title: A Hydrologic/Water Quality Model Applicati1

abstract: : This paper presents a procedure for standard application of hydrologic/water quality models. To date, most hydrologic/water quality modeling projects and studies have not utilized formal protocols, but rather have employed ad hoc approaches. The procedure proposed is an adaptation and extension of steps identified from relevant literature including guidance provided by the U.S. Environmental Protection Agency. This protocol provides guidance for establishing written plans prior to conducting modeling efforts. Eleven issues that should be addressed in model application plans were identified and discussed in the context of hydrologic/water quality studies. A graded approach for selection of the level of documentation for each item was suggested. The creation and use of environmental modeling plans is increasingly important as the results of modeling projects are used in decision-making processes that have significant implications. Standard modeling application protocols similar to the proposed procedure herein provide modelers with a roadmap to be followed, reduces modelers’ bias, enhances the reproducibility of model application studies, and eventually improves acceptance of modeling outcomes.


title: Time series feature extraction for data mining using DWT and DFT

abstract: A planter for plants comprising a lamp of the oil-burning type and comprising a glass base adapted to contain water, a burner having a wick extending therethrough, and a transparent chimney, the lower end of which is sealed relative to the perimeter of the burner and soil is disposed in at least the lower portion of said chimney and said wick extending into said soil to convey moisture from the base into said soil by capillary action.


title: Geo-topo maps: hybrid visualization of movement data over building floor plans and maps

abstract: We demonstrate how movements of multiple people or objects within a building can be displayed on a network representation of the building, where nodes are rooms and edges are doors. Our representation shows the direction of movements between rooms and the order in which rooms are visited, while avoiding occlusion or overplotting when there are repeated visits or multiple moving people or objects. We further propose the use of a hybrid visualization that mixes geospatial and topological (network-based) representations, enabling focus-in-context and multi-focal visualizations. An experimental comparison found that the topological representation was significantly faster than the purely geospatial representation for three out of four tasks.


title: Mobility and eccentricity analysis of a helical belt drive in-pipe robot

abstract: Pipeline grids of various size and material are pervasive in today's modern society. The frequent inspection and maintenance of such pipeline grids have presented a tremendous challenge. It is advocated that only advanced robot design embedded with intelligent electronics and control algorithms could perform the job. Given the ever increasing demands for intelligent in-pipe robots, various in-pipe drive mechanisms have been reported. One of the simplest is helical drives that have only one degree of freedom. All previously reported in-pipe helical drives are based on independent passive wheels that are tilted an angle. In this paper, the wheels are replaced by pulleys that are synchronized by a timing belt. This small change will result in significant improvement as will be highlighted in this paper. In this paper, the mobility of the robot design is analyzed. Uneven tracking force occurs at the bend is also illustrated. An experiment has been conducted to show the steady traction force.


title: Game Theory-Based Opponent Modeling in Large

abstract: We develop an algorithm for opponent modeling in large extensive-form games of imperfect information. It works by observing the opponent's action frequencies and building an opponent model by combining information from a precomputed equilibrium strategy with the observations. It then computes and plays a best response to this opponent model; the opponent model and best response are both updated continually in real time. The approach combines game-theoretic reasoning and pure opponent modeling, yielding a hybrid that can effectively exploit opponents after only a small number of interactions. Unlike prior opponent modeling approaches, ours is fundamentally game theoretic and takes advantage of recent algorithms for automated abstraction and equilibrium computation rather than relying on domain-specific prior distributions, historical data, or a handcrafted set of features. Experiments show that our algorithm leads to significantly higher win rates (than an approximate-equilibrium strategy) against several opponents in limit Texas Hold'em --- the most studied imperfect-information game in computer science --- including competitors from recent AAAI computer poker competitions.


title: A New Sampling Technique for Tensors

abstract: In this paper we propose new techniques to sample arbitrary third-order tensors, with an objective of speeding up tensor algorithms that have recently gained popularity in machine learning. Our main contribution is a new way to select, in a biased random way, only $O(n^{1.5}/\epsilon^2)$ of the possible $n^3$ elements while still achieving each of the three goals: \\ {\em (a) tensor sparsification}: for a tensor that has to be formed from arbitrary samples, compute very few elements to get a good spectral approximation, and for arbitrary orthogonal tensors {\em (b) tensor completion:} recover an exactly low-rank tensor from a small number of samples via alternating least squares, or {\em (c) tensor factorization:} approximating factors of a low-rank tensor corrupted by noise. \\ Our sampling can be used along with existing tensor-based algorithms to speed them up, removing the computational bottleneck in these methods.


title: Parallel corpora for medium density languages

abstract: The choice of natural language technology appropriate for a given language is greatly impacted by density (availability of digitally stored material). More than half of the world speaks medium density languages, yet many of the methods appropriate for high or low density languages yield suboptimal results when applied to the medium density case. In this paper we describe a general methodology for rapidly collecting, building, and aligning parallel corpora for medium density languages, illustrating our main points on the case of Hungarian, Romanian, and Slovenian. We also describe and evaluate the hybrid sentence alignment method we are using.


title: Controller design for a bipedal walking robot using variable stiffness actuators

abstract: The bipedal spring-loaded inverted pendulum (SLIP) model captures characteristic properties of human locomotion, and it is therefore often used to study human-like walking. The extended variable spring-loaded inverted pendulum (V-SLIP) model provides a control input for gait stabilization and shows robust and energy-efficient walking patterns. This work presents a control strategy that maps the conceptual V-SLIP model on a realistic model of a bipedal robot. This walker implements the variable leg compliance by means of variable stiffness actuators in the knees. The proposed controller consists of multiple levels, each level controlling the robot at a different level of abstraction. This allows the controller to control a simple dynamic structure at the top level and control the specific degrees of freedom of the robot at a lower level. The proposed controller is validated by both numeric simulations and preliminary experimental tests.


Preparation of carbon nitride films with high nitrogen content by electrodeposition from an organic solution


title: A hepatitis C-positive patient with new onset of nephrotic syndrome and systemic amyloidosis secondary to common variable immunodeficiency

abstract: Common variable immunodeficiency (CVID) is a heterogenous group of predominantly antibody-deficiency disorders that make up the greatest proportion of patients with symptomatic primary hypogammaglobulinemia. The rare coincidence of amyloidosis and hypogammaglobulinemia has been reported previously. Contrary to the usual insidious, slowly progressive disease following hepatitis C infection, a rapidly progressive cirrhotic form can develop in hypogammaglobulinemic patients. We report a HCV-positive patient with a new onset of nephrotic syndrome and systemic amyloidosis secondary to CVID. Blood analyses showed serum creatinine of 1.8 mg/dL and serum albumin of 3.1 gm/dL; 24-h urinary protein was 11 800 mg/day. Serum immunoglobulin levels were IgG 340 mg/dL, IgM 18 mg/dL, IgA 11 mg/dL. Duodenal biopsy revealed AA-type amyloidosis with potassium permanganate and Congo red staining. After a month of antiproteinuric therapy, the proteinuria was reduced to 3350 mg/day.


title: Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts

abstract: Electronic patient records remain a rather unexplored, but potentially rich data source for discovering correlations between diseases. We describe a general approach for gathering phenotypic descriptions of patients from medical records in a systematic and non-cohort dependent manner. By extracting phenotype information from the free-text in such records we demonstrate that we can extend the information contained in the structured record data, and use it for producing fine-grained patient stratification and disease co-occurrence statistics. The approach uses a dictionary based on the International Classification of Disease ontology and is therefore in principle language independent. As a use case we show how records from a Danish psychiatric hospital lead to the identification of disease correlations, which subsequently can be mapped to systems biology frameworks.


title: Learning rich features from objectness estimation for human lying-pose detection

abstract: Lying-pose human detection is an active research field of computer vision in recent years. It has a good theoretical significance and furthermore many applications, such as victim detection or home service robot. But the study on lying-pose human detection in low-altitude overlooking images have many unsolved problems owing to multiple poses, arbitrary orientation, in-plane rotation, perspective distortion, and time-consuming. In this paper, the proposed framework of human lying-pose detection is optimization and machine learning algorithms inspired by processes of neurobiology suggest and human vision system to select possible object locations. First, the proposed model effectively utilizes binarized normed gradient features to obtain the objectness rapidly based on the vision saliency. Further, deep-learning techniques based on the convolution neural network are trained for learning rich feature hierarchies, in order to obtain the object of lying-pose human from objectness estimation, unlike the classical sliding-window algorithm. Eventually, employed pyramid mean-shift algorithm and rotation-angle recovery method to find position and direction of human lying-pose. The experimental results show that our method is rapid and efficient, and that it achieves state-of-the-art results with our XMULP dataset.


title: CELF++: Optimizing the greedy algorithm for influence maximization in social networks

abstract: Kempe et al. [4] (KKT) showed the problem of influence maximization is NP-hard and a simple greedy algorithm guarantees the best possible approximation factor in PTIME. However, it has two major sources of inefficiency. First, finding the expected spread of a node set is #P-hard. Second, the basic greedy algorithm is quadratic in the number of nodes. The first source is tackled by estimating the spread using Monte Carlo simulation or by using heuristics[4, 6, 2, 5, 1, 3]. Leskovec et al. proposed the CELF algorithm for tackling the second. In this work, we propose CELF++ and empirically show that it is 35-55% faster than CELF.


title: Automatic visual inspection of printed circuit board for defect detection and classification

abstract: Inspection of printed circuit board (PCB) has been a crucial process in the electronic manufacturing industry to guarantee product quality & reliability, cut manufacturing cost and to increase production. The PCB inspection involves detection of defects in the PCB and classification of those defects in order to identify the roots of defects. In this paper, all 14 types of defects are detected and are classified in all possible classes using referential inspection approach. The proposed algorithm is mainly divided into five stages: Image registration, Pre-processing, Image segmentation, Defect detection and Defect classification. The algorithm is able to perform inspection even when captured test image is rotated, scaled and translated with respect to template image which makes the algorithm rotation, scale and translation in-variant. The novelty of the algorithm lies in its robustness to analyze a defect in its different possible appearance and severity. In addition to this, algorithm takes only 2.528 s to inspect a PCB image. The efficacy of the proposed algorithm is verified by conducting experiments on the different PCB images and it shows that the proposed afgorithm is suitable for automatic visual inspection of PCBs.


title: Column-based micro-beamformer for improved 2D beamforming using a matrix array transducer

abstract: In a 3-D medical ultrasound imaging system, a matrix array probe with 2-D positioning of the elements allows high resolution of ultrasound images due to its capability of two-dimensional dynamic focusing. However, the hundreds (up to thousands) of elements in the matrix array make the fabrication of the transducers and cables challenging. In this paper1, to achieve high quality of 3-D ultrasound images with low hardware complexity, we introduce a column-based micro-beamformer (CMB) in which a column in the matrix array is considered as a sub-array and then elevational and lateral beamformings are sequentially conducted in the analog and digital stages, respectively. For the performance evaluation of the proposed beamformer, the beam pattern simulations are conducted and point-spread-functions are obtained. In addition, root-mean-square-errors (RMSEs) in round-trip time delays were measured over the depths. Compared to a conventional micro-beamformer, the CMB produced more tightly focused beam patterns and showed almost equivalent performance to that of fully sampled array. In the near field, the mean RMSEs of the proposed and conventional beamformers were 274 ns and 18.1 ns (improved 93.4% of delay accuracy), respectively.


title: A novel approach for denoising and enhancement of extremely low-light video

abstract: In this paper, a novel approach for noise reduction and enhancement of extremely low-light video is proposed. For noise removal, a motion adaptive temporal filtering based on a Kalman structured updating is presented. Dynamic range of denoised video is increased by adjustment of RGB histograms using Gamma correction with adaptive clipping thresholds. Finally, residual noise is removed using a nonlocal means (NLM) denoising filter. The proposed method works directly on the color filter array (CFA) raw video for achieving low memory consumption1.


title: Home Bias at Home: Local Equity Preference in Domestic Portfolios

abstract: The strong bias in favor of domestic securities is a well-documented characteristic of international investment portfolios, yet we show that the preference for investing close to home also applies to portfolios of domestic stocks. Specifically, U.S. investment managers exhibit a strong preference for locally headquartered firms, particularly small, highly levered firms that produce nontraded goods. These results suggest that asymmetric information between local and nonlocal investors may drive the preference for geographically proximate investments, and the relation between investment proximity and firm size and leverage may shed light on several well-documented asset pricing anomalies.


title: Student-Centered and Teacher-Centered Classroom Management: A Case Study of Three Elementary Teachers.

abstract: The major purpose of this case study was to document the classroom management beliefs and practices of three teachers reputed to implement student-centered instruction and to examine the relationship between their instructional and managerial approaches. More specifically, do teachers who use student-centered instruction also implement studentcentered management? Results indicate that, although all three teachers used an eclectic approach, two teachers tended to be more student-centered while one was more teachercentered with respect to classroom management. All three teachers’ approaches also reflected the principles of “good classroom management” derived from studies conducted in the 1960’s and 1970’s in traditional transmission classrooms. Results also indicate that the teachers did think about the relationship between instruction and classroom management, but not in terms of using student-centered management to support their student-centered instruction. Rather, they thought about what management strategies were necessary to successfully implement a particular lesson.


title: Arabic Ontology Learning from Un-structured Text

abstract: Ontology Learning (OL) from a text is a process that consists of text processing, knowledge extraction, and ontology construction. For Arabic language, text processing, and knowledge extraction tasks are not mature as for Latin languages. They have not been integrated into the full Arabic OL pipeline. Currently, there is very little automated support for using knowledge from Arabic literature in semantically-enabled systems. This paper demonstrates the feasibility of using some existing OL methods for Arabic text and elicits proposals for further work toward building open domain OL systems for Arabic. This is done by building an OL system based on some available NLP tools for Arabic text utilizing GATE text analysis system for corpus and annotation management. The prototype is evaluated similarly to other OL systems and its performance is promising and recommended to enable more effective research and application of Arabic ontology learning.


title: A Fast Fixed Point Algorithm for Total Variation Deblurring and Segmentation

abstract: In this paper, we propose a fast fixed point algorithm and apply it to total variation (TV) deblurring and segmentation. The TV-based models can be written in the form of a general minimization problem. The novel method is derived from the idea of establishing the relation between solutions of the general minimization problem and new variables, which can be obtained by a fixed point algorithm efficiently. Under gentle conditions it provides a platform to develop efficient numerical algorithms for various image processing tasks. We then specialize this fixed point methodology to the TV-based image deblurring and segmentation models, and the resulting algorithms are compared with the split Bregman method, which is a strong contender for the state-of-the-art algorithms. Numerical experiments demonstrate that the algorithm proposed here performs favorably.


title: Coming of Age (Digitally): An Ecological View of Social Media Use among College Students

abstract: We take an ecological approach to studying social media use and its relation to mood among college students. We conducted a mixed-methods study of computer and phone logging with daily surveys and interviews to track college students' use of social media during all waking hours over seven days. Continual and infrequent checkers show different preferences of social media sites. Age differences also were found. Lower classmen tend to be heavier users and to primarily use Facebook, while upper classmen use social media less frequently and utilize sites other than Facebook more often. Factor analysis reveals that social media use clusters into patterns of content-sharing, text-based entertainment/discussion, relationships, and video consumption. The more constantly one checks social media daily, the less positive is one's mood. Our results suggest that students construct their own patterns of social media usage to meet their changing needs in their environment. The findings can inform further investigation into social media use as a benefit and/or distraction for students.


title: Robust misinterpretation of confidence intervals

abstract: Null hypothesis significance testing (NHST) is undoubtedly the most common inferential technique used to justify claims in the social sciences. However, even staunch defenders of NHST agree that its outcomes are often misinterpreted. Confidence intervals (CIs) have frequently been proposed as a more useful alternative to NHST, and their use is strongly encouraged in the APA Manual. Nevertheless, little is known about how researchers interpret CIs. In this study, 120 researchers and 442 students—all in the field of psychology—were asked to assess the truth value of six particular statements involving different interpretations of a CI. Although all six statements were false, both researchers and students endorsed, on average, more than three statements, indicating a gross misunderstanding of CIs. Self-declared experience with statistics was not related to researchers’ performance, and, even more surprisingly, researchers hardly outperformed the students, even though the students had not received any education on statistical inference whatsoever. Our findings suggest that many researchers do not know the correct interpretation of a CI. The misunderstandings surrounding p-values and CIs are particularly unfortunate because they constitute the main tools by which psychologists draw conclusions from data.


title: Supervised Learning of Large Perceptual Organization: Graph Spectral Partitioning and Learning Automata

abstract: Perceptual organization offers an elegant framework to group low-level features that are likely to come from a single object. We offer a novel strategy to adapt this grouping process to objects in a domain. Given a set of training images of objects in context, the associated learning process decides on the relative importance of the basic salient relationships such as proximity, parallelness, continuity, junctions, and common region toward segregating the objects from the background. The parameters of the grouping process are cast as probabilistic specifications of Bayesian networks that need to be learned. This learning is accomplished using a team of stochastic automata in an N-player cooperative game framework. The grouping process, which is based on graph partitioning is able to form large groups from relationships defined over a small set of primitives and is fast. We statistically demonstrate the robust performance of the grouping and the learning frameworks on a variety of real images. Among the interesting conclusions is the significant role of photometric attributes in grouping and the ability to form large salient groups from a set of local relations, each defined over a small number of primitives.


Deep Learning and Quantum Physics : A Fundamental Bridge


title: Cloud Based Emergency Health Care Information Service in India

abstract: A hospital is a health care organization providing patient treatment by expert physicians, surgeons and equipments. A report from a health care accreditation group says that miscommunication between patients and health care providers is the reason for the gap in providing emergency medical care to people in need. In developing countries, illiteracy is the major key root for deaths resulting from uncertain diseases constituting a serious public health problem. Mentally affected, differently abled and unconscious patients can't communicate about their medical history to the medical practitioners. Also, Medical practitioners can't edit or view DICOM images instantly. Our aim is to provide palm vein pattern recognition based medical record retrieval system, using cloud computing for the above mentioned people. Distributed computing technology is coming in the new forms as Grid computing and Cloud computing. These new forms are assured to bring Information Technology (IT) as a service. In this paper, we have described how these new forms of distributed computing will be helpful for modern health care industries. Cloud Computing is germinating its benefit to industrial sectors especially in medical scenarios. In Cloud Computing, IT-related capabilities and resources are provided as services, via the distributed computing on-demand. This paper is concerned with sprouting software as a service (SaaS) by means of Cloud computing with an aim to bring emergency health care sector in an umbrella with physical secured patient records. In framing the emergency healthcare treatment, the crucial thing considered necessary to decide about patients is their previous health conduct records. Thus a ubiquitous access to appropriate records is essential. Palm vein pattern recognition promises a secured patient record access. Likewise our paper reveals an efficient means to view, edit or transfer the DICOM images instantly which was a challenging task for medical practitioners in the past years. We have developed two services for health care. 1. Cloud based Palm vein recognition system 2. Distributed Medical image processing tools for medical practitioners.


title: Object level fusion of extended dynamic objects

abstract: This paper presents an approach to enable general tracking of extended objects of multiple sensors. Expert information in each input providing sensor module is mapped into simple model parameters and allows the fusion center to use a generalized version of such information. The model type and parameters are presented and a classical Kalman based fusion is extended with a method for integrated extent handling. By this approach the central fusion node can take into account both the track level information and extent estimate from each sensor. The proposed method is compared with a classic method of fusing the object center and the extent estimate separately. Simulated data is used to show that our proposed approach is general in the sense that it can be used on various setups without adaption. Detailed performance results are given based on estimation errors of the extended object space vector. The findings based on simulated data are completed by real world data from a front facing sensor setup. It is shown that the proposed method offers a benefit in position accuracy, especially when the measurement information does not contain a complete extent information in all directions.


title: A representation language language

abstract: The field of AI is strewn with knowledge representation languages. The language designer typically has one particular application domain in mind; as subsequent types of applications are tried, what had originally been useful features become undesirable limitations, and the language is overhauled or scrapped. One remedy to this bleak cycle might be to construct a representational scheme whose domain is the field of representational languages itself. Toward this end, we designed and implemented RLL, a frame-based Representation Languange Language. The components of representation languages in general (such as slots and inheritance mechanisms) and of RLL itself are encoded declaratively as frames. Modifying these frames can change the semantics of RLL, by radically altering the character of the RLL environment.


title: A Study in User-Centered Design and Evaluation of Mental Tasks for BCI

abstract: Current brain-computer interfacing (BCI) research focuses on detection performance, speed, and bit rates. However, this is only a small part of what is important to the user. From human-computer interaction (HCI) research, we can apply the paradigms of user-centered design and evaluation, to improve the usability and user experience. Involving the users in the design process may also help in moving beyond the limited mental tasks that are currently common in BCI systems. To illustrate the usefulness of these methods to BCI, we involved potential users in the design process of a BCI system, resulting in three new mental tasks. The experience of using these mental tasks was then evaluated within a prototype BCI system using a commercial online role-playing game. Results indicate that user preference for certain mental tasks is primarily based on the recognition of brain activity by the system, and secondly on the ease of executing the task.


title: Evaluating the quality of graph embeddings via topological feature reconstruction

abstract: In this paper we study three state-of-the-art, but competing, approaches for generating graph embeddings using unsupervised neural networks. Graph embeddings aim to discover the ‘best’ representation for a graph automatically and have been applied to graphs from numerous domains, including social networks. We evaluate their effectiveness at capturing a good representation of a graph's topological structure by using the embeddings to predict a series of topological features at the vertex level. We hypothesise that an ‘ideal’ high quality graph embedding should be able to capture key parts of the graph's topology, thus we should be able to use it to predict common measures of the topology, for example vertex centrality. This could also be used to better understand which topological structures are truly being captured by the embeddings. We first review these three graph embedding techniques and then evaluate how close they are to being ‘ideal’. We provide a framework, with extensive experimental evaluation on empirical and synthetic datasets, to assess the effectiveness of several approaches at creating graph embeddings which capture detailed topological structure.


title: PARADIGM SHIFTS IN FISHERIES STOCK ASSESSMENT: FROM INTEGRATED ANALYSIS TO BAYESIAN ANALYSIS AND BACK AGAIN

abstract: . I describe integrated analysis and Bayesian analysis, which have been two of the most influential paradigms in fisheries stock assessment during the last two decades of the twentieth century. These two paradigms have generally been considered complementary, rather than competing. However, recent advances in integrated analysis, including the special case of meta-analysis, have made Bayesian analysis somewhat redundant. I describe how data used to create priors for use in Bayesian analysis can be integrated directly into the analyses. This provides a much more convenient way of accurately including the information and associated uncertainty into the analyses. I discuss how there is still a need to describe the uncertainty and suggest that research should focus on the most appropriate methods for doing this.


title: Deep Physiological Affect Network for the Recognition of Human Emotions

abstract: Here we present a robust physiological model for the recognition of human emotions, called Deep Physiological Affect Network. This model is based on a convolutional long short-term memory (ConvLSTM) network and a new temporal margin-based loss function. Formulating the emotion recognition problem as a spectral-temporal sequence classification problem of bipolar EEG signals underlying brain lateralization and photoplethysmogram signals, the proposed model improves the performance of emotion recognition. Specifically, the new loss function allows the model to be more confident as it observes more of specific feelings while training ConvLSTM models. The function is designed to result in penalties for the violation of such confidence. Our experiments on a public dataset show that our deep physiological learning technology significantly increases the recognition rate of state-of-the-art techniques by 15.96% increase in accuracy. An extensive analysis of the relationship between participants' emotion ratings and physiological changes in brain lateralization function during the experiment is also presented.


title: Soft-DTW: a Differentiable Loss Function for Time-Series

abstract: We propose in this paper a differentiable learning loss between time series, building upon the celebrated dynamic time warping (DTW) discrepancy. Unlike the Euclidean distance, DTW can compare time series of variable size and is robust to shifts or dilatations across the time dimension. To compute DTW, one typically solves a minimal-cost alignment problem between two time series using dynamic programming. Our work takes advantage of a smoothed formulation of DTW, called soft-DTW, that computes the soft-minimum of all alignment costs. We show in this paper that soft-DTW is a differentiable loss function, and that both its value and gradient can be computed with quadratic time/space complexity (DTW has quadratic time but linear space complexity). We show that this regularization is particularly well suited to average and cluster time series under the DTW geometry, a task for which our proposal significantly outperforms existing baselines. Next, we propose to tune the parameters of a machine that outputs time series by minimizing its fit with ground-truth labels in a soft-DTW sense.


title: SemanticPaint: Interactive 3D Labeling and Learning at your Fingertips

abstract: We present a new interactive and online approach to 3D scene understanding. Our system, SemanticPaint, allows users to simultaneously scan their environment whilst interactively segmenting the scene simply by reaching out and touching any desired object or surface. Our system continuously learns from these segmentations, and labels new unseen parts of the environment. Unlike offline systems where capture, labeling, and batch learning often take hours or even days to perform, our approach is fully online. This provides users with continuous live feedback of the recognition during capture, allowing to immediately correct errors in the segmentation and/or learning—a feature that has so far been unavailable to batch and offline methods. This leads to models that are tailored or personalized specifically to the user's environments and object classes of interest, opening up the potential for new applications in augmented reality, interior design, and human/robot navigation. It also provides the ability to capture substantial labeled 3D datasets for training large-scale visual recognition systems.


title: Deep Edge-Aware Filters

abstract: There are many edge-aware filters varying in their construction forms and filtering properties. It seems impossible to uniformly represent and accelerate them in a single framework. We made the attempt to learn a big and important family of edge-aware operators from data. Our method is based on a deep convolutional neural network with a gradient domain training procedure, which gives rise to a powerful tool to approximate various filters without knowing the original models and implementation details. The only difference among these operators in our system becomes merely the learned parameters. Our system enables fast approximation for complex edge-aware filters and achieves up to 200x acceleration, regardless of their originally very different implementation. Fast speed can also be achieved when creating new effects using spatially varying filter or filter combination, bearing out the effectiveness of our deep edge-aware filters.


title: Compression garments versus compression bandaging in decongestive lymphatic therapy for breast cancer-related lymphedema: a randomized controlled trial

abstract: Background ::: Lymphedema as a result of curative surgery for breast cancer can lead to long-term morbidity. Decongestive lymphatic therapy (DLT) is recognized as an optimal management strategy for patients with moderate symptomatologies, but there is little data in regard to the most effective means of providing compression therapy within a DLT protocol. We conducted a randomized trial of two forms of compression therapy within the initial treatment phase of a DLT protocol for breast cancer-related lymphedema.


title: Non-iterative, feature-preserving mesh smoothing

abstract: With the increasing use of geometry scanners to create 3D models, there is a rising need for fast and robust mesh smoothing to remove inevitable noise in the measurements. While most previous work has favored diffusion-based iterative techniques for feature-preserving smoothing, we propose a radically different approach, based on robust statistics and local first-order predictors of the surface. The robustness of our local estimates allows us to derive a non-iterative feature-preserving filtering technique applicable to arbitrary "triangle soups". We demonstrate its simplicity of implementation and its efficiency, which make it an excellent solution for smoothing large, noisy, and non-manifold meshes.


Automatic Cephalometric X-Ray Landmark Detection Challenge 2014 : A machine learning tree-based approach


title: Context tracker: Exploring supporters and distracters in unconstrained environments

abstract: Visual tracking in unconstrained environments is very challenging due to the existence of several sources of varieties such as changes in appearance, varying lighting conditions, cluttered background, and frame-cuts. A major factor causing tracking failure is the emergence of regions having similar appearance as the target. It is even more challenging when the target leaves the field of view (FoV) leading the tracker to follow another similar object, and not reacquire the right target when it reappears. This paper presents a method to address this problem by exploiting the context on-the-fly in two terms: Distracters and Supporters. Both of them are automatically explored using a sequential randomized forest, an online template-based appearance model, and local features. Distracters are regions which have similar appearance as the target and consistently co-occur with high confidence score. The tracker must keep tracking these distracters to avoid drifting. Supporters, on the other hand, are local key-points around the target with consistent co-occurrence and motion correlation in a short time span. They play an important role in verifying the genuine target. Extensive experiments on challenging real-world video sequences show the tracking improvement when using this context information. Comparisons with several state-of-the-art approaches are also provided.


title: Circular and Elliptical CPW-Fed Slot and Microstrip-Fed Antennas for Ultrawideband Applications

abstract: This letter presents novel circular and elliptical coplanar waveguide (CPW)-fed slot and mictrostip-fed antenna designs targeting the 3.1?10.6 GHz band. The antennas are comprised of elliptical or circular stubs that excite similar-shaped slot apertures. Four prototypes have been examined, fabricated and experimentally tested, the three being fed by a CPW and the fourth by a microstrip line, exhibiting a very satisfactory behavior throughout the 7.5 GHz of the allocated bandwidth in terms of impedance matching $(hbox VSWR?2)$, radiation efficiency and radiation pattern characteristics. Measured impedance bandwidths of beyond 175% will be presented.


title: Metaphor Identification in Large Texts Corpora

abstract: Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms’ performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus.


title: Learning Camera Viewpoint Using CNN to Improve 3D Body Pose Estimation

abstract: The objective of this work is to estimate 3D human pose from a single RGB image. Extracting image representations which incorporate both spatial relation of body parts and their relative depth plays an essential role in accurate3D pose reconstruction. In this paper, for the first time, we show that camera viewpoint in combination to 2D joint lo-cations significantly improves 3D pose accuracy without the explicit use of perspective geometry mathematical models.To this end, we train a deep Convolutional Neural Net-work (CNN) to learn categorical camera viewpoint. To make the network robust against clothing and body shape of the subject in the image, we utilized 3D computer rendering to synthesize additional training images. We test our framework on the largest 3D pose estimation bench-mark, Human3.6m, and achieve up to 20% error reduction compared to the state-of-the-art approaches that do not use body part segmentation.


title: Acute Lymphoblastic Leukemia

abstract: Acute lymphoblastic leukemia and lymphoblastic lymphoma constitute a family of genetically heterogeneous lymphoid neoplasms derived from B- and T-lymphoid progenitors. Diagnosis is based on morphologic, immunophenotypic, and genetic features that allow differentiation from normal progenitors and other hematopoietic and nonhematopoietic neoplasms. Current intensive chemotherapy regimens have accomplished overall cure rates of 85% to 90% in children and 40% to 50% in adults, with outcomes depending on the genetic subtype of disease and clinical features at presentation. Therapy is optimized using minimal residual disease studies that employ flow cytometric and molecular methodologies, and are important determinants of prognosis. Genetic analyses currently underway are likely to provide insight into biology, mechanisms of relapse, pharmacogenetics, and new potential therapeutic targets, which should aid in further improvement of outcome in this disease.


title: Computational challenges for sentiment analysis in life sciences

abstract: Data and connectivity between users form the core of social networks. Every status, post, friendship, tweet, re-tweet, tag or image generates a massive amount of structured and unstructured data. Deriving meaning from this data and, in particular, extracting behavior and emotions of individual users, as well as of user communities, is the goal of sentiment analysis and affective computing and represents a significant challenge. Social networks also represent a potentially infinite source of applications for both research and commercial purposes and are adaptable to many different areas, including life science. Nevertheless, collecting, sharing, storing and analyzing social networks data pose several challenges to computer scientists, such as the management of highly unstructured data, big data, and the need for real-time computation. In this paper we give a brief overview of some concrete examples of applying sentiment analysis to social networks for healthcare purposes, we present the current type of tools existing for sentiment analysis, and summarize the challenges involved in this process focusing on the role of high performance computing.


title: A Modelling Approach Based on Fuzzy Agents

abstract: Modelling of complex systems is mainly based on the decomposition of these systems in autonomous elements, and the identification and definitio9n of possible interactions between these elements. For this, the agent-based approach is a modelling solution often proposed. Complexity can also be due to external events or internal to systems, whose main characteristics are uncertainty, imprecision, or whose perception is subjective (i.e. interpreted). Insofar as fuzzy logic provides a solution for modelling uncertainty, the concept of fuzzy agent can model both the complexity and uncertainty. This paper focuses on introducing the concept of fuzzy agent: a classical architecture of agent is redefined according to a fuzzy perspective. A pedagogical illustration of fuzzy agentification of a smart watering system is then proposed.


title: A Secure, Intelligent, and Smart-Sensing Approach for Industrial System Automation and Transmission over Unsecured Wireless Networks

abstract: In Industrial systems, Supervisory control and data acquisition (SCADA) system, the pseudo-transport layer of the distributed network protocol (DNP3) performs the functions of the transport layer and network layer of the open systems interconnection (OSI) model. This study used a simulation design of water pumping system, in-which the network nodes are directly and wirelessly connected with sensors, and are monitored by the main controller, as part of the wireless SCADA system. This study also intends to focus on the security issues inherent in the pseudo-transport layer of the DNP3 protocol. During disassembly and reassembling processes, the pseudo-transport layer keeps track of the bytes sequence. However, no mechanism is available that can verify the message or maintain the integrity of the bytes in the bytes received/transmitted from/to the data link layer or in the send/respond from the main controller/sensors. To properly and sequentially keep track of the bytes, a mechanism is required that can perform verification while bytes are received/transmitted from/to the lower layer of the DNP3 protocol or the send/respond to/from field sensors. For security and byte verification purposes, a mechanism needs to be proposed for the pseudo-transport layer, by employing cryptography algorithm. A dynamic choice security buffer (SB) is designed and employed during the security development. To achieve the desired goals of the proposed study, a pseudo-transport layer stack model is designed using the DNP3 protocol open library and the security is deployed and tested, without changing the original design.


title: Stochastic Geometry for Wireless Networks

abstract: Covering point process theory, random geometric graphs and coverage processes, this rigorous introduction to stochastic geometry will enable you to obtain powerful, general estimates and bounds of wireless network performance and make good design choices for future wireless architectures and protocols that efficiently manage interference effects. Practical engineering applications are integrated with mathematical theory, with an understanding of probability the only prerequisite. At the same time, stochastic geometry is connected to percolation theory and the theory of random geometric graphs and accompanied by a brief introduction to the R statistical computing language. Combining theory and hands-on analytical techniques with practical examples and exercises, this is a comprehensive guide to the spatial stochastic models essential for modelling and analysis of wireless network performance.


title: Image to Image Translation for Domain Adaptation

abstract: We propose a general framework for unsupervised domain adaptation, which allows deep neural networks trained on a source domain to be tested on a different target domain without requiring any training annotations in the target domain. This is achieved by adding extra networks and losses that help regularize the features extracted by the backbone encoder network. To this end we propose the novel use of the recently proposed unpaired image-toimage translation framework to constrain the features extracted by the encoder network. Specifically, we require that the features extracted are able to reconstruct the images in both domains. In addition we require that the distribution of features extracted from images in the two domains are indistinguishable. Many recent works can be seen as specific cases of our general framework. We apply our method for domain adaptation between MNIST, USPS, and SVHN datasets, and Amazon, Webcam and DSLR Office datasets in classification tasks, and also between GTA5 and Cityscapes datasets for a segmentation task. We demonstrate state of the art performance on each of these datasets.


title: Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations

abstract: Deep convolutional neural network (CNN) inference requires significant amount of memory and computation, which limits its deployment on embedded devices. To alleviate these problems to some extent, prior research utilize low precision fixed-point numbers to represent the CNN weights and activations. However, the minimum required data precision of fixed-point weights varies across different networks and also across different layers of the same network. In this work, we propose using floating-point numbers for representing the weights and fixed-point numbers for representing the activations. We show that using floating-point representation for weights is more efficient than fixed-point representation for the same bit-width and demonstrate it on popular large-scale CNNs such as AlexNet, SqueezeNet, GoogLeNet and VGG-16. We also show that such a representation scheme enables compact hardware multiply-and-accumulate (MAC) unit design. Experimental results show that the proposed scheme reduces the weight storage by up to 36% and power consumption of the hardware multiplier by up to 50%.


title: Learning at a distance I. Statistical learning of non-adjacent dependencies

abstract: Abstract In earlier work we have shown that adults, young children, and infants are capable of computing transitional probabilities among adjacent syllables in rapidly presented streams of speech, and of using these statistics to group adjacent syllables into word-like units. In the present experiments we ask whether adult learners are also capable of such computations when the only available patterns occur in non-adjacent elements. In the first experiment, we present streams of speech in which precisely the same kinds of syllable regularities occur as in our previous studies, except that the patterned relations among syllables occur between non-adjacent syllables (with an intervening syllable that is unrelated). Under these circumstances we do not obtain our previous results: learners are quite poor at acquiring regular relations among non-adjacent syllables, even when the patterns are objectively quite simple. In subsequent experiments we show that learners are, in contrast, quite capable of acquiring patterned relations among non-adjacent segments—both non-adjacent consonants (with an intervening vocalic segment that is unrelated) and non-adjacent vowels (with an intervening consonantal segment that is unrelated). Finally, we discuss why human learners display these strong differences in learning differing types of non-adjacent regularities, and we conclude by suggesting that these contrasts in learnability may account for why human languages display non-adjacent regularities of one type much more widely than non-adjacent regularities of the other type.


title: A compact size and high efficiency CMOS-IPD rectenna using 2.5D wafer-level packing for a wireless power harvesting system

abstract: This work achieves a two-and-a-half-dimensional (2.5D) wafer-level radio frequency (RF) energy harvesting rectenna module with a compact size and high power conversion efficiency (PCE) that integrates a 2.45 GHz antenna in an integrated passive device (IPD) and a rectifier in a tsmcTM 0.18 μm CMOS process. The proposed rectifier provides a master-slave voltage doubling full-wave topology which can reach relatively high PCE by means of a relatively simple circuitry. The IPD antenna was stacked on top of the CMOS rectifier. The rectenna (including an antenna and rectifier) achieves an output voltage of 1.2 V and PCE of 47 % when the operation frequency is 2.45 GHz, with −12 dBm input power. The peak efficiency of the circuit is 83 % with −4 dBm input power. The die size of the RF harvesting module is less than 1 cm2. The performance of this module makes it possible to energy mobile device and it is also very suitable for wearable and implantable wireless sensor networks (WSN).


title: g-Miner: Interactive Visual Group Mining on Multivariate Graphs

abstract: With the rapid growth of rich network data available through various sources such as social media and digital archives,there is a growing interest in more powerful network visual analysis tools and methods. The rich information about the network nodes and links can be represented as multivariate graphs, in which the nodes are accompanied with attributes to represent the properties of individual nodes. An important task often encountered in multivariate network analysis is to uncover link structure with groups, e.g., to understand why a person fits a specific job or certain role in a social group well.The task usually involves complex considerations including specific requirement of node attributes and link structure, and hence a fully automatic solution is typically not satisfactory.In this work, we identify the design challenges for min-ing groups with complex criteria and present an interactive system, "g-Miner," that enables visual mining of groups on multivariate graph data. We demonstrate the effectiveness of our system through case study and in-depth expert inter-views. This work contributes to understanding the design of systems for leveraging users' knowledge progressively with algorithmic capacity for tackling massive heterogeneous information.


title: SURROGATE PREPOSTERIOR ANALYSES FOR PREDICTING AND ENHANCING IDENTIFIABILITY IN MODEL CALIBRATION

abstract: In physics-based engineering modeling and uncertainty quantification, distinguishing the effects of two main sources of uncertainty — calibration parameter uncertainty and model discrepancy — is challenging. Previous research has shown that identifiability, which is quantified by the posterior covariance of the calibration parameters, can sometimes be improved by experimentally measuring multiple responses of the system that share a mutual dependence on a common set of calibration parameters. In this paper, we address the issue of how to select the most appropriate subset of responses to measure experimentally, to best enhance identifiability. We use a preposterior analysis approach that, prior to conducting physical experiments but after conducting computer simulations, can predict the degree of identifiability that will result using different subsets of responses to measure experimentally. It predicts identifiability via the preposterior covariance from a modular Bayesian Monte Carlo analysis of a multi-response spatial random process (SRP) model. Furthermore, to handle the computational challenge in preposterior analysis, we propose a surrogate preposterior analysis based on Fisher information of the calibration parameters. The proposed methods are applied to a simply supported beam example to select two out of six responses to best improve identifiability. The estimated preposterior covariance is compared to the actual posterior covariance to demonstrate the effectiveness of the methods.


title: Zero Privacy: Schools Are Violating Students' Fourteenth Amendment Right of Privacy under the Guise of Enforcing Zero Tolerance Policies

abstract: Abstract: The Fourteenth Amendment to the United States Constitution provides a right of privacy that protects against unwarranted governmental interference with an individual's contraceptive choices. This privacy right protects minors as well as adults. School officials serve as government actors for the purpose of Fourteenth Amendment analysis. Zero tolerance drug policies are school disciplinary policies that mandate predetermined and frequently severe consequences for specific offenses, often including the possession of legally prescribed or legally obtained over-the-counter medication. Zero tolerance drug policies have resulted in the often very public discipline of students for possessing a wide array of otherwise legal medication, including birth control pills, without parental permission. This Comment argues that schools may not enforce their discipline policies in ways that violate a minor's right of privacy with regard to contraceptive choices. Zero tolerance policies as applied to minors in possession of legally obtained contraceptives must not force students to notify their parents of their procreative choices in order to comply with the policy. At a minimum, such policies must include a bypass option that enables students to avoid acquiring parental consent in order for those students to possess contraceptives at school. In addition, zero tolerance policies may not violate a minor's constitutional right to be free from state dissemination of their private affairs-a natural consequence of disciplining students in possession of contraceptives in violation of the zero tolerance policy. Erin is a typical high school junior.1 She has never been in trouble at school. One afternoon, a teacher sees Erin put a package of pills in her pocket. The teacher reports this to the school administration and Erin is called into the principal's office. Erin readily acknowledges that she is in possession of birth control pills, which she obtained legally at a local health clinic. The principal informs Erin that possession of medication without parental permission violates the school's "zero tolerance" policy,2 and that the school is required to suspend her under that policy. Erin has not told her parents that she is taking birth control pills, a fact that her parents necessarily become aware of when informed of her suspension by the school. Erin's school community and the local media also become aware of the fact that the school suspended her for possessing birth control pills.3 Furthermore, the district tells Erin that her permanent high school record will reflect that she was disciplined for illegal possession of a drug.4 Schools first implemented zero tolerance drug policies in the 1980s.5 These policies often ban the possession of prescription and over-the-counter medication,6 thereby including medical contraceptives such as birth control pills, hormonal patches, and the "morning after" pill.7 Many of these policies allow students to bring legally prescribed or over-the-counter medication to school only if the student's parent or guardian first approves the student's possession of the medication.8 A student who violates such a zero tolerance policy is subject to mandatory, predetermined, and often severe consequences.9 A minor's right of privacy regarding contraceptive choices is a fundamental right protected by the Fourteenth Amendment to the U.S. Constitution.10 State interference with this right is constitutional only where it advances a significant state interest in regulating the behavior of minors that is not present in the case of adults.11 In addition, a state regulation requiring parental consent or notification of a minor's procreative choices is unconstitutional unless it grants the minor access to an alternative procedure whereby she may avoid parental involvement.12 Moreover, where a minor's private choices involve the decision "whether to bear or beget a child,"13 certain safeguards must be in place to protect the confidentiality of the minor's decision-making. …


title: IoT based rail track health monitoring and information system

abstract: This paper describes an Internet of things (IoT) based track monitoring system for increasing the comfort of rail track transportation. Usually, rail tracks are inspected only at midnight and also the inspection is carried out only once in a month or lesser. Unfortunately, any emergency issues not detected within the stipulated time leads to train derailment and other safety concerns. Hence in-service vehicle has been introduced to increase the safety of train transportation. In this paper, track irregularities are detected using bogie and car body acceleration measurements and these measurements is further optimized using particle swarm optimization algorithm (PSO). The proposed system is compared with the conventional track geometry measurement system. The monitoring and control information can be sent through IoT. Simulation results are performed for two different speed scenarios and it shows that the proposed system produces better results than the traditional track geometry measurement system and the effectiveness of the proposed method.


title: Narcissism and coach interpersonal style: A self‐determination theory perspective

abstract: Athletes' sport experiences are often influenced by the interpersonal styles of communication used by their coaches. Research on personality antecedents of such styles is scarce. We examined the link between a well-researched personality trait, namely narcissism, and two types of coaching interpersonal style, namely autonomy-supportive and controlling styles. We also tested the mediating roles of dominance and empathic concern in explaining the relations between narcissism and the two coaching interpersonal styles. United Kingdom-based coaches (N = 211) from various sports completed a multi-section questionnaire assessing the study variables. Regression analyses revealed a positive direct relation between narcissism and controlling coach behaviors. Furthermore, empathy (but not dominance) mediated the positive and negative indirect effects of narcissism on controlling and autonomy-supported interpersonal styles, respectively. We discuss these findings in terms of their implications for coaching and the quality of athletes' sport experiences. Language: en


title: Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest

abstract: Objective: To summarize recent research and present a selection of the best papers published in 2015 in the field of clinical Natural Language Processing (NLP). Method: A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Section editors first selected a shortlist of candidate best papers that were then peer-reviewed by independent external reviewers. Results: The clinical NLP best paper selection shows that clinical NLP is making use of a variety of texts of clinical interest to contribute to the analysis of clinical information and the building of a body of clinical knowledge. The full review process highlighted five papers analyzing patient-authored texts or seeking to connect and aggregate multiple sources of information. They provide a contribution to the development of methods, resources, applications, and sometimes a combination of these aspects. Conclusions: The field of clinical NLP continues to thrive through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques to impact clinical practice. Foundational progress in the field makes it possible to leverage a larger variety of texts of clinical interest for healthcare purposes.


title: Análise da relação entre dor lombar e desequilíbrio de força muscular em bailarinas

abstract: OBJECTIVE: To investigate the relationship between low back pain and muscle strength imbalance in the lumbopelvic region of classical ballet dancers. METHODS: This cross-sectional exploratory study was developed in the ballet academies of Divinopolis and Oliveira, Minas Gerais State. Forty-two ballet dancers were selected and then divided into two groups (Pain and Painless Groups), according to the presence or absence of pain complaints in the low back. Firstly, the ballet dancers were submitted, by one examiner, to four tests of muscle strength imbalance in the lumbopelvic region: gluteus maximus × hamstrings muscles and gluteus maximus × paraspinal muscles in the hip extension movement; gluteus maximus and medius × tensor fascia latae muscles in the hip abduction movement; abdominal × hip flexors in the hip flexion movement. After that, a second examiner applied the muscular strength tests for the abdominal and paraspinal muscles. Chi-square tests were used to verify the association between low back pain and muscle imbalances. The level of significance was set at α = 0.05. RESULTS: Most of the ballet dancers, independently of the group, showed muscle strength imbalances in the lumbopelvic region. The abdominal × hip flexors test demonstrated a significant association with low back pain (p = 0.043). CONCLUSION: Muscle imbalances in the lumbopelvic region are common in classical ballet dancers. However, it is not possible to assure that these muscle imbalances are specific adaptations to the classical ballet or dysfunctions associated to algic symptoms resulted from training faults.


title: Cognitive, Metacognitive, and Motivational Aspects of Problem Solving

abstract: This article examines the role of cognitive, metacognitive, and motivational skills in problem solving. Cognitive skills include instructional objectives, components in a learning hierarchy, and components in information processing. Metacognitive skills include strategies for reading comprehension, writing, and mathematics. Motivational skills include motivation based on interest, self-efficacy, and attributions. All three kinds of skills are required for successful problem solving in academic settings.


title: How does leadership affect student achievement? Results from a national US survey

abstract: Using survey responses from a national sample of US teachers, this paper provides insight into 2 questions: (1) Do 3 specific attributes of leadership behavior – the sharing of leadership with teachers, the development of trust relationships among professionals, and the provision of support for instructional improvement – affect teachers' work with each other and their classroom practices? and (2) Do the behaviors of school leaders contribute to student achievement? We tie this investigation of school leader behaviors to 2 additional factors that have also received increasing attention in research because they have been shown to be related to student achievement: professional community and the quality of classroom instruction. Our analysis provides an empirical test of the notion that leadership variables are positively related to student learning. It also suggests that both shared and instructionally focused leadership are complementary approaches for improving schools.


title: CNN-Based Projected Gradient Descent for Consistent Image Reconstruction

abstract: We present a new image reconstruction method that replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained as image-to-image regressors have been successfully used to solve inverse problems in imaging. However, unlike existing iterative image reconstruction algorithms, these CNN-based approaches usually lack a feedback mechanism to enforce that the reconstructed image is consistent with the measurements. We propose a relaxed version of PGD wherein gradient descent enforces measurement consistency, while a CNN recursively projects the solution closer to the space of desired reconstruction images. We show that this algorithm is guaranteed to converge and, under certain conditions, converges to a local minimum of a non-convex inverse problem. Finally, we propose a simple scheme to train the CNN to act like a projector. Our experiments on sparse-view computed-tomography reconstruction show an improvement over total variation-based regularization, dictionary learning, and a state-of-the-art deep learning-based direct reconstruction technique.


title: Dynamic furniture modeling through assembly instructions

abstract: We present a technique for parsing widely used furniture assembly instructions, and reconstructing the 3D models of furniture components and their dynamic assembly process. Our technique takes as input a multi-step assembly instruction in a vector graphic format and starts to group the vector graphic primitives into semantic elements representing individual furniture parts, mechanical connectors (e.g., screws, bolts and hinges), arrows, visual highlights, and numbers. To reconstruct the dynamic assembly process depicted over multiple steps, our system identifies previously built 3D furniture components when parsing a new step, and uses them to address the challenge of occlusions while generating new 3D components incrementally. With a wide range of examples covering a variety of furniture types, we demonstrate the use of our system to animate the 3D furniture assembly process and, beyond that, the semantic-aware furniture editing as well as the fabrication of personalized furnitures.


title: Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

abstract: Intelligent personal assistant systems with either text-based or voice-based conversational interfaces are becoming increasingly popular around the world. Retrieval-based conversation models have the advantages of returning fluent and informative responses. Most existing studies in this area are on open domain"chit-chat"conversations or task / transaction oriented conversations. More research is needed for information-seeking conversations. There is also a lack of modeling external knowledge beyond the dialog utterances among current conversational models. In this paper, we propose a learning framework on the top of deep neural matching networks that leverages external knowledge for response ranking in information-seeking conversation systems. We incorporate external knowledge into deep neural models with pseudo-relevance feedback and QA correspondence knowledge distillation. Extensive experiments with three information-seeking conversation data sets including both open benchmarks and commercial data show that, our methods outperform various baseline methods including several deep text matching models and the state-of-the-art method on response selection in multi-turn conversations. We also perform analysis over different response types, model variations and ranking examples. Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.


IRS : An Issue Resolution System for Cyber Attack Classification and Management


title: Occlusion-aware Hand Pose Estimation Using Hierarchical Mixture Density Network

abstract: Hand pose estimation is to predict the pose parameters representing a 3D hand model, such as locations of hand joints. This problem is very challenging due to large changes in viewpoints and articulations, and intense self-occlusions, etc. Many researchers have investigated the problem from both aspects of input feature learning and output prediction modelling. Though effective, most of the existing discriminative methods only give a deterministic estimation of target poses. Also, due to their single-value mapping intrinsic, they fail to adequately handle self-occlusion problems, where occluded joints present multiple modes. In this paper, we tackle the self-occlusion issue and provide a complete description of observed poses given an input depth image through a hierarchical mixture density network (HMDN) framework. In particular, HMDN leverages the state-of-the-art CNN module to facilitate feature learning, while proposes a density in a two-level hierarchy to reconcile single-valued and multi-valued mapping in the output. The whole framework is naturally end-to-end trainable with a mixture of two differentiable density functions. HMDN produces interpretable and diverse candidate samples, and significantly outperforms the state-of-the-art algorithms on benchmarks that exhibit occlusions.


title: Facial Attractiveness: Beauty and the Machine

abstract: This work presents a novel study of the notion of facial attractiveness in a machine learning context. To this end, we collected human beauty ratings for data sets of facial images and used various techniques for learning the attractiveness of a face. The trained predictor achieves a significant correlation of 0.65 with the average human ratings. The results clearly show that facial beauty is a universal concept that a machine can learn. Analysis of the accuracy of the beauty prediction machine as a function of the size of the training data indicates that a machine producing human-like attractiveness rating could be obtained given a moderately larger data set.


title: fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural Networks on Embedded FPGAs

abstract: In recent years, Convolutional Neural Networks (ConvNets) have become an enabling technology for a wide range of novel embedded Artificial Intelligence systems. Across the range of applications, the performance needs vary significantly, from high-throughput video surveillance to the very low-latency requirements of autonomous cars. In this context, FPGAs can provide a potential platform that can be optimally configured based on the different performance needs. However, the complexity of ConvNet models keeps increasing making their mapping to an FPGA device a challenging task. This work presents fpgaConvNet, an end-to-end framework for mapping ConvNets on FPGAs. The proposed framework employs an automated design methodology based on the Synchronous Dataflow (SDF) paradigm and defines a set of SDF transformations in order to efficiently explore the architectural design space. By selectively optimising for throughput, latency or multiobjective criteria, the presented tool is able to efficiently explore the design space and generate hardware designs from high-level ConvNet specifications, explicitly optimised for the performance metric of interest. Overall, our framework yields designs that improve the performance by up to 6.65x over highly optimised embedded GPU designs for the same power constraints in embedded environments.


title: Intermittent computation without hardware support or programmer intervention

abstract: As computation scales downward in area, the limitations imposed by the batteries required to power that computation become more pronounced. Thus, many future devices will forgo batteries and harvest energy from their environment. Harvested energy, with its frequent power cycles, is at odds with current models of long-running computation. ::: ::: To enable the correct execution of long-running applications on harvested energy--without requiring special-purpose hardware or programmer intervention--we propose Ratchet. Ratchet is a compiler that adds lightweight checkpoints to unmodified programs that allow existing programs to execute across power cycles correctly. Ratchet leverages the idea of idempotency, decomposing programs into a continuous stream of re-executable sections connected by lightweight checkpoints, stored in non-volatile memory. We implement Ratchet on top of LLVM, targeted at embedded systems with high-performance non-volatile main memory. Using eight embedded systems benchmarks, we show that Ratchet correctly stretches program execution across frequent, random power cycles. Experimental results show that Ratchet enables a range of existing programs to run on intermittent power, with total run-time overhead averaging below 60%--comparable to approaches that require hardware support or programmer intervention.


title: Factored 3-way restricted boltzmann machines for modeling natural images

abstract: Deep belief nets have been successful in modeling handwritten characters, but it has proved more difficult to apply them to real images. The problem lies in the restricted Boltzmann machine (RBM) which is used as a module for learning deep belief nets one layer at a time. The Gaussian-Binary RBMs that have been used to model real-valued data are not a good way to model the covariance structure of natural images. We propose a factored 3-way RBM that uses the states of its hidden units to represent abnormalities in the local covariance structure of an image. This provides a probabilistic framework for the widely used simple/complex cell architecture. Our model learns binary features that work very well for object recognition on the “tiny images” data set. Even better features are obtained by then using standard binary RBM’s to learn a deeper model.


title: A Dirty Word or a Dirty World? Attribute Framing, Political Affiliation, and Query Theory

abstract: We explored the effect of attribute framing on choice, labeling charges for environmental costs as either an earmarked tax or an offset. Eight hundred ninety-eight Americans chose between otherwise identical products or services, where one option included a surcharge for emitted carbon dioxide. The cost framing changed preferences for self-identified Republicans and Independents, but did not affect Democrats' preferences. We explain this interaction by means of query theory and show that attribute framing can change the order in which internal queries supporting one or another option are posed. The effect of attribute labeling on query order is shown to depend on the representations of either taxes or offsets held by people with different political affiliations.


title: Modeling the cultural evolution of language

abstract: The paper surveys recent research on language evolution, focusing in particular on models of cultural evolution and how they are being developed and tested using agent-based computational simulations and robotic experiments. The key challenges for evolutionary theories of language are outlined and some example results are discussed, highlighting models explaining how linguistic conventions get shared, how conceptual frameworks get coordinated through language, and how hierarchical structure could emerge. The main conclusion of the paper is that cultural evolution is a much more powerful process that usually assumed, implying that less innate structures or biases are required and consequently that human language evolution has to rely less on genetic evolution.


title: Interoperability Scenarios to Measure Informal Learning Carried Out in PLEs

abstract: Achieving success in the application of Information and Communications Technology to teaching and learning processes can be challenging. One possible reason for this is the failure to take into account the student as end-user when implementing technologies. In particular, student requirements to access different sources of information and collaborate with other students during their learning can be constrained by the nature of institutional technology which presents barriers to integrating formal and informal learning: students do not only learn in the institutional context but from their experiences throughout the course of daily life. Institutional Learning Management Systems are not enough to meet these needs for increased integration of informal learning, technological practice and formal learning. The efforts to develop effective Personal Learning Environments attempt to solve this, providing a space where students can use the tools they want. But this presents a problem with regard to the management and measurement of learning success. This paper poses some interoperability scenarios to allow the assessment of the personalized informal activity, and in this way, obtain measurable information about the advantages of personalization in Learning.


title: Particle-tracking velocimetry with new algorithms

abstract: New algorithms for particle-tracking velocimetry are proposed and tested with typical particle images showing two-dimensional fluid flows. There are new ideas not only in the algorithm of the particle tracking itself but also in that of the individual-particle detection. The performance of the particle tracking is much improved by the new relaxation method and that of the individual-particle detection by the use of the dynamic threshold-binarization method. The special concern of the authors about the new algorithms is the applicability of particle-tracking velocimetry to high-density particle images, contrary to what is usually believed regarding this type of particle-imaging velocimetry. These new algorithms are first tested in synthetic images with a variety of particle parameters and then in other types of experimental visualizations showing jet and wake flows.


Categorical Data Analysis


title: The Decision-making Model for the Stock Market Under Uncertainty

abstract: The main purpose of this research is developing methods and models of decision-making to assess the stock market state, and predict the possible changes in the RTS index value. This article shows that the analytical models for assessing the stock market state do not give reliable results. The absence of the reliable estimates associated with the high degree of uncertainty, random, nonlinear and non-stationary process with a significant degree of aftereffect. In this paper, to formalize the securities market parameters it’s proposed the fuzzy sets method. To assess the stock market current state and make decisions the fuzzy situational analysis model (situational model) is applied. The analytical prediction results of the stock market and graph of the RTS index expected return changes in 2014-2016 are showed. The model of calculating the fuzzy inference rules truth degree to predict the RTS index is developed. The market parameters linguistic definition is given and the expert’s rules construction to predict the RTS index growth is shown. The program in Matlab environment is designed to perform research. The study result showed that the model allows for the RTS index prediction in the condition of incomplete initial data with a confidence level about 90%.


title: Analyzing Right-wing YouTube Channels: Hate, Violence and Discrimination

abstract: As of 2018, YouTube, the major online video sharing website, hosts multiple channels promoting right-wing content. In this paper, we observe issues related to hate, violence and discriminatory bias in a dataset containing more than 7,000 videos and 17 million comments. We investigate similarities and differences between users' comments and video content in a selection of right-wing channels and compare it to a baseline set using a three-layered approach, in which we analyze (a) lexicon, (b) topics and (c) implicit biases present in the texts. Among other results, our analyses show that right-wing channels tend to (a) contain a higher degree of words from"negative"semantic fields, (b) raise more topics related to war and terrorism, and (c) demonstrate more discriminatory bias against Muslims (in videos) and towards LGBT people (in comments). Our findings shed light not only into the collective conduct of the YouTube community promoting and consuming right-wing content, but also into the general behavior of YouTube users.


title: Cone-beam computed tomography in the assessment of periapical lesions in endodontically treated teeth

abstract: Objective ::: This study aimed to evaluate the relationship between the quality of the coronal restoration and the root canal filling on the periapical status of endodontically treated teeth using CBCT. ::: ::: ::: Materials and Methods ::: CBCT data were obtained from the records of patients who deny any dental treatment in the 2 years prior to the CBCT examination. CBCT images (90 kVp and 7 mA, exposure time of 23 s, and a voxel size of 0.2 mm, with a field of view of 13 cm × 13 cm) of 1011 endodontically treated teeth were observed. A score was given to the quality of the root filling and the quality of the coronal restoration. ::: ::: ::: Statistical Analysis Used ::: Data were statistically analyzed to correlate the periapical status with gender, dental group. and quality of endodontic treatment and restoration (Chi-square test with a significance level of P < 0.001). ::: ::: ::: Results ::: Absence of periapical periodontitis was found in 54.9% of the cases. The periapical outcome was not related to gender or dental group (P > 0.05). A statistically significant factor (Chi-square test, P < 0.0001) resulted when different qualities of sealing were compared. ::: ::: ::: Conclusions ::: CBCT showed that high-quality root canal treatments followed by an adequate coronal sealing restoration avoid the presence of periapical periodontitis in time.


title: Work engagement : an emerging concept in occupational health psychology

abstract: This position paper introduces the emerging concept of work engagement: a positive, fulfilling, affective-motivational state of work-related well-being that is characterized by vigour, dedication, and absorption. Although there are different views of work engagement, most scholars agree that engaged employees have high levels of energy and identify strongly with their work. The most often used instrument to measure engagement is the Utrecht Work Engagement Scale, a self-report instrument that has been validated in many countries across the world. Research on engagement has investigated how engagement differs from related concepts (e.g., workaholism, organizational commitment), and has focused on the most important predictors of work engagement. These studies have revealed that engagement is a unique concept that is best predicted by job resources (e.g., autonomy, supervisory coaching, performance feedback) and personal resources (e.g., optimism, self-efficacy, self-esteem). Moreover, the first studies have shown that work engagement is predictive of job performance and client satisfaction. The paper closes with an account of what we do not know about work engagement, and offers a brief research agenda for future work.


title: VFD machinery vibration fatigue life and multi-level inverter effect

abstract: The paper documents fatigue related mechanical failures in variable frequency drive (VFD) motor machinery due to mechanical vibrations excited by drive torque harmonics which are created by PWM switching. Present effort models the coupled system with full electrical system including rectifier, DC bus, inverter, motor, and an industrial mechanical system including flexible couplings, gearboxes and multiple inertias. The models are formed by a novel combining of a commercial motor code with a general, self-written mechanical code. The approach extends failure prediction beyond simple occurrence of resonance, to fatigue life evaluation based on Rain-flow algorithm, which is suitable for both steady state and transient startup mechanical response. The second contribution is a demonstration that the common use of multilevel inverter to reduce voltage/current harmonics may actually exacerbate resonance and fatigue failure. This is shown to be caused by a resulting amplitude increase of torque components in proximity to potential resonance frequencies.


title: Effects of different Gabor filters parameters on image retrieval by texture

abstract: Gabor filter is widely used to extract texture features from images for image retrieval. A number of parameters (number of scales and orientations and filter mask size) are used in the Gabor filter. In the reported work so far, these parameters seem to be chosen without proper explanations. In this paper, we investigate the effects of different Gabor filter parameters on texture retrieval.


title: Improving the Resolution of CNN Feature Maps Efficiently with Multisampling

abstract: We describe a new class of subsampling techniques for CNNs, termed multisampling, that significantly increases the amount of information kept by feature maps through subsampling layers. One version of our method, which we call checkered subsampling, significantly improves the accuracy of state-of-the-art architectures such as DenseNet and ResNet without any additional parameters and, remarkably, improves the accuracy of certain pretrained ImageNet models without any training or fine-tuning. We glean new insight into the nature of data augmentations and demonstrate, for the first time, that coarse feature maps are significantly bottlenecking the performance of neural networks in image classification.


title: struc2gauss: Structure Preserving Network Embedding via Gaussian Embedding

abstract: Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitations exist in state-of-the-art NE methods: structure preservation and uncertainty modeling. Almost all previous methods represent a node into a point in space and focus on the local structural information, i.e., neighborhood information. However, neighborhood information does not capture the global structural information and point vector representation fails in modeling the uncertainty of node representations. In this paper, we propose a new NE framework, struc2gauss, which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information. struc2gauss first employs a given node similarity metric to measure the global structural information, then generates structural context for nodes and finally learns node representations via Gaussian embedding. Different structural similarity measures of networks and energy functions of Gaussian embedding are investigated. Experiments conducted on both synthetic and real-world data sets demonstrate that struc2gauss effectively captures the global structural information while state-of-the-art network embedding methods fails to, outperforms other methods on the structure-based clustering task and provides more information on uncertainties of node representations.


title: A Wearable Two-Antenna System on a Life Jacket for Cospas-Sarsat Personal Locator Beacons

abstract: A wearable two-antenna system to be integrated on a life jacket and connected to Personal Locator Beacons (PLBs) of the Cospas-Sarsat system is presented. Each radiating element is a folded meandered dipole resonating at 406 MHz and includes a planar reflector realized by a metallic foil. The folded dipole and the metallic foil are attached on the opposite sides of the floating elements of the life jacket itself, so resulting in a mechanically stable antenna. The metallic foil improves antenna radiation properties even when the latter is close to the sea surface, shields the human body from EM radiation and makes the radiating system less sensitive to the human body movements. Prototypes have been realized and a measurement campaign has been carried out. The antennas show satisfactory performance also when the life jacket is worn by a user. The proposed radiating elements are intended for the use in a two-antenna scheme in which the transmitter can switch between them in order to meet Cospas-Sarsat system specifications. Indeed, the two antennas provide complementary radiation patterns so that Cospas-Sarsat requirements (satellite constellation coverage and EIRP profile) are fully satisfied.


title: Automatic accident detector and reporting system (Hardware and software) ECBA medical system

abstract: Nowadays, the increase of using vehicles on the roads may impose several issues in delivering emergency help during an accident of which may threaten human lives involved greatly. During the accident, many people may lose their lives because of the delayed medical services, or the ambulance is stuck in traffic jam, and accident victims might die while waiting for the first aid kits and medical assistance. In this work, we propose a solution in providing the medical assistance and first aid kits before the arrival of the ambulance, coined as ECBA (Everyone Can be Ambulance). ECBA system is a software and a hardware system that works together for accident detection, report and medical assistance system. It is designed to provide automatic accident reporting system and to save people's lives.


title: Semi-supervised Question Retrieval with Gated Convolutions

abstract: Question answering forums are rapidly growing in size with no effective automated ability to refer to and reuse answers already available for previous posted questions. In this paper, we develop a methodology for finding semantically related questions. The task is difficult since 1) key pieces of information are often buried in extraneous details in the question body and 2) available annotations on similar questions are scarce and fragmented. We design a recurrent and convolutional model (gated convolution) to effectively map questions to their semantic representations. The models are pre-trained within an encoder-decoder framework (from body to title) on the basis of the entire raw corpus, and fine-tuned discriminatively from limited annotations. Our evaluation demonstrates that our model yields substantial gains over a standard IR baseline and various neural network architectures (including CNNs, LSTMs and GRUs).


title: Homing socialbots: intrusion on a specific organization's employee using Socialbots

abstract: One dimension on the Internet, which has gained great popularity in recent years are the online social networks (OSNs). Users all over the globe write, share, and publish personal information about themselves, their friends, and their workplace. In this study we present a method for infiltrating specific users in targeted organizations by using organizational social networks topologies and Socialbots. The targeted organizations, which have been chosen by us, were technology-oriented organizations. Employees from this kind of organization should be more aware of the dangers of exposing private information. An infiltration is defined as accepting a Socialbot's friend request. Upon accepting a Socialbot's friend request, users unknowingly expose information about themselves and their workplace. To infiltrate this we had to use our Socialbots in a sophisticated manner. First, we had to gather information and recognize Facebook users who work in targeted organizations. Afterwards, we chose ten Facebook users from every targeted organization randomly. These ten users were chosen to be the specific users from targeted organizations of which we would like to infiltrate. The Socialbots sent friend requests to all specific users' mutual friends who worked or work in the same targeted organization. The rationale behind this idea was to gain as many mutual friends as possible and through this act increase the probability that our friend requests will be accepted by the targeted users. We tested the proposed method on targeted users from two different organizations. Our method was able to gain a successful percentage of 50% and 70% respectively. The results demonstrate how easily adversaries can infiltrate users they do not know and get full access to personal and valuable information. These results are more surprising when we emphasize the fact that we chose oriented users who should be more aware to the dangers of information leakage for this study on purpose. Moreover, the results indicate once again that users who are interested in protecting themselves should not disclose information in OSNs and should be cautious of accepting friendship requests from unknown persons.


title: A Zero-Crossing-Based 8-bit 200 MS/s Pipelined ADC

abstract: Zero-crossing-based circuits (ZCBC) are introduced as a generalization of comparator-based switched-capacitor circuits (CBSC). To demonstrate this concept, an 8-bit, 200 MS/s, pipelined ADC is implemented in a 0.18 CMOS technology. A dynamic zero-crossing detector and current source replace the functionality of an opamp to realize a precision charge transfer. Furthermore, current source splitting improves linearity at high speeds and bit decision flip-flops replace traditional bit decision comparators for increased speed. The complete ADC draws no static current and consumes 8.5 mW of power. The corresponding FOM is 0.38 pJ/step at 100 MS/s and 0.51 pJ/step at 200 MS/s.


title: Visual information retrieval from large distributed online repositories

abstract: Digital images and video are becoming an integral part of human communications. The ease of capturing and creating digital images has caused most on-line information sources look more “visual”. We use more and more visual content in expressing ideas, reporting, education, and entertainment. With the tremendous amount of visual information becoming on-line, how does one find visual information from distributed repositories efficiently, at least to the same extent as that of existing information retrieval systems. With the growing number of on-line users, how does one design a system with performance scalable to a large extent?


title: TopicSketch: Real-Time Bursty Topic Detection from Twitter

abstract: Twitter has become one of the largest microblogging platforms for users around the world to share anything happening around them with friends and beyond. A bursty topic in Twitter is one that triggers a surge of relevant tweets within a short period of time, which often reflects important events of mass interest. How to leverage Twitter for early detection of bursty topics has therefore become an important research problem with immense practical value. Despite the wealth of research work on topic modelling and analysis in Twitter, it remains a challenge to detect bursty topics in real-time. As existing methods can hardly scale to handle the task with the tweet stream in real-time, we propose in this paper $\sf {TopicSketch}$ , a sketch-based topic model together with a set of techniques to achieve real-time detection. We evaluate our solution on a tweet stream with over 30 million tweets. Our experiment results show both efficiency and effectiveness of our approach. Especially it is also demonstrated that $\sf {TopicSketch}$ on a single machine can potentially handle hundreds of millions tweets per day, which is on the same scale of the total number of daily tweets in Twitter, and present bursty events in finer-granularity.


title: A 2.45GHz CMOS power amplifier with high linearity

abstract: A 2.45GHz 0.18µm RF CMOS Class-AB power amplifier (PA) with high linearity and output power for WLAN is presented in this paper. The proposed power amplifier is implemented with a two-stage architecture which is followed by an off-chip output matching network. To improve the linearity, an integrated diode linearization circuit provides a compensation mechanism for the input capacitance variation of the active devices, improving the linearity from the gain compressing. Moreover, a simple LC second harmonic tank and optimum gate biasing point is applied for the cancellation of the nonlinear harmonic generated by g m . In order to demonstrate the feasibility of the technique, two types of PAs have been designed. The improved PA at 2.4V supply voltage, has a 22.5dB of power gain,5 dBm increase of P 1dB , 15% and 5dB improvement of PAE at P 1dB and IMD3, respectively, as compared with the traditional PA‥