Open-source indicators such as social media can be very effective precursors for forecasting future societal events. As events are often preceded by social indicators generated by groups of people speaking many different languages, multiple languages need to be considered to ensure comprehensive event forecasting. However, this leads to several technical challenges for traditional models: 1) high dimension, sparsity, and redundancy of features; 2) translation correlation among the multilingual features. and 3) lack of language-wise supervision. In order to simultaneously address these issues, we present a novel model capable of distant-supervision of heterogeneous multitask learning (DHML) for multilingual spatial social event forecasting. This model maps the multilingual heterogeneous features into several latent semantic spaces and then enforces a similar sparsity pattern across them all, using distant supervision across all the languages involved. Optimizing this model creates a difficult problem that is nonconvex and nonsmooth that can then be decomposed into simpler subproblems using the Alternative Direction Multiplier of Methods (ADMM). A novel dynamic programming-based algorithm is proposed to solve one challenging subproblem efficiently. Theoretical properties of the proposed algorithm are analyzed. The results of extensive experiments on multiple real-world datasets are presented to demonstrate the effectiveness, efficiency, and interpretability of the proposed approach.

Event scales are commonly used by practitioners to gauge subjective feelings on the magnitude and significance of social events. For example, the Centers for Disease Control and Prevention (CDC) utilizes a 10-level scale to distinguish the severity of flu outbreaks and governments typically categorize violent outbreaks based on their intensity as reflected in multiple aspects. Effective forecasting of future event scales can be used qualitatively to determine reasonable resource allocations and facilitate accurate proactive actions by practitioners. Existing spatial event forecasting methods typically focus on the occurrence of events rather than their ordinal event scales as this is very challenging in several respects, including 1) the ordinal nature of the event scale, 2) the spatial heterogeneity of event scaling in different geo-locations, 3) the incompleteness of scale label data for some spatial locations, and 4) the spatial correlation of event scale patterns. In order to address all these challenges concurrently, a MultI-Task Ordinal Regression (MITOR) framework is proposed to effectively forecast the scale of future events. Our model enforces similar feature sparsity patterns for different tasks while preserving the heterogeneity in their scale patterns. In addition, based on the first law of geography, we proposed to enforce spatially-closed tasks to share similar scale patterns with theoretical guarantees. Optimizing the proposed model amounts to a new non-convex and non-smooth problem with an isotonicity constraint, which is then solved by our new algorithm based on ADMM and dynamic programming. Extensive experiments on ten real-world datasets demonstrate the effectiveness and efficiency of the proposed model.

Segmenting temporal data sequences is an important problem which helps in understanding data dynamics in multiple applications such as epidemic surveillance, motion capture sequences, etc. In this paper, we give DASSA, the first self-guided and efficient algorithm to automatically find a segmentation that best detects the change of pattern in data sequences. To avoid introducing tuning parameters, we design DASSA to be a multi-level method which examines segments at each level of granularity via a compact data structure called the segment-graph. We build this data structure by carefully leveraging the information bottleneck method with the MDL principle to effectively represent each segment.Next, DASSA efficiently finds the optimal segmentation via a novel average-longest-path optimization on the segment-graph. Finally we show how the outputs from DASSA can be naturally interpreted to reveal meaningful patterns. We ran DASSA on multiple real datasets of varying sizes and it is very effective in finding the time-cut points of the segmentations (in some cases recovering the cut points perfectly) as well as in finding the corresponding changing patterns.

Artificial intelligence has made some great developments toward speeding up cancer diagnosis so far in 2017. Last month it was announced that AI from Sophia Genetics was helping to accelerate patient diagnosis across Latin America. Earlier this year researchers at Stanford University developed a deep learning algorithm that can analyse skin cancer as accurately as a human doctor. Now, Israel-based company, Medial EarlySign has announced the ability of its AI tool to identify the top 1% at highest risk of undiagnosed colorectal cancer (CRC). The machine learning developer announced the first-year results of its implementation with Maccabi Healthcare Services (MHS), for ColonFlag, a tool developed in collaboration with MHS to identify individuals with a high probability of having CRC.

Researchers from the D'Or Institute for Research and Education have used machine learning to train a computer to identify what song a participant is listening to by analyzing brain activity. The study, published in Scientific Reports, aims to advance brain decoding for future communication with patients without spoken words. A total of six volunteers listened to 40 pieces of classical, rock, pop and jazz music while undergoing magnetic resonance imaging (MRI). The MRI identified the neural fingerprint of each song in a participant's brain while a computer simultaneously learned the specific patterns occurring during each song. The computer included tonality, dynamics, rhythm and timbre in its analysis for an improved recall.

The sound of music can speak to one's soul in myriad ways and evidently is also true in regards how listening to different musical genres impact the brain. Functional magnetic resonance imaging (fMRI) data and computational algorithms were used in new research published in Scientific Reports on Feb. 2 to demonstrate that music genre can be identified through observing neurological responses to certain characteristics associated with that particular genre. "Our approach was capable of identifying musical pieces with improving accuracy across time and spatial coverage," said lead researcher Sebastian Hoefle, a doctoral candidate at the Federal University of Rio de Janeiro in Brazil. "Specifically, we showed that the distributed information in auditory cortices and the entropy of musical pieces enhanced overall identification accuracy up to 95 percent." Researchers investigated fMRI brain responses of six participants who listened to 40 musical pieces of various genres, including rock, pop, jazz, classical and folk without lyrics.

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Mind-reading technology may be closer to reality than you might think. An international team of scientists, including researchers from the D'Or Institute for Research and Education in Rio de Janeiro, have used a Magnetic Resonance (MR) machine to read people's minds and identify what song they were listening to. Six participants listened to 40 different pieces of music ranging from classical to rock, pop, jazz and other types while being monitored by the MR machine, which was linked to a computer. The computer was fitted with special software which learned to identify the'neural fingerprint' of each song, or the specific brain patterns associated with it. It did this by searching for musical features such as rhythm, tonality, dynamics and timbre.

Before i start to write what i'm supposed to, sorry for the bad english, i'm far from being fluent. So, i'm a software engineer student from Brazil and recently i had an idea to apply my machine learning knowledge into a personal project. I thought to myself: "What about applying machine learning algorithms to predict the competitive matches results?" Which features winning teams have in common? Which type of compositions have more win ratio above others?

The present work shows the application of transfer learning for a pre-trained deep neural network (DNN), using a small image dataset ($\approx$ 12,000) on a single workstation with enabled NVIDIA GPU card that takes up to 1 hour to complete the training task and archive an overall average accuracy of $94.7\%$. The DNN presents a $20\%$ score of misclassification for an external test dataset. The accuracy of the proposed methodology is equivalent to ones using HSI methodology $(81\%-91\%)$ used for the same task, but with the advantage of being independent on special equipment to classify wheat kernel for FHB symptoms.