Twitter has limited the number of accounts people can follow in one go, as tries to stop the rapid spread of spam and bots. People will now be banned from following more than 400 accounts in an attempt to make conversations on the platform more "healthy". It already stopped them from following more than 1,000. The move is part of a broad attempt to improve the platform, which has been repeatedly accused of encouraging disagreement as well as malicious and fake accounts. We'll tell you what's true.

Ford CEO Jim Hackett Tuesday joined the growing ranks of vehicle and tech execs willing to say publicly that self-driving cars won't arrive as soon as some had hoped. The industry "overestimated the arrival of autonomous vehicles," Hackett told the Detroit Economic Club. Though Ford is not wavering from its self-imposed due date of 2021 for its first purpose-built driverless car, Hackett acknowledged that the vehicle's "applications will be narrow, what we call geo-fenced, because the problem is so complex." Bloomberg earlier reported the comments. Hackett is the latest high-ranking industry insider to engage in public real talk about the prospects for self-driving cars, which back in 2016 seemed just around the corner.

Like millions of women, Diana Diller was a devoted user of the pregnancy-tracking app Ovia, logging in every night to record new details on a screen asking about her bodily functions, sex drive, medications and mood. When she gave birth last spring, she used the app to chart her baby's first online medical data -- including her name, her location and whether there had been any complications -- before leaving the hospital's recovery room. But someone else was regularly checking in, too: her employer, which paid to gain access to the intimate details of its workers' personal lives, from their trying-to-conceive months to early motherhood. Diller's bosses could look up aggregate data on how many workers using Ovia's fertility, pregnancy and parenting apps had faced high-risk pregnancies or gave birth prematurely; the top medical questions they had researched; and how soon the new moms planned to return to work. "Maybe I'm naive, but I thought of it as positive reinforcement: They're trying to help me take care of myself," said Diller, 39, an event planner in Los Angeles for the video game company Activision Blizzard.

Professor Thomas Müller and Professor Hans Briegel have been carrying out research on a machine learning model for several years that differs significantly from alternative artificial intelligence (AI) learning models. The philosopher from Konstanz and the theoretical physicist from the University of Innsbruck have integrated methods of philosophical action theory and quantum optics. Their "Projective Simulation" learning model has already been successfully applied in basic research. Together with the Innsbruck physicist Dr. Katja Ried, the researchers have now adapted this AI model for realistic application to biological systems. The current issue of the scientific journal PLoS One discusses how the learning model can be used to model and reproduce locusts' specific swarming behaviour.

Business consultancy Frost & Sullivan has made a series of predictions for the future of the patient monitoring device market, following its recently published analysis of investments and trends in the industry. The firm have suggested that artificial intelligence, brain-computer interface (BCI), wearables, smart prosthetics/implants, nano-robotics and smart fabrics are the top six trends in the industry. These predictions have been inspired by Frost & Sullivan's recent analysis, Patient Monitoring Industry--Analysis of Investment and Trends, 2018. As patient monitoring technology has improved, the ad hoc interval observations undertaken by nurses and other medical staff have in many cases been replaced by continuous monitoring devices that can track multiple biometrics. The six technologies outlined by Frost & Sullivan are all focused on providing real-time, measurable monitoring.

The inverse problem of electrical resistivity surveys (ERS) is difficult because of its nonlinear and ill-posed nature. For this task, traditional linear inversion methods still face challenges such as sub-optimal approximation and initial model selection. Inspired by the remarkable non-linear mapping ability of deep learning approaches, in this paper we propose to build the mapping from apparent resistivity data (input) to resistivity model (output) directly by convolutional neural networks (CNNs). However, the vertically varying characteristic of patterns in the apparent resistivity data may cause ambiguity when using CNNs with the weight sharing and effective receptive field properties. To address the potential issue, we supply an additional tier feature map to CNNs to help it get aware of the relationship between input and output. Based on the prevalent U-Net architecture, we design our network (ERSInvNet) which can be trained end-to-end and reach real-time inference during testing. We further introduce depth weighting function and smooth constraint into loss function to improve inversion accuracy for the deep region and suppress false anomalies. Four groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed methods. According to the comprehensive qualitative analysis and quantitative comparison, ERSInvNet with tier feature map, smooth constraints and depth weighting function together achieve the best performance.

Deep neural networks (DNNs) are known for their vulnerability to adversarial examples. These are examples that have undergone a small, carefully crafted perturbation, and which can easily fool a DNN into making misclassifications at test time. Thus far, the field of adversarial research has mainly focused on image models, under either a white-box setting, where an adversary has full access to model parameters, or a black-box setting where an adversary can only query the target model for probabilities or labels. Whilst several white-box attacks have been proposed for video models, black-box video attacks are still unexplored. To close this gap, we propose the first black-box video attack framework, called V-BAD. V-BAD is a general framework for adversarial gradient estimation and rectification, based on Natural Evolution Strategies (NES). In particular, V-BAD utilizes \textit{tentative perturbations} transferred from image models, and \textit{partition-based rectifications} found by the NES on partitions (patches) of tentative perturbations, to obtain good adversarial gradient estimates with fewer queries to the target model. V-BAD is equivalent to estimating the projection of an adversarial gradient on a selected subspace. Using three benchmark video datasets, we demonstrate that V-BAD can craft both untargeted and targeted attacks to fool two state-of-the-art deep video recognition models. For the targeted attack, it achieves $>$93\% success rate using only an average of $3.4 \sim 8.4 \times 10^4$ queries, a similar number of queries to state-of-the-art black-box image attacks. This is despite the fact that videos often have two orders of magnitude higher dimensionality than static images. We believe that V-BAD is a promising new tool to evaluate and improve the robustness of video recognition models to black-box adversarial attacks.

Signaling proteins are an important topic in drug development due to the increased importance of finding fast, accurate and cheap methods to evaluate new molecular targets involved in specific diseases. The complexity of the protein structure hinders the direct association of the signaling activity with the molecular structure. Therefore, the proposed solution involves the use of protein star graphs for the peptide sequence information encoding into specific topological indices calculated with S2SNet tool. The Quantitative Structure - Activity Relationship classification model obtained with Machine Learning techniques is able to predict new signaling peptides. The best classification model is the first signaling prediction model, which is based on eleven descriptors and it was obtained using the Support Vector Machines - Recursive Feature Elimination (SVM-RFE) technique with the Laplacian kernel (RFE-LAP) and an AUROC of 0.961. Testing a set of 3114 proteins of unknown function from the PDB database assessed the prediction performance of the model. Important signaling pathways are presented for three UniprotIDs (34 PDBs) with a signaling prediction greater than 98.0%.

Cluster analysis or clustering is the task of dividing a set of objects in such a way that elements in the same group or cluster are more similar, according to some dissimilarity measure, than elements in different groups. To achieve this task there are two main types of algorithms: partitioning algorithms, which try to split the data into k groups that usually minimize some optimality criteria, or agglomerative algorithms, which start with single observations and merge them into clusters according to some dissimilarity measure. Such methods have been investigated in a large amount of literature, hence we refer to [12] and references therein for an overview. Clustering techniques used as unsupervised classification procedures are increasingly more influential in people's life since they are used in credit scoring, article recommendation, risk assessment, spam filtering or sentencing recommendations in courts of law, among others. Hence controlling the outcome of such procedures, in particular ensuring that some variables which should not be taken into account due to moral or legal issues are not playing a role in the classification of the observations, has become an important field of research known as fair learning. We refer to [15], [3], [1] or [9] for an overview of such legal issues and mathematical solutions to address them. For instance avoiding discrimination against sensitive characteristics such as sex, race or age can not only be achieved using the naive solution of simply ignoring such protected attribute. Indeed, if the the data at hand reflects a real world bias, machine learning algorithms can pick on this behaviour and emulate it. More precisely, suppose we have data that includes information about attributes that we know or suspect that are biased with respect to the protected class.

Image recognition systems have demonstrated tremendous progress over the past few decades thanks, in part, to our ability of learning compact and robust representations of images. As we witness the wide spread adoption of these systems, it is imperative to consider the problem of unintended leakage of information from an image representation, which might compromise the privacy of the data owner. This paper investigates the problem of learning an image representation that minimizes such leakage of user information. We formulate the problem as an adversarial non-zero sum game of finding a good embedding function with two competing goals: to retain as much task dependent discriminative image information as possible, while simultaneously minimizing the amount of information, as measured by entropy, about other sensitive attributes of the user. We analyze the stability and convergence dynamics of the proposed formulation using tools from non-linear systems theory and compare to that of the corresponding adversarial zero-sum game formulation that optimizes likelihood as a measure of information content. Numerical experiments on UCI, Extended Yale B, CIFAR-10 and CIFAR-100 datasets indicate that our proposed approach is able to learn image representations that exhibit high task performance while mitigating leakage of predefined sensitive information.