A novel approach to online social influence maximization

Online social networks are becoming a true growth point of Internet. As individuals constantly desire to interact with each other, the ability for Internet to deliver this networking influence becomes much stronger. In this paper, we study the online social influence maximization problem, which is to find a small group of influential users that maximizes the spread of influence through networks. After a thorough analysis of existing models, especially two classical ones, namely Independent cascade and linear thresholds, we argue that their idea that each user can only be activated by its active neighbors is not applicable to online social networks, since in many applications there is no clear concept for the issue of "activation". In our proposed influence model, if there is a social influence path linking two nonadjacent individuals, the value of influence between these two individuals can be evaluated along the existing social path based on the influence transitivity property under certain constraints. To compute influence probabilities between two neighbors, we also develop a new method which leverages both structure of networks and historical data. With reasonably learned influence probabilities, we recast the problem of online influence maximization to a weighted maximum cut problem which analyzes the influence flow among graph vertices. By running a semidefinite program-based (GW) algorithm on a complete influence graph, an optimal seed set can be identified effectively. We also provide experimental results on real online social networks, showing that our algorithm significantly outperforms greedy methods.


Arabic Opinion Mining Using Parallel Decision Trees

Opinion mining is an interested area of research, which epitomize the customer reviews of a product or service and express whether the opinions are positive or negative. Various methods have been proposed as classifiers for opinion mining such as Naïve Bayesian, and Support vector machine, these methods classify opinion without giving us the reasons about why the instance opinion is classified to certain class. Therefore, in our work, we investigate opinion mining of Arabic text at the document level, by applying decision trees classification classifier to have clear, understandable rule, also we apply parallel decision trees classifiers to have efficient results. We applied parallel decision trees on two Arabic corpus of text documents by using parallel implementation of RapidMiner tools. In case of applying parallel decision tree family on OCA we get the best results of accuracy (93.83%), f-measure (93.22) and consumed time 42 Sec at thread 4, one of the resulted rule is Urdu language lines. In case of applying parallel decision tree family on BHA we get the best results of accuracy (90.63%), f-measure (82.29) and consumed time 219 Sec at thread 4, one of the resulted rule is Urdu language lines.


Surgical management of cloacal malformations: a review of 339 patients.

BACKGROUND/PURPOSE
The aim of this study was to describe lessons learned from the authors' series of patients with cloaca and convey the improved understanding and surgical treatment of the condition's wide spectrum of complexity.

METHODS
The medical records of 339 patients with cloaca operated on by the authors were retrospectively reviewed.

RESULTS
A total of 265 patients underwent primary operations, and 74 were secondary. All patients were approached posterior sagittally; 111 of them also required a laparotomy. The average length of the common channel was 4.7 cm for patients that required a laparotomy and 2.3 cm for those that did not. Vaginal reconstruction involved a vaginal pull-through in 196 patients, a vaginal flap in 38, vaginal switch in 30, and vaginal replacement in 75 (36 with rectum, 31 with ileum, and 8 with colon). One hundred twenty-two patients underwent a total urogenital mobilization. Complications included vaginal stricture or atresia in 17, urethral strictures in 6, and urethro-vaginal fistula in 19, all of which occurred before the introduction of the total urogenital mobilization. A total of 54% of all evaluated patients were continent of urine and 24% remain dry with intermittent catheterization through their native urethra and 22% through a Mitrofanoff-type of conduit. Seventy-eight percent of the patients with a common channel longer than 3 cm require intermittent catheterization compared with 28% when their common channel was shorter than 3 cm. Sixty percent of all cases have voluntary bowel movements (28% of them never soiled, and 72% soiled occasionally). Forty percent are fecally incontinent but remain clean when subjected to a bowel management program. Forty-eight patients born at other institutions with hydrocolpos were not treated correctly during the neonatal period. The surgeons failed to drain the dilated vaginas, which interfered with the drainage of the ureters and provoked urinary tract infections, pyocolpos, and/or vaginal perforation. In 24 patients, the colostomy was created too distally, and it interfered with the pull-through. Twenty-three patients suffered from colostomy prolapse. All of these patients required a colostomy, revision before the main repair. Thirty-six patients underwent reoperation because they had a persistent urogenital sinus after an operation done at another institution, and 38 patients underwent reoperation because they suffered from atresia or stenosis of the vagina or urethra. The series was divided into 2 distinct groups of patients: group A were those with a common channel shorter than 3 cm (62%) and group B had a common channel longer than 3 cm (38%).

CONCLUSIONS
The separation of these groups has important therapeutic and prognostic implications. Group A patients can be repaired posterior sagittally with a reproducible, relatively short operation. Because they represent the majority of patients, we believe that most well-trained pediatric surgeons can repair these type of malformations, and the prognosis is good. Group B patients (those with a common channel longer than 3 cm), usually require a laparotomy and have a much higher incidence of associated urologic problems. The surgeons who repair these malformations require special training in urology, and the operations are prolonged, technically demanding, and the functional results are not as good as in group A. It is extremely important to establish an accurate neonatal diagnosis, drain the hydrocolpos when present, and create an adequate, totally diverting colostomy, leaving enough distal colon available for the pull-through and fixing the colon to avoid prolapse. A correct diagnosis will allow the surgeon to repair the entire defect and avoid a persistent urogenital sinus. Cloacas comprise a spectrum of defects requiring a complex array of surgical decisions. The length of the common channel is an important determinant of the potential for urinary control, and predicts the extent of surgical repair.


Broadband Millimeter-Wave Power Combiner Using Compact SIW to Waveguide Transition

A novel broadband millimeter-wave passive power combiner based on substrate integrated waveguide (SIW) to waveguide transition is presented in this letter. Two transition circuits are symmetrically inserted into the E-plane of the rectangular waveguide and function as power-combining network. To satisfy the wideband request of the power combiner, a broadband and compact SIW-to-waveguide transition circuit has been developed using antisymmetric tapered probe. A Ka-band four-way power combiner was fabricated and the measured results agree well with the simulated ones. The measured results show that the proposed combiner achieved a bandwidth of 52% from 23.5 to 40 GHz with better than 15 dB return loss and an insertion loss of 0.75 to 1.4 dB. This millimeter-wave power combiner can be employed in high power-combining system for its simple structure and broadband performance.


Security Viewpoint in a Reference Architecture Model for Cyber-Physical Production Systems

Cyber-physical Production Systems (CPPS) are one of the technical driving forces behind the transformation of industrial production towards "digital factory of the future" in the context of Industry 4.0. Security is a major concern for such systems as they become more intelligent, interconnected, and coupled with physical devices. For various security activities from security analysis to designing security controls and architecture, a systematic and structured view and presentation of security-related information is required. Based on the draft standard of Reference Architecture Model for Industry 4.0 (RAMI 4.0), we propose a practical approach to establish a security viewpoint in the CPPS reference architecture model. We investigate the feasibility of using an architecture modeling tool to implement the concept and leverage existing work on models of layered architecture. We demonstrate the applicability for security analysis in two example case studies.


Dual Poisson-Disk Tiling: An Efficient Method for Distributing Features on Arbitrary Surfaces

This paper introduces a novel surface-modeling method to stochastically distribute features on arbitrary topological surfaces. The generated distribution of features follows the Poisson disk distribution, so we can have a minimum separation guarantee between features and avoid feature overlap. With the proposed method, we not only can interactively adjust and edit features with the help of the proposed Poisson disk map, but can also efficiently re-distribute features on object surfaces. The underlying mechanism is our dual tiling scheme, known as the dual Poisson-disk tiling. First, we compute the dual of a given surface parameterization, and tile the dual surface by our specially-designed dual tiles; during the pre-processing, the Poisson disk distribution has been pre-generated on these tiles. By dual tiling, we can nicely avoid the problem of corner heterogeneity when tiling arbitrary parameterized surfaces, and can also reduce the tile set complexity. Furthermore, the dual tiling scheme is non-periodic, and we can also maintain a manageable tile set. To demonstrate the applicability of this technique, we explore a number of surface-modeling applications: pattern and shape distribution, bump-mapping, illustrative rendering, mold simulation, the modeling of separable features in texture and BTF, and the distribution of geometric textures in shell space.


Revisiting Distributed Synchronous SGD

Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony. In contrast, the synchronous approach is often thought to be impractical due to idle time wasted on waiting for straggling workers. We revisit these conventional beliefs in this paper, and examine the weaknesses of both approaches. We demonstrate that a third approach, synchronous optimization with backup workers, can avoid asynchronous noise while mitigating for the worst stragglers. Our approach is empirically validated and shown to converge faster and to better test accuracies.


Highway long short-term memory RNNS for distant speech recognition

In this paper, we extend the deep long short-term memory (DL-STM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers. These direct links, called highway connections, enable unimpeded information flow across different layers and thus alleviate the gradient vanishing problem when building deeper LSTMs. We further introduce the latency-controlled bidirectional LSTMs (BLSTMs) which can exploit the whole history while keeping the latency under control. Efficient algorithms are proposed to train these novel networks using both frame and sequence discriminative criteria. Experiments on the AMI distant speech recognition (DSR) task indicate that we can train deeper LSTMs and achieve better improvement from sequence training with highway LSTMs (HLSTMs). Our novel model obtains 43.9/47.7% WER on AMI (SDM) dev and eval sets, outperforming all previous works. It beats the strong DNN and DLSTM baselines with 15.7% and 5.3% relative improvement respectively.


A REVIEW OF POINT CLOUDS SEGMENTATION AND CLASSIFICATION ALGORITHMS

Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.


Inferring user demographics and social strategies in mobile social networks

Demographics are widely used in marketing to characterize different types of customers. However, in practice, demographic information such as age, gender, and location is usually unavailable due to privacy and other reasons. In this paper, we aim to harness the power of big data to automatically infer users' demographics based on their daily mobile communication patterns. Our study is based on a real-world large mobile network of more than 7,000,000 users and over 1,000,000,000 communication records (CALL and SMS). We discover several interesting social strategies that mobile users frequently use to maintain their social connections. First, young people are very active in broadening their social circles, while seniors tend to keep close but more stable connections. Second, female users put more attention on cross-generation interactions than male users, though interactions between male and female users are frequent. Third, a persistent same-gender triadic pattern over one's lifetime is discovered for the first time, while more complex opposite-gender triadic patterns are only exhibited among young people.
 We further study to what extent users' demographics can be inferred from their mobile communications. As a special case, we formalize a problem of double dependent-variable prediction-inferring user gender and age simultaneously. We propose the WhoAmI method, a Double Dependent-Variable Factor Graph Model, to address this problem by considering not only the effects of features on gender/age, but also the interrelation between gender and age. Our experiments show that the proposed WhoAmI method significantly improves the prediction accuracy by up to 10% compared with several alternative methods.


An implementation of the earliest deadline first algorithm in Linux

Recently, many projects have been started to introduce some real-time mechanisms into general purpose operating systems (GPOS) in order to make them capable of providing the users with some temporal guarantees. Many of these projects focused especially on Linux for its capillary and widespread adoption throughout many different research and industrial environments.
 By tracking the kernel release cycle, we propose an efficient Earliest Deadline First implementation in the form of a patch-set against the 2.6.27 version, that is the latest released one, as of now. Our implementation provides the user with the possibility to choose SCHED_EDF as one of the possible scheduling policies for a task, with an enhanced version of the standard algorithm. In fact, we propose a new approach to shared resources' access which, differently from many other previous existing works, does not require the user to specify any parameters about the critical sections every task will enter during its execution.


Path-space manipulation of physically-based light transport

Industry-quality content creation relies on tools for lighting artists to quickly prototype, iterate, and refine final renders. As industry-leading studios quickly adopt physically-based rendering (PBR) across their art generation pipelines, many existing tools have become unsuitable as they address only simple effects without considering underlying PBR concepts and constraints. We present a novel light transport manipulation technique that operates directly on path-space solutions of the rendering equation. We expose intuitive direct and indirect manipulation approaches to edit complex effects such as (multi-refracted) caustics, diffuse and glossy indirect bounces, and direct/indirect shadows. With our sketch- and object-space selection, all built atop a parameterized regular expression engine, artists can search and isolate shading effects to inspect and edit. We classify and filter paths on the fly and visualize the selected transport phenomena. We survey artists who used our tool to manipulate complex phenomena on both static and animated scenes.


What's Your Next Move: User Activity Prediction in Location-based Social Networks

Location-based social networks have been gaining increasing popularity in recent years. To increase users’ engagement with location-based services, it is important to provide attractive features, one of which is geo-targeted ads and coupons. To make ads and coupon delivery more effective, it is essential to predict the location that is most likely to be visited by a user at the next step. However, an inherent challenge in location prediction is a huge prediction space, with millions of distinct check-in locations as prediction target. In this paper we exploit the check-in category information to model the underlying user movement pattern. We propose a framework which uses a mixed hidden Markov model to predict the category of user activity at the next step and then predict the most likely location given the estimated category distribution. The advantages of modeling the category level include a significantly reduced prediction space and a precise expression of the semantic meaning of user activities. Extensive experimental results show that, with the predicted category distribution, the number of location candidates for prediction is 5.45 times smaller, while the prediction accuracy is 13.21% higher.


Texture Segmentation Based Video Compression Using Convolutional Neural Networks

There has been a growing interest in using different approaches to improve the coding efficiency of modern video codec in recent years as demand for web-based video consumption increases. In this paper, we propose a model-based approach that uses texture analysis/synthesis to reconstruct blocks in texture regions of a video to achieve potential coding gains using the AV1 codec developed by the Alliance for Open Media (AOM). The proposed method uses convolutional neural networks to extract texture regions in a frame, which are then reconstructed using a global motion model. Our preliminary results show an increase in coding efficiency while maintaining satisfactory visual quality. Introduction With the increasing amount of videos being created and consumed, better video compression tools are needed to provide fast transmission and high visual quality. Modern video coding standards utilize spatial and temporal redundancy in the videos to achieve high coding efficiency and high visual quality with motion compensation techniques and 2-D orthogonal transforms. However, efficient exploitation of statistical dependencies measured by a mean squared error (MSE) does not always produce the best psychovisual result, and may require higher data rate to preserve detail information in the video. Recent advancement in GPU computing has enabled the analysis of large scale data using deep learning method. Deep learning techniques have shown promising performance in many applications such as object detection, natural language process, and synthetic images generation [1, 2, 3, 4]. Several methods have been developed for video applications to improve coding efficiency using deep learning. In [5], sample adaptive offset (SAO) is replaced by a CNN-based in-loop filter (IFCNN) to improve the coding efficiency in HEVC. By learning the predicted residue between the quantized reconstructed frames obtained after deblocking filter (DF) and the original frames, IFCNN is able to reconstruct video frames with higher quality without requiring any bit transmission during coding process. Similar to [5], [6] proposes a Variable-filter-size Residue-learning CNN (VRCNN) to improving coding efficiency by replacing DF and SAO in HEVC. VRCNN is based on the concept of ARCNN [7] which is originally designed for JPEG applications. Instead of only using spatial information to train a CNN to reduce the coding artifacts in HEVC, [8] proposed a spatial temporal residue network (STResNet) as an additional in-loop filter after SAO. A rate-distortion optimization strategy is used to control the on/off switch of the proposed in-loop filter. There are also some works that have been done in the decoder of HEVC to improve the coding efficiency. In [9], a deep CNN-based auto decoder (DCAD) is implemented in the decoder of HEVC to improve the video quality of decoded video. DCAD is trained to learn the predict residue between decoded video frames and original video frames. By adding the predicted residual generated from DCAD to the compressed video frames, this method enhances the compressed video frames to higher quality. In summary, the above methods improve the coding efficiency by enhancing the quality of reconstructed video frames. However, they require different trained models for video reconstruction at different quantization levels. We are interested in developing deep learning approaches to only encode visually relevant information and use a different coding method for “perceptually insignificant” regions in a frame, which can lead to substantial data rate reductions while maintaining visual quality. In particular, we have developed a model based approach that can be used to improve the coding efficiency by identifying texture areas in a video frame that contain detail irrelevant information, which the viewer does not perceive specific details and can be skipped or encoded at a much lower data rate. The task is then to divide a frame into “perceptually insignificant” texture region and then use a texture model for the pixels in that region. In 1959, Schreiber and colleagues proposed a coding method that divides an image into textures and edges and used it in image coding [10]. This work was later extended by using the human visual system and statistical model to determine the texture region [11, 12, 13]. More recently, several groups have focused on adapting perceptual based approaches to the video coding framework [14]. In our previous work [15], we introduced a texture analyzer before encoding the input sequences to identify detail irrelevant regions in the frame which are classified into different texture classes. At the encoder, no inter-frame prediction is performed for these regions. Instead, displacement of the entire texture region is modeled by just one set of motion parameters. Therefore, only the model parameters are transmitted to the decoder for reconstructing the texture regions using a texture synthesizer. Non-texture regions in the frame are coded conventionally. Since this method uses feature extraction based on texture segmentation technique, a proper set of parameters are required to achieve accurate texture segmentation for different videos. Deep learning methods usually do not require such parameter tuning for inference. As a result, deep learning techniques can be developed to perform texture segmentation and classification for the proposed model-based video coding. A Fisher vector convolution neural networks (FVCNN) that can produce segmentation labels for different texture classes was proposed in [16]. One of the advantage of FV-CNN is that the image input size is flexible and is not limited by the network architecture. Instead of doing pixel-wise classification on texture regions, a texture classification CNN network was described in [17]. To reduce computational expenses, [17] uses a small classification network to classify image patches with size of 227 × 227. A smaller network is needed to classify smaller image patches in our case. In this paper, we propose a block-based texture segmentation method to extract texture region in a video frame using convolutional neural networks. The block-based segmentation network classifies each 16 × 16 block in a frame as texture or non-texture. The identified texture region is then synthesized using the temporal correlations among the frames. Our method was implemented using the AOM/AV1 codec. Preliminary results show significant bitrate savings while maintaining a satisfactory visual quality.


Visual Descriptors for Dense Tensor Fields in Computational Turbulent Combustion: A Case Study

Simulation and modeling of turbulent flow, and of turbulent reacting flow in particular, involve solving for and analyzing time-dependent and spatially dense tensor quantities, such as turbulent stress tensors. The interactive visual exploration of these tensor quantities can effectively steer the computational modeling of combustion systems. In this article, the authors analyze the challenges in dense symmetric-tensor visualization as applied to turbulent combustion calculation; most notable among these challenges are the dataset size and density. They analyze, together with domain experts, the feasibility of using several established tensor visualization techniques in this application domain. They further examine and propose visual descriptors for volume rendering of the data. Of these novel descriptors, one is a density-gradient descriptor which results in Schlieren-style images, and another one is a classification descriptor inspired by machine-learning techniques. The result is a hybrid visual analysis tool to be utilized in the debugging, benchmarking and verification of models and solutions in turbulent combustion. The authors demonstrate this analysis tool on two example configurations, report feedback from combustion researchers, and summarize the design lessons learned. c © 2016 Society for Imaging Science and Technology. [DOI: 10.2352/J.ImagingSci.Technol.2016.60.1.010404] INTRODUCTION Computational simulation of turbulent combustion for gas turbine design has become increasingly important in the last two decades, due in part to environmental concerns and regulations on toxic emissions. Such modern gas turbine designs feature a variety of mixing fuel compositions and possible flow configurations,1,2 which make non-computational simulations difficult. The focus of the computational research effort in this direction is on the development of computational tools for the modeling and prediction of turbulent combustion flows. Received June 30, 2015; accepted for publication Nov. 4, 2015; published online Dec. 10, 2015. Associate Editor: Song Zhang. 1062-3701/2016/60(1)/010404/11/$25.00 Tensor quantities are common features in these turbulent combustion models. In particular, stress and strain tensors are often correlated to turbulent quantities—which appear unclosed in the mathematical formulation and thus need to be modeled as part of the computational simulation. Visual identification of the characteristics of such tensor quantities can bring significant insights into the computational modeling process. However, these computational tensor fields are very large and spatially dense—a good example of the Big Data revolution across sciences and engineering. Figure 1 shows an example turbulent combustion configuration, featuring a grid size of 106 and 6× 106 particles (shown as spheres); this dataset should be considered in contrast to traditional tensor datasets, which feature grid sizes in the 102 range. At such large scales, typical glyph encodings become cluttered and illegible. Furthermore, combustion experts seldom have an intuitive understanding of the tensor quantities. In this respect, froma tensor visualization perspective, workingwith these datasets poses an array of challenges. Are traditional tensor and flow representations useful in this context? Does increasing the level of complexity or expressiveness of such representations help or hinder? Is interaction speed more important than the benefits gained from complex descriptors? In this article, we address a specific application design problem. In the process of exploring the design space, we also investigate some of the larger visualization questions above, through the opportunity of a case study in the computational-combustion domain. In thiswork,motivated by an ongoing collaborationwith domain experts,3 we investigate the challenges associated with the exploratory visualization of tensor quantities in turbulent combustion simulations. We first provide a characterization of the problem domain, including a data analysis. Through a case study involving five senior combustion researchers, we then iteratively explore the space of tensor visual encodings. We implement and evaluate several J. Imaging Sci. Technol. 010404-1 Jan.-Feb. 2016 Marai et al.: Visual descriptors for dense tensor fields in computational turbulent combustion: a case study Figure 1. One timestep in an example turbulent combustion configuration. The grid size is 106. In this image, 6× 106 particles are shown as spheres. This dataset should be contrasted to traditional tensor datasets, which feature sparse grids in the 102 range. At this scale, typical glyph encodings become cluttered. approaches advocated by the visualization community in an interactive prototype, and we contrast these approaches with the best-of-breed visualization practices in the target domain. Based on domain expert feedback, we then focus our efforts on identifying effective visual descriptors for volume rendering of the combustion tensor data. Our contributions include a novel density-gradient descriptor and the adaptation of a machine-learning classification technique. Next, we evaluate the visual descriptors on two computational-combustion datasets of particular interest, and we show the importance of the proposed approach for debugging the numerical simulation of complex configurations. In an effort to better bridge the gap between the combustion and tensor visualization communities, we describe these tensor field datasets. Last but not least, we contribute a summary of design lessons learned from the study and from the application design process. To the best of our knowledge, this is the first formal, exploratory case study of tensor visualization techniques in the context of very large, high-density turbulent combustion flow. TENSORS IN TURBULENT COMBUSTION MODELING Turbulent Combustion Modeling. A sufficiently accurate, flexible and reliable model can be used for an in silico combustor rig test as a much cheaper alternative to the reallife rig tests employed in combustor design and optimization. In order to achieve such amodel, themethodology should be well tested and proven with lab-scale configurations. Multiple numerical approaches exist for the generation of such computational models of combustion, most notably Direct numerical simulation (DNS), Reynolds-averaged Navier–Stokes (RANS) and Large eddy simulation (LES). DNS, RANS and LES have complementary strengths. However, allmodels begin by describing the compressible reacting flow via a set of partial differential equations (PDEs) that represent the conservation of mass, momentum and energy. These PDEs are a fully coupled set of multi-dimensional non-linear equations and can be posed in a variety of forms depending on the flow conditions (compressibility, scale, flow regime, etc.).4 In this article, we exemplify the visualization of stress/strain tensors, and therefore restrict the presentation to the pertinent subset of these PDEs, namely the momentum transport equation. Stress, Strain and Turbulent Stress Tensors. A tensor is an extension of the concept of a scalar and a vector to higher orders. For example, while a stress vector is the force acting on a given unit surface, a stress tensor is defined as the components of stress vectors acting on each coordinate surface; thus, stress can be described by a symmetric second-order tensor (a matrix). The velocity stress and strain tensor fields aremanifested in the transport of fluid momentum, which is a vector quantity governed by the following conservation equation:


Towards the Semantic Web: Collaborative Tag Suggestions

Content organization over the Internet went through several interesting phases of evolution: from structured di rectories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step toward s the semantic web vision. Tagging allows ranking and dat a organization to directly utilize inputs from end us er , enabling machine processing of Web content. Since tags are c reated by individual users in a free form, one important prob lem facing tagging is to identify most appropriate tags, while eliminating noise and spam. For this purpose, we define a set o f general criteria for a good tagging system. These criteria include high coverage of multiple facets to ensure good recall, least effort to reduce the cost involved in browsing, and high popu larity to ensure tag quality. We propose a collaborative tag su gestion algorithm using these criteria to spot high-quality ags. The proposed algorithm employs a goodness measure for t ags derived from collective user authorities to combat spam. Th e goodness measure is iteratively adjusted by a reward-penalty lgorithm, which also incorporates other sources of tags, e.g. , content-based auto-generated tags. Our experiments based on My We b 2.0 show that the algorithm is effective.


The Effect of Focal Loss in Semantic Segmentation of High Resolution Aerial Image

The semantic segmentation of High Resolution Remote Sensing (HRRS) images is the fundamental research area of the earth observation. Convolutional Neural Network (CNN), which has achieved superior performance in computer vision task, is also useful for semantic segmentation of HRRS images. In this work, focal loss is used instead of cross-entropy loss in training of CNN to handle the imbalance in training data. To evaluate the effect of focal loss, we train SegNet and FCN with focal loss and confirm improvement in accuracy in ISPRS 2D Semantic Labeling Contest dataset, especially when $\gamma$ is 0.5 in SegNet.


Co-Design for Low Warpage and High Reliability in Advanced Package with TSV-Free Interposer (TFI)

TSV-Free Interposer (TFI) technology eliminates TSV fabrication and reduces manufacturing and material cost. Co-design modelling methodology is established for TFI technology with considering wafer process, package assembly and package/board level reliability and thermal performance to optimize structure design, wafer process, assembly process and material selection. Experimental results are used for validating warpage modelling results. Through wafer level modelling, suitable carrier wafer and EMC materials are recommended to control wafer warpage less than 2mm. Effects of package substrate coefficient of thermal expansion (CTE) and stiffener on assembly induced package warpage are simulated to reduce package warpage. The recommended materials and geometry design based on reliability are aligned with that from wafer and package warpage simulation results. The final test vehicle (TV) design and material selection are determined based on co-design modelling results for achieving successful TFI wafer process and package assembly process and long term package/board level reliability.


Learning Fine-grained Features via a CNN Tree for Large-scale Classification

We propose a novel approach to enhance the discriminability of Convolutional Neural Networks (CNN). The key idea is to build a tree structure that could progressively learn fine-grained features to distinguish a subset of classes, by learning features only among these classes. Such features are expected to be more discriminative, compared to features learned for all the classes. We develop a new algorithm to effectively learn the tree structure among a large number of classes. Experiments on large-scale image classification tasks demonstrate that our method could boost the performance of a given basic CNN model. Our method is quite general, hence it can potentially be used in combination with many other deep learning models.


SIMPLIFIED SOLAR TRACKING PROTOTYPE

Solar energy is rapidly advancing as an important means of renewable energy resource. More energy is produced by tracking the solar panel to remain aligned to the sun at a right angle to the rays of light. This paper describes in detail the design and construction of a prototype for solar tracking system with two degrees of freedom, which detects the sunlight using photocells. The control circuit for the solar tracker is based on a PIC16F84A microcontroller (MCU). This is programmed to detect the sunlight through the photocells and then actuate the motor to position the solar panel where it can receive maximum sunlight. Keyword: PIC MCU, tracking, photocell, frame, motion system


Real-time automatic tag recommendation

Tags are user-generated labels for entities. Existing research on tag recommendation either focuses on improving its accuracy or on automating the process, while ignoring the efficiency issue. We propose a highly-automated novel framework for real-time tag recommendation. The tagged training documents are treated as triplets of (words, docs, tags), and represented in two bipartite graphs, which are partitioned into clusters by Spectral Recursive Embedding (SRE). Tags in each topical cluster are ranked by our novel ranking algorithm. A two-way Poisson Mixture Model (PMM) is proposed to model the document distribution into mixture components within each cluster and aggregate words into word clusters simultaneously. A new document is classified by the mixture model based on its posterior probabilities so that tags are recommended according to their ranks. Experiments on large-scale tagging datasets of scientific documents (CiteULike) and web pages del.icio.us) indicate that our framework is capable of making tag recommendation efficiently and effectively. The average tagging time for testing a document is around 1 second, with over 88% test documents correctly labeled with the top nine tags we suggested.


Superfamilies of evolved and designed networks.

Complex biological, technological, and sociological networks can be of very different sizes and connectivities, making it difficult to compare their structures. Here we present an approach to systematically study similarity in the local structure of networks, based on the significance profile (SP) of small subgraphs in the network compared to randomized networks. We find several superfamilies of previously unrelated networks with very similar SPs. One superfamily, including transcription networks of microorganisms, represents "rate-limited" information-processing networks strongly constrained by the response time of their components. A distinct superfamily includes protein signaling, developmental genetic networks, and neuronal wiring. Additional superfamilies include power grids, protein-structure networks and geometric networks, World Wide Web links and social networks, and word-adjacency networks from different languages.


Improving Alzheimer's disease diagnosis with machine learning techniques.

There is not a specific test to diagnose Alzheimer's disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.


Cloud Automation: Precomputing Roadmaps for Flexible Manipulation

The goal of this article is to highlight the benefits of cloud automation for industrial adopters and some of the research challenges that must be addressed in this process. The focus is on the use of cloud computing for efficiently planning the motion of new robot manipulators designed for flexible manufacturing floors. In particular, different ways that a robot can interact with a computing cloud are considered, where an architecture that splits computation between the remote cloud and the robot appears advantageous. Given this synergistic robot-cloud architecture, this article describes how solutions from the recent literature can be employed on the cloud during a periodically updated preprocessing phase to efficiently answer manipulation queries on the robot given changes in the workspace. In this setup, interesting tradeoffs arise between path quality and computational efficiency, which are evaluated through simulation. These tradeoffs motivate further research on how motion planning should be executed given access to a computing cloud.


Soft Robot Arm Inspired by the Octopus

The octopus is a marine animal whose body has no rigid structures. It has eight arms composed of a peculiar muscular structure, named a muscular hydrostat. The octopus arms provide it with both locomotion and grasping capabilities, thanks to the fact that their stiffness can change over a wide range and can be controlled through combined contractions of the muscles. The muscular hydrostat can better be seen as a modifiable skeleton. Furthermore, the morphology the arms and the mechanical characteristics of their tissues are such that the interaction with the environment (i.e., water) is exploited to simplify control. Thanks to this effective mechanism of embodied intelligence, the octopus can control a very high number of degrees of freedom, with relatively limited computing resources. From these considerations, the octopus emerges as a good model for embodied intelligence and for soft robotics. The prototype of a robot arm has been built based on an artificial muscular hydrostat inspired to the muscular hydrostat of the Octopus vulgaris. The prototype presents the morphology of the biological model and the broad arrangement of longitudinal and transverse muscles. Actuation is obtained with cables (longitudinally) and with shape memory alloy springs (transversally). The robot arm combines contractions and it can show the basic movements of the octopus arm, like elongation, shortening and bending, in water. © Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2012


Power Modeling and Optimization for OLED Displays

Emerging organic light-emitting diode (OLED)-based displays obviate external lighting, and consume drastically different power when displaying different colors, due to their emissive nature. This creates a pressing need for OLED display power models for system energy management, optimization as well as energy-efficient GUI design, given the display content or even the graphical-user interface (GUI) code. In this work, we study this opportunity using commercial QVGA OLED displays and user studies. We first present a comprehensive treatment of power modeling of OLED displays, providing models that estimate power consumption based on pixel, image, and code, respectively. These models feature various tradeoffs between computation efficiency and accuracy so that they can be employed in different layers of a mobile system. We validate the proposed models using a commercial QVGA OLED module and a mobile device with a QVGA OLED display. Then, based on the models, we propose techniques that adapt GUIs based on existing mechanisms as well as arbitrarily under usability constraints. Our measurement and user studies show that more than 75 percent display power reduction can be achieved with user acceptance.


The 12-Core POWER8™ Processor With 7.6 Tb/s IO Bandwidth, Integrated Voltage Regulation, and Resonant Clocking

POWER8™ is a 12-core processor fabricated in IBM's 22 nm SOI technology with core and cache improvements driven by big data applications, providing 2.5× socket performance over POWER7+™. Core throughput is supported by 7.6 Tb/s of off-chip I/O bandwidth which is provided by three primary interfaces, including two new variants of Elastic Interface as well as embedded PCI Gen-3. Power efficiency is improved with several techniques. An on-chip controller based on an embedded PowerPC™ 405 processor applies per-core DVFS by adjusting DPLLs and fully integrated voltage regulators. Each voltage regulator is a highly distributed system of digitally controlled microregulators, which achieves a peak power efficiency of 90.5%. A wide frequency range resonant clock design is used in 13 clock meshes and demonstrates a minimum power savings of 4%. Power and delay efficiency is achieved through the use of pulsed-clock latches, which require statistical validation to ensure robust yield.


Visceral pain: the ins and outs, the ups and downs.

PURPOSE OF REVIEW
Visceral pain represents a major clinical problem, yet far less is known about its mechanisms compared with somatic pains, for example, from cutaneous and muscular structures.

RECENT FINDINGS
In this review, we describe the neuroanatomical bases of visceral pain signalling in the peripheral and central nervous system, comparing to somatic pains and also the channels and receptors involved in these events. We include an overview of potential new targets in the context of mechanisms of visceral pain and hypersensitivity.

SUMMARY
This review should inform on the recognition of what occurs in patients with visceral pain, why comorbidities are common and how analgesic treatments work.


On the Use of Logic Trees for Ground-Motion Prediction Equations in Seismic-Hazard Analysis

Logic trees are widely used in probabilistic seismic hazard analysis as a tool to capture the epistemic uncertainty associated with the seismogenic sources and the ground-motion prediction models used in estimating the hazard. Combining two or more ground-motion relations within a logic tree will generally require several conversions to be made, because there are several definitions available for both the predicted ground-motion parameters and the explanatory parameters within the predictive ground-motion relations. Procedures for making conversions for each of these factors are presented, using a suite of predictive equations in current use for illustration. The sensitivity of the resulting ground-motion models to these conversions is shown to be pronounced for some of the parameters, especially the measure of source-to-site distance, highlighting the need to take into account any incompatibilities among the selected equations. Procedures are also presented for assigning weights to the branches in the ground-motion section of the logic tree in a transparent fashion, considering both intrinsic merits of the individual equations and their degree of applicability to the particular application.


Privacy-Preserving Data Mining

A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a novel reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.


Executing SQL over encrypted data in the database-service-provider model

Rapid advances in networking and Internet technologies have fueled the emergence of the "software as a service" model for enterprise computing. Successful examples of commercially viable software services include rent-a-spreadsheet, electronic mail services, general storage services, disaster protection services. "Database as a Service" model provides users power to create, store, modify, and retrieve data from anywhere in the world, as long as they have access to the Internet. It introduces several challenges, an important issue being data privacy. It is in this context that we specifically address the issue of data privacy.There are two main privacy issues. First, the owner of the data needs to be assured that the data stored on the service-provider site is protected against data thefts from outsiders. Second, data needs to be protected even from the service providers, if the providers themselves cannot be trusted. In this paper, we focus on the second challenge. Specifically, we explore techniques to execute SQL queries over encrypted data. Our strategy is to process as much of the query as possible at the service providers' site, without having to decrypt the data. Decryption and the remainder of the query processing are performed at the client site. The paper explores an algebraic framework to split the query to minimize the computation at the client site. Results of experiments validating our approach are also presented.


An Anatomical Analysis of the Supratrochlear Artery: Considerations in Facial Filler Injections and Preventing Vision Loss.

BACKGROUND
Embolia cutis medicamentosa (ECM) is a rare phenomenon attributed to intra-arterial drug injection. Glabellar filler injections can result in potentially devastating visual loss from inadvertent retrograde arteriolar embolization due to the extensive vasculature within the upper face. The minimum amount of filler necessary to potentiate this complication has not yet been reported.

OBJECTIVES
We aim to determine the volume of filler necessary to occupy the supratrochlear artery from the glabella to the bifurcation of the ophthalmic and central retinal arteries. We specifically examine the volume of the supratrochlear artery from the glabella to orbital apex.

METHODS
The study was approved by Duke University Institutional Review Board and involved surgical dissection of six fresh tissue cadaver heads (12 hemifaces). The arterial system in each cadaver head was injected with latex for visualization. The supratrochlear arteries were isolated anteriorly from the glabella to the orbital apex posteriorly. Intra-orbital vessel radius and length were measured. The vessel volume was calculated by water displacement of the intra-arterial latex.

RESULTS
The vessel volumes ranged from 0.04 to 0.12 mL. The average vessel volume was calculated as 0.085 mL, the average length as 51.75 mm, and the average radius as 0.72 mm.

CONCLUSIONS
Vascular occlusion from filler injections can lead to devastating visual consequences due to inadvertent retrograde intra-arterial embolization. Our findings indicate that the average entire volume of the supratrochlear artery from the glabella to the orbital apex is 0.085 mL. Injectors should be aware that a bolus of this critical volume may lead to a significant adverse outcome.


Geometry-Based Symbolic Approximation for Fast Sequence Matching on Manifolds

In this paper, we consider the problem of fast and efficient indexing techniques for sequences evolving in non-Euclidean spaces. This problem has several applications in the areas of human activity analysis, where there is a need to perform fast search, and recognition in very high dimensional spaces. The problem is made more challenging when representations such as landmarks, contours, and human skeletons etc. are naturally studied in a non-Euclidean setting where even simple operations are much more computationally intensive than their Euclidean counterparts. We propose a geometry and data adaptive symbolic framework that is shown to enable the deployment of fast and accurate algorithms for activity recognition, dynamic texture recognition, motif discovery. Toward this end, we present generalizations of key concepts of piece-wise aggregation and symbolic approximation for the case of non-Euclidean manifolds. We show that one can replace expensive geodesic computations with much faster symbolic computations with little loss of accuracy in activity recognition and discovery applications. The framework is general enough to work across both Euclidean and non-Euclidean spaces, depending on appropriate feature representations without compromising on the ultra-low bandwidth, high speed and high accuracy. The proposed methods are ideally suited for real-time systems and low complexity scenarios.


Wireless powering for a self-propelled and steerable endoscopic capsule for stomach inspection.

This paper describes the integration of an active locomotion module in a wirelessly powered endoscopic capsule. The device is a submersible capsule optimized to operate in a fluid environment in a liquid-distended stomach. A 3D inductive link is used to supply up to 400mW to the embedded electronics and a set of 4 radio-controlled motor propellers. The design takes advantage of a ferrite-core in the receiving coil-set. This approach significantly improves the coupling with the external field source with respect to earlier work by the group. It doubles the power that can be received with a coreless coil-set under identical external conditions. The upper limit of the received power was achieved complying with the strict regulations for safe exposure of biological tissue to variable magnetic fields. The wireless transferred power was proven to be sufficient to achieve the speed of 7cm/s in any directions. An optimized locomotion strategy was defined which limits the power consumption by running only 2 motors at a time. A user interface and a joystick controller allow to fully drive the capsule in an intuitive manner. The device functionalities were successfully tested in a dry and a wet environment in a laboratory set-up.


Semantic full-text search with broccoli

We combine search in triple stores with full-text search into what we call \emph{semantic full-text search}. We provide a fully functional web application that allows the incremental construction of complex queries on the English Wikipedia combined with the facts from Freebase. The user is guided by context-sensitive suggestions of matching words, instances, classes, and relations after each keystroke. We also provide a powerful API, which may be used for research tasks or as a back end, e.g., for a question answering system. Our web application and public API are available under \url{http://broccoli.cs.uni-freiburg.de}.


Design and In Vitro Interference Test of Microwave Noninvasive Blood Glucose Monitoring Sensor

A design of a microwave noninvasive continuous blood glucose monitoring sensor and its interference test results are presented. The novelty of the proposed sensor is that it comprises two spatially separated split-ring resonators, where one interacts with the change in glucose level of a sample under test while the other ring is used as a reference. The reference ring has a slightly different resonant frequency and is desensitized to the sample owing to its location, thus allowing changes in temperature to be calibrated out. From an oral glucose tolerance test with two additional commercially available sensors (blood strip and continuous glucose monitor) in parallel, we obtained encouraging performance for our sensor comparable with those of the commercial sensors. The effects of endogenous interferents common to all subjects, i.e., common sugars, vitamins (ascorbic acid), and metabolites (uric acid) have also been investigated by using a large Franz cell assembly. From the interference test, it is shown that the change in sensor response is dominated by changes in glucose level for concentrations relevant to blood, and the effects of interferents are negligible in comparison.


Solving POMDPs with Levin Search and EIRA

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A Syntactic Resource for Thai: CG Treebank

This paper presents Thai syntactic resource: Thai CG treebank, a categorial approach of language resources. Since there are very few Thai syntactic resources, we designed to create treebank based on CG formalism. Thai corpus was parsed with existing CG syntactic dictionary and LALR parser. The correct parsed trees were collected as preliminary CG treebank. It consists of 50,346 trees from 27,239 utterances. Trees can be split into three grammatical types. There are 12,876 sentential trees, 13,728 noun phrasal trees, and 18,342 verb phrasal trees. There are 17,847 utterances that obtain one tree, and an average tree per an utterance is 1.85.


WarpGAN: Automatic Caricature Generation

We propose, WarpGAN, a fully automatic network that can generate caricatures given an input face photo. Besides transferring rich texture styles, WarpGAN learns to automatically predict a set of control points that can warp the photo into a caricature, while preserving identity. We introduce an identity-preserving adversarial loss that aids the discriminator to distinguish between different subjects. Moreover, WarpGAN allows customization of the generated caricatures by controlling the exaggeration extent and the visual styles. Experimental results on a public domain dataset, WebCaricature, show that WarpGAN is capable of generating a diverse set of caricatures while preserving the identities. Five caricature experts suggest that caricatures generated by WarpGAN are visually similar to hand-drawn ones and only prominent facial features are exaggerated. ∗ indicates equal contribution


A Generic Approach to Challenge Modeling for the Procedural Creation of Video Game Levels

This paper presents an approach to automatic video game level design consisting of a computational model of player enjoyment and a generative system based on evolutionary computing. The model estimates the entertainment value of game levels according to the presence of “rhythm groups,” which are defined as alternating periods of high and low challenge. The generative system represents a novel combination of genetic algorithms (GAs) and constraint satisfaction (CS) methods and uses the model as a fitness function for the generation of fun levels for two different games. This top-down approach improves upon typical bottom-up techniques in providing semantically meaningful parameters such as difficulty and player skill, in giving human designers considerable control over the output of the generative system, and in offering the ability to create levels for different types of games.


ScienceDirect The 2 nd International Workshop on Communications and Sensor Networks ( ComSense-2014 ) A cloud-based healthcare framework for security and patients ' data privacy using wireless body area networks

The recent developments in remote healthcare systems have witnessed significant interests from IT industry (Microsoft, Google, VMware etc) that provide ubiquitous and easily deployable healthcare systems. These systems provide a platform to share medical information, applications, and infrastructure in a ubiquitous and fully automated manner. Communication security and patients' data privacy are the aspects that would increase the confidence of users in such remote healthcare systems. This paper presents a secure cloud-based mobile healthcare framework using wireless body area networks (WBANs). The research work presented here is twofold: first, it attempts to secure the inter-sensor communication by multi-biometric based key generation scheme in WBANs; and secondly, the electronic medical records (EMRs) are securely stored in the hospital community cloud and privacy of the patients' data is preserved. The evaluation and analysis shows that the proposed multi-biometric based mechanism provides significant security measures due to its highly efficient key generation mechanism.


Emerging applications of stimuli-responsive polymer materials.

Responsive polymer materials can adapt to surrounding environments, regulate transport of ions and molecules, change wettability and adhesion of different species on external stimuli, or convert chemical and biochemical signals into optical, electrical, thermal and mechanical signals, and vice versa. These materials are playing an increasingly important part in a diverse range of applications, such as drug delivery, diagnostics, tissue engineering and 'smart' optical systems, as well as biosensors, microelectromechanical systems, coatings and textiles. We review recent advances and challenges in the developments towards applications of stimuli-responsive polymeric materials that are self-assembled from nanostructured building blocks. We also provide a critical outline of emerging developments.


Classification of basic footwork in fencing using accelerometer

Analysis and recognition of motion patterns from data acquired by body-worn inertial sensors is an emerging technology in sports. In this paper we propose an effective method for recognition of fencing footwork using a single body-worn accelerometer. We present a challenging dataset consisting of six actions, which were performed by ten persons and repeated ten times by each of them. We propose a segment-based SVM for time-series classification together with a set of informative features. We demonstrate that the method is competitive with 1-NN DTW in terms of classification accuracy. The proposed method achieves classification accuracy slightly better than 70% on the fencing footwork dataset.


MoveIt!: Autonomous Underwater Free-Floating Manipulation

Today, autonomous underwater vehicles (AUVs) are mostly used for survey missions, but many existing applications require manipulation capabilities, such as the maintenance of permanent observatories, submerged oil wells, cabled sensor networks, and pipes; the deployment and recovery of benthic stations; or the search and recovery of black boxes. Currently, these tasks require the use of work-class remotely operated vehicles (ROVs) deployed from vessels equipped with dynamic positioning, leaving such solutions expensive to adopt. To face these challenges during the last 25 years, scientists have researched the idea of increasing the autonomy of underwater intervention systems.


Automotive hardware development according to ISO 26262

This paper briefly explains ISO 26262, the new automotive standard for functional safety. The standard is for electrical/electronic systems mounted on series of passenger cars. In the standard, Automotive Safety Integrity Level (ASIL) is classified into four levels according to the required safety level. Each of the ASIL has its own process and steps for item development. This paper focuses on the item development at hardware level, especially for ASIL C and D. The hardware development procedure includes the calculation of single point metric and latent fault metric. Furthermore, we present overall calculation steps of controlling random hardware failure.


Mandibular Rim Trilogy with Botulinum Toxin Injection: Reduction, Projection, and Lift.

"Onabotulinum toxin A (Botox) revolution" has brought the fundamental change in the facial rejuvenation as well as the concept of microinjection. The aesthetic standard tends to be the "globalization"; however, Asians have different aesthetic cultures and unique facial features compared with Caucasians. A new rejuvenation concept is proposed during our practice; the Asian face should preserve the original facial identity during Botox treatments. The lower face is treated with botulinum toxin to achieve a harmonious facial profile. Twenty young females ranging in age from 30 to 45 years consented and received the three-pronged procedure from March 2014 to April 2015; photography at baseline and follow-up visit were taken and analyzed. After posttreatment for 2 months, significant improvement was observed compared with the baseline. And the reduced masseter prominence and prominent chin were obtained, showing a favorable facial contour and harmonious appearance during the follow-up. The novel three-pronged approach to lower facial rejuvenation was aimed at the Asian characteristic of hypertrophic masseter, chin retrusion, and the facial sagging during the aging process. Botox treatment was a quite effective and safe strategy to improve the appearance and contour of the lower face in Asian patients.


Adversarially Learned Inference

We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the space of latent variables. An adversarial game is cast between these two networks and a discriminative network is trained to distinguish between joint latent/data-space samples from the generative network and joint samples from the inference network. We illustrate the ability of the model to learn mutually coherent inference and generation networks through the inspections of model samples and reconstructions and confirm the usefulness of the learned representations by obtaining a performance competitive with state-of-the-art on the semi-supervised SVHN and CIFAR10 tasks.


Exploring the link between reading fiction and empathy : Ruling out individual differences and examining outcomes

Readers of fiction tend to have better abilities of empathy and theory of mind (Mar et al., 2006). We present a study designed to replicate this finding, rule out one possible explanation, and extend the assessment of social outcomes. In order to rule out the role of personality, we first identified Openness as the most consistent correlate. This trait was then statistically controlled for, along with two other important individual differences: the tendency to be drawn into stories and gender. Even after accounting for these variables, fiction exposure still predicted performance on an empathy task. Extending these results, we also found that exposure to fiction was positively correlated with social support. Exposure to nonfiction, in contrast, was associated with loneliness, and negatively related to social support.


BatchQC: interactive software for evaluating sample and batch effects in genomic data

Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. There are several existing batch adjustment tools for '-omics' data, but they do not indicate a priori whether adjustment needs to be conducted or how correction should be applied. We present a software pipeline, BatchQC, which addresses these issues using interactive visualizations and statistics that evaluate the impact of batch effects in a genomic dataset. BatchQC can also apply existing adjustment tools and allow users to evaluate their benefits interactively. We used the BatchQC pipeline on both simulated and real data to demonstrate the effectiveness of this software toolkit.

AVAILABILITY AND IMPLEMENTATION
BatchQC is available through Bioconductor: http://bioconductor.org/packages/BatchQC and GitHub: https://github.com/mani2012/BatchQC CONTACT: wej@bu.eduSupplementary information: Supplementary data are available at Bioinformatics online.


End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies

Steering a car through traffic is a complex task that is difficult to cast into algorithms. Therefore, researchers turn to training artificial neural networks from front-facing camera data stream along with the associated steering angles. Nevertheless, most existing solutions consider only the visual camera frames as input, thus ignoring the temporal relationship between frames. In this work, we propose a Convolutional Long Short-Term Memory Recurrent Neural Network (C-LSTM), that is end-to-end trainable, to learn both visual and dynamic temporal dependencies of driving. Additionally, We introduce posing the steering angle regression problem as classification while imposing a spatial relationship between the output layer neurons. Such method is based on learning a sinusoidal function that encodes steering angles. To train and validate our proposed methods, we used the publicly available Comma.ai dataset. Our solution improved steering root mean square error by 35% over recent methods, and led to a more stable steering by 87%.


The diagnosis and management of Piriformis Syndrome: myths and facts.

Piriformis Syndrome (PS) is an uncommon, controversial neuromuscular disorder that is presumed to be a compression neuropathy of the sciatic nerve at the level of the piriformis muscle (PM). The diagnosis is hampered by a lack of agreed upon clinical criteria and a lack of definitive investigations such as imaging or electrodiagnostic testing. Treatment has focused on stretching, physical therapies, local injections, including botulinum toxin, and surgical management. This article explores the various sources of controversy surrounding piriformis syndrome including diagnosis, investigation and management. We conclude with a proposal for diagnostic criteria which include signs and symptoms, imaging, and response to therapeutic injections.


Game strategies in network security

This paper presents a game-theoretic method for analyzing the security of computer networks. We view the interactions between an attacker and the administrator as a two-player stochastic game and construct a model for the game. Using a nonlinear program, we compute Nash equilibria or best-response strategies for the players (attacker and administrator). We then explain why the strategies are realistic and how administrators can use these results to enhance the security of their network.


Data mining in metric space: an empirical analysis of supervised learning performance criteria

Many criteria can be used to evaluate the performance of supervised learning. Different criteria are appropriate in different settings, and it is not always clear which criteria to use. A further complication is that learning methods that perform well on one criterion may not perform well on other criteria. For example, SVMs and boosting are designed to optimize accuracy, whereas neural nets typically optimize squared error or cross entropy. We conducted an empirical study using a variety of learning methods (SVMs, neural nets, k-nearest neighbor, bagged and boosted trees, and boosted stumps) to compare nine boolean classification performance metrics: Accuracy, Lift, F-Score, Area under the ROC Curve, Average Precision, Precision/Recall Break-Even Point, Squared Error, Cross Entropy, and Probability Calibration. Multidimensional scaling (MDS) shows that these metrics span a low dimensional manifold. The three metrics that are appropriate when predictions are interpreted as probabilities: squared error, cross entropy, and calibration, lay in one part of metric space far away from metrics that depend on the relative order of the predicted values: ROC area, average precision, break-even point, and lift. In between them fall two metrics that depend on comparing predictions to a threshold: accuracy and F-score. As expected, maximum margin methods such as SVMs and boosted trees have excellent performance on metrics like accuracy, but perform poorly on probability metrics such as squared error. What was not expected was that the margin methods have excellent performance on ordering metrics such as ROC area and average precision. We introduce a new metric, SAR, that combines squared error, accuracy, and ROC area into one metric. MDS and correlation analysis shows that SAR is centrally located and correlates well with other metrics, suggesting that it is a good general purpose metric to use when more specific criteria are not known.


Navigation in a small world

The small-world phenomenon — the principle that most of us are linked by short chains of acquaintances — was first investigated as a question in sociology and is a feature of a range of networks arising in nature and technology. Experimental study of the phenomenon revealed that it has two fundamental components: first, such short chains are ubiquitous, and second, individuals operating with purely local information are very adept at finding these chains. The first issue has been analysed, and here I investigate the second by modelling how individuals can find short chains in a large social network.


The link-prediction problem for social networks


An incremental strategy for tractor-trailer vehicle global trajectory optimization in the presence of obstacles

Trajectory planning is a critical aspect in driving an autonomous tractor-trailer vehicle. This study considers trajectory planning as a minimal-time optimal control problem that incorporates the kinematics, mechanical/physical constraints, environmental requirements and an optimization criterion. This formulation benefits in the ability to directly handle time-dependent requirements. A gradient-based numerical solver is adopted to tackle the formulated optimal control problem. An incremental strategy is proposed to enhance the global optimization ability of that solver and to help find satisfactory solutions in challenging cases. Specifically, when an optimal solution is found, we doubt whether that is merely a local optimum and then still expect to make evolution; when optimal solutions are not easy to obtain, we expect at least one feasible solution first, which is taken as a preliminary guess to assist the subsequent optimization process. Both procedures proceed alternatively until no progress can be made any further. Some intricate simulation results are well beyond manual operation ability. Moreover, our overall proposal, as a unified and open framework, can deal with a wide variety of user-specified requirements.


3D video fragments: dynamic point samples for real-time free-viewpoint video

We present 3D video fragments, a dynamic point sample framework for real-time 3D video. By generalizing 2D video pixels towards 3D irregular point samples we combine the simplicity of conventional 2D video processing with the power of more complex polygonal representations for 3D video. We propose a differential update scheme exploiting the spatio-temporal coherence of the video streams of multiple cameras. Updates are issued by operators such as inserts and deletes accounting for changes in the input video images. The operators from multiple cameras are processed, merged into a 3D video stream and transmitted to a remote site. We also introduce a novel concept for camera control which dynamically selects the set of relevant cameras for reconstruction. Moreover, it adapts to the processing load and rendering platform. Our framework is generic in the sense that it works with any realtime 3D reconstruction method which extracts depth from images. The video renderer displays 3D videos using an efficient point based splatting scheme and makes use of state-of-the-art vertex and pixel processing hardware for real-time visual processing.


Education , politics and opinions about climate change evidence for interaction effects

U.S. public opinion regarding climate change has become increasingly polarized in recent years, as partisan think tanks and others worked to recast an originally scientific topic into a political wedge issue. Nominally “scientific” arguments against taking anthropogenic climate change seriously have been publicized to reach informed but ideologically receptive audiences. Reflecting the success of such arguments, polls have noted that concern about climate change increased with education among Democrats, but decreased with education among Republicans. These observations lead to the hypothesis that there exist interaction (non-additive) effects between education or knowledge and political orientation, net of other background factors, in predicting public concern about climate change. Two regional telephone surveys, conducted in New Hampshire (n = 541) and Michigan (n = 1, 008) in 2008, included identical climate-change questions that provide opportunities to test this hypothesis. Multivariate analysis of both surveys finds significant interactions. These empirical results fit with theoretical interpretations and several other recent studies. They suggest that the classically identified social bases of concern about the environment in general, and climate in particular, have shifted in recent years. Narrowcast media, including the many Web sites devoted to discrediting climatechange concerns, provide ideal conduits for channeling contrarian arguments to an audience predisposed to believe and electronically spread them further. Activeresponse Web sites by climate scientists could prove critical to counterbalancing contrarian arguments. L. C. Hamilton (B) Sociology Department, University of New Hampshire, Durham, NH, 03824, USA e-mail: Lawrence.Hamilton@unh.edu


Male-to-female transsexualism: technique, results and 3-year follow-up in 50 patients.

AIM
To evaluate the functional and cosmetic results of male-to-female gender-transforming surgery.

PATIENTS AND METHODS
Between May 2001 and April 2008 we performed 50 male-to-female gender-transforming surgeries. All patients had been cross-dressing, living as women, and receiving estrogen and progesterone for at least 12 months, which was sufficient for breast development and atrophy of the testes and prostate to occur. This hormonal therapy was suspended 1 month before the operation.

RESULTS
The mean operative time was 190 min and the mean depth of the vagina was 10 cm. On follow-up, the most common complication (10%) was shrinkage of the neovagina, which could be corrected by a second surgical intervention. Of the 50 patients, 45 (90%) were satisfied with the esthetic results; 42 patients (84%) reported having regular sexual intercourse, 2 of whom had pain during intercourse. Of the 50 patients, 35 (70%) reported achieving clitoral orgasm.

CONCLUSION
Male-to-female gender-transforming surgery can assure satisfactory cosmetic and functional results, with a reduced intra- and postoperative morbidity. Nevertheless the experience of the surgeon and the center remains central to obtaining optimal results.


Forecasting short-term taxi demand using boosting-GCRF

It will be most efficient to frame operation strategies before actual taxi demand is revealed. But this is challenging due to limited knowledge of the taxi demand distribution in immediate future and is more prone to prediction errors. In this study, we develop the boosting Gaussian conditional random field (boosting-GCRF) model to accurately forecast the short-term taxi demand distribution using historical time-series demand over the study area. Comprehensive numerical experiments are conducted to evaluate the performance of boosting-GCRF as compared to five other benchmark algorithms. The results suggest that the boosting-GCRF is superior with the best modified mean absolute percentage error being 10.4%. The approach is observed to be robust based on its prediction performance on anomaly taxi demand data. In addition, the density functions generated by the boosting-GCRF model are found to well capture the actual distribution of the short-term taxi demand.


Computational Models of Reading: A Primer

This article brief ly reviews four computational models of reading and the types of empirical findings that they are designed to explain: (1) theDual-Route model of word identification (Coltheart et al., 2001); (2) the SimpleRecurrent Network model of sentence processing (Elman, 1990); (3) the Construction–Integration model of discourse representation (Kintsch, 1988); and (4) the E-Z Reader model of eye-movement control in reading (Reichle et al., 2012). These particular models are reviewed because they provide comprehensive accounts of a large number of empirical findings in each of their respective domains, and because they have advanced our understanding of the cognitive processes involved in reading by motivating new empirical studies. Future models of reading will build upon the success of their predecessors by integrating models from two or more of the aforementioned domains, thereby providing a means to examine the compatibility of their theoretical assumptions and more comprehensive accounts of the cognitive processes involved in reading. The perceptual, cognitive, and motor processes that support skilled reading are numerous, extremely complex, and work together in a highly coordinated manner. For those reasons, any deep understanding of what happens in the mind during reading requires the use of computational models that describe those processes and how they jointly determine both the on-line behavior that is observed during reading (e.g., the patterns of eye movements that readers make) and the products of that behavior (e.g., the mental representations that readers generate to understand the text). As will be discussed in this article, such models provide theoretical frameworks for understanding the mental processes involved in reading, but also advance our understanding by generating novel predictions that can be tested with experiments. In the remainder of this article, I will describe four existing models that have already played important roles in furthering our understanding of the psychology of reading. These models were selected for inclusion in this article because they account for a wide range of empirical phenomena within a broad area of reading research, and because they have motivated a large number of new empirical studies. The models were also selected because they collectively span the range of topics that have traditionally been studied by reading researchers: (1) word identification; (2) sentence processing; (3) the representation of discourse; and (4) how these different processes interact with other cognitive processes (e.g., attention) to determine the patterns of eye movements that are observed during reading. Because one goal of this article is to introduce the models in a way that is accessible to a broad audience, I will describe the models using schematic diagrams rather than their equations; for a more formal description of the models, see either the original articles or Reichle’s review of the models. Word-Identification Models Word-identification models describe how the visual features corresponding to a word on a printed page are used to access that word’s pronunciation and meaning, as well as behavioral deficits that result from impairment of these processes due to developmental delay or brain © 2015 The Author Language and Linguistics Compass © 2015 John Wiley & Sons Ltd


Factorized adaptation for deep neural network

In this paper, we propose a novel method to adapt context-dependent deep neural network hidden Markov model (CD-DNN-HMM) with only limited number of parameters by taking into account the underlying factors that contribute to the distorted speech signal. We derive this factorized adaptation method from the perspectives of joint factor analysis and vector Taylor series expansion, respectively. Evaluated on Aurora 4, the proposed method can get 19.0% and 10.6% relative word error rate reduction on test set B and D with only 20 adaptation utterances, and can have decent improvement with as few as two adaptation utterances. We also show that the proposed method is better than feature discriminative linear regression (fDLR), an existing DNN adaptation method. Its small number of parameters and short training time offer an attractive solution to low-footprint speech applications.


DeepPore: Fingerprint Pore Extraction Using Deep Convolutional Neural Networks

As technological developments have enabled high-quality fingerprint scanning, sweat pores, one of the Level 3 features of fingerprints, have been successfully used in automatic fingerprint recognition systems (AFRS). Since the pore extraction process is a critical step for AFRS, high accuracy is required. However, it is difficult to extract the pore correctly because the pore shape depends on the person, region, and pore type. To solve the problem, we have presented a pore extraction method using deep convolutional neural networks and pore intensity refinement. The deep networks are used to detect pores in detail using a large area of a fingerprint image. We then refine the pore information by finding local maxima to identify pores with different intensities in the fingerprint image. The experimental results show that our pore extraction method performs better than the state-of-the-art methods.


How does sexual minority stigma "get under the skin"? A psychological mediation framework.

Sexual minorities are at increased risk for multiple mental health burdens compared with heterosexuals. The field has identified 2 distinct determinants of this risk, including group-specific minority stressors and general psychological processes that are common across sexual orientations. The goal of the present article is to develop a theoretical framework that integrates the important insights from these literatures. The framework postulates that (a) sexual minorities confront increased stress exposure resulting from stigma; (b) this stigma-related stress creates elevations in general emotion dysregulation, social/interpersonal problems, and cognitive processes conferring risk for psychopathology; and (c) these processes in turn mediate the relationship between stigma-related stress and psychopathology. It is argued that this framework can, theoretically, illuminate how stigma adversely affects mental health and, practically, inform clinical interventions. Evidence for the predictive validity of this framework is reviewed, with particular attention paid to illustrative examples from research on depression, anxiety, and alcohol-use disorders.


Research on significance of VCPU scheduling for SR-IOV on NUMA platform

Virtualization is vital important in server consolidation. Using virtualization technology, a single physical machine can provide multiple isolated virtual machine for users, which will improve the overall system resources usage. However, In virtualization environment, Non-Uniform Memory Access Architecture (NUMA) problem is also a very important parameter that affects server's performance. To solve NUMA problem, many solutions have been proposed to reduce its performance impact on virtual machines and improve the overall system performance. Hardware Single Root I/O Virtualization (SR-IOV) is a solution for high performance network processing, but when SR-IOV technology is running on a NUMA platform, it also generates the traditional problem about NUMA.


Real-time procedural generation of 'pseudo infinite' cities

We present an approach to procedural generation of `pseudo infinite' virtual cities in real-time. The cities contain geometrically varied buildings that are generated as needed. The building generation parameters are created by a pseudo random number generator, seeded with an integer derived from the building's position. The varied building geometries are extruded from a set of floor plans. The floor plans for each building are created by combining randomly generated polygons in an iterative process. A display list caching and frustum filling approach manages the generation of buildings and the use of system resources. This approach has been implemented on commodity PC hardware, resulting in interactive frame rates.


Hard Words

How do children acquire the meaning of words? And why are words such as know harder for learners to acquire than words such as dog or jump? We suggest that the chief limiting factor in acquiring the vocabulary of natural languages consists not in overcoming conceptual difficulties with abstract word meanings but rather in mapping these meanings onto their corresponding lexical forms. This opening premise of our position, while controversial, is shared with some prior approaches. The present discussion moves forward from there to a detailed proposal for how the mapping problem for the lexicon is solved, as well as a presentation of experimental findings that support thisaccount.Wedescribeanoverlappingseriesofsteps throughwhichnovicesmove in representing the lexical forms and phrase structures of the exposure language, a probabilistic multiple-cue learning process known as syntactic bootstrapping. The machinery is set inmotionbyword-to-worldpairing, aprocedureavailable tonovices fromthe LANGUAGE LEARNING AND DEVELOPMENT, 1(1), 23–64 Copyright © 2005, Lawrence Erlbaum Associates, Inc.


Mid-face Rejuvenation Mr Dalvi Humzah and

With age, the mid-face changes due to a combination of remodelling of the maxilla and pyriform aperture, volumetric and positional changes of the superficial and deep mid-facial fat pads, and changes in skin tone and texture. It is widely accepted that these changes significantly influence many of the visual changes in the lower face. These may be effectively treated with a variety of injectable techniques to rejuvenate the mid-face. The current literature concerning the pertinent anatomy for mid-facial rejuvenation is therefore discussed within this article. Introduction In recent years, the way we analyse and plan the non-surgical approach to the mid-face has changed dramatically. The focus has shifted away from treating visible lines/folds as they present, and is now centred on treating the age-related changes at the root cause. The restoration of a dynamic and harmonious volume distribution is a key factor in non-surgical rejuvenation of the mid-face. There is a growing body of literature demonstrating the multifactorial nature of facial ageing. These indicate that facial analysis and subsequent rejuvenation should initially address such anatomical changes. An understanding of the anatomical changes associated with ageing is required to formulate effective strategies to rejuvenate the ageing mid-face.


Virtual Machine Migration in Cloud Computing

Cloud computing is the delivers the computing services over the internet. Cloud services help individuals and organization to use data that are managed by third parties or another person at remote locations. Virtual Machine (VM) is an emulation of a particular computer system. In cloud computing, Virtual machine migration is a useful tool for migrating Operating System instances across multiple physical machines. It is used to load balancing, fault management, low-level system maintenance and reduce energy consumption. There are various techniques and parameters available for VM migration. This paper presents the various virtual machine migration techniques.


The KUKA Control Toolbox: motion control of KUKA robot manipulators with MATLAB

The KUKA Control Toolbox (KCT) is a collection of MATLAB functions developed at the University of Siena, for motion control of KUKA robot manipulators. The toolbox, which is compatible with all 6 DOF small and low-payload KUKA robots that use the Eth.RSIXML, runs on a remote computer connected with the KUKA controller via TCP/IP. KCT includes more than 40 functions, spanning operations such as forward and inverse kinematics computation, point-to-point joint and Cartesian control, trajectory generation, graphical display, 3-D animation and diagnostics. Applicative examples show the flexibility of KCT and its easy interfacing with other toolboxes and external devices.


Saliency Detection via Absorbing Markov Chain

In this paper, we formulate saliency detection via absorbing Markov chain on an image graph model. We jointly consider the appearance divergence and spatial distribution of salient objects and the background. The virtual boundary nodes are chosen as the absorbing nodes in a Markov chain and the absorbed time from each transient node to boundary absorbing nodes is computed. The absorbed time of transient node measures its global similarity with all absorbing nodes, and thus salient objects can be consistently separated from the background when the absorbed time is used as a metric. Since the time from transient node to absorbing nodes relies on the weights on the path and their spatial distance, the background region on the center of image may be salient. We further exploit the equilibrium distribution in an ergodic Markov chain to reduce the absorbed time in the long-range smooth background regions. Extensive experiments on four benchmark datasets demonstrate robustness and efficiency of the proposed method against the state-of-the-art methods.


Human Activity Recognition and Pattern Discovery

In principle, activity recognition can be exploited to great societal benefits, especially in real-life, human centric applications such as elder care and healthcare. This article focused on recognizing simple human activities. Recognizing complex activities remains a challenging and active area of research and the nature of human activities poses different challenges. Human activity understanding encompasses activity recognition and activity pattern discovery. The first focuses on accurate detection of human activities based on a predefined activity model. An activity pattern discovery researcher builds a pervasive system first and then analyzes the sensor data to discover activity patterns.


Soft Biometrics in Online Social Networks: A Case Study on Twitter User Gender Recognition

The proliferate unstructured data generated in online social networks leads to significant research advances in the recognition of user profiles (e.g., age, gender, ethnicity, etc.), but meanwhile brings new challenges. These attributes are referred to as soft biometrics and provide a semantic description of users. Identifying users' soft biometric traits in online social networks is crucial for a variety of applications such as customized marketing, personalized recommendation, and urban planning. Compared to conventional studies, we address the importance and challenges of identifying soft biometrics in online social networks and provide a case study on gender recognition of Twitter users based on their tweets texts and profile images. We first apply an efficient approach to label the soft biometric attributes of users using their self-reported names, instead of labor-forced methodology such as manually labeling and crowdsourcing. Then we investigate the approaches using texts and profile images, respectively, including Hashtag-based TF-IDF, LDA Topic Distribution, SIFT-based TF-IDF and Convolutional Neural Networks (CNN). Finally, we propose an ensemble method by combining models learned from both texts and images to enhance gender identification. The performance in our extensive experiments is demonstrated to be comparative with state-of the-art work.