDigital reveal what to potentially expect on 20 February. When unfolded into a tablet, the Samsung Galaxy X does not appear to feature a front-facing camera, suggesting the South Korean firm has either decided to forego the technology or hide it within the phone's hardware. The lack of a camera at the front of the foldable smartphone allows it to be a truly full-screen device, with barely a hint of a bezel. If true, this could mean that Samsung has figured out a way to hide the camera beneath the screen, or has decided to hide the camera in a pop-up compartment behind the screen.

Apple could be planning to introduce an emojii version of its Siri virtual assistant, according to a new patent application from the tech giant. The patent request, filed with the US Patent and Trademark Office, describes an emoji-based avatar for a smart home speaker that can adapt to a user's mood. Though not mentioned by name in the patent, the description of the smart speaker accurately resembles that of the Apple HomePod. Apple's patent application describes a "humanistic avatar, a simplified graphical representation of a digital assistant such as an emoji-based avatar" – essentially a cartoon version of Siri. Depending on what request is made through the smart speaker, the emoji assistant would be able to react appropriately.