Although there are millions of transgender people in the world, a lack of information exists about their health issues. This issue has consequences for the medical field, which only has a nascent understanding of how to identify and meet this population's health-related needs. Social media sites like Twitter provide new opportunities for transgender people to overcome these barriers by sharing their personal health experiences. Our research employs a computational framework to collect tweets from self-identified transgender users, detect those that are health-related, and identify their information needs. This framework is significant because it provides a macro-scale perspective on an issue that lacks investigation at national or demographic levels. Our findings identified 54 distinct health-related topics that we grouped into 7 broader categories. Further, we found both linguistic and topical differences in the health-related information shared by transgender men (TM) as com-pared to transgender women (TW). These findings can help inform medical and policy-based strategies for health interventions within transgender communities. Also, our proposed approach can inform the development of computational strategies to identify the health-related information needs of other marginalized populations.

Hence, it is seldom the case where a single oracle labels an entire collection. Furthermore, the Web, through its social nature, also exploits the wisdom of crowds to annotate large collections of documents and images. By categorizing texts, tagging images or rating products and places, Web users are generating large volumes of labeled content. However, when learning supervised models from crowds, the quality of labels can vary significantly due to task subjectivity and differences in annotator reliability (or bias) [9], [10]. If we consider a sentiment analysis task, it becomes clear that the subjectiveness of the exercise is prone to generate considerably distinct labels from different annotators. Similarly, online product reviews are known to vary considerably depending on the personal biases and volatility of the reviewer's opinions. It is therefore essential to account for these issues when learning from this increasingly common type of data. Hence, the interest of researchers on building models that take the reliabilities of different annotators into consideration and mitigate the effect of their biases has spiked during the last few years (e.g.

Twitter has provided a great opportunity for public libraries to disseminate information for a variety of purposes. Twitter data have been applied in different domains such as health, politics, and history. There are thousands of public libraries in the US, but no study has yet investigated the content of their social media posts like tweets to find their interests. Moreover, traditional content analysis of Twitter content is not an efficient task for exploring thousands of tweets. Therefore, there is a need for automatic methods to overcome the limitations of manual methods. This paper proposes a computational approach to collecting and analyzing using Twitter Application Programming Interfaces (API) and investigates more than 138,000 tweets from 48 US west coast libraries using topic modeling. We found 20 topics and assigned them to five categories including public relations, book, event, training, and social good. Our results show that the US west coast libraries are more interested in using Twitter for public relations and book-related events. This research has both practical and theoretical applications for libraries as well as other organizations to explore social media actives of their customer and themselves.

This problem is known as phase retrieval and is encountered frequently in applications including X-ray crystallography [1, 2], astronomy [3], optics [4], tomography, microscopy, array imaging [5], acoustics [6], quantum mechanics [7] and ptychography [8], where it is extremely difficult or infeasible to measure phase information of signal while recording magnitude measurements is much easier. In its full generality, the inverse problem 1 is severely ill-posed due to its nonlinear and non-convex nature. Traditional approaches to overcome the ill posedness of phase retrieval generally falls into two categories. First approach is to introduce redundancy into measurement system, where we take more measurements than dimension of true signal x, i.e., m n usually in the form of oversampled Fourier transform [9], short-time Fourier transform [10], random Gaussian measurements [11], coded diffraction patterns using random masks or structured illuminations [12, 13], wavelet transform [14], and Gabor frames [15]. Second approach is to exploit some known knowledge about true signal x (prior information) such as sparsity [16, 17, 18] or non-negativity [19, 20].

A biomimetic machine intelligence algorithm, that holds promise in creating invariant representations of spatiotemporal input streams is the hierarchical temporal memory (HTM). This unsupervised online algorithm has been demonstrated on several machine-learning tasks, including anomaly detection. Significant effort has been made in formalizing and applying the HTM algorithm to different classes of problems. There are few early explorations of the HTM hardware architecture, especially for the earlier version of the spatial pooler of HTM algorithm. In this article, we present a full-scale HTM architecture for both spatial pooler and temporal memory. Synthetic synapse design is proposed to address the potential and dynamic interconnections occurring during learning. The architecture is interweaved with parallel cells and columns that enable high processing speed for the HTM. The proposed architecture is verified for two different datasets: MNIST and the European number plate font (EUNF), with and without the presence of noise. The spatial pooler architecture is synthesized on Xilinx ZYNQ-7, with 91.16% classification accuracy for MNIST and 90\% accuracy for EUNF, with noise. For the temporal memory sequence prediction, first and second order predictions are observed for a 5-number long sequence generated from EUNF dataset and 95% accuracy is obtained. Moreover, the proposed hardware architecture offers 1364X speedup over the software realization. These results indicate that the proposed architecture can serve as a digital core to build the HTM in hardware and eventually as a standalone self-learning system.

Checking various log files from different processes can be a tedious task as these logs contain lots of events, each with a (possibly large) number of attributes. We developed a way to automatically model log files with a dozen attributes and detect outlier traces in the data. For that we extend Dynamic Bayesian Networks to model the normal behavior found in log files. We introduce a new algorithm that is able to learn a model of a log file starting from the data itself. The model is capable of scoring traces even when new values or new combinations of values appear in the log file and has the ability to give a decomposition of the score indicating the root cause for the anomalies.

Nowadays, most of the objective speech quality assessment tools (e.g., perceptual evaluation of speech quality (PESQ)) are based on the comparison of the degraded/processed speech with its clean counterpart. The need of a "golden" reference considerably restricts the practicality of such assessment tools in real-world scenarios since the clean reference usually cannot be accessed. On the other hand, human beings can readily evaluate the speech quality without any reference (e.g., mean opinion score (MOS) tests), implying the existence of an objective and non-intrusive (no clean reference needed) quality assessment mechanism. In this study, we propose a novel end-to-end, non-intrusive speech quality evaluation model, termed Quality-Net, based on bidirectional long short-term memory. The evaluation of utterance-level quality in Quality-Net is based on the frame-level assessment. Frame constraints and sensible initializations of forget gate biases are applied to learn meaningful frame-level quality assessment from the utterance-level quality label. Experimental results show that Quality-Net can yield high correlation to PESQ (0.9 for the noisy speech and 0.84 for the speech processed by speech enhancement). We believe that Quality-Net has potential to be used in a wide variety of applications of speech signal processing.

The performance of Feedforward neural network (FNN) fully depends upon the selection of architecture and training algorithm. FNN architecture can be tweaked using several parameters, such as the number of hidden layers, number of hidden neurons at each hidden layer and number of connections between layers. There may be exponential combinations for these architectural attributes which may be unmanageable manually, so it requires an algorithm which can automatically design an optimal architecture with high generalization ability. Numerous optimization algorithms have been utilized for FNN architecture determination. This paper proposes a new methodology which can work on the estimation of hidden layers and their respective neurons for FNN. This work combines the advantages of Tabu search (TS) and Gradient descent with momentum backpropagation (GDM) training algorithm to demonstrate how Tabu search can automatically select the best architecture from the populated architectures based on minimum testing error criteria. The proposed approach has been tested on four classification benchmark dataset of different size Keywords: Tabu search (TS), Feedforward neural network (FNN), hidden layer, hidden neurons, optimization, architecture.

SAN FRANCISCO – Google's plan to launch a censored search engine in China requires more "transparency, oversight and accountability," hundreds of employees at the Alphabet Inc. unit said in an internal petition seen by Reuters on Thursday. Hoping to gain approval from the Chinese government to provide a mobile search service, the company plans to block some websites and search terms, Reuters reported this month, citing two people familiar with the matter. Disclosure of the secretive effort has disturbed some Google employees and human rights advocacy organizations. They are concerned that by agreeing to censorship demands, Google would validate China's prohibitions on free expression and violate the "don't be evil" clause in the company's code of conduct. After employees petitioned this year, Google announced it would not renew a project to help the U.S. military develop artificial intelligence technology for drones.

Artificial intelligence, in its many forms, will likely continue to redefine how individuals think about government, school, work, and daily life. And startups that are not leveraging AI are beginning to fall behind the rest of the pack. "AI will be a true differentiator for companies in the future. The companies that have mastered it will take off, those who haven't will dwindle (just like companies choosing to embrace software and the internet early on)," Scott Stephenson, founder and CEO of Deepgram, an automatic speech recognition company, told Crunchbase News in an email. And in a world where supergiant rounds are now quite common, AI startups have brought in some significant capital.