The use of photoplethysmogram signal (PPG) for heart and sleep monitoring is commonly found nowadays in smartphones and wrist wearables. Besides common usages, it has been proposed and reported that person information can be extracted from PPG for other uses, like biometry tasks. In this work, we explore several end-to-end convolutional neural network architectures for detection of human's characteristics such as gender or person identity. In addition, we evaluate whether speech/non-speech events may be inferred from PPG signal, where speech might translate in fluctuations into the pulse signal. The obtained results are promising and clearly show the potential of fully end-to-end topologies for automatic extraction of meaningful biomarkers, even from a noisy signal sampled by a low-cost PPG sensor. The AUCs for best architectures put forward PPG wave as biological discriminant, reaching $79\%$ and $89.0\%$, respectively for gender and person verification tasks. Furthermore, speech detection experiments reporting AUCs around $69\%$ encourage us for further exploration about the feasibility of PPG for speech processing tasks.

"We lived and breathed data science," DataRobot CEO Jeremy Achin says of himself and his cofounder ... [ ] Tom de Godoy. "And we asked ourselves, 'How would we automate our jobs?'" DataRobot wants to make machine learning so simple that a business analyst with basic training can run predictive models without breaking a sweat. The Boston-based startup just raised a $206 million Series E funding round led by Sapphire Ventures to expand the business, which sells software that helps companies across industries develop and deploy in-house AI models. The billion-dollar valuation makes it the highest-ranking of the "picks-and-shovels" startups featured on Forbes' inaugural AI 50 list (meaning the companies that provide tools to help their customers develop their own AI).

The latest report titled global Artificial Intelligence Platform Software market includes the comprehensive study of the present market scope and based on the research that is being carried out the analysts at The Research Insights state that the newest developments that are presently affecting the changing scenario products and services that have high rankings and great feedback are described wisely. The Artificial Intelligence platform provides tools and technologies to build applications with AI-rich capabilities. The algorithms used for formulating the AI platform provide logical models for application developers to fabricate various innovative applications with capabilities, such as speech and voice recognition, text recognition, and predictive analytics. The factors likely to drive the Artificial Intelligence platform market are the substantial increase in data generation, high demand for AI-based solutions, the need to enhance customer experience, and the increasing operational efficiency & reduced cost that AI platforms offer. Among end users, the BFSI segment is projected to have the largest share, while healthcare is expected to have the highest growth rate during the forecast period.

The Artificial Intelligence (AI) in accounting market size is expected to grow from USD 666 million in 2019 to USD 4,791 million by 2024, at a Compound Annual Growth Rate (CAGR) of 48.4% during the forecast period. The AI in accounting is driven by various factors, such as the growing need to automate accounting processes and support enhanced data-based advisory and decision making. However, growing concerns over high criticality of data volume and quality, and investment related issues with integration of AI in accounting can hinder the growth of the market. Services segment to grow at a higher CAGR during the forecast period The AI in accounting market based on component is segmented into solutions and services.The services segment is expected to grow at a rapid pace during the forecast period. The growth of this segment can be attributed to the increasing deployment of AI in accounting software tools and solutions, which leads to increasing the demand for pre- and post-deployment services.

Preserving differential privacy has been well studied under centralized setting. However, it's very challenging to preserve differential privacy under multiparty setting, especially for the vertically partitioned case. In this work, we propose a new framework for differential privacy preserving multiparty learning in the vertically partitioned setting. Our core idea is based on the functional mechanism that achieves differential privacy of the released model by adding noise to the objective function. We show the server can simply dissect the objective function into single-party and cross-party sub-functions, and allocate computation and perturbation of their polynomial coefficients to local parties. Our method needs only one round of noise addition and secure aggregation. The released model in our framework achieves the same utility as applying the functional mechanism in the centralized setting. Evaluation on real-world and synthetic datasets for linear and logistic regressions shows the effectiveness of our proposed method.

We consider Social Distance Games (SDGs), that is cluster formation games in which the utility of each agent only depends on the composition of the cluster she belongs to, proportionally to her harmonic centrality, i.e., to the average inverse distance from the other agents in the cluster. Under a non-cooperative perspective, we adopt Nash stable outcomes, in which no agent can improve her utility by unilaterally changing her coalition, as the target solution concept. Although a Nash equilibrium for a SDG can always be computed in polynomial time, we obtain a negative result concerning the game convergence and we prove that computing a Nash equilibrium that maximizes the social welfare is NP-hard by a polynomial time reduction from the NP-complete Restricted Exact Cover by 3-Sets problem. We then focus on the performance of Nash equilibria and provide matching upper bound and lower bounds on the price of anarchy of Θ(n), where n is the number of nodes of the underlying graph. Moreover, we show that there exists a class of SDGs having a lower bound on the price of stability of 6/5 − ε, for any ε > 0. Finally, we characterize the price of stability 5 of SDGs for graphs with girth 4 and girth at least 5, the girth being the length of the shortest cycle in the graph.

Visual objects are composed of a recursive hierarchy of perceptual wholes and parts, whose properties, such as shape, reflectance, and color, constitute a hierarchy of intrinsic causal factors of object appearance. However, object appearance is the compositional consequence of both an object's intrinsic and extrinsic causal factors, where the extrinsic causal factors are related to illumination, and imaging conditions. Therefore, this paper proposes a unified tensor model of wholes and parts, and introduces a compositional hierarchical tensor factorization that disentangles the hierarchical causal structure of object image formation, and subsumes multilinear block tensor decomposition as a special case. The resulting object representation is an interpretable combinatorial choice of wholes' and parts' representations that renders object recognition robust to occlusion and reduces training data requirements. We demonstrate ourapproach in the context of face recognition by training on an extremely reduced dataset of synthetic images, and report encouragingface verification results on two datasets - the Freiburg dataset, andthe Labeled Face in the Wild (LFW) dataset consisting of real world images, thus, substantiating the suitability of our approach for data starved domains.

Despite impressive performance on numerous visual tasks, Convolutional Neural Networks (CNNs) --- unlike brains --- are often highly sensitive to small perturbations of their input, e.g. adversarial noise leading to erroneous decisions. We propose to regularize CNNs using large-scale neuroscience data to learn more robust neural features in terms of representational similarity. We presented natural images to mice and measured the responses of thousands of neurons from cortical visual areas. Next, we denoised the notoriously variable neural activity using strong predictive models trained on this large corpus of responses from the mouse visual system, and calculated the representational similarity for millions of pairs of images from the model's predictions. We then used the neural representation similarity to regularize CNNs trained on image classification by penalizing intermediate representations that deviated from neural ones. This preserved performance of baseline models when classifying images under standard benchmarks, while maintaining substantially higher performance compared to baseline or control models when classifying noisy images. Moreover, the models regularized with cortical representations also improved model robustness in terms of adversarial attacks. This demonstrates that regularizing with neural data can be an effective tool to create an inductive bias towards more robust inference.

November 13, 2019 Abstract Collision avoidance is one of the most primary problems in the decentralized multiagent navigation: while the agents are moving towards their own targets, attentions should be paid to avoid the collisions with the others. In this paper, we introduced the concept of the local action cell, which provides for each agent a set of velocities that are safe to perform. Consequently, as long as the local action cells are updated on time and each agent selects its motion within the corresponding cell, there should be no collision caused. Furthermore, we coupled the local action cell with an adaptive learning framework, in which the performance of selected motions are evaluated and used as the references for making decisions in the following updates. The efficiency of the proposed approaches were demonstrated through the experiments for three commonly considered scenarios, where the comparisons have been made with several well studied strategies. 1 Introduction Collision-free navigation is a fundamental and important problem in the design of the multiagent systems, which are widely applied in the fields such as robots control and traffic engineering.

Latest Strategic Study Released on Global Software for Autonomous Cars Market with forecast till 2025, the report comprises of historical data and estimation of the Global Software for Autonomous Cars Market. The following Industry is shown to progress with a noteworthy rise in the Compound Annual Growth Rate (CAGR) during the forecast period owing to various factors driving the market. Some of the key players mentioned in this research are "Alphabet, Delphi Automotive, Intel, NVIDIA, QNX Software Systems, Tesla, Apple, Autotalks, Cisco, Cohda Wireless, Covisint, DeepMap & Nauto", etc. Rapid Growth Factors In addition, the market is growing at a fast pace and the report shows us that there are a couple of key factors behind that. The most important factor that's helping the market grow faster than usual is the tough competition. Business Strategies Key strategies in theGlobal Software for Autonomous Cars Market that includes product developments, partnerships, mergers and acquisitions, etc discussed in this report.