Life-expectancy is a complex outcome driven by genetic, socio-demographic, environmental and geographic factors. Increasing socio-economic and health disparities in the United States are propagating the longevity-gap, making it a cause for concern. Earlier studies have probed individual factors but an integrated picture to reveal quantifiable actions has been missing. Amidst growing concerns about the further widening of healthcare inequality and differential access created by Artificial Intelligence, it is imperative to explore it's potential for illuminating biases and enabling transparent policy decisions. In this work, we reveal actionable interventions for decreasing the longevity-gap in the United States by analyzing a County-level data resource with healthcare, socio-economic, behavioral, education and demographic features. We learn an ensemble-averaged structure, draw inferences using the joint probability distribution and extend it to a Bayesian Decision Network for identifying policy actions. We draw quantitative estimates for the positive roles of diversity, preventive-care quality and stable-families within the unified framework of our decision network. Finally, we make this analysis and dashboard available as an interactive web-application for enabling users and policy-makers to validate our insights on bridging the longevity-gap and explore the ones beyond reported in this work.

In this paper, we are concerned with the 2D and 3D geometric shape generation by prescribing a set of characteristic values of a specific geometric body. One of the major motivations of our study is the 3D human body generation in various applications. We develop a novel method that can generate the desired body with customized characteristic values. The proposed method follows a machine-learning flavour that generates the inferred geometric body with the input characteristic parameters from a training dataset. One of the critical ingredients and novelties of our method is the borrowing of inverse scattering techniques in the theory of wave propagation to the body generation. This is done by establishing a delicate one-to-one correspondence between a geometric body and the far-field pattern of a source scattering problem governed by the Helmholtz system. It in turn enables us to establish a one-to-one correspondence between the geometric body space and the function space defined by the far-field patterns. Hence, the far-field patterns can act as the shape generators. The shape generation with prescribed characteristic parameters is achieved by first manipulating the shape generators and then reconstructing the corresponding geometric body from the obtained shape generator by a stable multiple-frequency Fourier method. Our method is easy to implement and produces more efficient and stable body generations. We provide both theoretical analysis and extensive numerical experiments for the proposed method. The study is the first attempt to introduce inverse scattering approaches in combination with machine learning to the geometric body generation and it opens up many opportunities for further developments.

Ever increasing number of Android malware, has always been a concern for cybersecurity professionals. Even though plenty of anti-malware solutions exist, a rational and pragmatic approach for the same is rare and has to be inspected further. In this paper, we propose a novel two-set feature selection approach based on Rough Set and Statistical Test named as RSST to extract relevant system calls. To address the problem of higher dimensional attribute set, we derived suboptimal system call space by applying the proposed feature selection method to maximize the separability between malware and benign samples. Comprehensive experiments conducted on a dataset consisting of 3500 samples with 30 RSST derived essential system calls resulted in an accuracy of 99.9%, Area Under Curve (AUC) of 1.0, with 1% False Positive Rate (FPR). However, other feature selectors (Information Gain, CFsSubsetEval, ChiSquare, FreqSel and Symmetric Uncertainty) used in the domain of malware analysis resulted in the accuracy of 95.5% with 8.5% FPR. Besides, empirical analysis of RSST derived system calls outperform other attributes such as permissions, opcodes, API, methods, call graphs, Droidbox attributes and network traces.

Speaker verification (SV) systems using deep neural network embeddings, so-called the x-vector systems, are becoming popular due to its good performance superior to the i-vector systems. The fusion of these systems provides improved performance benefiting both from the discriminatively trained x-vectors and generative i-vectors capturing distinct speaker characteristics. In this paper, we propose a novel method to include the complementary information of i-vector and x-vector, that is called generative x-vector. The generative x-vector utilizes a transformation model learned from the i-vector and x-vector representations of the background data. Canonical correlation analysis is applied to derive this transformation model, which is later used to transform the standard x-vectors of the enrollment and test segments to the corresponding generative x-vectors. The SV experiments performed on the NIST SRE 2010 dataset demonstrate that the system using generative x-vectors provides considerably better performance than the baseline i-vector and x-vector systems. Furthermore, the generative x-vectors outperform the fusion of i-vector and x-vector systems for long-duration utterances, while yielding comparable results for short-duration utterances.

Abstract--The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical. To better enable the intelligent sensing at the front-end, instead of compressing and transmitting visual signals or the ultimately utilized toplayer deep learning features, we propose to compactly represent and convey the intermediate-layer deep learning features of high generalization capability, to facilitate the collaborating approach between front and cloud ends. This strategy enables a good balance among the computational load, transmission load and the generalization ability for cloud servers when deploying the deep neural networks for large scale cloud based visual analysis. Moreover, the presented strategy also makes the standardization of deep feature coding more feasible and promising, as a series of tasks can simultaneously benefit from the transmitted intermediate layers. We also present the results for evaluation of lossless deep feature compression with four benchmark data compression methods, which provides meaningful investigations and baselines for future research and standardization activities. ECENTLY, deep neural networks (DNNs) have demonstrated the state-of-the-art performance in various computer vision tasks, e.g., image classification [1], [2], [3], [4], image object detection [5], [6], visual tracking [7], visual retrieval [8]. In contrast to the handcrafted features such as Scale-Invariant Feature Transform (SIFT) [9], deep learning based approaches are able to learn representative features directly from the vast amounts of data. For image classification, which is the fundamental task of computer vision, the AlexNet model [1] has achieved 9% better classification accuracy than the previous handcrafted methods in the 2012 ImageNet competition [10], which provides a large scale training dataset with 1.2 million images and one thousand categories. Inspired by the fantastic progress of AlexNet, DNN models continue to be the undisputed leaders in the competition of ImageNet. In particular, both VGGNet [2] and GoogLeNet [11] announced promising performance in the ILSVRC 2014 classification challenge, which demonstrated that deeper and wider architectures can bring great benefits in learning better representations via large scale datasets. In 2016, He et al. also proposed residual blocks to enable very deep learning structure [3]. With the advances of network infrastructure, cloud-based applications are springing up in recent years. In particular, the front-end devices acquire information from users or the physical world, which are subsequently transmitted to the cloud end (i.e., data center) for further process and analyses. In particular, for visual analysis, the front-end devices deployed in the real world such as surveillance cameras and wearable devices acquire massive visual data which are transmitted to the cloud side for analyses, as shown in Figure 1.

Ranking is a fundamental and widely studied problem in scenarios such as search, advertising, and recommendation. However, joint optimization for multi-scenario ranking, which aims to improve the overall performance of several ranking strategies in different scenarios, is rather untouched. Separately optimizing each individual strategy has two limitations. The first one is lack of collaboration between scenarios meaning that each strategy maximizes its own objective but ignores the goals of other strategies, leading to a sub-optimal overall performance. The second limitation is the inability of modeling the correlation between scenarios meaning that independent optimization in one scenario only uses its own user data but ignores the context in other scenarios. In this paper, we formulate multi-scenario ranking as a fully cooperative, partially observable, multi-agent sequential decision problem. We propose a novel model named Multi-Agent Recurrent Deterministic Policy Gradient (MA-RDPG) which has a communication component for passing messages, several private actors (agents) for making actions for ranking, and a centralized critic for evaluating the overall performance of the co-working actors. Each scenario is treated as an agent (actor). Agents collaborate with each other by sharing a global action-value function (the critic) and passing messages that encodes historical information across scenarios. The model is evaluated with online settings on a large E-commerce platform. Results show that the proposed model exhibits significant improvements against baselines in terms of the overall performance.

Artificial intelligence has large potential to contribute to global economic activity. But widening gaps among countries, companies, and workers will need to be managed to maximize the benefits. The role of artificial intelligence (AI) tools and techniques in business and the global economy is a hot topic. This is not surprising given that AI might usher in radical--arguably unprecedented--changes in the way people live and work. The AI revolution is not in its infancy, but most of its economic impact is yet to come.

A team of experts from IIT-Kharagpur (IIT-Kgp) and Tata Medical Centre (TMC), Kolkata, has devised a computer-assisted model they say can automatically grade breast cancer aggressiveness, even in remote settings, providing fresh impetus to AI-based medical technology in India. It also seeks to reduce human error in identifying breast cancer of various levels of aggressiveness to assist in distinguishing normal and low and higher risk malignant tumours. To do that, the team tapped into deep learning, a form of AI concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. "The idea is to assess and identify the cancer that's of high risk. This software allows accurate identification of the aggressive cancers anywhere, even in the remotest part of the country, allowing faster referral and quicker treatment for patients, irrespective of their geographical location," Sanjoy Chatterjee, senior clinical oncologist at TMC, told IANS.

Most AI platform suppliers have been obsessed lately with autonomous vehicles. This week, Nvidia escalated the obsession by spreading the epidemic to "autonomous machines." At Nvidia's GPU Technology Conference held here, CEO Jensen Huang wound up and pitched Nvidia AGX, a series of embedded AI high-performance computers built around Nvidia's new Xavier processors, for a host of robotic and autonomous machines. Phil Magney, founder and principal advisor at VSI Labs, called Nvidia "shrewd" to extend the reach of the architecture, since most competitors are focusing exclusively on automated cars. "As we know, there are lots of human driven machines out there where removing the operator is the goal. Nvidia's new partners in Japan have their bases covered with these announcements."

Both Reuters and the Wall Street Journal have learned that the Trump administration is likely to formally announce its latest tariffs on Chinese goods within the next few days (possibly as soon as September 17th). Imports for "internet technology products," circuit boards and other electronics are still likely to become more expensive, although the tariff level is reportedly set at 10 percent, not the originally proposed 25 percent also used for earlier tariffs. The administration may have lowered the tariffs to reduce the chances that companies would instantly raise prices to make up for the higher costs. As before, the tariffs are meant to pressure China into curbing trade policies deemed unfair, including attempts to acquire US technologies and subsidize tech categories like AI and robotics. There are hints of the two sides resuming talks that could mitigate or end the trade war, but Trump hasn't been willing to wait for these talks before imposing new tariffs.