title: Olfactory Assays for Mouse Models of Neurodegenerative Disease

abstract: In many neurodegenerative diseases and particularly in Parkinson’s disease, deficits in olfaction are reported to occur early in the disease process and may be a useful behavioral marker for early detection. Earlier detection in neurodegenerative disease is a major goal in the field because this is when neuroprotective therapies have the best potential to be effective. Therefore, in preclinical studies testing novel neuroprotective strategies in rodent models of neurodegenerative disease, olfactory assessment could be highly useful in determining therapeutic potential of compounds and translation to the clinic. In the present study we describe a battery of olfactory assays that are useful in measuring olfactory function in mice. The tests presented in this study were chosen because they measure olfaction abilities in mice related to food odors, social odors, and non-social odors. These tests have proven useful in characterizing novel genetic mouse models of Parkinson’s disease as well as in testing potential disease-modifying therapies.


title: Rapid Assessment of Wetland Ecosystem Services (RAWES): An example from Colombo, Sri Lanka

abstract: Wetlands make essential positive contributions to multiple dimensions of human wellbeing. However, recognition of these benefits is often lacking in decision-making, compromising the wellbeing of both the ecosystem and is many linked human beneficiaries. Wetland site managers, decision-makers and stakeholders all need to better understand the benefits provided by wetlands. Despite a plethora of available assessment techniques, very few approaches are genuinely rapid, applicable across different wetland types or consider the realities of time and money resource constraints. The Rapid Assessment of Wetland Ecosystem Services (RAWES) approach is presented as a method that meets these needs, illustrated through its practical application in over 60 different wetland sites supporting development of a Wetland Strategy for the Metro Colombo Region, Sri Lanka. The approach is based on the trained, local assessors using a variety of field indicators in order to assess the positive or negative contribution over 30 wetland ecosystem services provide at local, regional or global scales. Outputs are simplified, signalling to decision-makers the diversity of interlinked ecosystem service outcomes consequent from management policies and actions.


title: A Mixed-reality Learning Environment

abstract: Abstract The Industry 4.0 vision anticipates that internet technologies will find their way into future factories replacing traditional components by dynamic and intelligent cyber-physical systems (CPS) that combine the physical objects with their digital representation. Reducing the gap between the real and digital world makes the factory environment more flexible, more adaptive, but also demand broader skill of human workers. Interdisciplinary competencies from engineering, information technology, and computer science are required in order to understand and manage the diverse interrelations between physical objects and their digital counterpart. This paper proposes a system architecture for a mixed-reality based learning environment, which combines physical objects and visualization of its digital content via Augmented Reality. It allows to make the dynamic interrelations between real and digital factory visible and tangible. The proposed learning environment is not meant to work as a stand-alone solution, but should enrich existing academic and advanced training curricula.


title: Inferring Twitter user locations with 10 km accuracy

abstract: Geographic locations of users form an important axis in public polls and localized advertising, but are not available by default. The number of users who make their locations public or use GPS tagging is relatively small, compared to the huge number of users in online social networking services and social media platforms. In this work we propose a new framework to infer a user's main location of activities in Twitter using their textual contents. Our approach is based on a probabilistic generative model that filters local words, employs data binning for scalability, and applies a map projection technique for performance. For Korean Twitter users, we report that 60% of users are identified within 10 km of their locations, a significant improvement over existing approaches.


title: MR imaging in patients with male-to-female sex reassignment surgery: postoperative anatomy and complications

abstract: Objective:To investigate the role of MRI in the evaluation of both the new female anatomy and complications in male-to-female sex reassignment surgery (MtF-SRS).Methods:71 consecutive patients with MtF-SRS had 74 MRI [age range, 21–63 years; mean (±standard deviation) age, 36 ± 10 years; median age, 37 years]. In 47 patients, MRI was performed to rule out early post-operative complications after gender conversion (n = 40), vaginoplasty (n = 6) or remodelling of the labia majora (n = 1). In 27 patients, MRI was performed 1–20 years after MtF-SRS for late post-operative complications, pain or dysuria, inflammatory changes or poor cosmetic outcome. Three patients had MRI both before and after the operation.Results:MRI allowed investigation of the new female anatomy in all cases. Soon after MtF-SRS, a small amount of blood was identified in all patients around the neoclitoris, urethral plaque and labia. Post-operative complications were clinically significant fluid collections (n = 5), labial abscesses (n = 2...


title: A classification-based approach to question routing in community question answering

abstract: Community-based Question and Answering (CQA) services have brought users to a new era of knowledge dissemination by allowing users to ask questions and to answer other users' questions. However, due to the fast increasing of posted questions and the lack of an effective way to find interesting questions, there is a serious gap between posted questions and potential answerers. This gap may degrade a CQA service's performance as well as reduce users' loyalty to the system. To bridge the gap, we present a new approach to Question Routing, which aims at routing questions to participants who are likely to provide answers. We consider the problem of question routing as a classification task, and develop a variety of local and global features which capture different aspects of questions, users, and their relations. Our experimental results obtained from an evaluation over the Yahoo!~Answers dataset demonstrate high feasibility of question routing. We also perform a systematical comparison on how different types of features contribute to the final results and show that question-user relationship features play a key role in improving the overall performance.


title: A Closer Look at Who “Chokes Under Pressure”

abstract: Previous research has shown that the higher one's working memory capacity, the more likely his/her performance is to be negatively impacted by performance pressure. In the current research we examined potential explanations for this finding by assessing the relation between pressure-induced performance deficits (i.e. “choking under pressure”) in math-based problem solving and individual differences in both working memory (as assessed via complex span tasks) and attentional control (as assessed via two measures from an Eriksen Flanker task). We find higher working memory only relates to “choking under pressure” when individuals were low in attentional control. These results further elucidate the mechanism by which high-pressure scenarios can lead to errors in performance and carry implications for developing effective intervention strategies to prevent poor performance in high-stakes situations.


title: Long short-term memory model for predicting productivity of drilling space units

abstract: In the oil and gas industry, there is a growing demand for application of big data analytics and artificial intelligence (AI) technologies to optimize operations and reduce cost. In this study, we work on the productivity prediction, which is an important and challenging task for operators. Unlike previous studies where full field or single well analysis was conducted, we focus on more active operation units, drilling space units. Moreover, significant information is extracted from geology reports which are saved in scanned PDF files and well logs. By using the extracted information, long short-term memory (LSTM) model which could take the special-temporal changes of DSUs into consideration is employed to predict the DSUs' productivity. After rigorous validation, it is found that the accuracy of LSTM model could reach to more than 60%, which is 10% higher than a multilayer perceptron (MLP) model proposed in a previous research.


title: Load Aware Adaptive Scheduling for Energy Efficient Suburban Massive MIMO Networks

abstract: This paper proposes an adaptive scheduling strategy to improve the energy efficiency of massive MIMO. The proposed technique combines a novel adaptive discontinuous transmission (ADTx) and antenna optimization that can optimize the number of active antennas and transmission period such that data can be delivered to users with the minimum power. The proposed ADTx utilizes a DTx principle and adaptive precoding technique selection. On the other hand, antennas optimization is used to relax the number of active antennas once the required period has been calculated. Although massive MIMO is initially designed to utilize the multiplexing users found in dense users scenario, this paper discusses the impact of the proposed technique to low density scenarios, in particular suburban scenario, because major parts of cellular coverage are low density. It will be shown that the proposed scheme provides significant energy efficiency (EE) and QoS improvement that can support green cellular network.


title: Remapping Hand Movements in a Novel Geometrical Environment

abstract: The issue of how the Euclidean properties of space are represented in the nervous system is a main focus in the study of visual perception, but is equally relevant to motor learning. The goal of our experiments was to investigate how the properties of space guide the remapping of motor coordination. Subjects wore an instrumented data glove that recorded the finger motions. Signals generated by the glove operated a remotely controlled endpoint: a cursor on a computer monitor. The subjects were instructed to execute movements of this endpoint with controlled motions of the fingers. This required inverting a highly redundant map from fingers to cursor motions. We found that 1) after training with visual feedback of the final error (but not of the ongoing cursor motion), subjects learned to map cursor locations into configurations of the fingers; 2) extended practice of movement led to more rectilinear cursor movement, a trend facilitated by training under continuous visual feedback of cursor motions; 3) with...


title: Numerical methods for the Stokes and Navier–Stokes equations driven by threshold slip boundary conditions

abstract: Abstract In this article, we discuss the numerical solution of the Stokes and Navier–Stokes equations completed by nonlinear slip boundary conditions of friction type in two and three dimensions. To solve the Stokes system, we first reduce the related variational inequality into a saddle point–point problem for a well chosen augmented Lagrangian. To solve this saddle point problem we suggest an alternating direction method of multiplier together with finite element approximations. The solution of the Navier–Stokes system combines finite element approximations, time discretization by operator splitting and augmented Lagrangian method. Numerical experiment results for two and three dimensional flow confirm the interest of these approaches.


title: Reverse Nested Miller Compensation Using Current Buffers in a Three-Stage LDO

abstract: A novel frequency compensation scheme called reverse nested Miller compensation using current buffers (RNMCCB) for three-stage amplifiers is proposed. As opposed to previous reverse nested schemes, our work uses inverting gain stages for both the second and third stages. The outer compensation loop utilizes a current mirror as an inverting current buffer (CB), and the inner loop uses a common-gate amplifier as a CB, creating two left-half-plane (LHP) zeros. We introduce a simple and effective method of placing a resistor in series with a CB for accurate placement of LHP zeros. As a design example of the RNMCCB scheme, we propose a three-stage low dropout voltage regulator (LDO) in a 0.5-?m CMOS process to supply 1.21 V to a load ranging from 1 ?A to 100 mA. Our design goals were to simultaneously achieve very high current efficiency and very low transient output voltage variation. As such, we achieved a 99.95% current efficiency and a maximum load transient output voltage variation of ±48 mV with an output capacitor of 100 nF. Experimental results, in good agreement with theoretical analysis, validate the novel RNMCCB frequency compensation scheme.


Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations


title: Layered Diffusion based Coverage Control in Wireless Sensor Networks

abstract: Coverage is an important performance metric for many applications, such as surveillance in wireless sensor networks. Coverage control is used to select as few active nodes as possible from all deployed sensor nodes, such that sufficient coverage of the monitored area can be guaranteed while reducing the energy consumption of each individual sensor node to prolong the network lifetime. This paper classifies three types of coverage control protocols based on the available information about nodes' distances or locations, and reviews several representative protocols for each type. We also propose a new distributed and localized coverage control protocol, called Layered Diffusion-based Coverage Control (LDCC). The LDCC protocol does not require information about the node location coordinates when selecting active nodes. Instead, it exploits hop count information, which is easily obtained in a WSN, to select active sensor nodes. Furthermore, the LDCC protocol is very simple and does not require any sophisticated computation such as distance or covered area computation. Our simulation results show that the LDCC protocol achieves a high coverage ratio while incurring very low message overhead compared with other existing protocols. Furthermore, simulation results suggest that in a large-scale sensor network with medium to large localization errors, LDCC performs even better than location-based coverage control protocols.


An empirical study on reverse psychology applied in advertising messages


title: Lexicon-Based Sentiment Analysis of Twitter Messages in Spanish Análisis de Sentimiento basado en lexicones de mensajes de Twitter en español

abstract: Lexicon-Based approaches to Sentiment Analysis (SA) differ from the more common machine-learning based approaches in that the former rely solely on previously generated lexical resources that store polarity information for lexical items, which are then identified in the texts, assigned a polarity tag, and finally weighed, to come up with an overall score for the text. Such SA systems have been proved to perform on par with supervised, statistical systems, with the added benefit of not requiring a training set. However, it remains to be seen whether such lexically-motivated systems can cope equally well with extremely short texts, as generated on social networking sites, such as Twitter. In this paper we perform such an evaluation using Sentitext, a lexicon-based SA tool for Spanish.


title: Reference ranges for foetal nasal bone length, prenasal thickness, and interocular distance at 18 to 24 weeks’ gestation in low-risk pregnancies

abstract: The aim of the present study was to establish the normal ranges for foetal nasal bone length (NBL), prenasal skin thickness (PNT), interocular distance (IOD), and ratio of prenasal thickness to- nasal bone length (PNT/ NBL) at 18–24 weeks using two-dimensional (2D) ultrasound. This study was a retrospective study of prenatal ultrasonographic records from 407 foetuses between 18 and 24 weeks gestational age (GA). The NBL, PNT, IOD, PNT/ NBL ratio, biparietal diameter (BPD), and femur length (FL) were investigated. The relationships among NBL, PNT, IOD, PNT/ NBL, and GA were evaluated. Additionally, descriptive statistics for NBL, PNT, and IOD values for each gestational week were obtained. There was a significant association between GA and NBL, PNT, and IOD between 18 and 24 weeks. NBL increased from a mean of 5.5 mm to 8.3 mm, PNT increased from a mean of 3.5 mm to 5.1 mm, and IOD increased from a mean of 11.1 mm to 14.5 mm. PNT/NBL ratio did not change with gestational age. This study showed normal ranges for NBL, PNT, IOD, and PNT/ NBL ratios for foetuses between 18 and 24 weeks in low-risk pregnancies. There was a positive linear relationship between GA and NBL, PNT, and IOD. The PNT/NBL ratio might be a more useful measurement than NBL or PNT alone.


title: Semantically Decomposing the Latent Spaces of Generative Adversarial Networks

abstract: We propose a new algorithm for training generative adversarial networks that jointly learns latent codes for both identities (e.g. individual humans) and observations (e.g. specific photographs). By fixing the identity portion of the latent codes, we can generate diverse images of the same subject, and by fixing the observation portion, we can traverse the manifold of subjects while maintaining contingent aspects such as lighting and pose. Our algorithm features a pairwise training scheme in which each sample from the generator consists of two images with a common identity code. Corresponding samples from the real dataset consist of two distinct photographs of the same subject. In order to fool the discriminator, the generator must produce pairs that are photorealistic, distinct, and appear to depict the same individual. We augment both the DCGAN and BEGAN approaches with Siamese discriminators to facilitate pairwise training. Experiments with human judges and an off-the-shelf face verification system demonstrate our algorithm's ability to generate convincing, identity-matched photographs.


title: Learning to Disentangle Factors of Variation with Manifold Interaction

abstract: Many latent factors of variation interact to generate sensory data; for example, pose, morphology and expression in face images. In this work, we propose to learn manifold coordinates for the relevant factors of variation and to model their joint interaction. Many existing feature learning algorithms focus on a single task and extract features that are sensitive to the task-relevant factors and invariant to all others. However, models that just extract a single set of invariant features do not exploit the relationships among the latent factors. To address this, we propose a higher-order Boltzmann machine that incorporates multiplicative interactions among groups of hidden units that each learn to encode a distinct factor of variation. Furthermore, we propose correspondence-based training strategies that allow effective disentangling. Our model achieves state-of-the-art emotion recognition and face verification performance on the Toronto Face Database. We also demonstrate disentangled features learned on the CMU Multi-PIE dataset.


title: A procedural procedural level generator generator

abstract: Procedural content generation (PCG) is concerned with automatically generating game content, such as levels, rules, textures and items. But could the content generator itself be seen as content, and thus generated automatically? This would be very useful if one wanted to avoid writing a content generator for a new game, or if one wanted to create a content generator that generates an arbitrary amount of content with a particular style or theme. In this paper, we present a procedural procedural level generator generator for Super Mario Bros. It is an interactive evolutionary algorithm that evolves agent-based level generators. The human user makes the aesthetic judgment on what generators to prefer, based on several views of the generated levels including a possibility to play them, and a simulation-based estimate of the playability of the levels. We investigate the characteristics of the generated levels, and to what extent there is similarity or dissimilarity between levels and between generators.


title: Document categories in the ISI Web of Knowledge: Misunderstanding the Social Sciences?

abstract: Thomson Reuter's ISI Web of Knowledge (or ISI for short) is used in the majority of benchmarking analyses and bibliometric research projects. Therefore, it is important to be aware of the limitations of data provided by ISI. This article deals with a limitation that disproportionally affects the Social Sciences: ISI's misclassification of journal articles containing original research into the "review" or "proceedings paper" category. I report on a comprehensive, 11 year analysis, of document categories for 27 journals in nine Social Science and Science disciplines. I show that although ISI's "proceedings paper" and "review" classifications seem to work fairly well in the Sciences, they illustrate a profound misunderstanding of research and publication practices in the Social Sciences.


title: Learning a locality preserving subspace for visual recognition

abstract: We have demonstrated that the face recognition performance can be improved significantly in low dimensional linear subspaces. Conventionally, principal component analysis (PCA) and linear discriminant analysis (LDA) are considered effective in deriving such a face subspace. However, both of them effectively see only the Euclidean structure of face space. We propose a new approach to mapping face images into a subspace obtained by locality preserving projections (LPP) for face analysis. We call this Laplacianface approach. Different from PCA and LDA, LPP finds an embedding that preserves local information, and obtains a face space that best detects the essential manifold structure. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. We compare the proposed Laplacianface approach with eigenface and fisherface methods on three test datasets. Experimental results show that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.


title: A Re-examination of Machine Learning Approaches for Sentence-Level MT Evaluation

abstract: Recent studies suggest that machine learning can be applied to develop good automatic evaluation metrics for machine translated sentences. This paper further analyzes aspects of learning that impact performance. We argue that previously proposed approaches of training a HumanLikeness classifier is not as well correlated with human judgments of translation quality, but that regression-based learning produces more reliable metrics. We demonstrate the feasibility of regression-based metrics through empirical analysis of learning curves and generalization studies and show that they can achieve higher correlations with human judgments than standard automatic metrics.


title: Learning compositional sparse models of bimodal percepts

abstract: Various perceptual domains have underlying compositional semantics that are rarely captured in current models. We suspect this is because directly learning the compositional structure has evaded these models. Yet, the compositional structure of a given domain can be grounded in a separate domain thereby simplifying its learning. To that end, we propose a new approach to modeling bimodal percepts that explicitly relates distinct projections across each modality and then jointly learns a bimodal sparse representation. The resulting model enables compositionality across these distinct projections and hence can generalize to unobserved percepts spanned by this compositional basis. For example, our model can be trained on red triangles and blue squares; yet, implicitly will also have learned red squares and blue triangles. The structure of the projections and hence the compositional basis is learned automatically for a given language model. To test our model, we have acquired a new bimodal dataset comprising images and spoken utterances of colored shapes in a table-top setup. Our experiments demonstrate the benefits of explicitly leveraging compositionality in both quantitative and human evaluation studies.


title: Regression convolutional network for vanishing point detection

abstract: This paper presents a detection method for estimation of vanishing point position with designed regression convolutional neural network. Due to the deep structures of convolutional networks, global high-level features are extracted from the whole image, which helps to locate the vanishing point. In this paper, we provide a new structure of regression neural network based on AlexNet. The structure consists of five convolutional layers, four fully connected layers, Tanh activation function and regression loss function. We feed the neural net with a small number of training dataset and the result proves that this method is adaptable. Compare to classical method, deep learning is more effective on blurred pictures and complex circumstances.


title: How to Read Less: Better Machine Assisted Reading Methods for Systematic Literature Reviews.

abstract: Context: Systematic literature reviews (SLRs) are the primary method for aggregating and synthesizing evidence in evidence-based software engineering. Primary study selection is a critical and time-consuming SLR step in which reviewers use titles, abstracts, or even full texts to evaluate thousands of studies to find the dozens of them that are relevant to the research questions. Objective: We seek to reduce the effort of primary study selection in SLRs with machine assisted reading techniques. Method: In this paper we explore and refactor the state-of-the-art machine assisted reading techniques from both evidence-based medicine and legal electronic discovery to support SLRs. By refactoring those methods, we discovered FASTREAD, which is a new state-of-the-art in machine assisted primary studies for SLRs. Tested on two data sets generated from existing SLRs of Hall, Wahono, et al., FASTREAD outperforms the current state-of-the-art methods. Results: Using FASTREAD, it is possible to find 90% of the studies found by standard manual methods, but after only reading less than 10% of the candidate studies. Conclusions: With the help of FASTREAD, conducting an SLR is much more efficient and less difficult. Software Engineering researchers now have no excuse: they should conduct SLRs.


title: Progressive Attention Networks for Visual Attribute Prediction

abstract: We propose a novel attention model which can accurately attend to target objects of various scales and shapes in images. The model is trained to gradually suppress irrelevant regions in an input image via a progressive attentive process over multiple layers of a convolutional neural network. The attentive process in each layer determines whether to pass or suppress features at certain spatial locations for use in the next layer. We further employ local contexts to estimate attention probability at each location since it is difficult to infer accurate attention by observing a feature vector from a single location only. The experiments on synthetic and real datasets show that the proposed attention network outperforms traditional attention methods in visual attribute prediction tasks.


title: Image based Arabic Sign Language Recognition System

abstract: Through history, humans have used many ways of communication such as gesturing, sounds, drawing, writing, and speaking. However, deaf and speaking impaired people cannot use speaking to communicate with others, which may give them a sense of isolation within their societies. For those individuals, sign language is their principal way to communicate. However, most people (who can hear) do not know the sign language. In this paper, we aim to automatically recognize Arabic Sign Language (ArSL) alphabets using an image-based methodology. More specifically, various visual descriptors are investigated to build an accurate ArSL alphabet recognizer. The extracted visual descriptors are conveyed to One-Versus-All Support Vector Machine (SVM). The analysis of the results shows that Histograms of Oriented Gradients (HOG) descriptor outperforms the other considered descriptors. Thus, the ArSL gesture models that are learned by One-Versus-All SVM using HOG descriptors are deployed in the proposed system.


title: Resolver Models for Manufacturing

abstract: This paper develops a new mathematical model, for pancake resolvers, that is dependent on a set of variables controlled by a resolver manufacturer-the winding parameters. This model allows a resolver manufacturer to manipulate certain in-process controllable variables in order to readjust already assembled resolvers that, without any action, would be scrapped for the production line. The developed model follows a two-step strategy where, on a first step, a traditional transformer's model computes the resolver nominal conditions and, on a second step, a linear model computes the corrections on the controllable variables, in order to compensate small deviations in design assumptions, caused by the variability of the manufacturing process. An experimental methodology for parameter identification is presented. The linearized model develops a complete new approach to simulate the product characteristics of a pancake resolver from the knowledge of manufacturer controllable variables (winding parameters). This model had been simulated and experimentally tested in a resolver manufacturing plant. The performed tests prove the efficiency of the developed model, stabilizing the product specifications in a dynamic environment with high variability of the production processes. All experiments had been led at the resolver manufacturer Tyco Electronics-Evora plant.


title: Systems Thinking Approach to Robotics Curriculum in Schools

abstract: This chapter presents a systems thinking approach for the conceptualization, design, and implementation of robotics curriculum to scaffold students’ learning of important Science, Technology, Engineering, and Mathematics (STEM) concepts and processes. This approach perceives the curriculum as a system of integrated elements and allows for the investigation of the interdependencies amongst the elements and the dynamics of the curriculum as a whole. Through this approach, we believe that students can be provided with robotics curriculum units that facilitate the learning of STEM “Big Ideas” of and about STEM. A STEM “Big Idea” is central to the understanding and application of STEM across a wide range of fields, one that links numerous STEM discipline understandings. Robotics is a rich context in which students can establish deep knowledge and robust understanding of STEM “Big Ideas”. Curriculum units based on this systems thinking approach can do much to ensure that students engaged in robotics activities focus not only on the completion of robotics tasks but also on the social construction of integrated networks of authentic STEM knowledge centred around “Big Ideas” of and about STEM.


title: Receiver and resource allocation optimization for uplink NOMA in 5G wireless networks

abstract: Non-Orthogonal Multiple Access (NOMA) is a promising multiple access technique for 5G wireless networks. In uplink NOMA, more than one user can use the same subcarrier, which increases the system throughput and fairness. Due to the non-orthogonality among the users, joint processing is required at the receiver to retrieve the users' signals. In this paper, we propose solutions to optimize the receiver and the radio resource allocation of uplink NOMA. First, we propose an iterative multiuser detection and decoding (MUDD) to improve the performance of the multiuser detector by utilizing the information derived by the channel decoder. In addition, a novel subcarrier and power allocation algorithm is proposed for NOMA scheme that maximizes the users' weighted sum-rate. The link-level performance evaluation has shown that the proposed iterative MUDD approach brings about 4.5 dB and 9.8 dB performance improvements at block error rate equal to 10−2 comparing to conventional multiuser detection, for QPSK and 16QAM modulations, respectively. Furthermore, the system-level results show that the proposed resource allocation algorithm achieves the system spectral efficiency upper-bound, making NOMA significantly outperforms OFDMA in terms of spectral efficiency and fairness.


title: Sampled Policy Gradient for Learning to Play the Game Agar.io

abstract: In this paper, a new offline actor-critic learning algorithm is introduced: Sampled Policy Gradient (SPG). SPG samples in the action space to calculate an approximated policy gradient by using the critic to evaluate the samples. This sampling allows SPG to search the action-Q-value space more globally than deterministic policy gradient (DPG), enabling it to theoretically avoid more local optima. SPG is compared to Q-learning and the actor-critic algorithms CACLA and DPG in a pellet collection task and a self play environment in the game this http URL. The online game this http URL has become massively popular on the internet due to intuitive game design and the ability to instantly compete against players around the world. From the point of view of artificial intelligence this game is also very intriguing: The game has a continuous input and action space and allows to have diverse agents with complex strategies compete against each other. The experimental results show that Q-Learning and CACLA outperform a pre-programmed greedy bot in the pellet collection task, but all algorithms fail to outperform this bot in a fighting scenario. The SPG algorithm is analyzed to have great extendability through offline exploration and it matches DPG in performance even in its basic form without extensive sampling.


title: A Deep Encoder-Decoder Networks for Joint Deblurring and Super-Resolution

abstract: In this paper, we propose an end-to-end convolution neural network (CNN) to restore a clear high-resolution image from a severely blurry image. It's a highly ill-posed problem and brings tremendous challenges to state-of-art deblurring or super-resolution (SR) methods. A straightforward way to solve this problem is to concatenate two types of networks directly. However, experiments show that the concatenation of independent networks increases computation complexity instead of generating satisfying high-resolution images. Consequently, we focus on designing a single deep network to solve the deblurring and SR problems in parallel. Our method, called ED-DSRN, extends the traditional Super-Resolution network by adding a deblurring branch that shares the same feature maps extracted from an encoder-decoder module with the original SR branch. Extensive experiments show that our method produces remarkable deblurred and super-resolved images simultaneously with high efficiency.


title: Mutual-Information-Private Online Gradient Descent Algorithm

abstract: A user implemented privacy preservation mechanism is proposed for the online gradient descent (OGD) algorithm. Privacy is measured through the information leakage as quantified by the mutual information between the users outputs and learners inputs. The input perturbation mechanism proposed can be implemented by individual users with a space and time complexity that is independent of the horizon T. For the proposed mechanism, the information leakage is shown to be bounded by the Gaussian channel capacity in the full information setting. The regret bound of the privacy preserving learning mechanism is identical to the non private OGD with only differing in constant factors.


title: Body Sensors: Wireless Access to Physiological Data

abstract: An area often neglected in software technology is where software meets biology. We'll approach this topic with one of the major interfaces between human beings and information technology: body sensors. These sensors mostly operate as interfaces to relatively small software components attached to or implanted into human bodies, such as pacemakers. They provide bidirectional communication interfaces between a person and a remote information system that provides healthcare services, diagnosis, or upgrades. The authors offer an overview of this rather old yet fast-evolving technology and show how to cope with design constraints.


title: Stream reasoning and complex event processing in ETALIS

abstract: Addressing dynamics and notifications in the Semantic Web realm has recently become an important area of research. Run time data is continuously generated by multiple social networks, sensor networks, various on-line services and so forth. How to get advantage of this continuously arriving data events remains a challenge --that is, how to integrate heterogeneous event streams, combine them with background knowledge e.g., an ontology, and perform event processing and stream reasoning. In this paper we describe ETALIS --a system which enables specification and monitoring of changes in near real time. Changes can be specified as complex event patterns, and ETALIS can detect them in real time. Moreover the system can perform reasoning over streaming events with respect to background knowledge. ETALIS implements two languages for specification of event patterns: ETALIS Language for Events, and Event Processing SPARQL. ETALIS has various applicabilities in capturing changes in semantic networks, broadcasting notifications to interested parties, and creating further changes based on processing of the temporal, static, or slowly evolving knowledge.


Primary Cutaneous CD 4 + Small-to Medium-Sized Pleomorphic T-Cell Lymphoma : Report of a Case With Spontaneous Resolution


Building Lexical Vector Representations from Concept Definitions


title: Joint CSIT Acquisition Based on Low-Rank Matrix Completion for FDD Massive MIMO Systems

abstract: Channel state information at the transmitter (CSIT) is essential for frequency-division duplexing (FDD) massive MIMO systems, but conventional solutions involve overwhelming overhead both for downlink channel training and uplink channel feedback. In this letter, we propose a joint CSIT acquisition scheme to reduce the overhead. Particularly, unlike conventional schemes where each user individually estimates its own channel and then feed it back to the base station (BS), we propose that all scheduled users directly feed back the pilot observation to the BS, and then joint CSIT recovery can be realized at the BS. We further formulate the joint CSIT recovery problem as a low-rank matrix completion problem by utilizing the low-rank property of the massive MIMO channel matrix, which is caused by the correlation among users. Finally, we propose a hybrid low-rank matrix completion algorithm based on the singular value projection to solve this problem. Simulations demonstrate that the proposed scheme can provide accurate CSIT with lower overhead than conventional schemes.


title: Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

abstract: Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry and recreation. However, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter- individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both offline and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.


title: Weighed Down by Stigma: How Weight-Based Social Identity Threat Contributes to Weight Gain and Poor Health

abstract: Weight stigma is pervasive, and a number of scholars argue that this profound stigma contributes to the negative effects of weight on psychological and physical health. Some lay individuals and health professionals assume that stigmatizing weight can actually motivate healthier behaviors and promote weight loss. However, as we review, weight stigma is consistently associated with poorer mental and physical health outcomes. In this article, we propose a social identity threat model elucidating how weight stigma contributes to weight gain and poorer mental and physical health among overweight individuals. We propose that weight-based social identity threat increases physiological stress, undermines self-regulation, compromises psychological health, and increases the motivation to avoid stigmatizing domains (e.g., the gym) and escape the stigma by engaging in unhealthy weight loss behaviors. Given the prevalence of overweight and obesity in the US, weight stigma thus has the potential to undermine the health and wellbeing of millions of Americans.


title: Aspect extraction for opinion mining with a deep convolutional neural network

abstract: In this paper, we present the first deep learning approach to aspect extraction in opinion mining. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about. We used a 7-layer deep convolutional neural network to tag each word in opinionated sentences as either aspect or non-aspect word. We also developed a set of linguistic patterns for the same purpose and combined them with the neural network. The resulting ensemble classifier, coupled with a word-embedding model for sentiment analysis, allowed our approach to obtain significantly better accuracy than state-of-the-art methods.


title: Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions

abstract: In this paper we provide the largest published comparison of translation quality for phrase-based SMT and neural machine translation across 30 translation directions. For ten directions we also include hierarchical phrase-based MT. Experiments are performed for the recently published United Nations Parallel Corpus v1.0 and its large six-way sentence-aligned subcorpus. In the second part of the paper we investigate aspects of translation speed, introducing AmuNMT, our efficient neural machine translation decoder. We demonstrate that current neural machine translation could already be used for in-production systems when comparing words-per-second ratios.


title: Soft tissue enhancement around dental implants

abstract: Peri-implant plastic surgery aims at improving the esthetic aspects of smile appearance and masticatory function. Enhancement of the esthetic appearance can lend significant support to patients wishing to experience more effective and successful interactions with others in personal, social and workplace situations. This article reviews pre-implant anatomic features that influence the outcome of dental implant therapy and presents a range of surgical modalities aimed at enhancing the appearance of peri-implant soft tissue.


title: Dynamic braking of induction motor - Analysis of conventional methods and an efficient multistage braking model

abstract: In this paper, a detailed analysis is made on capacitor self excitation and DC injection methods of dynamic braking of induction motor through MATLAB/SIMULINK simulations. The effectiveness of these conventional dynamic braking methods is carefully analyzed while changing various parameters. The speed range and time duration for which these methods are the most effective is carefully observed during changing load conditions. The effects of changing capacitor values and DC injection voltage levels on the effective speed range have been studied and the time durations in which these methods are most effective are also analyzed. Using the analysis data obtained, the parameters of a reliable fast multistage dynamic braking model are arrived upon for its best possible performance. The performance of this model, which efficiently dissipates the kinetic energy of the motor in a very short duration of time, is checked through simulations.


title: [Evaluation of efficacy and tolerance of amodiaquine versus chloroquine in the treatment of uncomplicated malaria outbreak in children of Gabon].

abstract: We conducted a prospective study from September 1997 to January 1998 in Libreville (Gabon). Fifty-three (53) children with uncomplicated P. falciparum malaria were included and divided into two groups. The first group (27 patients) was treated with amodiaquine and the second (26 patients) with chloroquine. The efficacy and tolerance of amodiaquine 30 mg/kg base over 3 days (10 mg/kg daily) and chloroquine 25 mg/kg base over 3 days (10 mg/kg day 0, 10 mg/kg day 1, 5 mg/kg day 3) were estimated at days 7 and 14. Clinical examination and parasitaemia were assessed on days 0, 1, 2, 3, 7, 14. Haematological and biochemical parameters were determined on days 0 and 7. Amodiaquine was shown to be more effective than chloroquine in clinical response and ridding patients of parasites: adequate clinical response was significantly higher with amodiaquine than chloroquine [100% (27/27) versus 45% (9/20), p < 0.0005]. Rates for early treatment failure (ETF) and late treatment failure (LTF) were respectively 35% and 12% with chloroquine. The parasitological success rate was significantly higher with amodiaquine than chloroquine on days 7 [93% (25/27) versus 62% (13/21), p < 0.008] and 14 [100% (13/13) versus 44% (4/9), p < 0.01]. The RI resistance type was 7% in the amodiaquine group. The rate of in vivo chloroquino-resistance was 53%, essentially of RII and RIII type. Overall, the two drugs were well tolerated.


title: Abstract Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry

abstract: Reliable prediction of sales can improve the quality of business strategy. In this research, fuzzy logic and artificial neural network are integrated into the fuzzy back-propagation network (FBPN) for sales forecasting in Printed Circuit Board (PCB) industry. The fuzzy back propagation network is constructed to incorporate production-control expert judgments in enhancing the model's performance. Parameters chosen as inputs to the FBPN are no longer considered as of equal importance, but some sales managers and production control experts are requested to express their opinions about the importance of each input parameter in predicting the sales with linguistic terms, which can be converted into pre-specified fuzzy numbers. The proposed system is evaluated through the real world data provided by a printed circuit board company and experimental results indicate that the Fuzzy back-propagation approach outperforms other three different forecasting models in MAPE measures.


title: A neural network method for prediction of 2006 World Cup Football Game

abstract: A neural network method is adopted to predict the football game's winning rate of two teams according to their previous stage's official statistical data of 2006 World Cup Football Game. The adopted prediction model is based on multi-layer perceptron (MLP) with back propagation learning rule. The input data are transformed to the relative ratios between two teams of each game. New training samples are added to the training samples at the previous stages. By way of experimental results, the determined neural network architecture for MLP is 8 inputs, 11 hidden nodes, and 1 output (8-11-1). The learning rate and momentum coefficient are sequentially determined by experiments as well. Based on the adopted MLP prediction method, the prediction accuracy can achieve 76.9% if the draw games are excluded.


title: Metaheuristics for dynamic combinatorial optimization problems

abstract: Many real-world optimization problems are combinatorial optimization problems subject to dynamic environments. In such dynamic combinatorial optimization problems (DCOPs), the objective, decision variables and/or constraints may change over time, and so solving DCOPs is a challenging task. Metaheuristics are a good choice of tools to tackle DCOPs because many metaheuristics are inspired by natural or biological evolution processes, which are always subject to changing environments. In recent years, DCOPs have attracted a growing interest from the metaheuristics community. This paper is a tutorial on metaheuristics for DCOPs. We cover the definition of DCOPs, typical benchmark problems and their characteristics, methodologies and performance measures, real-world case study and key challenges in the area. Some future research directions are also pointed out in this paper.


title: When Positive Sentiment is Not so Positive: Textual Analytics and Bank Failures

abstract: We extend beyond healthiness assessment of banks using quantitative financial data by applying textual sentiment analysis. Looking at 10-K annual reports for a large sample of banks in the 2000-2014 period, 52 public bank holding companies that were associated with bank failures during the global financial crisis serve as a natural experiment. Utilizing negative and positive dictionaries proposed by Loughran and McDonald (2011), we find that both sentiments on average discriminate between failed and non-failed banks 80% of the time. However, we find that positive sentiment contains stronger predictive power than negative sentiment; out of ten failed banks, on average positive sentiment can identify seven true events, whereas negative sentiment identifies five failed banks at most. While one would link financial soundness with more positive sentiment, it appears that failed banks exhausted more positive sentiment than their non-failed peers, whether ex-ante in anticipation of good news or ex-post to conceal financial distress.


title: Control over brain activation and pain learned by using real-time functional MRI

abstract: If an individual can learn to directly control activation of localized regions within the brain, this approach might provide control over the neurophysiological mechanisms that mediate behavior and cognition and could potentially provide a different route for treating disease. Control over the endogenous pain modulatory system is a particularly important target because it could enable a unique mechanism for clinical control over pain. Here, we found that by using real-time functional MRI (rtfMRI) to guide training, subjects were able to learn to control activation in the rostral anterior cingulate cortex (rACC), a region putatively involved in pain perception and regulation. When subjects deliberately induced increases or decreases in rACC fMRI activation, there was a corresponding change in the perception of pain caused by an applied noxious thermal stimulus. Control experiments demonstrated that this effect was not observed after similar training conducted without rtfMRI information, or using rtfMRI information derived from a different brain region, or sham rtfMRI information derived previously from a different subject. Chronic pain patients were also trained to control activation in rACC and reported decreases in the ongoing level of chronic pain after training. These findings show that individuals can gain voluntary control over activation in a specific brain region given appropriate training, that voluntary control over activation in rACC leads to control over pain perception, and that these effects were powerful enough to impact severe, chronic clinical pain.


Predicting function from sequence in venom peptide families


title: Non-intrusive Zigbee power meter for load monitoring in smart buildings

abstract: Energy efficiency in smart buildings requires distributed sensing infrastructure to monitor the power consumption of appliances, machines and lighting sources. The analysis of current and voltage waveforms is fundamental for gathering diagnostic information about the power quality and for reducing power wastage. Moreover, it enables Non-intrusive Load Monitoring (NILM), which is the process of disaggregating a household's total electricity consumption into its contributing appliances, by analysing the voltage and current changes. In this paper, an innovative full Energy-neutral (i.e. battery free) and Non-intrusive Wireless Energy Meter (NIWEM) is presented to measure current, voltage and power factor. As key features, the NIWEM is completely non-invasive and it can self-sustain its operations by harvesting energy from the monitored load. It also features a standard (Zigbee) wireless interface for communication with the smart-building system. Experimental results have confirmed that complete energy sustainability can be achieved also with very low-power loads.


title: The Value of Semantic Parse Labeling for Knowledge Base Question Answering

abstract: We demonstrate the value of collecting semantic parse labels for knowledge base question answering. In particular, (1) unlike previous studies on small-scale datasets, we show that learning from labeled semantic parses significantly improves overall performance, resulting in absolute 5 point gain compared to learning from answers, (2) we show that with an appropriate user interface, one can obtain semantic parses with high accuracy and at a cost comparable or lower than obtaining just answers, and (3) we have created and shared the largest semantic-parse labeled dataset to date in order to advance research in question answering.


title: ElSe: Ellipse Selection for Robust Pupil Detection in Real-World Environments

abstract: Fast and robust pupil detection is an essential prerequisite for video-based eye-tracking in real-world settings. Several algorithms for image-based pupil detection have been proposed in the past, their applicability, however, is mostly limited to laboratory conditions. In real-world scenarios, automated pupil detection has to face various challenges, such as illumination changes, reflections (on glasses), make-up, non-centered eye recording, and physiological eye characteristics. We propose ElSe, a novel algorithm based on ellipse evaluation of a filtered edge image. We aim at a robust, inexpensive approach that can be integrated in embedded architectures, e.g., driving. The proposed algorithm was evaluated against four state-of-the-art methods on over 93,000 hand-labeled images from which 55,000 are new eye images contributed by this work. On average, the proposed method achieved a 14.53% improvement on the detection rate relative to the best state-of-the-art performer. Algorithm and data sets are available for download: ftp://emmapupildata@messor.informatik.uni-tuebingen.de (password:eyedata).


title: The Optimal Noise-Adding Mechanism in Differential Privacy

abstract: Differential privacy is a framework to quantify to what extent individual privacy in a statistical database is preserved while releasing useful aggregate information about the database. In this paper, within the classes of mechanisms oblivious of the database and the queries beyond the global sensitivity, we characterize the fundamental tradeoff between privacy and utility in differential privacy, and derive the optimal $\epsilon $ -differentially private mechanism for a single real-valued query function under a very general utility-maximization (or cost-minimization) framework. The class of noise probability distributions in the optimal mechanism has staircase-shaped probability density functions which are symmetric (around the origin), monotonically decreasing and geometrically decaying. The staircase mechanism can be viewed as a geometric mixture of uniform probability distributions, providing a simple algorithmic description for the mechanism. Furthermore, the staircase mechanism naturally generalizes to discrete query output settings as well as more abstract settings. We explicitly derive the parameter of the optimal staircase mechanism for $\ell _{1}$ and $\ell _{2}$ cost functions. Comparing the optimal performances with those of the usual Laplacian mechanism, we show that in the high privacy regime ( $\epsilon $ is small), the Laplacian mechanism is asymptotically optimal as $\epsilon \to 0$ ; in the low privacy regime ( $\epsilon $ is large), the minimum magnitude and second moment of noise are $\Theta (\Delta e^{(-{\epsilon }/{2})})$ and $\Theta (\Delta ^{2} e^{(-{2\epsilon }/{3})})$ as $\epsilon \to +\infty $ , respectively, while the corresponding figures when using the Laplacian mechanism are ${\Delta }/{\epsilon }$ and ${2\Delta ^{2}}/{\epsilon ^{2}}$ , where $\Delta $ is the sensitivity of the query function. We conclude that the gains of the staircase mechanism are more pronounced in the moderate-low privacy regime.


Iranian Traditional Music Dastgah Classification


title: Exploring Trade-off’s Between Centralized versus Decentralized Automotive Architectures Using a Virtual Integration Environment

abstract: The large variety of architectural dimensions in automotive electronics design, for example, bus protocols, number of nodes, sensors and actuators interconnections and power distribution topologies, makes architecture design task a very complex but crucial design step especially for OEMs. This situation motivates the need for a design environment that accommodates the integration of a variety of models in a manner that enables the exploration of design alternatives in an efficient and seamless fashion. Exploring these design alternatives in a virtual environment and evaluating them with respect to metrics such as cost, latency, flexibility and reliability provide an important competitive advantage to OEMs and help minimize integration risks later in the design cycle. In particular, the choice of the degree of decentralization of the architecture has become a crucial issue in automotive electronics. In this paper, we demonstrate how a rigorous methodology (Platform-Based Design) and the Metropolis framework can be used to find the balance between centralized and decentralized architectures.


title: Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

abstract: A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.


title: Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making

abstract: The use of “Big Data” in policy and decision making is a current topic of debate. The 2013 murder of Drummer Lee Rigby in Woolwich, London, UK led to an extensive public reaction on social media, providing the opportunity to study the spread of online hate speech (cyber hate) on Twitter. Human annotated Twitter data was collected in the immediate aftermath of Rigby's murder to train and test a supervised machine learning text classifier that distinguishes between hateful and/or antagonistic responses with a focus on race, ethnicity, or religion; and more general responses. Classification features were derived from the content of each tweet, including grammatical dependencies between words to recognize “othering” phrases, incitement to respond with antagonistic action, and claims of well-founded or justified discrimination against social groups. The results of the classifier were optimal using a combination of probabilistic, rule-based, and spatial-based classifiers with a voted ensemble meta-classifier. We demonstrate how the results of the classifier can be robustly utilized in a statistical model used to forecast the likely spread of cyber hate in a sample of Twitter data. The applications to policy and decision making are discussed.


title: Wide impedance and pattern bandwidth leaky wave antenna realized on a substrate integrated waveguide using fractal shaped slots

abstract: This paper presents some initial observations on the application of fractal shaped slots in a substrate integrated waveguide based leaky wave antenna. Significant to the design issues are obtaining a nearly 1 GHz impedance bandwidth with a second iteration fractal shaped slot ranging from 9.25 GHz to 10.25 GHz. Pattern stability is observed with near identical radiation pattern within the frequency range obtained from resonance characteristics. The antenna gain varies from 7.01 dBi to 7.77 dBi.


title: Applications of neural networks for control of a double inverted pendulum

abstract: A study regarding the swing-up and stabilization problem of a double pendulum on a cart is presented. Current methods of feed forward control, energy based control, and passivity based control are used to select a design for the swing-up controller. The stabilization is done using an LQR controller. The nonlinear controller is analyzed with respect to being approximated by a neural network [1].


title: Improving long short-term memory networks using maxout units for large vocabulary speech recognition

abstract: s have been shown to give state-of-the-art performance on many speech recognition tasks. To achieve a further performance improvement, in this paper, maxout units are proposed to be integrated with the LSTM cells, considering those units have brought significant improvements to deep feed-forward neural networks. A novel architecture was constructed by replacing the input activation units (generally tanh) in the LSTM networks with maxout units. We implemented the LSTM network training on multi-GPU devices with truncated BPTT, and empirically evaluated the proposed designs on a large vocabulary Mandarin conversational telephone speech recognition task. The experimental results support our claim that the performance of LSTM based acoustic models can be further improved using the maxout units.


title: Factors analysis of IPv6 user acceptance against security aspects based on concept of technology acceptance model (TAM) and technology threat avoidance theory (TTAT)

abstract: The evolution of IPv6 technology had become a worldwide trend and showed a significant increase, particularly with the near-coming era named “Internet of Things” or so-called IOT. Concomitant with the transition process from version 4 to version 6, there are open security hole that considered to be vulnerable, mainly against cyber-attacks that poses a threat to companies implements IPv6 network topology. The purpose of this research is to create a model of acceptance of the factors that influenced the behavior of individuals in providing security within IPv6 network topology and analysis of factors that affects the acceptance of individuals in anticipating security with regards to IPv6 network topology. This study was conducted using both, quantitative method focuses on statistical processing on the result of questionnaire filled by respondents using Structural Equation Modeling (SEM), as well as qualitative method to conduct Focus Group Discussion (FGD) and interviews with experts from various background such as: practitioners, academician and government representatives. The results showed ease of use provides insignificant correlation to the referred behavior of avoiding threat on IPv6 environment.


title: An Ontology-Based System for Cancer Diseases Knowledge Management

abstract: Cancer is a class of diseases characterized by out-of-control cell growth. There are over 200 different types of cancer, and each is classified by the type of cell that is initially affected. This paper discusses the technical aspects of some of the ontology-based medical systems for cancer diseases. It also proposes an ontology based system for cancer diseases knowledge management. The system can be used to help patients, students and physicians to decide what cancer type the patient has, what is the stage of the cancer and how it can be treated. The system performance and accuracy are acceptable, with a cancer diseases classification accuracy of 92%.


E-waste recycling practices in Bangladesh


title: A continuous-time sigma-delta modulator with 88-dB dynamic range and 1.1-MHz signal bandwidth

abstract: This paper presents the design and experimental results of a continuous-time /spl Sigma//spl Delta/ modulator for ADSL applications. Multibit nonreturn-to-zero (NRZ) DAC pulse shaping is used to reduce clock jitter sensitivity. The nonzero excess loop delay problem in conventional continuous-time /spl Sigma//spl Delta/ modulators is solved by our proposed architecture. A prototype third-order continuous-time /spl Sigma//spl Delta/ modulator with 5-bit internal quantization was realized in a 0.5-/spl mu/m double-poly triple-metal CMOS technology, with a chip area of 2.4 /spl times/ 2.4 mm/sup 2/. Experimental results show that the modulator achieves 88-dB dynamic range, 84-dB SNR, and 83-dB SNDR over a 1.1-MHz signal bandwidth with an oversampling ratio of 16, while dissipating 62 mW from a 3.3-V supply.


title: Perceived 'usefulness' of online consumer reviews: An exploratory investigation across three services categories

abstract: Online reviews, a form of online word-of-mouth (eWOM), have recently become one of the most important sources of information for modern consumers. Recent scholarship involving eWOM often focuses on the transmission and impact of online reviews but sheds less light on the underlying processes that drive consumers' receptions of them. Similarly, few studies have explored the recipients' perspectives in the context of various services. This study addresses the aforementioned gaps in extant literature. The research model in this study is built upon the rich stream of literature related to how people are influenced by information and is tested on reviews collected from Yelp.com, a popular online advisory website dedicated to services businesses throughout the United States. The results of the study show that a combination of both reviewer and review characteristics are significantly correlated with the perceived usefulness of reviews. The study also finds several results that are anomalous to established knowledge related to consumers' information consumption, both offline and online. The authors present the results of the study and discuss their significance for research and practice.


title: Efficient spotlight SAR raw signal simulation of extended scenes

abstract: Synthetic aperture radar (SAR) raw signal simulation is a powerful tool for designing new sensors, testing processing algorithms, planning missions, and devising inversion algorithms. In this paper, a spotlight SAR raw signal simulator for distributed targets is presented. The proposed procedure is based on a Fourier domain analysis: a proper analytical reformulation of the spotlight SAR raw signal expression is presented. It is shown that this reformulation allows us to design a very efficient simulation scheme that employs fast Fourier transform codes. Accordingly, the computational load is dramatically reduced with respect to a time-domain simulation and this, for the first time, makes spotlight simulation of extended scenes feasible.


title: 1/f noise of NMOS and PMOS transistors and their implications to design of voltage controlled oscillators

abstract: Low frequency noise of NMOS and PMOS transistors in a 0.25 /spl mu/m foundry CMOS process with a pure SiO/sub 2/ gate oxide layer is characterized for the entire range of MOSFET operation. Surprisingly, the measurement results showed that surface channel PMOS transistors have about an order of magnitude lower 1/f noise than NMOS transistors especially at V/sub GS/-V/sub TH/ less than /spl sim/0.4 V The data were used to show that a VCO using all surface channel PMOS transistors can have /spl sim/14 dB lower close-in phase noise compared to that for a VCO using all surface channel NMOS transistors.


title: Real-time underwater 3D scene reconstruction using commercial depth sensor

abstract: This paper presents preliminary work to utilize a commercial time of flight depth camera for real-time 3D scene reconstruction of underwater objects. Typical RGB stereo camera imaging for 3D capturing suffers from blur and haziness due to turbidity of water in addition to critical dependence on light, either from natural or artificial sources. We propose a method for repurposing the low-cost Microsoft Kinect™ Time of Flight camera for underwater environment enabling dense depth data acquisition that can be processed in real time. Our motivation is the ease of use and low cost of the device for high quality real-time scene reconstruction as compared to multi view stereo cameras, albeit at a smaller range. Preliminary results of depth data acquisition and surface reconstruction in underwater environment are also presented. The novelty of our work is the utilization of the Kinect depth camera for real-time 3D mesh reconstruction and our main objective is to develop an economical and compact solution for underwater 3D mapping.


title: Role of music in morphine rewarding effects in mice using conditioned place preference method.

abstract: This research aims at studying the neuroendocrine effects of music on creating morphine dependence in mice using conditioned place preference (CPP). The mice treated with 10 mg/kg morphine subcutaneously, fast music and slow music. Morphine was used to create dependence. In order to recognize the morphine rewarding effects, CPP technique was used. In the conditioning stage that lasted for 8 days, different groups of mice, after receiving the treatment were randomly placed in compartment for 30 minutes. The post-conditioning stage included the fourth day, the ninth day, the 12th day and the 16th day. Comparing place preference between morphine group and the control group, a significant increase ( p<0.05) was observed in the place preference of morphine group, while a significant decrease ( p<0.05) was demonstrated in the place preferences of morphine + taxi girl music group compared with morphine group alone. In addition morphine + alone in the rain music group demonstrated a significantly increased conditioned place preference (p<0.05) compared with the morphine group. Alone in the rain music acts as a positive pleasant emotion increasing the dopaminergic activity in the Nucleus Accumbens (NAc) and Ventral Tegmental Area (VTA) and through associated learning mechanisms of reward-related behavior increases morphine addiction. However, taxi girl music may act as unpleasant experiences producing negative emotions and reducing morphine addiction.


title: Organizational Ambidexterity: Antecedents, Outcomes, and Moderators

abstract: Organizational ambidexterity, defined as an organization's ability to be aligned and efficient in its management of today's business demands while simultaneously being adaptive to changes in the environment, has gained increasing interest in recent years. In this article, the authors review various literature streams to develop a comprehensive model that covers research into the antecedents, moderators, and outcomes of organizational ambidexterity. They indicate gaps within and across different research domains and point to important avenues for future research.


title: Drug repositioning for Alzheimer's disease

abstract: Existing drugs for Alzheimer's disease provide symptomatic benefit for up to 12 months, but there are no approved disease-modifying therapies. Given the recent failures of various novel disease-modifying therapies in clinical trials, a complementary strategy based on repositioning drugs that are approved for other indications could be attractive. Indeed, a substantial body of preclinical work indicates that several classes of such drugs have potentially beneficial effects on Alzheimer's-like brain pathology, and for some drugs the evidence is also supported by epidemiological data or preliminary clinical trials. Here, we present a formal consensus evaluation of these opportunities, based on a systematic review of published literature. We highlight several compounds for which sufficient evidence is available to encourage further investigation to clarify an optimal dose and consider progression to clinical trials in patients with Alzheimer's disease.


title: Minimum trajectory error training for deep neural networks, combined with stacked bottleneck features.

abstract: Recently, Deep Neural Networks (DNNs) have shown promise as an acoustic model for statistical parametric speech synthesis. Their ability to learn complex mappings from linguistic features to acoustic features has advanced the naturalness of synthesis speech significantly. However, because DNN parameter estimation methods typically attempt to minimise the mean squared error of each individual frame in the training data, the dynamic and continuous nature of speech parameters is neglected. In this paper, we propose a training criterion that minimises speech parameter trajectory errors, and so takes dynamic constraints from a wide acoustic context into account during training. We combine this novel training criterion with our previously proposed stacked bottleneck features, which provide wide linguistic context. Both objective and subjective evaluation results confirm the effectiveness of the proposed training criterion for improving model accuracy and naturalness of synthesised speech.


title: Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier

abstract: Current satellite imaging technology enables shooting high-resolution pictures of the ground. As any other kind of digital images, overhead pictures can also be easily forged. However, common image forensic techniques are often developed for consumer camera images, which strongly differ in their nature from satellite ones (e.g., compression schemes, post-processing, sensors, etc.). Therefore, many accurate state-of-the-art forensic algorithms are bound to fail if blindly applied to overhead image analysis. Development of novel forensic tools for satellite images is paramount to assess their authenticity and integrity. In this paper, we propose an algorithm for satellite image forgery detection and localization. Specifically, we consider the scenario in which pixels within a region of a satellite image are replaced to add or remove an object from the scene. Our algorithm works under the assumption that no forged images are available for training. Using a generative adversarial network (GAN), we learn a feature representation of pristine satellite images. A one-class support vector machine (SVM) is trained on these features to determine their distribution. Finally, image forgeries are detected as anomalies. The proposed algorithm is validated against different kinds of satellite images containing forgeries of different size and shape.


title: Regularized Evolution for Image Classifier Architecture Search

abstract: The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier— AmoebaNet-A—that surpasses hand-designs for the first time. To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-theart 83.9% top-1 / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures.


title: The Need for Speed of AI Applications: Performance Comparison of Native vs. Browser-based Algorithm Implementations

abstract: AI applications pose increasing demands on performance, so it is not surprising that the era of client-side distributed software is becoming important. On top of many AI applications already using mobile hardware, and even browsers for computationally demanding AI applications, we are already witnessing the emergence of client-side (federated) machine learning algorithms, driven by the interests of large corporations and startups alike. Apart from mathematical and algorithmic concerns, this trend especially demands new levels of computational efficiency from client environments. Consequently, this paper deals with the question of state-of-the-art performance by presenting a comparison study between native code and different browser-based implementations: JavaScript, ASM.js as well as WebAssembly on a representative mix of algorithms. Our results show that current efforts in runtime optimization push the boundaries well towards (and even beyond) native binary performance. We analyze the results obtained and speculate on the reasons behind some surprises, rounding the paper off by outlining future possibilities as well as some of our own research efforts.


title: Aurora: Providing Trusted System Services for Enclaves On an Untrusted System

abstract: Intel SGX provisions shielded executions for security-sensitive computation, but lacks support for trusted system services (TSS), such as clock, network and filesystem. This makes \textit{enclaves} vulnerable to Iago attacks~\cite{DBLP:conf/asplos/CheckowayS13} in the face of a powerful malicious system. To mitigate this problem, we present Aurora, a novel architecture that provides TSSes via a secure channel between enclaves and devices on top of an untrusted system, and implement two types of TSSes, i.e. clock and end-to-end network. We evaluate our solution by porting SQLite and OpenSSL into Aurora, experimental results show that SQLite benefits from a \textit{microsecond} accuracy trusted clock and OpenSSL gains end-to-end secure network with about 1ms overhead.


title: UnDeepVO: Monocular Visual Odometry Through Unsupervised Deep Learning

abstract: We propose a novel monocular visual odometry (VO) system called UnDeepVO in this paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera and the depth of its view by using deep neural networks. There are two salient features of the proposed UnDeepVO: one is the unsupervised deep learning scheme, and the other is the absolute scale recovery. Specifically, we train UnDeepVO by using stereo image pairs to recover the scale but test it by using consecutive monocular images. Thus, UnDeepVO is a monocular system. The loss function defined for training the networks is based on spatial and temporal dense information. A system overview is shown in Fig. 1. The experiments on KITTI dataset show our UnDeepVO outperforms other monocular VO methods in terms of pose accuracy.


title: Desulfation of lead-acid battery by high frequency pulse

abstract: One of the major disadvantages of lead-acid batteries is sulfation, which decreases batteries' efficiency. Sulfate results in higher internal resistance and capacity reduction. This article presents desulfation of lead-acid battery by using high frequency pulse. The results showed that after the lead-acid battery was charged with high frequency pulse, the battery had lower internal resistance. The voltage of the fully-charged battery and the cold cranking amps were higher, resulting in better battery performance.


title: A 110 nW Resistive Frequency Locked On-Chip Oscillator with 34.3 ppm/°C Temperature Stability for System-on-Chip Designs

abstract: This work presents a sub-μW on-chip oscillator for fully integrated system-on-chip designs. The proposed oscillator introduces a resistive frequency locked loop topology for accurate clock generation. In this topology, a switched-capacitor circuit is controlled by an internal voltage-controlled oscillator (VCO), and the equivalent resistance of this switched-capacitor is matched to a temperature-compensated on-chip resistor using an ultra-low power amplifier. This design yields a temperature-compensated frequency from the internal VCO. The approach eliminates the traditional comparator from the oscillation loop; this comparator typically consumes a significant portion of the total oscillator power and limits temperature stability in conventional RC relaxation oscillators due to its temperature-dependent delay. A test chip is fabricated in 0.18 μm CMOS that exhibits a temperature coefficient of 34.3 ppm/°C with long-term stability of less than 7 ppm (12 second integration time) while consuming 110 nW at 70.4 kHz. A radio transmitter circuit that uses the proposed oscillator as a baseband timing source is also presented to demonstrate a system-on-chip design using this oscillator.


title: Forces and moments generated by removable thermoplastic aligners: Incisor torque, premolar derotation, and molar distalization

abstract: Introduction The exact force systems as well as their progressions generated by removable thermoplastic appliances have not been investigated. Thus, the purposes of this experimental study were to quantify the forces and moments delivered by a single aligner and a series of aligners (Invisalign; Align Technology, Santa Clara, Calif) and to investigate the influence of attachments and power ridges on the force transfer. Methods We studied 970 aligners of the Invisalign system (60 series of aligners). The aligners came from 30 consecutive patients, of which 3 tooth movements (incisor torque, premolar derotation, molar distalization) with 20 movements each were analyzed. The 3 movement groups were subdivided so that 10 movements were supported with an attachment and 10 were not. The patients' ClinCheck (Align Technology, Santa Clara, Calif) was planned so that the movements to be investigated were performed in isolation in the respective quadrant. Resin replicas of the patients' intraoral situation before the start of the investigated movement were taken and mounted in a biomechanical measurement system. An aligner was put on the model, the force systems were measured, and the calculated movements were experimentally performed until no further forces or moments were generated. Subsequently, the next aligners were installed, and the measurements were repeated. Results The initial mean moments were about 7.3 N·mm for maxillary incisor torque and about 1.0 N for distalization. Significant differences in the generated moments were measured in the premolar derotation group, whether they were supported with an attachment (8.8 N·mm) or not (1.2 N·mm). All measurements showed an exponential force change. Conclusions Apart from a few maximal initial force systems, the forces and moments generated by aligners of the Invisalign system are within the range of orthodontic forces. The force change is exponential while a patient is wearing removable thermoplastic appliances.


title: Malicious Behavior Detection using Windows Audit Logs

abstract: As antivirus and network intrusion detection systems have increasingly proven insufficient to detect advanced threats, large security operations centers have moved to deploy endpoint-based sensors that provide deeper visibility into low-level events across their enterprises. Unfortunately, for many organizations in government and industry, the installation, maintenance, and resource requirements of these newer solutions pose barriers to adoption and are perceived as risks to organizations' missions. To mitigate this problem we investigated the utility of agentless detection of malicious endpoint behavior, using only the standard built-in Windows audit logging facility as our signal. We found that Windows audit logs, while emitting manageable sized data streams on the endpoints, provide enough information to allow robust detection of malicious behavior. Audit logs provide an effective, low-cost alternative to deploying additional expensive agent-based breach detection systems in many government and industrial settings, and can be used to detect, in our tests, 83% percent of malware samples with a 0.1% false positive rate. They can also supplement already existing host signature-based antivirus solutions, like Kaspersky, Symantec, and McAfee, detecting, in our testing environment, 78% of malware missed by those antivirus systems.


title: Automatic Number Plate Recognition for Australian Conditions

abstract: We consider the task of recognition of Australian vehicle number plates (also called license plates or registration plates in other countries). A system for Australian number plate recognition must cope with wide variations in the appearance of the plates. Each state uses its own range of designs with font variations between the designs. There are special designs issued for significant events such as the Sydney 2000 Olympic Games. Also, vehicle owners may place the plates inside glass covered frames or use plates made of non-standard materials. These issues compound the complexity of automatic number plate recognition, making existing approaches inadequate. We have developed a system that incorporates a novel combination of image processing and artificial neural network technologies to successfully locate and read Australian vehicle number plates in digital images. Commercial application of the system is envisaged.


title: Online Detection Technique of 3D Defects for Steel Strips Based on Photometric Stereo

abstract: 2D Detection with gray level images is popularly employed in current surface inspection systems of steel strips, which lead to high false detection rate because of interference of pseudo-defects, such as iron oxide, water stains, and grease. Therefore, an online detection technique of 3D defects based on photometric stereo was developed in this paper. The acquired 3D information is utilized to distinguish real 3D defects from pseudo-defects, which can significant improve the performance of surface inspection system. The designed inspection system was equipped with lasers and linear CCD camera to scan the moving surface of steel strips, referred as the photometric stereo laser scanning system. Samples of hot and cold-rolled steels, with pseudo and real defects such as roll imprints, cracks and scratches on surface, were scanned and showed obvious distinguishment.


title: A Deep-Learning-Based Forecasting Ensemble to Predict Missing Data for Remote Sensing Analysis

abstract: The problem of missing data in remote sensing analysis is manifold. The situation becomes more serious during multitemporal analysis when data at various a-periodic timestamps are missing. In this work, we have proposed a deep-learning-based framework ( Deep-STEP_FE ) for reconstructing the missing data to facilitate analysis with remote sensing time series. The idea is to utilize the available data from both earlier and subsequent timestamps, while maintaining the causality constraint in spatiotemporal analysis. The framework is based on an ensemble of multiple forecasting modules , built upon the observed data in the time-series sequence. The coupling between the forecasting modules is accomplished with the help of dummy data, initially predicted using the earlier part of the sequence. Then, the dummy data are progressively improved in an iterative manner so that it can best conform to the next part of the sequence. Each of the forecasting modules in the ensemble is based on Deep-STEP, a variant of the deep stacking network learning approach. The work has been validated using a case study on predicting the missing images in normalized difference vegetation index time series, derived from Landsat-7 TM-5 satellite imagery over two spatial zones in India . Comparative performance analysis demonstrates the effectiveness of the proposed forecasting ensemble.


title: Practical Path Guiding for Efficient Light-Transport Simulation

abstract: We present a robust, unbiased technique for intelligent light-path construction in path-tracing algorithms. Inspired by existing path-guiding algorithms, our method learns an approximate representation of the scene's spatio-directional radiance field in an unbiased and iterative manner. To that end, we propose an adaptive spatio-directional hybrid data structure, referred to as SD-tree, for storing and sampling incident radiance. The SD-tree consists of an upper part-a binary tree that partitions the 3D spatial domain of the light field-and a lower part-a quadtree that partitions the 2D directional domain. We further present a principled way to automatically budget training and rendering computations to minimize the variance of the final image. Our method does not require tuning hyperparameters, although we allow limiting the memory footprint of the SD-tree. The aforementioned properties, its ease of implementation, and its stable performance make our method compatible with production environments. We demonstrate the merits of our method on scenes with difficult visibility, detailed geometry, and complex specular-glossy light transport, achieving better performance than previous state-of-the-art algorithms.


title: 3D Object Pose Estimation Using Viewpoint Generative Learning

abstract: Conventional local features such as SIFT or SURF are robust to scale and rotation changes but sensitive to large perspective change. Because perspective change always occurs when 3D object moves, using these features to estimate the pose of a 3D object is a challenging task. In this paper, we extend one of our previous works on viewpoint generative learning to 3D objects. Given a model of a textured object, we virtually generate several patterns of the model from different viewpoints and select stable keypoints from those patterns. Then our system learns a collection of feature descriptors from the stable keypoints. Finally, we are able to estimate the pose of a 3D object by using these robust features. In our experimental results, we demonstrate that our system is robust against large viewpoint change and even under partial occlusion.


title: Detection of Slang Words in e-Data using semi-Supervised Learning

abstract: The proposed algorithmic approach deals with finding the sense of a word in an electronic data. Now a day, in different communication mediums like internet, mobile services etc. people use few words, which are slang in nature. This approach detects those abusive words using supervised learning procedure. But in the real life scenario, the slang words are not used in complete word forms always. Most of the times, those words are used in different abbreviated forms like sounds alike forms, taboo morphemes etc. This proposed approach can detect those abbreviated forms also using semi supervised learning procedure. Using the synset and concept analysis of the text, the probability of a suspicious word to be a slang word is also evaluated.


Crowd documentation: Exploring the coverage and the dynamics of api discussions on stack overflow,” Georgia Institute of Technology


title: Grammar Induction with Neural Language Models: An Unusual Replication

abstract: A substantial thread of recent work on latent tree learning has attempted to develop neural network models with parse-valued latent variables and train them on non-parsing tasks, in the hope of having them discover interpretable tree structure. In a recent paper, Shen et al. (2018) introduce such a model and report near-state-of-the-art results on the target task of language modeling, and the first strong latent tree learning result on constituency parsing. In an attempt to reproduce these results, we discover issues that make the original results hard to trust, including tuning and even training on what is effectively the test set. Here, we attempt to reproduce these results in a fair experiment and to extend them to two new datasets. We find that the results of this work are robust: All variants of the model under study outperform all latent tree learning baselines, and perform competitively with symbolic grammar induction systems. We find that this model represents the first empirical success for latent tree learning, and that neural network language modeling warrants further study as a setting for grammar induction.


title: Visual Exploration of Climate Variability Changes Using Wavelet Analysis

abstract: Due to its nonlinear nature, the climate system shows quite high natural variability on different time scales, including multiyear oscillations such as the El Nino southern oscillation phenomenon. Beside a shift of the mean states and of extreme values of climate variables, climate change may also change the frequency or the spatial patterns of these natural climate variations. Wavelet analysis is a well established tool to investigate variability in the frequency domain. However, due to the size and complexity of the analysis results, only few time series are commonly analyzed concurrently. In this paper we will explore different techniques to visually assist the user in the analysis of variability and variability changes to allow for a holistic analysis of a global climate model data set consisting of several variables and extending over 250 years. Our new framework and data from the IPCC AR4 simulations with the coupled climate model ECHAM5/MPI-OM are used to explore the temporal evolution of El Nino due to climate change.


title: Face Recognition via Active Annotation and Learning

abstract: In this paper, we introduce an active annotation and learning framework for the face recognition task. Starting with an initial label deficient face image training set, we iteratively train a deep neural network and use this model to choose the examples for further manual annotation. We follow the active learning strategy and derive the Value of Information criterion to actively select candidate annotation images. During these iterations, the deep neural network is incrementally updated. Experimental results conducted on LFW benchmark and MS-Celeb-1M challenge demonstrate the effectiveness of our proposed framework.


title: Grey Matter Changes Associated with Heavy Cannabis Use: A Longitudinal sMRI Study

abstract: Cannabis is the most frequently used illicit drug worldwide. Cross-sectional neuroimaging studies suggest that chronic cannabis exposure and the development of cannabis use disorders may affect brain morphology. However, cross-sectional studies cannot make a conclusive distinction between cause and consequence and longitudinal neuroimaging studies are lacking. In this prospective study we investigate whether continued cannabis use and higher levels of cannabis exposure in young adults are associated with grey matter reductions. Heavy cannabis users (N = 20, age baseline M = 20.5, SD = 2.1) and non-cannabis using healthy controls (N = 22, age baseline M = 21.6, SD = 2.45) underwent a comprehensive psychological assessment and a T1- structural MRI scan at baseline and 3 years follow-up. Grey matter volumes (orbitofrontal cortex, anterior cingulate cortex, insula, striatum, thalamus, amygdala, hippocampus and cerebellum) were estimated using the software package SPM (VBM-8 module). Continued cannabis use did not have an effect on GM volume change at follow-up. Cross-sectional analyses at baseline and follow-up revealed consistent negative correlations between cannabis related problems and cannabis use (in grams) and regional GM volume of the left hippocampus, amygdala and superior temporal gyrus. These results suggests that small GM volumes in the medial temporal lobe are a risk factor for heavy cannabis use or that the effect of cannabis on GM reductions is limited to adolescence with no further damage of continued use after early adulthood. Long-term prospective studies starting in early adolescence are needed to reach final conclusions.


title: Immediate and long-term effects of 2016 Zika Outbreak: A Twitter-based study

abstract: Twitter is becoming a popular social media platform for syndromic surveillance. We explored the Twitter discussion in the context of the 2016 Zika Outbreak. The Zika virus can have severe long-term (such as microcephaly in newborns) effects and not so serious immediate (such as fever or headache) effects. We explored whether social media discussions effectively capture the severity of these long-term concerns. We performed volumetric and text mining analysis, such as the co-occurrence of words and hierarchical clustering, to explore the different underlying themes in the Twitter discussion regarding the immediate and long-term concerns. Our findings suggest that the concerns related to the long-term consequences are dominant and consistent, but this is not the case for the immediate effects.