Query: Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications

Relevant passages:
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera <=SEP=> KinectFusion enables a user holding and moving a standard Kinect camera to rapidly create detailed 3D reconstructions of an indoor scene. Only the depth data from Kinect is used to track the 3D pose of the sensor and reconstruct, geometrically precise, 3D models of the physical scene in real-time. The capabilities of KinectFusion, as well as the novel GPU-based pipeline are described in full. Uses of the core system for low-cost handheld scanning, and geometry-aware augmented reality and physics-based interactions are shown. Novel extensions to the core GPU pipeline demonstrate object segmentation and user interaction directly in front of the sensor, without degrading camera tracking or reconstruction. These extensions are used to enable real-time multi-touch interactions anywhere, allowing any planar or non-planar reconstructed physical surface to be appropriated for touch.

RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments <=SEP=> RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. RGB-D cameras rely on either structured light patterns combined with stereo sensing [6,10] or time-of-flight laser sensing [1] to generate depth estimates that can be associated with RGB pixels. Very soon, small, high-quality RGB-D cameras developed for computer gaming and home entertainment applications will become available at cost below $100. In this paper we investigate how such cameras can be used in the context of robotics, specifically for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. The robotics and computer vision communities have developed a variety of techniques for 3D mapping based on laser range scans [8, 11], stereo cameras [7], monocular cameras [3], and unsorted collections of photos [4]. While RGB-D cameras provide the opportunity to build 3D maps of unprecedented richness, they have drawbacks that make their application to 3D mapping difficult: They provide depth only up to a limited distance (typically less than 5m), depth values are much noisier than those provided by laser scanners, and their field of view (∼ 60◦) is far more constrained than that of specialized cameras or laser scanners typically used for 3D mapping (∼ 180◦). In our work, we use a camera developed by PrimeSense [10]. The key insights of this investigation are: first, that existing frame matching techniques are not sufficient to provide robust visual odometry with these cameras; second, that a tight integration of depth and color information can yield robust frame matching and loop closure detection; third, that building on best practice techniques in SLAM and computer graphics makes it possible to build and visualize accurate and extremely rich 3D maps with such cameras; and, fourth, that it will be feasible to build complete robot navigation and interaction systems solely based on cheap depth cameras.

Time-of-Flight sensor calibration for accurate range sensing <=SEP=> Over the past years Time-of-Flight (ToF) sensors have become a considerable alternative to conventional distance sensing techniques like laser scanners or image based stereo-vision. Due to the ability to provide full-range distance information at high frame-rates, ToF sensors achieve a significant impact onto current research areas like online object recognition, collision prevention or scene and object reconstruction. Nevertheless, ToF-cameras like the Photonic Mixer Device (PMD) still exhibit a number of error sources that affect the accuracy of measured distance information. For this reason, major error sources for ToFcameras will be discussed, along with a new calibration approach that combines intrinsic, distance as well as a reflectivity related error calibration in an overall, easy to use system and thus significantly reduces the number of necessary reference images. The main contribution, in this context, is a new intensity-based calibration model that requires less input data compared to other models and thus significantly contributes to the reduction of calibration data. 2010 Elsevier Inc. All rights reserved.

Parallel Tracking and Mapping for Small AR Workspaces <=SEP=> This paper presents a method of estimating camera pose in an unknown scene. While this has previously been attempted by adapting SLAM algorithms developed for robotic exploration, we propose a system specifically designed to track a hand-held camera in a small AR workspace. We propose to split tracking and mapping into two separate tasks, processed in parallel threads on a dual-core computer: one thread deals with the task of robustly tracking erratic hand-held motion, while the other produces a 3D map of point features from previously observed video frames. This allows the use of computationally expensive batch optimisation techniques not usually associated with real-time operation: The result is a system that produces detailed maps with thousands of landmarks which can be tracked at frame-rate, with an accuracy and robustness rivalling that of state-of-the-art model-based systems.

CALIBRATION OF A PMD-CAMERA USING A PLANAR CALIBRATION PATTERN TOGETHER WITH A MULTI-CAMERA SETUP <=SEP=> We discuss the joint calibration of novel 3D range cameras based on the time-of-flight principle with the Photonic Mixing Device (PMD) and standard 2D CCD cameras. Due to the small field-of-view (fov) and low pixel resolution, PMD-cameras are difficult to calibrate with traditional calibration methods. In addition, the 3D range data contains systematic errors that need to be compensated. Therefore, a calibration method is developed that can estimate full intrinsic calibration of the PMD-camera including optical lens distortions and systematic range errors, and is able to calibrate the external orientation together with multiple 2D cameras that are rigidly coupled to the PMD-camera. The calibration approach is based on a planar checkerboard pattern as calibration reference, viewed from multiple angles and distances. By combining the PMD-camera with standard CCD-cameras the internal camera parameters can be estimated more precisely and the limitations of the small fov can be overcome. Furthermore we use the additional cameras to calibrate the systematic depth measurement error of the PMD-camera. We show that the correlation between rotation and translation estimation is significantly reduced with our method.

Learning Linear Threshold Functions in the Presence of Classification Noise <=SEP=> I show that the linear threshold functions are polynomially learnable in the presence of classification noise, i.e., polynomial in <italic>n</italic>, 1/ε, 1/δ, and 1/&sgr;, where <italic>n</italic> is the number of Boolean attributes, ε and δ are the usual accuracy and confidence parameters, and &sgr; indicates the minimum distance of any example from the target hyperplane, which is assumed to be lower than the average distance of the examples from any hyperplane. This result is achieved by modifying the Perceptron algorithm—for each update, a weighted average of a sample of misclassified examples and a correction vector is added to the current weight vector. Similar modifications are shown for the Weighted Majority algorithm. The correction vector is simply   the mean of the normalized examples. In the special case of Boolean threshold functions, the modified Perceptron algorithm performs <italic>O</italic> (<italic>n</italic><supscrpt>2</supscrpt>ε<supscrpt>−2</supscrpt> ) iterations over <italic>O</italic>(<italic>n</italic><supscrpt>4</supscrpt>ε<supscrpt> −2</supscrpt>ln(<italic>n</italic>/(δε))) examples. This improves on the previous classification-noise result of Angluin and Laird to a much larger concept class with a similar number of examples, but with multiple iterations over the examples.

Usability Evaluation of Google Classroom : Basis for the Adaptation of GSuite E-Learning Platform <=SEP=> Electronic learning is a technology learning that plays an important role in modern education and training. Its great contribution lies in the fact that content is available at any place and device from a fixed device to mobile device. Nowadays, education is accessible everywhere with the use of technology. There are several LMS (Learning Management Systems) available. One of the new tool available was released by Google under GSuite. Pangasinan State University is currently subscribed to GSuite for Education, and recently Google introduces Classroom as an eLearning platform for an educational institution. This research aims to evaluate the new product, its functionalities for the purpose of adapting and deployment. The main objective of this paper is to identify the usability and evaluation of the Learning Management System (LMS) Google Classroom, its functionalities, features, and satisfaction level of the students. Based on the result, the respondents agreed that GSuite classroom is recommended. The result of this study will be the proposed e-learning platform for Pangasinan State University, Lingayen Campus initially need in the College of Hospitality Management, Business and Public Administration.

Learning to Read Chest X-Ray Images from 16000+ Examples Using CNN <=SEP=> Chest radiography (chest X-ray) is a low-cost yet effective and widely used medical imaging procedures. The lacking of qualified radiologist seriously limits the applicability of the technique. We explore the possibility of designing a computer-aided diagnosis for chest X-rays using deep convolutional neural networks. Using a real-world dataset of 16,000 chest X-rays with natural language diagnosis reports, we can train a multi-class classification model from images and preform accurate diagnosis, without any prior domain knowledge.

Automatic detection of discontinuities from 3D point clouds for the stability analysis of jointed rock masses <=SEP=> We describe an strategy for automatic detection of discontinuities in jointed rock masses from a 3D point cloud. The method consists on a sequence of processes for feature extraction and characterization of the discontinuities that use voxelization, robust detection of planes by sequential RANSAC and clustering. The results obtained by applying this methodology to the 3D point cloud are a set of discontinuity families, characterized by their dips and dip directions, and surface wedges. This geometric information is essential for a later stability analysis of the jointed rock mass. The ultimate aim of our investigation is the automatic extraction of discontinuity families for stability analysis of tunnels excavated in jointed rock masses.

Efficient calibration for rssi-based indoor localization by bayesian experimental design on multi-task classification <=SEP=> RSSI-based indoor localization is getting much attention. Thanks to a number of researchers, the localization accuracy has already reached a sufficient level. However, it is still not easy-to-use technology because of its heavy installation cost. When an indoor localization system is installed, it needs to collect RSSI data for training classifiers. Existing techniques need to collect enough data at each location. This is why the installation cost is very heavy. We propose a technique to gather data efficiently by using machine learning techniques. Our proposed algorithm is based on multi-task learning and Bayesian optimization. This algorithm can remove the need to collect data of all location labels and select location labels to acquire new data efficiently. We verify this algorithm by using a Wi-Fi RSSI dataset collected in a building. The empirical results suggest that the algorithm is superior to an existing algorithm applying single-task learning and Active Class Selection.

Deep Neural Architecture for Multi-Modal Retrieval based on Joint Embedding Space for Text and Images <=SEP=> Recent advances in deep learning and distributed representations of images and text have resulted in the emergence of several neural architectures for cross-modal retrieval tasks, such as searching collections of images in response to textual queries and assigning textual descriptions to images. However, the multi-modal retrieval scenario, when a query can be either a text or an image and the goal is to retrieve both a textual fragment and an image, which should be considered as an atomic unit, has been significantly less studied. In this paper, we propose a gated neural architecture to project image and keyword queries as well as multi-modal retrieval units into the same low-dimensional embedding space and perform semantic matching in this space. The proposed architecture is trained to minimize structured hinge loss and can be applied to both cross- and multi-modal retrieval. Experimental results for six different cross- and multi-modal retrieval tasks obtained on publicly available datasets indicate superior retrieval accuracy of the proposed architecture in comparison to the state-of-art baselines.

From “classic” child abuse and neglect to the new era of maltreatment <=SEP=> The evolution of the concept of child abuse leads to consider new types of maltreatment that in the future will certainly be taken into account with a new era of social pediatrics.Pediatric care has been based on the increased awareness of the importance of meeting the psychosocial and developmental needs of children and of the role of families in promoting the health.

Overcoming Security Challenges in Microservice Architectures <=SEP=> The microservice architectural style is an emerging trend in software engineering that allows building highly scalable and flexible systems. However, current state of the art provides only limited insight into the particular security concerns of microservice system. With this paper, we seek to unravel some of the mysteries surrounding microservice security by: providing a taxonomy of microservices security; assessing the security implications of the microservice architecture; and surveying related contemporary solutions, among others Docker Swarm and Netflix security decisions. We offer two important insights. On one hand, microservice security is a multi-faceted problem that requires a layered security solution that is not available out of the box at the moment. On the other hand, if these security challenges are solved, microservice architectures can improve security; their inherent properties of loose coupling, isolation, diversity, and fail fast all contribute to the increased robustness of a system. To address the lack of security guidelines this paper describes the design and implementation of a simple security framework for microservices that can be leveraged by practitioners. Proof-of-concept evaluation results show that the performance overhead of the security mechanisms is around 11%.

vDesign: a CAVE-based virtual design environment using hand interactions <=SEP=> The cave automatic virtual environment (CAVE) system is one of the most fully immersive systems for virtual reality environments. By providing users with realistic perception and immersive experience, CAVE systems have been widely used in many fields, including military, education, health care, entertainment, design, and others. In this paper, we focus on the design applications in the CAVE. The design applications involve many interactions between the user and the CAVE. However, the conventional interaction tool, the wand, cannot provide fast and convenient interactions. In this paper, we propose vDesign, a CAVE-based virtual design environment using hand interactions. The hand interactions in vDesign are classified into menu navigation and object manipulations. For menu navigation, we define two interactions: activating the main menu and selecting a menu item. For object manipulations, we define three interactions: moving, rotating, and scaling an object. By using the proposed hand interactions, we develop the functions of image segmentation and image composition in vDesign. With the image segmentation function, the designer can select and cut the interested objects from different images. With the image composition function, the designer can manipulate the segmented objects and combine them as a composite image. We implemented the vDesign prototype in CAVE and conducted experiments to evaluate the interaction performance in terms of manipulation time and distortion. The experimental results demonstrated that the proposed hand interactions can provide faster and more accurate interactions compared to the traditional wand interactions.

A support vector approach for cross-modal search of images and texts <=SEP=> Building bilateral semantic associations between images and texts is among the fundamental problems in computer vision. In this paper, we study two complementary cross-modal prediction tasks: (i) predicting text(s) given a query image (“Im2Text”), and (ii) predicting image(s) given a piece of text (“Text2Im”). We make no assumption on the specific form of text; i.e., it could be either a set of labels, phrases, or even captions. We pose both these tasks in a retrieval framework. For Im2Text, given a query image, our goal is to retrieve a ranked list of semantically relevant texts from an independent text-corpus (i.e., texts with no corresponding images). Similarly, for Text2Im, given a query text, we aim to retrieve a ranked list of semantically relevant images from a collection of unannotated images (i.e., images without any associated textual meta-data). We propose a novel Structural SVM based unified framework for these two tasks, and show how it can be efficiently trained and tested. Using a variety of loss functions, extensive experiments are conducted on three popular datasets (two medium-scale datasets containing few thousands of samples, and one web-scale dataset containing one million samples). Experiments demonstrate that our framework gives promising results compared to competing baseline cross-modal search techniques, thus confirming its efficacy.

M-Learning: A New Paradigm of Learning Mathematics in Malaysia <=SEP=> M-Learning is a new learning paradigm of the new social structure with mobile and wireless technologies. Smart school is one of the four flagship applications for Multimedia Super Corridor (MSC) under Malaysian government initiative to improve education standard in the country. With the advances of mobile devices technologies, mobile learning could help the government in realizing the initiative. This paper discusses the prospect of implementing mobile learning for primary school students. It indicates significant and challenges and analysis of user perceptions on potential mobile applications through a survey done in primary school context. The authors propose the m-Learning for mathematics by allowing the extension of technology in the traditional classroom in term of learning and teaching.

Brand Name Logo Recognition of Fast Food and Healthy Food among Children <=SEP=> The fast food industry has been increasingly criticized for creating brand loyalty in young consumers. Food marketers are well versed in reaching children and youth given the importance of brand loyalty on future food purchasing behavior. In addition, food marketers are increasingly targeting the Hispanic population given their growing spending power. The fast food industry is among the leaders in reaching youth and ethnic minorities through their marketing efforts. The primary objective of this study was to determine if young children recognized fast food restaurant logos at a higher rate than other food brands. Methods Children (n = 155; 53% male; 87% Hispanic) ages 4–8 years were recruited from elementary schools and asked to match 10 logo cards to products depicted on a game board. Parents completed a survey assessing demographic and psychosocial characteristics associated with a healthy lifestyle in the home. Results Older children and children who were overweight were significantly more likely to recognize fast food restaurant logos than other food logos. Moreover, parents’ psychosocial and socio-demographic characteristics were associated with the type of food logo recognized by the children. Conclusions Children’s high recognition of fast food restaurant logos may reflect greater exposure to fast food advertisements. Families’ socio-demographic characteristics play a role in children’s recognition of food logos.

LSTM Model to Forecast Time Series for EC2 Cloud Price <=SEP=> With the widespread use of spot instances in Amazon EC2, which utilize unused capacity with unfixed price, predicting price is important for users. In this paper, we try to forecast spot instance price by using long short-term memory (LSTM) algorithm to predict time series of the prices. We apply cross validation technique to our data set, and extract some features; this help our model to learn deeply. We make the prediction for 10 regions, and measure the performance using root-mean-square error (RMSE). We apply our data to other statistical models to compare their performance; we found that our model works more efficiently, also the error is decreasing more with cross validation and the result is much faster. Based on our result we try to find the availability zone that less become over on-demand price and less changing price over time, which help users to choose the most stable availability zone.

Automatic scraper of celebrity images from heterogeneous websites based on face recognition and sorting for profiling <=SEP=> Now days it has become trend to follow all the celebrities as we consider them as our role models. So instead of searching the images of various celebrities in different websites we can find them in a single website by sorting all the images. Reliable database of images is essential for any image recognition system. Through Internet we find all the required images. These images will serve as samples for automatic recognition system. With these images we do face detection, face recognition, face sorting using various techniques like local binary patterns, haar cascades. We make an overall analysis of the detector. Using opencv we detect and recognize images. Similarity matching is done to check how the images are related to each other. Collection of images is based on user defined templates, which are in web browser environment. With the help of this system one can give their requirement and the image of celebrity is displayed based on that.

Convolution Kernels on Discrete Structures <=SEP=> We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on a innnite set from kernels involving generators of the set. The family of kernels generated generalizes the family of radial basis kernels. It can also be used to deene kernels in the form of joint Gibbs probability distributions. Kernels can be built from hidden Markov random elds, generalized regular expressions, pair-HMMs, or ANOVA de-compositions. Uses of the method lead to open problems involving the theory of innnitely divisible positive deenite functions. Fundamentals of this theory and the theory of reproducing kernel Hilbert spaces are reviewed and applied in establishing the validity of the method.

What Does This Imply? Examining the Impact of Implicitness on the Perception of Hate Speech <=SEP=> 

An autonomous polymerization motor powered by DNA hybridization. <=SEP=> We present a synthetic molecular motor capable of autonomous nanoscale transport in solution. Inspired by bacterial pathogens such as Rickettsia rickettsii, which locomote by inducing the polymerization of the protein actin at their surfaces to form 'comet tails', the motor operates by polymerizing a double-helical DNA tail2. DNA strands are propelled processively at the living end of the growing polymers, demonstrating autonomous locomotion powered by the free energy of DNA hybridization.

0.18-V input charge pump with forward body biasing in startup circuit using 65nm CMOS <=SEP=> In this paper, a 0.18-V input three-stage charge pump circuit applying forward body bias is proposed. In the developed charge pump, all the MOSFETs are forward body biased by using the inter-stage/output voltages. By applying the proposed charge pump as the startup in the boost converter, the lower kick-up input voltage of the boost converter can be achieved. To verify the circuit characteristics, four test circuits have been implemented by using 65nm CMOS process. The measured available output current of the proposed charge pump under 0.18-V input voltage can be improved more than 150%. In addition, the boost converter can successfully been boosted from 0.18-V input to the 0.74-V output under 6mA output current. The proposed circuit is suitable for extremely low voltage applications such as harvesting energy sources.

A Blockchain-Based Micro Economy Platform for Distributed Infrastructure Initiatives <=SEP=> Distributed Infrastructure Initiatives (DIIs) are communities that collaboratively produce and consume infrastructure. To develop a healthy ecosystem, DIIs require an economic model that balances supply and demand, but there is currently a lack of tooling to support implementing these. In this research, we propose an architecture for a platform that enables DIIs to implement such models, focused around a digital currency based on blockchain technology. The currency is issued according to the amount participants contribute to the initiative, which is quantified based on operational metrics gathered from the infrastructure. Furthermore, the platform enables participants to deploy smart contracts which encode self-enforcing agreements about the infrastructure services they exchange. The architecture has been evaluated through a case study at The Things Network (TTN) a global distributed crowdsourced Internet of Things initiative. The case study revealed that the architecture is effective for the selected case at TTN. In addition, the results motivate future research lines to support scalability (i.e., to deploy the architecture on a larger scale) and security.

Safe robotic grasping: Minimum impact-force grasp selection <=SEP=> This paper addresses the problem of selecting from a choice of possible grasps, so that impact forces will be minimised if a collision occurs while the robot is moving the grasped object along a post-grasp trajectory. Such considerations are important for safety in human-robot interaction, where even a certified “human-safe” (e.g. compliant) arm may become hazardous once it grasps and begins moving an object, which may have significant mass, sharp edges or other dangers. Additionally, minimising collision forces is critical to preserving longevity of robots which operate in uncertain and hazardous environments, e.g. robots deployed for nuclear decommissioning, where removing a damaged robot from a contaminated zone for repairs may be extremely difficult and costly. Also, unwanted collisions between a robot and critical infrastructure (e.g. pipework) in such high-consequence environments can be disastrous. In this paper we investigate how the safety of the post-grasp motion can be considered during the pre-grasp approach phase, so that the selected grasp is optimal in terms of applying minimum impact forces if a collision occurs during the desired post-grasp manipulation. We build on the methods of augmented robot-object dynamic model and “effective mass” and propose a method for combining these concepts with modern grasp and trajectory planners, to enable the robot to achieve a grasp which maximises the safety of the post-grasp trajectory, by minimising potential collision forces. We demonstrate the effectiveness of our approach through several experiments with both simulated and real robots.

Quantifying Political Polarity Based on Bipartite Opinion Networks <=SEP=> Political inclinations of individuals (liberal vs. conservative) largely shape their opinions on several issues such as abortion, gun control, nuclear power, etc. These opinions are openly exerted in online forums, news sites, the parliament, and so on. In this paper, we address the problem of quantifying political polarity of individuals and of political issues for classification and ranking. We use signed bipartite networks to represent the opinions of individuals on issues, and formulate the problem as a node classification task. We propose a linear algorithm that exploits network effects to learn both the polarity labels as well as the rankings of people and issues in a completely unsupervised manner. Through extensive experiments we demonstrate that our proposed method provides an effective, fast, and easy-to-implement solution, while outperforming three existing baseline algorithms adapted to signed networks, on real political forum and US Congress datasets. Experiments on a wide variety of synthetic graphs with varying polarity and degree distributions of the nodes further demonstrate the robustness of our approach. Introduction Many individuals use online media to exert their opinions on a variety of topics. Hotly debated topics include liberal vs. conservative policies such as tax cuts and gun control, social issues such as abortion and same-sex marriage, environmental issues such as climate change and nuclear power plants, etc. These openly debated issues in blogs, forums, and news websites shape the nature of public opinion and affect the direction of politics, media, and public policy. In this paper, we address the problem of quantifying political polarity in opinion datasets. Given a set of individuals from two opposing camps (liberal vs. conservative) debating a set of issues or exerting opinions on a set of subjects (e.g. human subjects, political issues, congressional bills), we aim to address two problems: (1) classify which person lies in which camp, and which subjects are favored by each camp; and (2) rank the people and the subjects by the magnitude or extent of their polarity. Here while the classification enables us to determine the two camps, ranking helps us understand the extremity to which a person/subject is polarized; e.g. same-sex marriage may be highly polarized among the Copyright c © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. two camps (liberals being strictly in favor, and conservatives strictly being against), while gun control may not belong to a camp fully favoring or opposing it (i.e., is less polarized). Ranking also helps differentiate moderate vs. extreme individuals, as well as unifying vs. polarizing subjects; such as (unifying) bills voted in the same way, e.g. all ‘yea’ by the majority of congressmen vs. (polarizing) bills that are voted quite oppositely by the two camps. A large body of prior research focuses on sentiment analysis on politically oriented text (Cohen and Ruths 2013; Conover et al. 2011b; 2011a; Pak and Paroubek 2010; Tumasjan et al. 2010). The main goal of these works is to classify political text. In this work, on the other hand, we deal with network data to classify its nodes. Moreover, these methods mostly rely on supervised techniques whereas we focus on un/semi-supervised classification. Other prior research on polarization have exploited link mining and graph clustering to study the social structure on social media networks (Adamic and Glance 2005; Livne et al. 2011; Conover et al. 2011b; Guerra et al. 2013) where the edges depict the ‘mention’ or ‘hyperlink’ relations and not opinions. Moreover, these works do not perform ranking. Different from previous works, our key approach is to exploit network effects to both classify and rank individuals and political subjects by their polarity. The opinion datasets can be effectively represented as signed bipartite networks, where one set of nodes represent individuals, the other set represent subjects, and signed edges between the individuals and subjects depict the +/− opinions. As such, we cast the problem as a node classification task on such networks. Our main contributions can be summarized as follows: • We cast the political polarity classification and ranking problem as a node classification task on edge-signed bipartite opinion networks. • We propose an algorithm, called signed polarity propagation (SPP), that computes probabilities (i.e., polarity scores) of nodes of belonging to one of two classes (e.g., liberal vs. conservative), and use these scores for classification and ranking. Our method is easy-to-implement and fast—running time grows linearly with network size. • We show the effectiveness of our algorithm, in terms of both prediction and ranking, on synthetic and real datasets with ground truth from the US Congress and Political Forum. Further, we modify three existing algorithms to handle signed networks, and compare them to SPP. Our experiments reveal the advantages and robustness of our method on diverse settings with various polarity and degree distributions. Related Work Scoring and ranking the nodes of a graph based on the network structure has been studied extensively, with wellknown algorithms like PageRank (Brin and Page 1998), and HITS (Kleinberg 1998). These, however, are applied on graphs where the edges are unsigned and therefore cannot be directly used to compute polarity scores. Computing polarity scores can be cast as a network classification problem, where the task is to assign probabilities (i.e., scores) to nodes of belonging to one of two classes, which is the main approach we take in our work. There exist a large body of work on network-based classification (Getoor et al. 2007; Neville and Jensen 2003; Macskassy and Provost 2003). Semi-supervised algorithms based on network propagation have also been used in classifying political orientation (Lin and Cohen 2008; Zhou, Resnick, and Mei 2011). However, all the existing methods work with unsigned graphs in which the edges do not represent opinions but simply relational connections such as HTML hyperlinks between blog articles or ‘mention’ relations in Twitter. Signed networks have only recently attracted attention (Leskovec, Huttenlocher, and Kleinberg 2010b). Most existing studies focused on tackling the edge sign prediction problem (Yang et al. 2012; Chiang et al. 2011; Leskovec, Huttenlocher, and Kleinberg 2010a; Symeonidis, Tiakas, and Manolopoulos 2010). Other works include the study of trust/distrust propagation (Guha et al. 2004; DuBois, Golbeck, and Srinivasan 2011; Huang et al. 2012), and product/merchant quality estimation from reviews (McGlohon, Glance, and Reiter 2010). These works do not address the problem of classifying and ranking nodes in signed graphs. With respect to studies on political orientation through social media, (Adamic and Glance 2005; Adar et al. 2004) use link mining and graph clustering to analyze political communities in the blogosphere. While these and most clustering algorithms are designed to work with unsigned graphs, there also exist approaches for clustering signed graphs (Traag and Bruggeman 2009; Lo et al. 2013; Zhang et al. 2013). Clustering, however, falls short in scoring the nodes and hence quantifying polarity for ranking. Related, (Livne et al. 2011) utilize graph and text mining techniques to analyze differences between political parties and their online media usage in conveying a cohesive message. Most recently, (Cohen and Ruths 2013) use supervised classification techniques to classify three groups of Twitter users with varying political activity (figures, active, and modest) by their political orientation. Other works that exploit supervised classification using text features include (Conover et al. 2011a; Golbeck and Hansen 2011; Pennacchiotti and Popescu 2011). Similar to (Adamic and Glance 2005), there exist related works that study the social structure for measuring polarity. These works rely on the existence of (unsigned) social links between the users of social media and study the communities induced by polarized debate; an immediate consequence of the homophily principle, which states that people with similar beliefs and opinions tend to establish social ties. (Livne et al. 2011) and (Conover et al. 2011b) both use modularity (Newman 2006) as a measure of segregation between political groups in Twitter. (Guerra et al. 2013) compare modularity of polarized and non-polarized networks and propose two new measures of polarity based on community boundaries. Again, these works do not study the opinion networks with signed edges. As our main task is quantifying polarity, work on sentiment analysis is also related. There exist a long list of works on sentiment and polarity prediction in political text (Tumasjan et al. 2010; Awadallah, Ramanath, and Weikum 2010; Conover et al. 2011b; He et al. 2012), as well as in tweets, blogs, and news articles (Pak and Paroubek 2010; Godbole, Srinivasaiah, and Skiena 2007; Thomas, Pang, and Lee 2006; Balasubramanyan et al. 2012). These differ from our work as they use text-based sentiment analysis, while we focus on the network effects in signed graphs. Proposed Method Problem Overview We consider the problem of polarity prediction and ranking in opinion datasets (e.g. forums, blogs, the congress). Opinion datasets mainly consist of a set of people (e.g. users in a forum, representatives in The House) and a set of subjects (e.g. political issues, political people, congressional bills). Each person often maintains a positive or negative opinion toward a particular subject (e.g. a representative votes ‘yes’ for a bill, a person ‘likes’ a political individual). This opinion is often an exposition of the person’s latent political leaning—liberal or conservative. For example, we could think of a person with strong negative opinion toward gay&lesbian rights to be more conservati

miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades <=SEP=> microRNAs (miRNAs) are a large class of small non-coding RNAs which post-transcriptionally regulate the expression of a large fraction of all animal genes and are important in a wide range of biological processes. Recent advances in high-throughput sequencing allow miRNA detection at unprecedented sensitivity, but the computational task of accurately identifying the miRNAs in the background of sequenced RNAs remains challenging. For this purpose, we have designed miRDeep2, a substantially improved algorithm which identifies canonical and non-canonical miRNAs such as those derived from transposable elements and informs on high-confidence candidates that are detected in multiple independent samples. Analyzing data from seven animal species representing the major animal clades, miRDeep2 identified miRNAs with an accuracy of 98.6-99.9% and reported hundreds of novel miRNAs. To test the accuracy of miRDeep2, we knocked down the miRNA biogenesis pathway in a human cell line and sequenced small RNAs before and after. The vast majority of the >100 novel miRNAs expressed in this cell line were indeed specifically downregulated, validating most miRDeep2 predictions. Last, a new miRNA expression profiling routine, low time and memory usage and user-friendly interactive graphic output can make miRDeep2 useful to a wide range of researchers.

GA3C: GPU-based A3C for Deep Reinforcement Learning <=SEP=> We introduce and analyze the computational aspects of a hybrid CPU/GPU implementation of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. Our analysis concentrates on the critical aspects to leverage the GPU’s computational power, including the introduction of a system of queues and a dynamic scheduling strategy, potentially helpful for other asynchronous algorithms as well. We also show the potential for the use of larger DNN models on a GPU. Our TensorFlow implementation achieves a significant speed up compared to our CPU-only implementation, and it will be made publicly available to other researchers.

1 Augmented Reality <=SEP=> This paper is an overview of technologies that fall under the term augmented reality. Augmented reality refers to a system in which the physical surroundings of a person are mixed with real-time computer generated information creating an enhanced perception of surrounding environment. Being partly virtual and real, augmented reality applications have quite extreme requirements to be practical to use. It also has very much potential in numerous different application areas. These issues make augmented reality both an interesting and challenging subject from scientific and business perspectives.

Arrested development? Reconsidering dual-systems models of brain function in adolescence and disorders <=SEP=> The dual-systems model of a ventral affective system, whose reactivity confers risks and liabilities, and a prefrontal control system, whose regulatory capacities buffer against these vulnerabilities, is an intuitive account that pervades many fields in the cognitive neurosciences--especially in the study of populations that differ from neurotypical adults, such as adolescents or individuals with affective or impulse regulation disorders. However, recent evidence that is inconsistent with dual-systems models illustrates the complexity of developmental and clinical variations in brain function. Building new models to account for this complexity is critical to progress in these fields, and will be facilitated by research that emphasizes network-based approaches and maps relationships between structure and function, as well as brain and behavior, over time.