title: Communication-Efficient Algorithms for Decentralized and Stochastic Optimization

abstract: We present a new class of decentralized first-order methods for nonsmooth and stochastic optimization problems defined over multiagent networks. Considering that communication is a major bottleneck in decentralized optimization, our main goal in this paper is to develop algorithmic frameworks which can significantly reduce the number of inter-node communications. Our major contribution is to present a new class of decentralized primal–dual type algorithms, namely the decentralized communication sliding (DCS) methods, which can skip the inter-node communications while agents solve the primal subproblems iteratively through linearizations of their local objective functions. By employing DCS, agents can find an $$\epsilon $$-solution both in terms of functional optimality gap and feasibility residual in $${{\mathcal {O}}}(1/\epsilon )$$ (resp., $${{\mathcal {O}}}(1/\sqrt{\epsilon })$$) communication rounds for general convex functions (resp., strongly convex functions), while maintaining the $${{\mathcal {O}}}(1/\epsilon ^2)$$ (resp., $$\mathcal{O}(1/\epsilon )$$) bound on the total number of intra-node subgradient evaluations. We also present a stochastic counterpart for these algorithms, denoted by SDCS, for solving stochastic optimization problems whose objective function cannot be evaluated exactly. In comparison with existing results for decentralized nonsmooth and stochastic optimization, we can reduce the total number of inter-node communication rounds by orders of magnitude while still maintaining the optimal complexity bounds on intra-node stochastic subgradient evaluations. The bounds on the (stochastic) subgradient evaluations are actually comparable to those required for centralized nonsmooth and stochastic optimization under certain conditions on the target accuracy.


title: Evaluating stereo and motion cues for visualizing information nets in three dimensions

abstract: This article concerns the benefits of presenting abstract data in 3D. Two experiments show that motion cues combined with stereo viewing can substantially increase the size of the graph that can be preceived. The first experiment was designed to provide quantitiative measurements of how much more (or less) can be understood in 3D than in 2D. The 3D display used was configured so that the image on the monitor was coupled to the user's actual eye positons (and it was updated in real-time as the user moved) as well as bring in stereo. Thus the effect was like a local “virtual reality” display located in the vicinity of the computer monitor. The results from this study show that head-coupled stereo viewing can increase the size of an abstract graph that can be understood by a factor of three; using stereo alone provided an increase by a factor of 1.6 and head coupling along produced an increase by a factor of 2.2. The second experiment examined a variety of motion cues provided by head-coupled perspective (as in virtual reality displays), head-guided motion and automatic rotation, respectively, both with and without stereo in each case. The results show that structured 3D motion and stereo viewing both help in understanding, but that the kind of motion is not particularly important; all improve performance, and all are more significant than stereo cues. These results provide strong reasons for using advanced 3D graphics for interacting with a large variety of information structures.


title: ShapeWorld - A new test methodology for multimodal language understanding

abstract: We introduce a novel framework for evaluating multimodal deep learning models with respect to their language understanding and generalization abilities. In this approach, artificial data is automatically generated according to the experimenter's specifications. The content of the data, both during training and evaluation, can be controlled in detail, which enables tasks to be created that require true generalization abilities, in particular the combination of previously introduced concepts in novel ways. We demonstrate the potential of our methodology by evaluating various visual question answering models on four different tasks, and show how our framework gives us detailed insights into their capabilities and limitations. By open-sourcing our framework, we hope to stimulate progress in the field of multimodal language understanding.


title: Semantic representation of scientific literature: bringing claims, contributions and named entities onto the Linked Open Data cloud

abstract: Processing (NLP) for Rhetorical Entity (RE) detection; (ii) Named Entity (NE) recognition based on the Linked Open Data (LOD) cloud; and (iii) automatic knowledge base construction for both NEs and REs using semantic web ontologies that interconnect entities in documents with the machine-readable LOD cloud. Results. We present a complete workflow to transform scientific literature into a semantic knowledge base, based on the W3C standards RDF and RDFS. A text mining pipeline, implemented based on the GATE framework, automatically extracts rhetorical entities of type Claims and Contributions from full-text scientific literature. These REs are further enriched with named entities, represented as URIs to the linked open data cloud, by integrating the DBpedia Spotlight tool into our workflow. Text mining results are stored in a knowledge base through a flexible export process that provides for a dynamic mapping of semantic annotations to LOD vocabularies through rules stored in the knowledge base. We created a gold standard corpus from computer science conference proceedings and journal articles, where Claim and Contribution sentences are manually annotated with their respective types using LOD URIs. The performance of the RE detection phase is evaluated against this corpus, where it achieves an average Fmeasure of 0.73. We further demonstrate a number of semantic queries that show how the generated knowledge base can provide support for numerous use cases in managing scientific literature. Availability. All software presented in this paper is available under open source licenses at http://www.semanticsoftware.info/semantic-scientific-literature-peerj-20... [19]. Development releases of individual components are additionally available on our GitHub page at https://github.com/SemanticSoftwareLab [20]. URL https://peerj.com/articles/cs-37/ [21] DOI 10.7717/peerj-cs.37 [22] Copyright © 2015 Sateli and Witte. Distributed under Creative Commons CC-BY 4.0. History Submitted 4 August 2015 Accepted 13 November 2015 Published 9 December 2015 Acknowledgments This work was partially funded by an NSERC Discovery Grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Attachment Size peerj-cs-37.pdf [23] 8.69 MB Semantics for the Masses Except where otherwise noted, all original content on this site is copyright by its author and licensed under a Creative Commons Attribution-Share Alike 2.5 Canada License. Source URL (retrieved on 2016-07-30 23:58 ): http://www.semanticsoftware.info/biblio/semantic-representation-scientific-literature-peerj-compsci-2015 Links: [1] http://www.semanticsoftware.info/users/bahar [2] http://www.semanticsoftware.info/taxonomy/term/418 [3] http://www.semanticsoftware.info/taxonomy/term/391 [4] http://www.semanticsoftware.info/category/blog-tags/natural-language-processing [5] http://www.semanticsoftware.info/taxonomy/term/419 [6] http://www.semanticsoftware.info/taxonomy/term/390 [7] http://www.semanticsoftware.info/category/blog-tags/semantic-publishing [8] http://www.semanticsoftware.info/category/blog-tags/semantic-web [9] http://www.semanticsoftware.info/category/topic/semantic-web [10] http://www.semanticsoftware.info/category/topic/semantic-computing [11] http://www.semanticsoftware.info/category/topic/nlp [12] http://www.semanticsoftware.info/category/topic/text-mining [13] http://www.semanticsoftware.info/biblio/author/73 [14] http://www.semanticsoftware.info/biblio/author/1 [15] http://www.semanticsoftware.info/biblio/author/161 [16] http://www.semanticsoftware.info/biblio/keyword/16 [17] http://www.semanticsoftware.info/biblio/keyword/104 [18] http://www.semanticsoftware.info/biblio/keyword/2 [19] http://www.semanticsoftware.info/semantic-scientific-literature-peerj-2015-supplements [20] https://github.com/SemanticSoftwareLab [21] https://peerj.com/articles/cs-37/ [22] http://dx.doi.org/10.7717/peerj-cs.37 [23] http://www.semanticsoftware.info/system/files/peerj-cs-37.pdf


title: Capturing the human figure through a wall

abstract: We present RF-Capture, a system that captures the human figure -- i.e., a coarse skeleton -- through a wall. RF-Capture tracks the 3D positions of a person's limbs and body parts even when the person is fully occluded from its sensor, and does so without placing any markers on the subject's body. In designing RF-Capture, we built on recent advances in wireless research, which have shown that certain radio frequency (RF) signals can traverse walls and reflect off the human body, allowing for the detection of human motion through walls. In contrast to these past systems which abstract the entire human body as a single point and find the overall location of that point through walls, we show how we can reconstruct various human body parts and stitch them together to capture the human figure. We built a prototype of RF-Capture and tested it on 15 subjects. Our results show that the system can capture a representative human figure through walls and use it to distinguish between various users.


Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery


Dead simple OWL design patterns


Text Mining in Organizational Research


title: 3D-FlashMap: A physical-location-aware block mapping strategy for 3D NAND flash memory

abstract: Three-dimensional (3D) flash memory is emerging to fulfil the ever-increasing demands of storage capacity. In 3D NAND flash memory, multiple layers are stacked to increase bit density and reduce bit cost of flash memory. However, the physical architecture of 3D flash memory leads to a higher probability of disturbance to adjacent physical pages and greatly increases bit error rates. This paper presents 3D-FlashMap, a novel physical-location-aware block mapping strategy for three-dimensional NAND flash memory. 3D-FlashMap permutes the physical mapping of blocks and maximizes the distance between consecutively logical blocks, which can significantly reduce the disturbance to adjacent physical pages and effectively enhance the reliability. We apply 3D-FlashMap to a representative flash storage system. Experimental results show that the proposed scheme can reduce uncorrectable page errors by 85% with less than 2% space overhead in comparison with the baseline scheme.


title: Solving and Rating Sudoku Puzzles with Genetic Algorithms

abstract: This paper discusses solving and generating Sudoku puzzles with evolutionary algorithms. Sudoku is a Japanese number puzzle game that has become a worldwide phenomenon. As an optimization problem Sudoku belongs to the group of combinatorial problems, but it is also a constraint satisfaction problem. The objective of this paper is to test if genetic algorithm optimization is an efficient method for solving Sudoku puzzles and to generate new puzzles. Another goal is to find out if the puzzles, that are difficult for human solver, are also difficult for the genetic algorithms. In that case it would offer an opportunity to use genetic algorithm solver to test the difficulty levels of new Sudoku puzzles, i.e. to use it as a rating machine.


title: Analysis of the subcomponents and cortical terminations of the perisylvian superior longitudinal fasciculus: a fiber dissection and DTI tractography study

abstract: The anatomy of the perisylvian component of the superior longitudinal fasciculus (SLF) has recently been reviewed by numerous diffusion tensor imaging tractography (DTI) studies. However, little is known about the exact cortical terminations of this tract. The aim of the present work is to isolate the different subcomponents of this tract with fiber dissection and DTI tractography, and to identify the exact cortical connections. Twelve postmortem human hemispheres (6 right and 6 left) were dissected using the cortex-sparing fiber dissection. In addition, three healthy brains were analyzed using DTI-based tractography software. The different components of the perisylvian SLF were isolated and the fibers were followed until the cortical terminations. Three segments of the perisylvian SLF were identified: (1) anterior segment, connecting the supramarginal gyrus and superior temporal gyrus with the precentral gyrus, (2) posterior segment, connecting the posterior portion of the middle temporal gyrus with the angular gyrus, and (3) long segment of the arcuate fasciculus that connects the middle and inferior temporal gyri with the precentral gyrus and posterior portion of the inferior and middle frontal gyri. In the present study, three different components of the perisylvian SLF were identified. For the first time, our dissections revealed that each component was connected to a specific cortical area within the frontal, parietal and temporal lobes. By accurately depicting not only the trajectory but also cortical connections of this bundle, it is possible to develop new insights into the putative functional role of this tract.


title: Learning Scene Flow in 3D Point Clouds

abstract: Many applications in robotics and human-computer interaction can benefit from an understanding of 3D motion of points in a dynamic environment, widely noted as scene flow. While most previous methods focus on solving the problem with stereo and RGB-D images, few try to estimate scene flow directly from point clouds. In this work, we propose a novel deep neural network named $\textit{FlowNet3D}$ that learns scene flow from point clouds in an end-to-end fashion. Our network simultaneously learns deep hierarchical point cloud features, flow embeddings as well as how to smooth the output. We evaluate the network on both challenging synthetic data and real LiDAR scans from KITTI. Trained on synthetic data only, our network is able to generalize well to real scans. Benefited from learning directly in point clouds, our model achieved significantly more accurate scene flow results compared with various baselines on stereo images and RGB-D images.


title: Speech Emotion Recognition with Data Augmentation and Layer-wise Learning Rate Adjustment

abstract: In this work, we design a neural network for recognizing emotions in speech, using the standard IEMOCAP dataset. Following the latest advances in audio analysis, we use an architecture involving both convolutional layers, for extracting high-level features from raw spectrograms, and recurrent ones for aggregating long-term dependencies. Applying techniques of data augmentation, layer-wise learning rate adjustment and batch normalization, we obtain highly competitive results, with 64.5% weighted accuracy and 61.7% unweighted accuracy on four emotions. Moreover, we show that the model performance is strongly correlated with the labeling confidence, which highlights a fundamental difficulty in emotion recognition.


title: Utilizing instantaneous driving direction for enhancing lane-marking detection

abstract: Our earlier lane-marking detection method identified lane-markings appearing on an input image based on the intensity contrast between lane-markings pixels and their neighboring pixels. This detection results in outputs with nearly-zero false negatives, but with many false positives. To filter out these false positives in a principled way, we utilize the driving direction of a roadway. We do this because longitudinal lane-markings delineate the driving direction of a road and the orientations of any true, longitudinal lane-markings appearing on input images should be aligned with this direction. To approximate the driving direction of a road, we detect the vanishing point on a horizon line and draw a line to link the image coordinates of the detected vanishing point to those of the center of the image bottom. We then filter out any lane-marking blobs if their orientations are not aligned with that of the approximated driving direction. Through testing with streets and inter-city highway images, the proposed method demonstrates its effectiveness.


title: Agile Spectrum Imaging: Programmable Wavelength Modulation for Cameras and Projectors

abstract: We advocate the use of quickly-adjustable, computer-controlled color spectra in photography, lighting and displays. We present an optical relay system that allows mechanical or electronic color spectrum control and use it to modify a conventional camera and projector. We use a diffraction grating to disperse the rays into different colors, and introduce a mask (or LCD/DMD) in the optical path to modulate the spectrum. We analyze the trade-offs and limitations of this design, and demonstrate its use in a camera, projector and light source. We propose applications such as adaptive color primaries, metamer detection, scene contrast enhancement, photographing fluorescent objects, and high dynamic range photography using spectrum modulation.


title: Design and Construction of a Rogowski Coil for Measuring Wide Pulsed Current

abstract: In this paper, the design, installation, and performance of a Rogowski coil are presented. Based on the lumped-element model of Rogowski coil, the low-frequency distortion of wide pulsed current measurement is studied. The optimal damping resistance of the external-integrating Rogowski coil is obtained under the sinusoidal steady-state excitation. The amplitude-frequency characteristics of measuring systems (MSs) based on Rogowski coils with an RC integrator and an active integrator, respectively, are compared, and the actual bandwidths of the two MSs are also determined by the measurement accuracy. As a result of using an active integrator, the measuring bandwidth is broadened and the transient performance of the MS is improved compared with that of the MS with an RC integrator. Some measures of resisting electromagnetic interference caused by the Marx generator and spark gap switch are described. In the end, the experimental results of the MS based on the Rogowski coil in SG-III are presented.


title: Are the Results of the Beery-Buktenica Developmental Test of Visual-Motor Integration and Its Subtests Related to Achievement Test Scores?

abstract: ABSTRACT:Purpose.Although visual analysis, motor coordination, and visual-motor integration can each affect performance on a test of visual motor integration, previous studies have not reported the relative importance of these components to the relation between visual motor integration and learning


title: Skipping Word: A Character-Sequential Representation based Framework for Question Answering

abstract: Recent works using artificial neural networks based on word distributed representation greatly boost the performance of various natural language learning tasks, especially question answering. Though, they also carry along with some attendant problems, such as corpus selection for embedding learning, dictionary transformation for different learning tasks, etc. In this paper, we propose to straightforwardly model sentences by means of character sequences, and then utilize convolutional neural networks to integrate character embedding learning together with point-wise answer selection training. Compared with deep models pre-trained on word embedding (WE) strategy, our character-sequential representation (CSR) based method shows a much simpler procedure and more stable performance across different benchmarks. Extensive experiments on two benchmark answer selection datasets exhibit the competitive performance compared with the state-of-the-art methods.


title: Goal profiles, mental toughness and its influence on performance outcomes among Wushu athletes

abstract: This study examined the association between goal orientations and mental toughness and its influence on performance outcomes in competition. Wushu athletes (n = 40) competing in Intervarsity championships in Malaysia completed Task and Ego Orientations in Sport Questionnaire (TEOSQ) and Psychological Performance Inventory (PPI). Using cluster analysis techniques including hierarchical methods and the non-hierarchical method (k-means cluster) to examine goal profiles, a three cluster solution emerged viz. cluster 1 - high task and moderate ego (HT/ME), cluster 2 - moderate task and low ego (MT/LE) and, cluster 3 - moderate task and moderate ego (MT/ME). Analysis of the fundamental areas of mental toughness based on goal profiles revealed that athletes in cluster 1 scored significantly higher on negative energy control than athletes in cluster 2. Further, athletes in cluster 1 also scored significantly higher on positive energy control than athletes in cluster 3. Chi-square (χ 2 ) test revealed no significant differences among athletes with different goal profiles on performance outcomes in the competition. However, significant differences were observed between athletes (medallist and non medallist) in self-confidence (p = 0.001) and negative energy control (p = 0.042). Medallist’s scored significantly higher on self-confidence (mean = 21.82 ± 2.72) and negative energy control (mean = 19.59 ± 2.32) than the non-medallists (self confidence-mean = 18.76 ± 2.49; negative energy control mean = 18.14 ± 1.91).


title: Fourier Analysis for Demand Forecasting in a Fashion Company

abstract: In the fashion industry, demand forecasting is particularly complex: companies operate with a large variety of short lifecycle products, deeply influenced by seasonal sales, promotional events, weather conditions, advertising and marketing campaigns, on top of festivities and socio-economic factors. At the same time, shelf-out-of-stock phenomena must be avoided at all costs. Given the strong seasonal nature of the products that characterize the fashion sector, this paper aims to highlight how the Fourier method can represent an easy and more effective forecasting method compared to other widespread heuristics normally used. For this purpose, a comparison between the fast Fourier transform algorithm and another two techniques based on moving average and exponential smoothing was carried out on a set of 4- year historical sales data of a €60+ million turnover medium- to large-sized Italian fashion company, which operates in the women's textiles apparel and clothing sectors. The entire analysis was performed on a common spreadsheet, in order to demonstrate that accurate results exploiting advanced numerical computation techniques can be carried out without necessarily using expensive software.


Neonatal screening for haemoglobinopathies.


title: Design of Compact Beam-Steering Antennas Using a Metasurface Formed by Uniform Square Rings

abstract: In this paper, we propose two designs of slot-fed metasurface antenna for beam steering applications. Both designs have the same two-layer stacked configuration consisting of either a single-slot or a double-slot radiator and a metasurface superstrate. In contrast to existing phase-gradient metasurfaces with varying unit elements for beam steering, our proposed metasurface is formed by uniform square rings. At operating frequencies, each square ring acts as an individual resonating element. By exciting the elements in sequence with different phase delays through the slot radiator, the generated antenna beam can be steered. In the first design, the position of the single-slot radiator is shifted to achieve the beam steering functionality; while for the second antenna, to avoid mechanically moving the feeding slot, a double-slot-feeding structure is proposed with two slots excited with varying phase differences. Simulation and experimental results show that beam steering angles of −35° to 35° can be achieved for the single-slot-feeding design and beam steering angles of −30° to 30° are realizable for the double-slot-feeding configuration. Comparing with conventional phased array antennas, our proposed designs have compact size with moderate to high antenna gain.


title: Multi-scale Pyramid Feature Maps for Object Detection

abstract: this paper presents how we can achieve the state-of-the-art accuracy in multi-scale objection detection task while adopting and combining the recent technical innovation in deep learning. Following the common pipeline of CNN feature extraction, we mainly design the architecture of feature extraction which exploits the idea of feature pyramid. We further add an extra 1*1 convolution layer to benefit feature extraction, via the batch normalization. In addition, the designed network architecture for feature extraction combines low-resolution and high-resolution feature layers to predict the category of the object in images. The new architecture is trained with the help of batch normalization, mean pooling based on plateau detection. The proposed architecture shows competitive results compared to some state-of-the-art algorithms both in accuracy and in speed on some datasets.


title: Artificial Intelligence in Precision Cardiovascular Medicine

abstract: Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine.


title: CYC: a mid-term report

abstract: Abstract After explicating the need for a large common-sense knowledge base spanning human consensus knowledge, we report on many of the lessons learned over the first five years of attempting its construction. We have come a long way, in terms of methodology, representation language, techniques for efficient inferencing, the ontology of the knowledge base, and in terms of the environment and infrastructure in which it is being built. We describe the evolution of Cyc, its current state, and close with a look ahead at our plans and expectations for the coming five years, including an argument for how and why the project may conclude at that time.


title: Growing neural gas for temporal clustering

abstract: Conventional clustering techniques provide a static snapshot of each vectorpsilas commitment to every group. With additive datasets, however, existing methods may not be sufficient for adapting to the presence of new clusters or even the merging of existing data-dense regions. To overcome this deficit, we explore the use of growing neural gas for temporal clustering and provide evidence that this new algorithm is capable of detecting cluster structures that incrementally emerge.


title: Evolving Intelligent Mario Controller by Reinforcement Learning

abstract: Artificial Intelligence for computer games is an interesting topic which attracts intensive attention recently. In this context, Mario AI Competition modifies a Super Mario Bros game to be a benchmark software for people who program AI controller to direct Mario and make him overcome the different levels. This competition was handled in the IEEE Games Innovation Conference and the IEEE Symposium on Computational Intelligence and Games since 2009. In this paper, we study the application of Reinforcement Learning to construct a Mario AI controller that learns from the complex game environment. We train the controller to grow stronger for dealing with several difficulties and types of levels. In controller developing phase, we design the states and actions cautiously to reduce the search space, and make Reinforcement Learning suitable for the requirement of online learning.


title: Shifting attention across near and far spaces: Implications for the use of hands-free cell phones while driving

abstract: In three experiments, participants performed two tasks concurrently during driving. In the peripheral detection task, they responded manually to visual stimuli delivered through a LED placed on the internal rear mirror; in the conversation task, they were engaged in a conversation with a passenger, or through earphone-operated, loudspeaker-operated, or hand-held cell phones. Results showed that drivers were slower at responding to the visual stimuli when conversing through a hand-held cell phone or an earphone-operated cell phone than when conversing through a loudspeaker-operated cell phone or with a passenger. These results suggest that due to the brain coding the space into multiple representations, devices that make phone conversations taking place in the near, personal space make drivers slower at responding to visual stimuli, compared to devices that make the conversation occurring in a far space.


title: Polyglot Contextual Representations Improve Crosslingual Transfer

abstract: We introduce Rosita, a method to produce multilingual contextual word representations by training a single language model on text from multiple languages. Our method combines the advantages of contextual word representations with those of multilingual representation learning. We produce language models from dissimilar language pairs (English/Arabic and English/Chinese) and use them in dependency parsing, semantic role labeling, and named entity recognition, with comparisons to monolingual and non-contextual variants. Our results provide further evidence for the benefits of polyglot learning, in which representations are shared across multiple languages.


Nephridial excretion of ammonia and urea in the freshwater leech, Poecilobdella viridis as a function of temperature and photoperiod


title: The Evolutionary Genetics of Personality

abstract: Genetic influences on personality differences are ubiquitous, but their nature is not well understood. A theoretical framework might help, and can be provided by evolutionary genetics. We assess three evolutionary genetic mechanisms that could explain genetic variance in personality differences: selective neutrality, mutation-selection balance, and balancing selection. Based on evolutionary genetic theory and empirical results from behaviour genetics and personality psychology, we conclude that selective neutrality is largely irrelevant, that mutation-selection balance seems best at explaining genetic variance in intelligence, and that balancing selection by environmental heterogeneity seems best at explaining genetic variance in personality traits. We propose a general model of heritable personality differences that conceptualises intelligence as fitness components and personality traits as individual reaction norms of genotypes across environments, with different fitness consequences in different environmental niches. We also discuss the place of mental health in the model. This evolutionary genetic framework highlights the role of gene-environment interactions in the study of personality, yields new insight into the person-situation-debate and the structure of personality, and has practical implications for both quantitative and molecular genetic studies of personality. Copyright # 2007 John Wiley & Sons, Ltd.


title: STATISTICAL ANALYSIS OF PARTICULATE MATTER DATA IN DOHA, QATAR

abstract: Pollution in Doha is measured using passive, active and automatic sampling. In this paper we consider data automatically sampled in which various pollutants were continually collected and analysed every hour. At each station the sample is analysed on-line and in real time and the data is stored within the analyser, or a separate logger so it can be downloaded remotely by a modem. The accuracy produced enables pollution episodes to be analysed in detail and related to traffic flows, meteorology and other variables. Data has been collected hourly over more than 6 years at 3 different locations, with measurements available for various pollutants – for example, ozone, nitrogen oxides, sulphur dioxide, carbon monoxide, THC, methane and particulate matter (PM1.0, PM2.5 and PM10), as well as meteorological data such as humidity, temperature, and wind speed and direction. Despite much care in the data collection process, the resultant data has long stretches of missing values, when the equipment has malfunctioned – often as a result of more extreme conditions. Our analysis is twofold. Firstly, we consider ways to “clean” the data, by imputing missing values, including identified outliers. The second aspect specifically considers prediction of each particulate (PM1.0, PM2.5 and PM10) 24 hours ahead, using current (and previous) pollution and meteorological data. In this case, we use vector autoregressive models, compare with decision trees and propose variable selection criteria which explicitly adapt to missing data. Our results show that the regression tree models, with no variable transformations, perform the best, and that attempts to impute missing values are hampered by non-random missingness.


title: MRI of fetal GI tract abnormalities

abstract: We describe the magnetic resonance (MR) patterns of a variety of fetal gastrointestinal (GI) abnormalities. Thirty-two fetuses between 23 and 38 weeks' gestation with abnormal appearance of the GI tract by ultrasound underwent MR imaging with T1- and T2-weighted sequences. The MR aspect of intestinal atresia (duodenal atresia, one case; small bowel atresia, nine cases) included dilatation of the bowel loops, accurate assessment of the normal bowel distal to the atresia (except in the patient with multiple atresia and apple-peel syndrome), and micro-rectum with decreased T1 signal (except in the patient with duodenal atresia). Megacystis-microcolon-intestinal hypoperistalsis syndrome (one case) was indicated by an abnormal signal of the entire bowel and an abnormal pattern for the urinary tract. Meconium pseudocysts (two cases) were easily differentiated from enteric cysts (two cases). High anorectal malformations with (two cases) or without (one case) urinary fistula and cloacal malformation (one case) are described and MR findings are discussed. The capability of MR imaging to demonstrate the normal bowel with intraperitoneal anomalies (e.g., congenital diaphragmatic hernia, and sacrococcygeal teratoma) is emphasized. MR imaging is informative in the diagnosis of GI tract abnormalities, especially the severe malformations, with much more accuracy than sonography.


title: Brain-Computer Interface in Stroke Rehabilitation

abstract: Recent advances in computer science enabled people with severe motor disabilities to use brain-computer interfaces (BCI) for communication, control, and even to restore their motor disabilities. This paper reviews the most recent works of BCI in stroke rehabilitation with a focus on methodology that reported on data collected from stroke patients and clinical studies that reported on the motor improvements of stroke patients. Both types of studies are important as the former advances the technology of BCI for stroke, and the latter demonstrates the clinical efficacy of BCI in stroke. Finally some challenges are discussed. Category: Human computing


title: Semantic 3D Octree Maps based on Conditional Random Fields

abstract: In this paper we present a 3D semantic outdoor mapping system with multi-label and resolution octree maps based on the OctoMap mapping framework. The semantic labeling of point clouds uses conditional random fields. Speeding up the conditional random field, we use an adaptive graph downsampling method based on voxel grids and the histogram-of-oriented-residuals operator to describe the local point cloud distribution. We validate the proposed classification and map representation approach on real-world 3D point cloud data. The presented classification approach achieves an overall precision about 96 %. The integration of the classification results into the map data structure offers the opportunity to solve complex task settings. Furthermore, the runtime of the presented approach allows an integration of the classification into a real-time 3D semantic outdoor mapping system.


title: Privacy Paradox Revised: Pre-Existing Attitudes, Psychological Ownership, and Actual Disclosure

abstract: Prior research has pointed to discrepancies between users' privacy concerns and disclosure behaviors, denoted as the privacy paradox, and repeatedly highlighted the importance to find explanations for this dichotomy. In this regard, three approaches have been proposed by prior literature: (1) use of actual disclosure behavior rather than behavioral intentions, (2) systematic distinction between pre-existing attitudes and situation-specific privacy considerations, and (3) limited and irrational cognitive processes during decision-making. The current research proposes an experiment capable to test these three assumptions simultaneously. More precisely, the authors aim to explore the contextual nature of privacy-related decisions by systematically manipulating (1) individuals' psychological ownership with regard to own private information, and (2) individuals' affective states, while measuring (3) pre-existing attitudes as well as situation-specific risk and benefit perceptions, and (4) intentions as well as actual disclosure. Thus, the proposed study strives to uniquely add to the understanding of the privacy paradox.


Severe post-tonsillectomy haemorrhage treated with selective embolisation: a pseudoaneurysm of the lingual artery -


title: Development of Bump Support Film (BSF) for expanding the size of WLCSP

abstract: Solder joint crack is increasing with increasing of warpage caused by larger Wafer Level Chip Size Package (WLCSP). In this study, we studied new Bump Support Film (BSF) made of back grind tape and thermosetting epoxy resin, which covers the bottom of bumps by heat lamination. We succeeded to form the BSF without voids at the bottom of the bumps by optimizing the thickness of thermosetting epoxy resin layer. We also succeeded to remove residues of BSF at top of the bumps by dicing process. In addition, the board level test of IC chip with BSF shows no electrical defect after reflow process.


Cytoscape.js: a graph theory library for visualisation and analysis


title: Experience Report: Understanding Cross-Platform App Issues from User Reviews

abstract: App developers publish apps on different platforms, such as Google Play, App Store, and Windows Store, to maximize the user volumes and potential revenues. Due to the different characteristics of the platforms and the different user preference (e.g., Android is more customized than iOS), app testing cases on these three platforms should also be designed differently. Comprehensive app testing can be time-consuming for developers. Therefore, understanding the differences of the app issues on these platforms can facilitate the testing process. In this paper, we propose a novel framework named CrossMiner to analyze the essential app issues and explore whether the app issues exhibit differently on the three platforms. Based on five million user reviews, the framework automatically captures the distributions of seven app issues, i.e., "battery", "crash", "memory", "network", "privacy", "spam", and "UI". We discover that the apps for different platforms indeed generate different issue distributions, which can be employed by app developers to schedule and design the testing cases. The verification based on the official user forums also demonstrates the effectiveness of our framework. Furthermore, we also identify that the issues related to "crash" and "network" are more concerned by users than the other issues on these three platforms. To assist developers in gaining a deep insight on the user issues, we also prioritize the user reviews corresponding to the issues. Overall, we aim at understanding the differences of issues on different platforms and facilitating the testing process for app developers.


title: The Ruhr Basin 1850–1980: A Case of Large-Scale Environmental Pollution

abstract: The industrial area along the Ruhr has had a dramatic and impressive history. For the last 100 years, it has been the biggest industrial region not just in Germany, but in the whole of Europe; its industrial strength was envied and it was admired, especially since the region became industrialized within a few decades. In fact, industrialization happened so rapidly that the area was called Prussia’s Wild West: contemporaries were only able to describe what happened by referring to the American example.


title: A 100 W 5.1-Channel Digital Class-D Audio Amplifier With Single-Chip Design

abstract: A 100 W, 5.1-channel, single-chip, digital-input class-D audio amplifier with a low-voltage (LV) digital circuit and high-voltage (HV) switching power stage is designed for moderate-performance and cost-effective speaker systems. The LV portion, including multi-channel audio processors, delta-sigma modulators (DSMs), and pulse-width modulation (PWM) generators, is implemented with a standard CMOS digital cell-library. A dual-loop resonator is proposed to increase the stable input range of the DSM so that the low-distortion output power of the class-D amplifier can be increased. For the HV portion, distortion caused by parasitic resistances of the power stage is analyzed to obtain a better design. A multi-phase PWM switching technique is proposed to prevent the multi-channel output stages from simultaneously switching, and thus the supply bouncing can be reduced. An over-current protection circuit with high supply noise immunity is also presented. Fabricated with 0.35/3-μm 3.3/18-V 1P3M CMOS technology, the 5.1-channel amplifier achieves a total root-mean-square (RMS) output power of 100 W, a distortion of less than 0.7%, and a power efficiency of 88% with a total chip area of 48.9 mm2.


title: Interactive online learning for obstacle classification on a mobile robot

abstract: We present an architecture for incremental online learning in high-dimensional feature spaces and apply it on a mobile robot. The model is based on learning vector quantization, approaching the stability-plasticity problem of incremental learning by adaptive insertions of representative vectors. We employ a cost-function-based learning vector quantization approach and introduce a new insertion strategy optimizing a cost-function based on a subset of samples. We demonstrate this model within a real-time application for a mobile robot scenario, where we perform interactive real-time learning of visual categories.


title: A Modular Dielectric Elastomer Actuator to Drive Miniature Autonomous Underwater Vehicles

abstract: In this paper we present the design of a fin-like dielectric elastomer actuator (DEA) that drives a miniature autonomous underwater vehicle (AUV). The fin-like actuator is modular and independent of the body of the AUV. All electronics required to run the actuator are inside the 100 mm long 3D-printed body, allowing for autonomous mobility of the AUV. The DEA is easy to manufacture, requires no pre-stretch of the elastomers, and is completely sealed for underwater operation. The output thrust force can be tuned by stacking multiple actuation layers and modifying the Young's modulus of the elastomers. The AUV is reconfigurable by a shift of its center of mass, such that both planar and vertical swimming can be demonstrated on a single vehicle. For the DEA we measured thrust force and swimming speed for various actuator designs ran at frequencies from 1 Hz to 5 Hz. For the AUV we demonstrated autonomous planar swimming and closed-loop vertical diving. The actuators capable of outputting the highest thrust forces can power the AUV to swim at speeds of up to 0.55 body lengths per second. The speed falls in the upper range of untethered swimming robots powered by soft actuators. Our tunable DEAs also demonstrate the potential to mimic the undulatory motions of fish fins.


title: Sample-efficient Policy Optimization with Stein Control Variate

abstract: Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems. However, it still often suffers from the large variance issue on policy gradient estimation, which leads to poor sample efficiency during training. In this work, we propose a control variate method to effectively reduce variance for policy gradient methods. Motivated by the Stein's identity, our method extends the previous control variate methods used in REINFORCE and advantage actor-critic by introducing more general action-dependent baseline functions. Empirical studies show that our method significantly improves the sample efficiency of the state-of-the-art policy gradient approaches.


title: Bicuspid aortic valve and ascending aortic aneurysm – genetic background of the disease

abstract: Department of Cardiac Surgery and Transplantology, Institute of Cardiology, Warsaw, PolandPostep Kardiol Inter 2012; 8, 2 (28): 114 –119 DOI: 10.5114/pwki.2012.29652 Key words: bicuspid aortic valve, aortic regurgitation, aortic aneurysm, aortic dissection, inheritanceCorresponding author:Justyna Rybicka MD, Department of Congenital Heart Diseases, Institute of Cardiology, 42 Alpejska, 04-628 Warsaw, Poland, tel.: +48 22 34 34 263, fax: +48 22 34 34 538, e-mail: jrybicka@ikard.plPraca wplynela: 27.03.2012, wersja poprawiona:15.05.2012, przyjeta do druku: 28.05.2012.


title: Compact waveguide monopulse comparator at Ka-band for monopulse tracking

abstract: In this paper, a compact waveguide monopulse comparator at Ka-band for monopulse tracking is presented. Simulated results show a bandwidth of nearly 6% for a return loss of better than 10 dB, an isolation of better than 40 dB between the ports, and a null depth better than 40 dB. There is an imbalance of +0.1/-0.8 dB and 1° in magnitude and phase respectively at the output ports.


title: A Deep Hierarchical Approach to Lifelong Learning in Minecraft

abstract: We propose a lifelong learning system that has the ability to reuse and transfer knowledge from one task to another while efficiently retaining the previously learned knowledge-base. Knowledge is transferred by learning reusable skills to solve tasks in Minecraft, a popular video game which is an unsolved and high-dimensional lifelong learning problem. These reusable skills, which we refer to as Deep Skill Networks, are then incorporated into our novel Hierarchical Deep Reinforcement Learning Network (H-DRLN) architecture using two techniques: (1) a deep skill array and (2) skill distillation, our novel variation of policy distillation (Rusu et. al. 2015) for learning skills. Skill distillation enables the HDRLN to efficiently retain knowledge and therefore scale in lifelong learning, by accumulating knowledge and encapsulating multiple reusable skills into a single distilled network. The H-DRLN exhibits superior performance and lower learning sample complexity compared to the regular Deep Q Network (Mnih et. al. 2015) in sub-domains of Minecraft.


Listening to Music and Active Music Therapy in Behavioral Disturbances in Dementia: A Crossover Study


title: The credibility of advertising vs publicity for new FMCGs in Turkey

abstract: Purpose – The purpose of this study is to investigate the credibility of advertising vs publicity; to examine the credibility of advertising including a promotional endorser (APE) and publicity including a promotional endorser (PPE); to compare the credibility of advertising across the different demographic segments; and to explore the important factors affecting consumers' shopping considerations of new fast moving consumer goods (FMCGs) in Turkey.Design/methodology/approach – The data was gathered by a telephone survey from a sample of 717 of which 348 respondents wanted to participate from three of Turkey's largest cities, Istanbul, Ankara, and Izmir. The systematic sampling was exercised to select the sample. χ2 and t‐tests were computed and the results of them were significant at 0.05 level.Findings – For announcing new FMCGs in Turkey, advertising was found to be more credible by the participants with higher income. Respondents' shopping decisions of new FMCGs were affected by price and quality more...


title: A split-face comparison of Q-switched Nd:YAG 1064-nm laser for facial rejuvenation in Nevus of Ota patients

abstract: We aimed to investigate the efficacy and safety of using the 1064-nm Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser (QSNYL) for skin rejuvenation in patients with Nevus of Ota. A retrospective, randomized, split-faced, clinical study was conducted. Twenty-nine patients with unilateral moderate to severe Nevus of Ota were enrolled. The participants completed 3-13 sessions of QSNYL treatments 3-6 months apart. Two independent physicians compared the treated and untreated sides of the face to evaluate the clearance of Nevus of Ota, the wrinkle severity rating scale (WSRS), the global aesthetic improvement scale (GAIS), and adverse event reporting. Patients' satisfaction levels were also considered. Of the 29 patients, 28 (96.6%) achieved nearly complete pigmentation clearance. After an average of 7.76 ± 2.99 sessions, statistically significant improvement in wrinkles and skin texture were observed, compared with the untreated side. The degree of skin rejuvenation was positively correlated with the number of treatment sessions. No clinically adverse effects were observed. Repeated QSNYL treatments not only remove the pigment in Nevus of Ota effectively and safely but also improve facial rejuvenation.


title: Clinical Research on Acupuncture: Part 1. What Have Reviews of the Efficacy and Safety of Acupuncture Told Us So Far?

abstract: UNLABELLED ::: OVERVIEW AND METHODS: This paper discusses those medical conditions in which clinical trials of acupuncture have been conducted, and where meta-analyses or systematic reviews have been published. It focuses on the general conclusions of these reviews by further examining official reviews conducted in the United States, United Kingdom, Europe, and Canada each of which examined available systematic reviews. While all reviews agree that the methodological rigor of acupuncture clinical trials has generally been poor and that higher quality clinical trials are necessary, this has not completely hampered the interpretation of the results of these clinical trials. In some conditions the evidence of efficacy has clearly reached a sufficient critical mass from enough well-designed studies to draw clear conclusions; for the rest, the evidence is difficult to clearly interpret. This paper also examines conclusions from the same international reviews on the safety and adverse effects of acupuncture. Here, conclusions are more easily drawn and there is good agreement about the safety of acupuncture. ::: ::: ::: RESULTS AND CONCLUSIONS ::: General international agreement has emerged that acupuncture appears to be effective for postoperative dental pain, postoperative nausea and vomiting, and chemotherapy-related nausea and vomiting. For migraine, low-back pain, and temporomandibular disorders the results are considered positive by some and difficult to interpret by others. For a number of conditions such as fibromyalgia, osteoarthritis of the knee, and tennis elbow the evidence is considered promising, but more and better quality research is needed. For conditions such as chronic pain, neck pain, asthma, and drug addiction the evidence is considered inconclusive and difficult to interpret. For smoking cessation, tinnitus, and weight loss the evidence is usually regarded as negative. Reviews have concluded that while not free from serious adverse events, they are rare and that acupuncture is a relatively safe procedure.


title: Bio-Inspired Imprecise Computational Blocks for Efficient VLSI Implementation of Soft-Computing Applications

abstract: The conventional digital hardware computational blocks with different structures are designed to compute the precise results of the assigned calculations. The main contribution of our proposed Bio-inspired Imprecise Computational blocks (BICs) is that they are designed to provide an applicable estimation of the result instead of its precise value at a lower cost. These novel structures are more efficient in terms of area, speed, and power consumption with respect to their precise rivals. Complete descriptions of sample BIC adder and multiplier structures as well as their error behaviors and synthesis results are introduced in this paper. It is then shown that these BIC structures can be exploited to efficiently implement a three-layer face recognition neural network and the hardware defuzzification block of a fuzzy processor.


title: Task dependent modulation of exogenous attention: Effects of target duration and intervening events

abstract: Inhibition of return (IOR) consists of slower reaction times in response to stimuli appearing at previously attended or inspected locations. The exact mechanisms underlying the effect have not yet been determined. In the present work, we manipulated two variables, target duration and intervening event (fixation cue between cue and target), through which we modulated the IOR effect as a function of task. When the target was presented until response, the presence of an intervening event made the cueing effect more negative in all tasks, although facilitation in the absence of an intervening event was only observed in discrimination and go–no-go tasks. When the target duration was 50 ms, the effect of the intervening event on cueing was also only observed for the discrimination and go–no-go tasks. Target duration had no effect at all in the discrimination task. Possible mechanisms for these modulations (detection cost and spatial selection benefit, both of which are based on cue–target integration processes) are discussed.


title: Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization

abstract: We prove that many mirror descent algorithms for online convex optimization (such as online gradient descent) have an equivalent interpretation as follow-the-regularizedleader (FTRL) algorithms. This observation makes the relationships between many commonly used algorithms explicit, and provides theoretical insight on previous experimental observations. In particular, even though the FOBOS composite mirror descent algorithm handles L1 regularization explicitly, it has been observed that the FTRL-style Regularized Dual Averaging (RDA) algorithm is even more effective at producing sparsity. Our results demonstrate that the key difference between these algorithms is how they handle the cumulative L1 penalty. While FOBOS handles the L1 term exactly on any given update, we show that it is effectively using subgradient approximations to the L1 penalty from previous rounds, leading to less sparsity than RDA, which handles the cumulative penalty in closed form. The FTRL-Proximal algorithm, which we introduce, can be seen as a hybrid of these two algorithms, and significantly outperforms both on a large, realworld dataset.


title: A novel PWM_OFF_PWM mode for braking operation of brushless DC motor

abstract: The conventional pulse width modulation (PWM) modes for braking operation in Brushless DC motor may cause diode freewheeling in inactive phase during normal conduction period which will aggravate torque ripple. In this paper, a novel braking PWM modulation mode named PWM_OFF_PWM is proposed to eliminate the diode freewheeling. The performance of the proposed PWM_OFF_PWM mode on diode freewheeling and commutation torque ripple are analyzed. During normal conduction period, the proposed PWM_OFF_PWM mode is confirmed to give no contribution to diode freewheeling; during commutation period, the speed range where commutation torque ripple can be suppressed is derived. At last, simulation results verify the feasibility of the proposed braking PWM_OFF_PWM mode and the correctness of the theoretical analysis.


title: Cognitive Analytics: A Step Towards Tacit Knowledge?

abstract: Tacit Knowledge (TK) generally refers to information that is difficult to convey, store, or transfer explicitly. KT is a key challenge for corporations interested in capturing information in Knowledge Management (KM) systems that is generally lost with attrition or other human factors (e.g., dimensia). In particular, the challenge is in the capture of implicit information (e.g., additional related data, perspectives, and other frames of reference) – in a manner in which it can later be utilized. This paper suggests the use of Cognitive Computing (Analytics) as an advanced approach to capture and extract tacit knowledge. KM involves the process of identifying, capturing, extending, sharing, and ultimately exploiting individual or organizational knowledge. Today’s KM requires a multi-disciplinary approach, capable of extending itself to deal with large volumes of disparate data types and emerging technologies that provide a broad set of search and analytics capabilities to meet an organization’s need to innovate and thrive. Many organizations have extended their KM to include a variety of unstructured text (e.g., documents and web pages) and multimedia (e.g., pictures, audio and video). The last decade has shown a strong focus on analytics. Analytics provide large organizations the ability to deal with the exponential growth in data volumes and the complexities associated with effectively and efficiently exploiting corporate or organizational data – thus allowing them to dynamically meet internal goals, as well as survive in very competitive environments. This paper provides an overview of various analytic approaches that have been applied to KM over the years, and the state of the art in analytics (Cognitive Computing); and it identifies additional capabilities and technologies in the horizon.


title: A fast algorithm for Steiner trees

abstract: Given an undirected distance graph G=(V, E, d) and a set S, where V is the set of vertices in G, E is the set of edges in G, d is a distance function which maps E into the set of nonnegative numbers and S?V is a subset of the vertices of V, the Steiner tree problem is to find a tree of G that spans S with minimal total distance on its edges. In this paper, we analyze a heuristic algorithm for the Steiner tree problem. The heuristic algorithm has a worst case time complexity of O(¦S¦¦V¦ 2) on a random access computer and it guarantees to output a tree that spans S with total distance on its edges no more than 2(1?1/l) times that of the optimal tree, where l is the number of leaves in the optimal tree.


title: A K-Band High Power and High Isolation Stacked-FET Single Pole Double Throw MMIC Switch Using Resonating Capacitor

abstract: Author(s): Nguyen, DP; Pham, AV; Aryanfar, F | Abstract: © 2016 IEEE. A K-band monolithic microwave integrated circuit (MMIC) transmit and receive (T/R) single pole double throw (SPDT) switch with low insertion loss, high isolation and ultra-high output power is demonstrated using 0.15-μm Gallium Arsenide (GaAs) technology. A shunt field effect transistor (FET) configuration is used to provide low insertion loss and high isolation while the stacked-FET is employed to improve power handling capability. The novel GaAs switch exhibits a minimum measured insertion loss of 1.4 dB and less than 2.5 dB from 22 GHz to 26 GHz as well as 44 dB isolation. The measured input 1-dB power compression point (P1,dB) exceeds 4 W.


title: Emerging Mobile Communication Technologies for Healthcare System in 5G Network

abstract: Recent development in the field of mobile communication technologies have opened innovative prospects for latest and state-of-the-art means of healthcare system. However, implementation of healthcare system using mobile and wireless system (2G, 3G, and 4G) poses new challenges related to excessive usage of data rates and bandwidth, and delay. Therefore, keeping in view the above problem, in this paper we present a novel 5G system architecture for healthcare system. The proposed system exploits Transparent Interconnection of Lots of Links (TRILL) protocol for mobility management and data packet delivery. In addition, each evolved node B (eNB) act as a routing bridge (RB), where data delivery operations are achieved between RB using TRILL mechanism. Moreover, a scheme for mobility management is also proposed that is used for mapping identifies (IP address) and MAC address of RB. Moreover, in order to perform efficient handover, a technique is also presented that is based on the locator of the users. Numerical analysis show that the proposed system architecture outperform exiting 4G in term of data transmission delay, data tunneling overhead, and queuing delay.


title: Preschoolers' Emotion Regulation Strategy Understanding: Relations with Emotion Socialization and Child Self-regulation

abstract: Preschool-age children's ability to verbally generate strategies for regulating anger and sadness, and to recognize purported effective strategies for these emotions, were examined in relation to child factors (child age, temperament, and language ability) and maternal emotion socialization (supportiveness and structuring in response to child distress). The relation between strategy understanding and actual self-regulation was also examined. In a sample of 116 boys and girls, 4-year-olds recognized and generated strategies for anger more than 3-year-olds but 3- and 4-year-olds recognized and generated strategies similarly for sadness. Age effects for strategy generation were explained by expressive language skill. Maternal support in response to child distress was related to strategy recognition and generation but in different ways. Maternal structuring was related only to strategy generation for anger. Child strategy understanding of anger and sadness predicted different child behaviors when children had to deal with frustration alone. The findings suggest that emotion regulation strategy understanding can be assessed in young children and that such understanding has implications for self-regulatory behavior.


Informational Urbanism. A Conceptual Framework of Smart Cities


title: A Two Phase Hybrid Classifier based on Structure Similarities and Textural Features for Accurate Meningioma Classification

abstract: Meningioma subtype classification is a complex pattern classification problem of digital pathology due to het-erogeneity issues of tumor texture, low inter-class and high intra-class texture variations of tumor samples, and architec-tural variations of cellular components. The basic aim is the achievement of significantly high classification results for all the subtypes of meningioma while dealing with inherent complexity and texture variations. The ultimate goal is to mimic the prognosis decision of expert pathologists and assist newer pathologists in making right and quick decisions. In this paper, a novel hybrid classification framework based on nuclei shape matching and texture analysis is proposed for classification of four subtypes of grade-I benign meningioma. Meningothelial and fibroblastic subtypes are classified on basis of nuclei shape matching through skeletons and shock graphs while an optimized texture-based evolutionary framework is designed for the classification of transi-tional and psammomatous subtypes. Classifier-based evolutionary feature selection is performed using Genetic Algorithm (GA) in combination with Support Vector Machine (SVM) to select the optimal combination of higher-order statistical features extracted from morphologically processed RGB color channel images. The proposed hybrid classifier employed leave-one-patient-out 5-fold cross validation and achieved an overall 95.63% mean classification accuracy.


title: "Aiming at a Moving Target": Social Science and the Recent Rebellions in Eastern Europe.

abstract: One week before the mass demonstrations that led to the collapse of the Czechoslovak Communist regime in November, 1989, a Charter 77 activist, Jan Urban, proposed that the group contest the national elections to be held in June of 1991. Urban's friends laughed at his proposals for being hopelessly utopian. But a week later, thousands of students filled Wenceslas Square, a general strike was called, and the regime collapsed. The leaders of the future Civic Forum, who had been carefully watching events in Hungary, Poland and the GDR, had little idea that their own country was ripe for revolution.'


title: Two-stage genetic programming (2SGP) for the credit scoring model

abstract: Abstract Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed for significantly improving the accuracy of the credit scoring models. In this paper, two-stage genetic programming (2SGP) is proposed to deal with the credit scoring problem by incorporating the advantages of the IF–THEN rules and the discriminant function. On the basis of the numerical results, we can conclude that 2SGP can provide the better accuracy than other models.


title: TensiStrength: Stress and relaxation magnitude detection for social media texts

abstract: Computer systems need to be able to react to stress in order to perform optimally on some tasks. This article describes TensiStrength, a system to detect the strength of stress and relaxation expressed in social media text messages. TensiStrength uses a lexical approach and a set of rules to detect direct and indirect expressions of stress or relaxation, particularly in the context of transportation. It is slightly more effective than a comparable sentiment analysis program, although their similar performances occur despite differences on almost half of the tweets gathered. The effectiveness of TensiStrength depends on the nature of the tweets classified, with tweets that are rich in stress-related terms being particularly problematic. Although generic machine learning methods can give better performance than TensiStrength overall, they exploit topic-related terms in a way that may be undesirable in practical applications and that may not work as well in more focused contexts. In conclusion, TensiStrength and generic machine learning approaches work well enough to be practical choices for intelligent applications that need to take advantage of stress information, and the decision about which to use depends on the nature of the texts analysed and the purpose of the task.


title: Examining Alignment of Frames Using Actor-Network Constructs: The Implementation of an IT Project

abstract: IT projects often cannot succeed without bridging different stakeholders’ perspectives on system design and function during implementation. These perspectives often start as competing technology frames consisting of different beliefs, interests, technology evaluation routines and artifact characteristics. For successful implementation of IT, these competing frames usually need to be stabilized into a dominant frame which is widely accepted. Prior research has offered few clues on the mechanisms that are at work in aligning competing frames. This paper uses a case study to propose mechanisms for aligning frames—namely, two actor-network theory constructs of “black-boxing” and “obligatory points of passage”—that aid in translating the competing frames into a truce frame. The examined properties of these mechanisms are described and analyzed, thereby extending the literature on technology frames by providing evidence of generic aligning mechanisms.


title: Broadband CPW Feed for Millimeter-Wave SIW-Based Antipodal Linearly Tapered Slot Antennas

abstract: To achieve broadband performances in the millimeter-wave range, antipodal linearly tapered slot antenna (ALTSA) designs with new combined substrate integrated waveguide (SIW) and regular coplanar waveguide (CPW) feeds are presented and studied. This feed structure eliminates the fabrication of air bridges in direct CPW-fed tapered slot antennas (TSAs). Two millimeter-wave design techniques are introduced for the selected 41–61 GHz and 90–120 GHz frequency ranges, demonstrating very good impedance match and nearly constant gain, beamwidth, and cross-polarization levels over bandwidths of 39% and 28%, respectively. The design procedure is validated by comparing simulated results with measurements performed on a 21–31 GHz (38% bandwidth) prototype. Very good agreement between measured and calculated performance characteristics is obtained with only cross-polarization levels slightly higher than predicted. The structural design parameters and dimensions of all three designs are given.


title: Emotion recognition and brain mapping for sentiment analysis: A review

abstract: The rapid growth of the Internet has caused the increase in the amount of textual information available, such as in blogs, discussion forums and review sites on the web, where the texts surely have the emotion content. Emotion is one appearence of people behaviour and it is an important performance in human computer interaction (HCI). Human express the emotion in the form of facial expression, speech and writing text. Recently, researchers in computational linguistic (CL) areas are interested in the attention of emotion for Sentiment Analysis (SA). SA naturally observes the emotion conveyed by a text, and at the same time, distinguishing positive and negative valence. The wide areas of CL research, actually considerable for investigating the emotion dimension detection and searching the approaches and techniques in the term of emotion recognition (ER). There are two significant trends of research in the area, the emotion recognition based on state affective computing and the real time using brain signal machines. The two areas have the same aim for getting the improvement result in sentiment analysis with the mapping of emotion recognition provided. The exclusive work on emotion detection is comparatively rare and lacks empirical evaluation research. This paper provides the overview of past and recent research on emotion detection as well as some approaches and techniques used and shows the linked between both SA and ER.


title: Habanero-Java library: a Java 8 framework for multicore programming

abstract: With the advent of the multicore era, it is clear that future growth in application performance will primarily come from increased parallelism. We believe parallelism should be introduced early into the Computer Science curriculum to educate students on the fundamentals of parallel computation. In this paper, we introduce the newly-created Habanero-Java library (HJlib), a pure Java 8 library implementation of the pedagogic parallel programming model [12]. HJlib has been used in teaching a sophomore-level course titled "Fundamentals of Parallel Programming" at Rice University. HJlib adds to the Java ecosystem a powerful and portable task parallel programming model that can be used to parallelize both regular and irregular applications. By relying on simple orthogonal parallel constructs with important safety properties, HJlib allows programmers with a basic knowledge of Java to get started with parallel programming concepts by writing or refactoring applications to harness the power of multicore architecture. The HJlib APIs make extensive use of lambda expressions and can run on any Java 8 JVM. HJlib runtime feedback capabilities, such as the abstract execution metrics and the deadlock detector, help the programmer to obtain feedback on theoretical performance as well as the presence of potential bugs in their program. Being an implementation of a pedagogic programming model, HJlib is also an attractive tool for both educators and researchers. HJlib is actively being used in multiple research projects at Rice and also by external independent collaborators. These projects include exploitation of homogeneous and heterogeneous multicore parallelism in big data applications written for the Hadoop platform [20, 43].


title: Numerical simulation of liver perfusion: from CT scans to FE model

abstract: Abstract —We use a collection of Python programs for numerical simulationof liver perfusion. We have an application for semi-automatic generation ofa ﬁnite element mesh of the human liver from computed tomography scansand for reconstruction of the liver vascular structure. When the real vasculartrees can not be obtained from the CT data we generate artiﬁcial trees usingthe constructive optimization method. The generated FE mesh and vasculartrees are imported into SfePy (Simple Finite Elements in Python) and numericalsimulations are performed in order to get the pressure distribution and perfusionﬂows in the liver tissue. In the post-processing steps we calculate transport of acontrast ﬂuid through the liver parenchyma. Index Terms —segmentation, liver perfusion, multicompartment model, ﬁniteelement method, ﬁnite volume method 1 I NTRODUCTION A patient speciﬁc numerical modelling of liver perfusionrequires cooperation of people with different specializations.Our research group consists of cyberneticians, informaticians,mechanicians and physicians who formulate problems to besolved and who are able to judge the outcomes of mathemati-cal models and simulations from the point of view of medicine.The research is motivated by the needs of surgeons, they wouldlike to have efﬁcient tools for better planning of liver surgeriesand who would also appreciate to be able to predict changes ofliver perfusion caused by diseases or after surgical resections.The task of numerical modelling of a human liver canbe divided into two sub-problems. First of all, the geometryof larger vascular structures and hepatic parenchyma mustbe identiﬁed from data obtained by computed tomography(CT) or magnetic resonance imaging (MRI) examinations.With the knowledge of liver shape and vascular structures,the numerical simulations of liver perfusion can be performedusing different mathematical models of blood ﬂow at differentspatial scales. The question, how to obtain all the necessaryparameters of our models (permeabilities, etc.) is out ofthe scope of this paper. More information can be found in[Roh12b] or [Coo12].The mathematical model of tissue perfusion presented inthis paper has been already used for similar problems, e.g. for


title: Evolving the optimal racing line in a high-end racing game

abstract: Finding a racing line that allows to achieve a competitive lap-time is a key problem in real-world car racing as well as in the development of non-player characters for a commercial racing game. Unfortunately, solving this problem generally requires a domain expert and a trial-and-error process. In this work, we show how evolutionary computation can be successfully applied to solve this task in a high-end racing game. To this purpose, we introduce a novel encoding for the racing lines based on a set of connected Be´zier curves. In addition, we compare two different methods to evaluate the evolved racing lines: a simulation-based fitness and an estimation-based fitness; the former does not require any previous knowledge but is rather expensive; the latter is much less expensive but requires few domain knowledge and is not completely accurate. Finally, we test our approach using The Open Racing Car Simulator (TORCS), a state-of-the-art open source simulator, as a testbed.


title: BRIEF REPORT Imprisoned by the past: Unhappy moods lead to a retrospective bias to mind wandering

abstract: Evidence suggests that mind wandering is a frequent accompaniment to an unhappy mood. Building on such work, two laboratory experiments used mood induction to assess whether the greater frequency of mind wandering in a low mood is also accompanied by a shift towards a focus on events from the past. Experiment 1 induced moods via video and induction of an unhappy mood was associated with a greater tendency for past-related mind wandering as measured by a post-task questionnaire. In Experiment 2, negative and positive moods were induced in a group of participants using the Velten mood-induction procedure and the temporal focus of mind wandering was measured using experience sampling probes. Analyses indicated that induction of an unhappy mood led to an increase in past-related mind wandering and the magnitude of this change increased with scores on a measure of depressive symptoms. Together these experiments suggest that when the mind wanders in an unhappy mood it is drawn to events from its past.


title: Big Data Model of Security Sharing Based on Blockchain

abstract: The rise of big data age in the Internet has led to the explosive growth of data size. However, trust issue has become the biggest problem of big data, leading to the difficulty in data safe circulation and industry development. The blockchain technology provides a new solution to this problem by combining non-tampering, traceable features with smart contracts that automatically execute default instructions. In this paper, we present a credible big data sharing model based on blockchain technology and smart contract to ensure the safe circulation of data resources.


title: CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning

abstract: Extreme Multi-label Learning (XML) considers large sets of items described by a number of labels that can exceed one million. Tree-based methods, which hierarchically partition the problem into small scale sub-problems, are particularly promising in this context to reduce the learn-ing/prediction complexity and to open the way to parallelization. However, the current best approaches do not exploit tree randomization which has shown its efficiency in random forests and they resort to complex partitioning strategies. To overcome these limits, we here introduce a new random forest based algorithm with a very fast partitioning approach called CRAFTML. Experimental comparisons on nine datasets from the XML literature show that it outperforms the other tree-based approaches. Moreover with a paral-lelized implementation reduced to five cores, it is competitive with the best state-of-the-art methods which run on one hundred-core machines.


A Named Entity Recognition Shootout for German


title: All-in and Bad Beat: Professional Poker Players and Pathological Gambling

abstract: Previous research has shown that online poker players may be at a high risk of developing gambling problems and that there are various personality traits that may be associated with such risk. An online survey comprising 256 poker players (50.8 % professional players and 49.2 % recreational players) examined various aspects of their poker playing behavior. The goals of the study were to investigate: (i) the playing motivations of recreational and professional Texas Hold’em poker players, (ii) the psychological characteristics of online poker players in relation to narcissism, impulsivity, self-esteem, and problem gambling, and (iii) the differences between professional and recreational poker player on these psychological variables. Results showed that only 1.6 % poker players met the DSM-IV-TR diagnostic criteria for pathological gambling (n = 4). Generally, poker players had low scores on narcissism and impulsivity, and high scores on self-esteem. Implications for the study of online poker gambling are also discussed.


title: f-VAEs: Improve VAEs with Conditional Flows

abstract: In this paper, we integrate VAEs and flow-based generative models successfully and get f-VAEs. Compared with VAEs, f-VAEs generate more vivid images, solved the blurred-image problem of VAEs. Compared with flow-based models such as Glow, f-VAE is more lightweight and converges faster, achieving the same performance under smaller-size architecture.


title: Measuring Inconsistency in a Network Intrusion Detection Rule Set Based on Snort

abstract: In this preliminary study, we investigate how inconsistency in a network intrusion detection rule set can be measured. To achieve this, we first examine the structure of these rules which are based on Snort and incorporate regular expression (Regex) pattern matching. We then identify primitive elements in these rules in order to translate the rules into their (equivalent) logical forms and to establish connections between them. Additional rules from background knowledge are also introduced to make the correlations among rules more explicit. We measure the degree of inconsistency in formulae of such a rule set (using the Scoring function, Shapley inconsistency values and Blame measure for prioritized knowledge) and compare the informativeness of these measures. Finally, we propose a new measure of inconsistency for prioritized knowledge which incorporates the normalized number of atoms in a language involved in inconsistency to provide a deeper inspection of inconsistent formulae. We conclude that such measures are useful for the network intrusion domain assuming that introducing expert knowledge for correlation of rules is feasible.


title: CONCURRENCY CONTROL SCHEMES IN NEWSQL SYSTEMS

abstract: OLTP applications, in the current times, require support for ACID transactions and concurrency. But these properties each come with their trade-off. NewSQL systems have emerged as the solution to this conundrum, by overhauling the traditional architecture of RDBMSs. Concurrency control scheme selected has a major impact on the architectural performance. In this paper we firstly discuss the older concurrency control schemes used in the conventional SQL systems like two phase locking protocol and later discuss the modern concurrency control schemes used mainly in the modern NewSQL based systems such as multi version concurrency control, basic timestamp concurrency control, optimistic concurrency control, Timestamp ordering with partition level locking. Later, we have discussed the contemporary NewSQL systems with emphasis on underlying architecture and schemes used for controlling concurrency.


Optical PUFs Reloaded


title: Article Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation

abstract: Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from LiDAR sensors is presented. The 3D point cloud is first segmented into voxels, which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a new metric that combines both segmentation and classification results simultaneously is presented. The effects of voxel size and incorporation of RGB color and laser reflectance intensity on the classification results are also discussed. The method is evaluated on standard data sets using different metrics to demonstrate its efficacy.


title: Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey

abstract: Hyperspectral unmixing (HU) is a very useful and increasingly popular preprocessing step for a wide range of hyperspectral applications. However, the HU research has been constrained a lot by three factors: (a) the number of hyperspectral images (especially the ones with ground truths) are very limited; (b) the ground truths of most hyperspectral images are not shared on the web, which may cause lots of unnecessary troubles for researchers to evaluate their algorithms; (c) the codes of most state-of-the-art methods are not shared, which may also delay the testing of new methods. ::: Accordingly, this paper deals with the above issues from the following three perspectives: (1) as a profound contribution, we provide a general labeling method for the HU. With it, we labeled up to 15 hyperspectral images, providing 18 versions of ground truths. To the best of our knowledge, this is the first paper to summarize and share up to 15 hyperspectral images and their 18 versions of ground truths for the HU. Observing that the hyperspectral classification (HyC) has much more standard datasets (whose ground truths are generally publicly shared) than the HU, we propose an interesting method to transform the HyC datasets for the HU research. (2) To further facilitate the evaluation of HU methods under different conditions, we reviewed and implemented the algorithm to generate a complex synthetic hyperspectral image. By tuning the hyper-parameters in the code, we may verify the HU methods from four perspectives. The code would also be shared on the web. (3) To provide a standard comparison, we reviewed up to 10 state-of-the-art HU algorithms, then selected the 5 most benchmark HU algorithms, and compared them on the 15 real hyperspectral datasets. The experiment results are surely reproducible; the implemented codes would be shared on the web.


title: A 30-Gb/s 1.37-pJ/b CMOS Receiver for Optical Interconnects

abstract: This paper presents a digitally controlled 1-V 30-Gb/s 1.37-pJ/b optical receiver in 65-nm CMOS technology. This receiver consists of an inverter-based inductive transimpedance amplifier, a fully integrated low-dropout regulator, a main amplifier, a three-stage-cascaded continuous-time linear equalizer (CTLE), a two-stage limiting amplifier, and an output driver. The CTLE consists of three cascaded stages with different peaking frequencies (5, 12, and 20 GHz) offering 16 dB of adjustable low-frequency gain to accommodate different photodetector (PD) characteristics. The electrical measurements demonstrate a maximum transimpedance gain of 83 dBΩ (14,125 Ω), an output swing of 300 mV, a −3-dB bandwidth of 24 GHz, and a power consumption of 41 mW. Moreover, optical measurement results with a 28-Gb/s PD show that the receiver achieves 10−12 BER at 30 Gb/s for a 215 −1 PRBS input with a −5.6-dBm input sensitivity. Using a lower bandwidth 14-Gb/s PD, the receiver can still reach 30 Gb/s at 10−12 BER with only a 0.6-dB degradation in input sensitivity, demonstrating the effectiveness of the proposed receiver and the programmable-cascaded CTLE. The core area occupies 0.26 mm2.


title: Joint Image-Text News Topic Detection and Tracking by Multimodal Topic And-Or Graph

abstract: This paper presents a novel method for automatically detecting and tracking news topics from multimodal TV news data. We propose a multimodal topic and-or graph (MT-AOG) to jointly represent textual and visual elements of news stories and their latent topic structures. An MT-AOG leverages a context-sensitive grammar that can describe the hierarchical composition of news topics by semantic elements about people involved, related places, and what happened, and model contextual relationships between elements in the hierarchy. We detect news topics through a cluster sampling process which groups stories about closely related events together. Swendsen–Wang cuts, an effective cluster sampling algorithm, is adopted for traversing the solution space and obtaining optimal clustering solutions by maximizing a Bayesian posterior probability. The detected topics are then continuously tracked and updated with incoming news streams. We generate topic trajectories to show how topics emerge, evolve, and disappear over time. The experimental results show that our method can explicitly describe the textual and visual data in news videos and produce meaningful topic trajectories. Our method also outperforms previous methods for the task of document clustering on Reuters-21578 dataset and our novel dataset, UCLA Broadcast News dataset.


title: Code Conjurer: Pulling Reusable Software out of Thin Air

abstract: For many years, the IT industry has sought to accelerate the software development process by assembling new applications from existing software assets. However, true component-based reuse of the form Douglas Mcllroy envisaged in the 1960s is still the exception rather than the rule, and most of the systematic software reuse practiced today uses heavyweight approaches such as product-line engineering or domain-specific frameworks. By component, we mean any cohesive and compact unit of software functionality with a well-defined interface - from simple programming language classes to more complex artifacts such as Web services and Enterprise JavaBeans.


title: Molecular profiling assays in breast cancer: are we ready for prime time?

abstract: In this review, we will present the current data on commercially available molecular profiling assays in breast cancer and discuss the challenges surrounding their incorporation into routine clinical practice as prognostic and predictive tools.


title: Image Super-Resolution via Adaptive $\ell _{p} (0<p<1)$ Regularization and Sparse Representation

abstract: Previous studies have shown that image patches can be well represented as a sparse linear combination of elements from an appropriately selected over-complete dictionary. Recently, single-image super-resolution (SISR) via sparse representation using blurred and downsampled low-resolution images has attracted increasing interest, where the aim is to obtain the coefficients for sparse representation by solving an l0 or l1 norm optimization problem. The l0 optimization is a nonconvex and NP-hard problem, while the l1 optimization usually requires many more measurements and presents new challenges even when the image is the usual size, so we propose a new approach for SISR recovery based on regularization nonconvex optimization. The proposed approach is potentially a powerful method for recovering SISR via sparse representations, and it can yield a sparser solution than the l1 regularization method. We also consider the best choice for lp regularization with all p in (0, 1), where we propose a scheme that adaptively selects the norm value for each image patch. In addition, we provide a method for estimating the best value of the regularization parameter λ adaptively, and we discuss an alternate iteration method for selecting p and λ . We perform experiments, which demonstrates that the proposed regularization nonconvex optimization method can outperform the convex optimization method and generate higher quality images.


title: Ego-motion estimation and moving object tracking using multi-layer LIDAR

abstract: This paper presents an approach for the robust recognition of a complex and dynamic driving environment, such as an urban area, using on-vehicle multi-layer LIDAR. The multi-layer LIDAR alleviates the consequences of occlusion by vertical scanning; it can detect objects with different heights simultaneously, and therefore the influence of occlusion can be curbed. The road environment recognition algorithm proposed in this paper consists of three procedures: ego-motion estimation, construction and updating of a 3-dimensional local grid map, and the detection and tracking of moving objects. The integration of these procedures enables us to estimate ego-motion accurately, along with the positions and states of moving objects, the free area where vehicles and pedestrians can move freely, and the ‘unknown’ area, which have never previously been observed in a road environment.


Recurrent Neural Network with Word Embedding for Complaint Classification


title: A Case Study on Improving Capacity Delivery of Battery Packs via Reconfiguration

abstract: Cell imbalance in large battery packs degrades their capacity delivery, especially for cells connected in series where the weakest cell dominates their overall capacity. In this article, we present a case study of exploiting system reconfigurations to mitigate the cell imbalance in battery packs. Specifically, instead of using all the cells in a battery pack to support the load, selectively skipping cells to be discharged may actually enhance the pack’s capacity delivery. Based on this observation, we propose CSR, a Cell Skipping-assisted Reconfiguration algorithm that identifies the system configuration with (near)-optimal capacity delivery. We evaluate CSR using large-scale emulation based on empirically collected discharge traces of 40 lithium-ion cells. CSR achieves close-to-optimal capacity delivery when the cell imbalance in the battery pack is low and improves the capacity delivery by about 20% and up to 1x in the case of a high imbalance.


title: Novel insights into skeletal muscle function by mechanomyography: from the laboratory to the field

abstract: Purpose ::: The review aimed to provide a wider overview on the new application fields of MMG signal. A particular emphasis on measurements reliability and sensitivity was also given.


title: Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency

abstract: Learning to reconstruct depths in a single image by watching unlabeled videos via deep convolutional network (DCN) is attracting significant attention in recent years. In this paper, we introduce a surface normal representation for unsupervised depth estimation framework. Our estimated depths are constrained to be compatible with predicted normals, yielding more robust geometry results. Specifically, we formulate an edge-aware depth-normal consistency term, and solve it by constructing a depth-to-normal layer and a normal-to-depth layer inside of the DCN. The depth-to-normal layer takes estimated depths as input, and computes normal directions using cross production based on neighboring pixels. Then given the estimated normals, the normal-to-depth layer outputs a regularized depth map through local planar smoothness. Both layers are computed with awareness of edges inside the image to help address the issue of depth/normal discontinuity and preserve sharp edges. Finally, to train the network, we apply the photometric error and gradient smoothness for both depth and normal predictions. We conducted experiments on both outdoor (KITTI) and indoor (NYUv2) datasets, and show that our algorithm vastly outperforms state of the art, which demonstrates the benefits from our approach.


title: A fast algorithm for bottom-up document layout analysis

abstract: This paper describes a new bottom-up method for document layout analysis. The algorithm was implemented in the CLIDE (Chemical Literature Data Extraction) system, but the method described here is suitable for a broader range of documents. It is based on Kruskal's algorithm and uses a special distance-metric between the components to construct the physical page structure. The method has all the major advantages of bottom-up systems: independence from different text spacing and independence from different block alignments. The algorithms computational complexity is reduced to linear by using heuristics and path-compression.


title: TorqueBAR: an ungrounded haptic feedback device

abstract: Kinesthetic feedback is a key mechanism by which people perceive object properties during their daily tasks - particularly inertial properties. For example, transporting a glass of water without spilling, or dynamically positioning a handheld tool such as a hammer, both require inertial kinesthetic feedback. We describe a prototype for a novel ungrounded haptic feedback device, the TorqueBAR, that exploits a kinesthetic awareness of dynamic inertia to simulate complex coupled motion as both a display and input device. As a user tilts the TorqueBAR to sense and control computer programmed stimuli, the TorqueBAR's centre-of-mass changes in real-time according to the user's actions. We evaluate the TorqueBAR using both quantitative and qualitative techniques, and we describe possible applications for the device such as video games and real-time robot navigation.


title: Fine-to-Coarse Global Registration of RGB-D Scans

abstract: RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality. However, it is still challenging to register RGB-D images from a hand-held camera over a long video sequence into a globally consistent 3D model. Current methods often can lose tracking or drift and thus fail to reconstruct salient structures in large environments (e.g., parallel walls in different rooms). To address this problem, we propose a "fine-to-coarse" global registration algorithm that leverages robust registrations at finer scales to seed detection and enforcement of new correspondence and structural constraints at coarser scales. To test global registration algorithms, we provide a benchmark with 10,401 manually-clicked point correspondences in 25 scenes from the SUN3D dataset. During experiments with this benchmark, we find that our fine-to-coarse algorithm registers long RGB-D sequences better than previous methods.