title: Conpair: concordance and contamination estimator for matched tumor–normal pairs

abstract: MOTIVATION ::: Sequencing of matched tumor and normal samples is the standard study design for reliable detection of somatic alterations. However, even very low levels of cross-sample contamination significantly impact calling of somatic mutations, because contaminant germline variants can be incorrectly interpreted as somatic. There are currently no sequence-only based methods that reliably estimate contamination levels in tumor samples, which frequently display copy number changes. As a solution, we developed Conpair, a tool for detection of sample swaps and cross-individual contamination in whole-genome and whole-exome tumor-normal sequencing experiments. ::: ::: ::: RESULTS ::: On a ladder of in silico contaminated samples, we demonstrated that Conpair reliably measures contamination levels as low as 0.1%, even in presence of copy number changes. We also estimated contamination levels in glioblastoma WGS and WXS tumor-normal datasets from TCGA and showed that they strongly correlate with tumor-normal concordance, as well as with the number of germline variants called as somatic by several widely-used somatic callers. ::: ::: ::: AVAILABILITY AND IMPLEMENTATION ::: The method is available at: https://github.com/nygenome/conpair CONTACT: egrabowska@gmail.com or mczody@nygenome.orgSupplementary information: Supplementary data are available at Bioinformatics online.


Integrating Internal And External Stakeholders Into A Successful Abet Accreditation Team


Rate-Distortion Optimization for Video Compression


title: VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop

abstract: We present a new neural text to speech (TTS) method that is able to transform text to speech in voices that are sampled in the wild. Unlike other systems, our solution is able to deal with unconstrained voice samples and without requiring aligned phonemes or linguistic features. The network architecture is simpler than those in the existing literature and is based on a novel shifting buffer working memory. The same buffer is used for estimating the attention, computing the output audio, and for updating the buffer itself. The input sentence is encoded using a context-free lookup table that contains one entry per character or phoneme. The speakers are similarly represented by a short vector that can also be fitted to new identities, even with only a few samples. Variability in the generated speech is achieved by priming the buffer prior to generating the audio. Experimental results on several datasets demonstrate convincing capabilities, making TTS accessible to a wider range of applications. In order to promote reproducibility, we release our source code and models.


title: Voltage and electric field distribution along porcelain long rod insulator string in AC 500kV transmission line

abstract: Porcelain long rod insulators are widely used in transmission lines while many of their features are lack of research. To determine the voltage and electric field distribution of porcelain long rod insulators helps to guide the existing operational strategy. In accordance with the insulator structure and the actual working conditions, the simulative calculation of porcelain long rod insulator string in 500kV transmission line is carried out through finite element method in three-dimensional model considering the effects of tower, bundle conductors, grading ring, shielding line and so on. The simulative calculation is in comparison with disk ceramic insulator string. The calculation is based on method of solving electrostatic field and all insulator strings are assumed to be clean and dry. The result shows that the voltage distribution of porcelain long rod insulator string vary through structure height, which presents a decline curve of three parts, and the electric potential of two ends declines faster than the middle. Each single section insulator also presents a decline curve that two ends declines faster than the middle. The voltage distribution of three insulators are about for 5:2:3, which means over 50% operating voltage concentrates on the single unit of high voltage polarity for the long rod insulator string. The electric field strength distribution of porcelain long rod insulator string presents a U-shaped curve and each single section insulator also presents a U-shaped curve. The maximum field strength lies on the first rod core areas of umbrella skirt root of the high voltage end. Compared with disk ceramic insulator string, porcelain long rod insulator has less fittings, leading to faster voltage decline in both ends. The surface electric field strength of porcelain long rod insulator in both ends is higher than disk ceramic insulator string, its grading ring could be optimized further. In summary, the voltage and electric field distribution of porcelain long rod insulator is more uneven, which may lead to higher risk of corona, flashover or even breakdown.


title: Echo State Gaussian Process

abstract: Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of benchmark tasks. In this paper, we introduce a novel Bayesian approach toward ESNs, the echo state Gaussian process (ESGP). The ESGP combines the merits of ESNs and Gaussian processes to provide a more robust alternative to conventional reservoir computing networks while also offering a measure of confidence on the generated predictions (in the form of a predictive distribution). We exhibit the merits of our approach in a number of applications, considering both benchmark datasets and real-world applications, where we show that our method offers a significant enhancement in the dynamical data modeling capabilities of ESNs. Additionally, we also show that our method is orders of magnitude more computationally efficient compared to existing Gaussian process-based methods for dynamical data modeling, without compromises in the obtained predictive performance.


Identification of Fasciola spp. in the East of Iran, Based on the Spermatogenesis and Nuclear Ribosomal DNA ( ITS1 ) and Mitochondrial ( ND1 ) Genes


title: Domestic electricity use: A high-resolution energy demand model

abstract: The pattern of electricity use in an individual domestic dwelling is highly dependent upon the activities of the occupants and their associated use of electrical appliances. This paper presents a high-resolution model of domestic electricity use that is based upon a combination of patterns of active occupancy (i.e. when people are at home and awake), and daily activity profiles that characterise how people spend their time performing certain activities. One-min resolution synthetic electricity demand data is created through the simulation of appliance use; the model covers all major appliances commonly found in the domestic environment. In order to validate the model, electricity demand was recorded over the period of a year within 22 dwellings in the East Midlands, UK. A thorough quantitative comparison is made between the synthetic and measured data sets, showing them to have similar statistical characteristics. A freely downloadable example of the model is made available and may be configured to the particular requirements of users or incorporated into other models.


title: Object Oriented Metrics and Quality Attributes: A Survey

abstract: Design phase is considered one of the most important phases of software development process. Considering design quality is a significant indicator to guarantee a good product. Despite the fact that this phase is more important, it takes less effort in processing but it can take a large part of the total effort consumed in correcting earlier issues that resulted from bad design decisions. If the design quality of a product wasn't previously considered, the cost of fixing bad design issues after software delivery will be very high. Recently researchers have introduced several object oriented metrics for identifying design problems and avoiding such issues at earlier development phases. This paper initiates a survey regarding object-oriented metrics and their quality attributes. Also it shows the relationship between internal and external quality attributes and identify a group of practical and common used metrics which having a significant impact on design quality.


title: Mining concepts from code with probabilistic topic models

abstract: We develop and apply statistical topic models to software as a means of extracting concepts from source code. The effectiveness of the technique is demonstrated on 1,555 projects from SourceForge and Apache consisting of 113,000 files and 19 million lines of code. In addition to providing an automated, unsupervised, solution to the problem of summarizing program functionality, the approach provides a probabilistic framework with which to analyze and visualize source file similarity. Finally, we introduce an information-theoretic approach for computing tangling and scattering of extracted concepts, and present preliminary results


Trojaning Attack on Neural Networks


title: Next Generation Automotive Architecture Modeling and Exploration for Autonomous Driving

abstract: To support emerging applications in autonomous and semi-autonomous driving, next-generation automotive systems will be equipped with an increasing number of heterogeneous components (sensors, actuators and computation units connected through various buses), and have to process a high volume of data to percept the environment accurately and efficiently. Challenges for such systems include system integration, prediction, verification and validation. In this work, we propose an architecture modeling and exploration framework for evaluating various software and hardware architecture options. The framework will facilitate system integration and optimization, and enable validation of various design metrics such as timing, reliability, security and performance.


title: Learning a classification model for segmentation

abstract: We propose a two-class classification model for grouping. Human segmented natural images are used as positive examples. Negative examples of grouping are constructed by randomly matching human segmentations and images. In a preprocessing stage an image is over-segmented into super-pixels. We define a variety of features derived from the classical Gestalt cues, including contour, texture, brightness and good continuation. Information-theoretic analysis is applied to evaluate the power of these grouping cues. We train a linear classifier to combine these features. To demonstrate the power of the classification model, a simple algorithm is used to randomly search for good segmentations. Results are shown on a wide range of images.


title: Model predictive control for PV maximum power point tracking of single-phase submultilevel inverter

abstract: Dynamic behavior of solar energy resource entails the need of robust controllers that can converge to the maximum power point (MPP) to maximize energy harvest. This paper explores an improved Perturb and Observe (P&O) technique that combines a fixed step model predictive controller (MPC), to speed up the control loop, applied to a boost converter. The proposed MPC Maximum Power Point Tracking (MPPT) technique is of higher efficacy and robustness over conventional MPPT. The improved MPC-MPPT is tested for the first time on an MPC strategy of seven-level subMultilevel Inverter (sMI) using three power arms cascaded with the H-bridge inverter. Such topology brings about many sizable benefits such as reduced number of power switches and their gate drivers when compared to the traditional multilevel inverter. MPC is also used as the control strategy for the sMI to eliminate complexities in the space vector pulse width modulation (SVPWM) and overcome the weaknesses of the inner control loop performance.


title: Requirements elicitation for the design of context-aware applications in a ubiquitous environment

abstract: With the invention of new interaction devices and the requirements for ubiquitous access to application systems, user's interactions have moved beyond the desktop and evolved into a trend of ongoing development. The context in which the application is being used becomes an integral part of the activity carried out with the system. The inclusion of context-awareness provides convenience and efficiency to users for their ubiquitous access. Traditional human-computer interface (HCI) theories are now inadequate for developing these context-aware applications, as we believe that the notion of context should be extended to different categories: computing contexts, user contexts, and physical contexts for ubiquitous computing. This demands a new paradigm for system requirements elicitation and design in order to make good use of such extended context information. In this paper, we introduce a methodology for the elicitation of context-aware requirements and the matching of context-awareness features to the target context by capability matching. Based on this model, we analyze the design issues of some common ubiquitous access situations and show how to fit them systematically into a context-aware application by considering the requirements of a ubiquitous tourist application.


title: Esophageal Atresia and Tracheo-Esophageal Fistula — 25 Years' Experience and Current Management

abstract: Abstract ::: A review of the experience with esophageal atresia and tracheoesophageal fistula over a 25-year period appears to lead to the advisability of the following procedures in surgical management: ::: ::: • Emergency gastrostomy under local anesthesia in all patients. ::: ::: • Extrapleural interruption of tracheo-esophageal fistula and end-to-end esophago-esophagostomy in patients who have the common type of upper esophageal atresia with distal tracheo-esophageal fistula. ::: ::: • Upper esophageal stretching and eventual esophago-esophagostomy in patients with proximal and distal esophageal atresia with or without proximal tracheo-esophageal fistula.


title: Scene Classification via Triplet Networks

abstract: Scene classification is a fundamental task for automatic remote sensing image understanding. In recent years, convolutional neural networks have become a hot research topic in the remote sensing community, and have made great achievements in scene classification. Deep convolutional networks are primarily trained in a supervised way, requiring huge volumes of labeled training samples. However, clearly labeled remote sensing data are usually limited. To address this issue, in this paper, we propose a novel scene classification method via triplet networks, which use weakly labeled images as network inputs. Besides, we initiate a theoretical study on the three existing loss functions for triplet networks, analyzing their different underlying mechanisms for dealing with “hard” and/or “easy” triplets during training. Furthermore, four new loss functions are constructed, aiming at laying more stress on “hard” triplets to improve classification accuracy. Extensive experiments have been conducted, and the experimental results show that triplet networks coupled with our proposed losses achieve a state-of-the-art performance in scene classification tasks.


title: Unified Approach to Prefiltered Environment Maps

abstract: Different methods for prefiltered environment maps have been proposed, each of which has different advantages and disadvantages. We present a general notation for prefiltered environment maps, which will be used to classify and compare the existing methods. Based on that knowledge we develop three new algorithms: 1. A fast hierarchical prefiltering method that can be utilized for all previously proposed prefiltered environment maps. 2. A technique for hardware-accelerated prefiltering of environment maps that achieves interactive rates even on low-end workstations. 3. Anisotropic environment maps using the Banks model.


title: Learning feed-forward one-shot learners

abstract: One-shot learning is usually tackled by using generative models or discriminative embeddings. Discriminative methods based on deep learning, which are very effective in other learning scenarios, are ill-suited for one-shot learning as they need large amounts of training data. In this paper, we propose a method to learn the parameters of a deep model in one shot. We construct the learner as a second deep network, called a learnet, which predicts the parameters of a pupil network from a single exemplar. In this manner we obtain an efficient feed-forward one-shot learner, trained end-to-end by minimizing a one-shot classification objective in a learning to learn formulation. In order to make the construction feasible, we propose a number of factorizations of the parameters of the pupil network. We demonstrate encouraging results by learning characters from single exemplars in Omniglot, and by tracking visual objects from a single initial exemplar in the Visual Object Tracking benchmark.


title: Fully automatic detection of lung nodules in CT images using a hybrid feature set

abstract: Purpose: The aim of this study was to develop a novel technique for lung nodule detection using an optimized feature set. This feature set has been achieved after rigorous experimentation, which has helped in reducing the false positives significantly. ::: ::: ::: ::: Method: The proposed method starts with preprocessing, removing any present noise from input images, followed by lung segmentation using optimal thresholding. Then the image is enhanced using multiscale dot enhancement filtering prior to nodule detection and feature extraction. Finally, classification of lung nodules is achieved using Support Vector Machine (SVM) classifier. The feature set consists of intensity, shape (2D and 3D) and texture features, which have been selected to optimize the sensitivity and reduce false positives. In addition to SVM, some other supervised classifiers like K‐Nearest‐Neighbor (KNN), Decision Tree and Linear Discriminant Analysis (LDA) have also been used for performance comparison. The extracted features have also been compared class‐wise to determine the most relevant features for lung nodule detection. The proposed system has been evaluated using 850 scans from Lung Image Database Consortium (LIDC) dataset and k‐fold cross‐validation scheme. ::: ::: ::: ::: Results: The overall sensitivity has been improved compared to the previous methods and false positives per scan have been reduced significantly. The achieved sensitivities at detection and classification stages are 94.20% and 98.15%, respectively, with only 2.19 false positives per scan. ::: ::: ::: ::: Conclusions: It is very difficult to achieve high performance metrics using only a single feature class therefore hybrid approach in feature selection remains a better choice. Choosing right set of features can improve the overall accuracy of the system by improving the sensitivity and reducing false positives.


title: Design of 13.56 MHz Smartcard Stickers with Ferrite for Payment and Authentication

abstract: We present an adhesive sticker which allows contactless Smartcard function widely independent of the object material which it is sticked to. Recently interest raised for such a sticker to be attached on any mobile phone for contactless payment applications. This is seen as precessor of near field communication (NFC) technology helping to introduce contactless payment with mobile phone. In this context we consider antenna design with field simulation software and evaluate electrical antenna parameters of samples fabricated in the Smartcard process flow. Aspects for conformance to the ISO/IEC14443-2 and NFC card standard are considered and an optimized composite design is proposed.


title: Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs

abstract: In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs. We propose a novel self-attention based slot matching model which exploits the inherent structure of query graphs, our logical form of choice. Our proposed model generally outperforms other ranking models on two QA datasets over the DBpedia knowledge graph, evaluated in different settings. We also show that domain adaption and pre-trained language model based transfer learning yield improvements, effectively offsetting the general lack of training data.


title: Scale-free Networks Well Done

abstract: We bring rigor to the vibrant activity of detecting power laws in empirical degree distributions in real-world networks. We first provide a rigorous definition of power-law distributions, equivalent to the definition of regularly varying distributions in statistics. This definition allows the distribution to deviate from a pure power law arbitrarily but without affecting the power-law tail exponent. We then identify three estimators of these exponents that are proven to be statistically consistent -- that is, converging to the true exponent value for any regularly varying distribution -- and that satisfy some additional niceness requirements. Finally, we apply these estimators to a representative collection of synthetic and real-world data. According to their estimates, real-world scale-free networks are definitely not as rare as one would conclude based on the popular but unrealistic assumption that real-world data comes from power laws of pristine purity, void of noise and deviations.


title: Neural Basis of Video Gaming: A Systematic Review

abstract: Background Video gaming is an increasingly popular activity in contemporary society, especially among young people, and video games are increasing in popularity not only as a research tool but also as a field of study. Many studies have focused on the neural and behavioral effects of video games, providing a great deal of video game derived brain correlates in recent decades. There is a great amount of information, obtained through a myriad of methods, providing neural correlates of video games. Objectives We aim to understand the relationship between the use of video games and their neural correlates, taking into account the whole variety of cognitive factors that they encompass. Methods A systematic review was conducted using standardized search operators that included the presence of video games and neuro-imaging techniques or references to structural or functional brain changes. Separate categories were made for studies featuring Internet Gaming Disorder and studies focused on the violent content of video games. Results A total of 116 articles were considered for the final selection. One hundred provided functional data and twenty two measured structural brain changes. One-third of the studies covered video game addiction, and 14% focused on video game related violence. Conclusions Despite the innate heterogeneity of the field of study, it has been possible to establish a series of links between the neural and cognitive aspects, particularly regarding attention, cognitive control, visuospatial skills, cognitive workload and reward processing. However, many aspects could be improved. The lack of standardization in the different aspects of video game-related research, such as the participants’ characteristics, the features of each video game genre and the diverse study goals could contribute to discrepancies in many related studies.


title: Fast Sliding Window Classification with Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) have repeatedly been shown to be the state of the art method for natural signal classification -- image classification in particular. Unfortunately, due to the high model complexity CNNs often cannot be used for object detection tasks with real-time constraints, where many predictions have to be made on sub-windows of a large input image. We demonstrate how two recent advances in CNN efficiency can be combined, with modifications, to provide a substantial speedup for sliding window classification. An in depth analysis of the various factors that can impact performance is presented.


title: Prototype Design of a Modular Ultrawideband Wavelength-Scaled Array of Flared Notches

abstract: A prototype modular ultrawideband wavelength- scaled array of flared notches has been designed, built, measured and validated with full-wave modeling tools. Wavelength-scaled arrays operate over ultrawide bandwidths with significantly-reduced element counts, maintaining a relatively-constant beam size by utilizing phased-array radiators of different size. The prototype phased array presented here is designed to operate over an 8:1 bandwidth (1-8 GHz), demonstrating a 12-degree beam capacity at 2 GHz, 4 GHz, and 8 GHz. The architecture achieves a reduction in element count by a factor of 6.4-only 160 elements per polarization as compared to a conventional 1024-element phased array of the same aperture size-at the cost of reduced beamwidth capacity in the higher frequency range. Performance metrics (active VSWR and radiation characteristics) of the wavelength-scaled array are measured and validated against full-wave simulations. The technology is presented as a viable alternative to more expensive conventional ultrawideband arrays with dense uniform element layouts.


title: Channel Capacity of Electromagnetic Nanonetworks in the Terahertz Band

abstract: Nanotechnology is enabling the development of devices in a scale ranging from one to a few hundred nanometers. Coordination and information sharing among these nano-devices will lead towards the development of future nanonetworks, rising new applications of nanotechnology in the medical, environmental and military fields. Despite the major progress in nano-device design and fabrication, it is still not clear how these atomically precise machines will communicate. The latest advancements in graphene- based electronics have opened the door to electromagnetic communication among nano-devices in the terahertz band (0.1-10 THz). This frequency band can potentially provide very large bandwidths, ranging from the entire band to several gigahertz- wide windows, depending on the transmission distance and the molecular composition of the channel. In this paper, the capacity of the terahertz channel is numerically evaluated by using a new terahertz propagation model, for different channel molecular compositions, and under different power allocation schemes. A novel communication technique based on the transmission of ultra-short pulses, less than one picosecond long, is motivated and quantitatively compared to the capacity- optimal power allocation scheme. The results show that for the very short range, up to a few tens of millimeters, the transmission of short pulses offer a realistic and still efficient way to exploit the terahertz channel.


title: Topology Cuts: A Novel Min-Cut/Max-Flow Algorithm for Topology Preserving Segmentation in N-D Images

abstract: Topology is an important prior in many image segmentation tasks. In this paper, we design and implement a novel graph-based min-cut/max-flow algorithm that incorporates topology priors as global constraints. We show that the optimization of the energy function we consider here is NP-hard. However, our algorithm is guaranteed to find an approximate solution that conforms to the initialization, which is a desirable property in many applications since the globally optimum solution does not consider any initialization information. The key innovation of our algorithm is the organization of the search for maximum flow in a way that allows consideration of topology constraints. In order to achieve this, we introduce a label attribute for each node to explicitly handle the topology constraints, and we use a distance map to keep track of those nodes that are closest to the boundary. We employ the bucket priority queue data structure that records nodes of equal distance and we efficiently extract the node with minimal distance value. Our methodology of embedding distance functions in a graph-based algorithm is general and can also account for other geometric priors. Experimental results show that our algorithm can efficiently handle segmentation cases that are challenging for graph-cut algorithms. Furthermore, our algorithm is a natural choice for problems with rich topology priors such as object tracking.


title: InferSpark: Statistical Inference at Scale

abstract: The Apache Spark stack has enabled fast large-scale data processing. Despite a rich library of statistical models and inference algorithms, it does not give domain users the ability to develop their own models. The emergence of probabilistic programming languages has showed the promise of developing sophisticated probabilistic models in a succinct and programmatic way. These frameworks have the potential of automatically generating inference algorithms for the user defined models and answering various statistical queries about the model. It is a perfect time to unite these two great directions to produce a programmable big data analysis framework. We thus propose, InferSpark, a probabilistic programming framework on top of Apache Spark. Efficient statistical inference can be easily implemented on this framework and inference process can leverage the distributed main memory processing power of Spark. This framework makes statistical inference on big data possible and speed up the penetration of probabilistic programming into the data engineering domain.


title: 2D-3D fusion for layer decomposition of urban facades

abstract: We present a method for fusing two acquisition modes, 2D photographs and 3D LiDAR scans, for depth-layer decomposition of urban facades. The two modes have complementary characteristics: point cloud scans are coherent and inherently 3D, but are often sparse, noisy, and incomplete; photographs, on the other hand, are of high resolution, easy to acquire, and dense, but view-dependent and inherently 2D, lacking critical depth information. In this paper we use photographs to enhance the acquired LiDAR data. Our key observation is that with an initial registration of the 2D and 3D datasets we can decompose the input photographs into rectified depth layers. We decompose the input photographs into rectangular planar fragments and diffuse depth information from the corresponding 3D scan onto the fragments by solving a multi-label assignment problem. Our layer decomposition enables accurate repetition detection in each planar layer, using which we propagate geometry, remove outliers and enhance the 3D scan. Finally, the algorithm produces an enhanced, layered, textured model. We evaluate our algorithm on complex multi-planar building facades, where direct autocorrelation methods for repetition detection fail. We demonstrate how 2D photographs help improve the 3D scans by exploiting data redundancy, and transferring high level structural information to (plausibly) complete large missing regions.


title: Decentralized control for renewable DC Microgrid with composite energy storage system and UC voltage restoration connected to the grid

abstract: In this paper we propose a new decentralized control strategy applied to a DC Microgrid in order to manage the power delivery of storage devices into a common DC-link, avoiding high-bandwidth communication (HBC) between the storage devices (SD) and alternative sources. Batteries and Ultracapacitors (UC) are used as SD and the common DC-link is fed by alternative sources such as photovoltaic panels, wind turbines and fuel cells as well. The batteries are used to supply/absorb extra power in steady-state regime while the UC absorbs the power transients caused by variations on the power production or load connections. The proposed strategy uses as input for the batteries control only the DC-link voltage and state of charge (SOC), while for the UC only the DC-link voltage and UC terminal voltage are used to achieve the power sharing among the storage devices, equalization of the batteries and voltage restoration of the UC without HBC. The DC-link voltage is not restored in order to work as the sharing signal between storage devices. Additionally, the DC Microgrid is connected to the AC grid in order to deliver the extra power to the distributed system; however, the voltage variation on the DC-link does not affect the power quality of the produced energy. Simulated and experimental results are presented to demonstrate the feasibility of the proposed approach.


title: Sensitivity and Generalization in Neural Networks: an Empirical Study

abstract: In practice it is often found that large over-parameterized neural networks generalize better than their smaller counterparts, an observation that appears to conflict with classical notions of function complexity, which typically favor smaller models. In this work, we investigate this tension between complexity and generalization through an extensive empirical exploration of two natural metrics of complexity related to sensitivity to input perturbations. Our experiments survey thousands of models with various fully-connected architectures, optimizers, and other hyper-parameters, as well as four different image classification datasets. We find that trained neural networks are more robust to input perturbations in the vicinity of the training data manifold, as measured by the norm of the input-output Jacobian of the network, and that it correlates well with generalization. We further establish that factors associated with poor generalization $-$ such as full-batch training or using random labels $-$ correspond to lower robustness, while factors associated with good generalization $-$ such as data augmentation and ReLU non-linearities $-$ give rise to more robust functions. Finally, we demonstrate how the input-output Jacobian norm can be predictive of generalization at the level of individual test points.


title: Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs

abstract: Betweenness centrality quantifies the importance of nodes in a graph in many applications, including network analysis, community detection and identification of influential users. Typically, graphs in such applications evolve over time. Thus, the computation of betweenness centrality should be performed incrementally. This is challenging because updating even a single edge may trigger the computation of all-pairs shortest paths in the entire graph. Existing approaches cannot scale to large graphs: they either require excessive memory (i.e., quadratic to the size of the input graph) or perform unnecessary computations rendering them prohibitively slow. We propose $i$ Central ; a novel incremental algorithm for computing betweenness centrality in evolving graphs. We decompose the graph into biconnected components and prove that processing can be localized within the affected components. $i$ Central is the first algorithm to support incremental betweeness centrality computation within a graph component. This is done efficiently, in linear space; consequently, $i$ Central scales to large graphs. We demonstrate with real datasets that the serial implementation of $i$ Central is up to 3.7 times faster than existing serial methods. Our parallel implementation that scales to large graphs, is an order of magnitude faster than the state-of-the-art parallel algorithm, while using an order of magnitude less computational resources.


title: A Novel Interface for Higher-Dimensional Classification of Volume Data

abstract: In the traditional volume visualization paradigm, the user specifies a transfer function that assigns each scalar value to a color and opacity by defining an opacity and a color map function. The transfer function has two limitations. First, the user must define curves based on histogram and value rather than seeing and working with the volume itself. Second, the transfer function is inflexible in classifying regions of interest, where values at a voxel such as intensity and gradient are used to differentiate material, not talking into account additional properties such as texture and position. We describe an intuitive user interface for specifying the classification functions that consists of the users painting directly on sample slices of the volume. These painted regions are used to automatically define high-dimensional classification functions that can be implemented in hardware for interactive rendering. The classification of the volume is iteratively improved as the user paints samples, allowing intuitive and efficient viewing of materials of interest.


title: Nature inspired techniques for data clustering

abstract: Nature is always a source of inspiration. In last few decades, the research is stimulated on new computing paradigms and result of this effort is emergence of new problem solving techniques like Nature Inspired Computing, Evolutionary Computing. Nature inspired problem solving techniques are widely used to solve complex problems. These techniques are widely used due to their decentralized and self-organized behavior. Such behavior is observed in social systems such as artificial bee colony algorithm, particle swarm optimization, ant colony optimization, bat algorithm, firefly algorithm, glowworm swarm optimization etc. In this paper we have given overview of nature inspired techniques used for data clustering, hybridization with traditional clustering techniques and their effectiveness.


title: SpamGuru: An Enterprise Anti-Spam Filtering System

abstract: Spam-reduction techniques have developed rapidly over the last few years, as spam volumes have increased. We believe that no one anti-spam solution is the “right” answer, and that the best approach is a multifaceted one, combining various forms of filtering with infrastructure changes, financial changes, legal recourse, and more, to provide a stronger barrier to spam than can be achieved with one solution alone. SpamGuru addresses the part of this multi-faceted approach that can be handled by technology on the recipient’s side, using plug-in tokenizers and parsers, plug-in classification modules, and machine-learning techniques to achieve high hit rates and low false-positive rates.


title: Accelerating Low Bit-Width Deep Convolution Neural Network in MRAM

abstract: Deep Convolution Neural Network (CNN) has achieved outstanding performance in image recognition over large scale dataset. However, pursuit of higher inference accuracy leads to CNN architecture with deeper layers and denser connections, which inevitably makes its hardware implementation demand more and more memory and computational resources. It can be interpreted as ‘CNN power and memory wall’. Recent research efforts have significantly reduced both model size and computational complexity by using low bit-width weights, activations and gradients, while keeping reasonably good accuracy. In this work, we present different emerging nonvolatile Magnetic Random Access Memory (MRAM) designs that could be leveraged to implement ‘bit-wise in-memory convolution engine’, which could simultaneously store network parameters and compute low bit-width convolution. Such new computing model leverages the ‘in-memory computing’ concept to accelerate CNN inference and reduce convolution energy consumption due to intrinsic logic-in-memory design and reduction of data communication.


preference. Brand Preferences in Children 3 The Effect of Movies and Television Commercials


title: Imaging Calcium in Neurons

abstract: Calcium ions generate versatile intracellular signals that control key functions in all types of neurons. Imaging calcium in neurons is particularly important because calcium signals exert their highly specific functions in well-defined cellular subcompartments. In this Primer, we briefly review the general mechanisms of neuronal calcium signaling. We then introduce the calcium imaging devices, including confocal and two-photon microscopy as well as miniaturized devices that are used in freely moving animals. We provide an overview of the classical chemical fluorescent calcium indicators and of the protein-based genetically encoded calcium indicators. Using application examples, we introduce new developments in the field, such as calcium imaging in awake, behaving animals and the use of calcium imaging for mapping single spine sensory inputs in cortical neurons in vivo. We conclude by providing an outlook on the prospects of calcium imaging for the analysis of neuronal signaling and plasticity in various animal models.


title: Non-Threshold Quantum Secret Sharing Schemes in the Graph State Formalism

abstract: In a recent work, Markham and Sanders have proposed a framework to study quantum secret sharing (QSS) schemes using graph states. This framework unified three classes of QSS protocols, namely, sharing classical secrets over private and public channels, and sharing quantum secrets. However, most work on secret sharing based on graph states focused on threshold schemes. In this paper, we focus on general access structures. We show how to realize a large class of arbitrary access structures using the graph state formalism. We show an equivalence between $[[n,1]]$ binary quantum codes and graph state secret sharing schemes sharing one bit. We also establish a similar (but restricted) equivalence between a class of $[[n,1]]$ Calderbank-Shor-Steane (CSS) codes and graph state QSS schemes sharing one qubit. With these results we are able to construct a large class of quantum secret sharing schemes with arbitrary access structures.


title: Simple real-time human detection using a single correlation filter

abstract: This paper presents an extremely simple human detection algorithm based on correlating edge magnitude images with a filter. The key is the technology used to train the filter: Average of Synthetic Exact Filters (ASEF). The ASEF based detector can process images at over 25 frames per second and achieves a 94.5% detection rate with less than one false detection per frame for sparse crowds. Filter training is also fast, taking only 12 seconds to train the detector on 32 manually annotated images. Evaluation is performed on the PETS 2009 dataset and results are compared to the OpenCV cascade classifier and a state-of-the-art deformable parts based person detector.


title: A 3-D Camera With Adaptable Background Light Suppression Using Pixel-Binning and Super-Resolution

abstract: This paper presents a CMOS time-of-flight (TOF) 3-D camera employing a column-level background light (BGL) suppression scheme for high-resolution outdoor imaging. The use of the column-level approach minimizes a pixel size for high-density pixel arrays. Pixel-binning and super-resolution can be adaptably applied for an optimal BGL suppression at given spatiotemporal resolutions. A prototype sensor has been fabricated by using 0.11 μm CMOS processes. The sensor achieved a fill factor of 24% in a pixel pitch of 5.9 μm which is the smallest among all the reported TOF cameras up to date. Measurement results showed the temporal noise of 1.47 cm-rms with a 100 ms integration time at a demodulation frequency of 12.5 MHz using a white target at 1 m distance. The non-linearity was measured as 1% over the range of 0.75 m ~ 4 m. The BGL suppression over 100 klx was achieved from indoor and outdoor experiments, while the BGL-induced offset was maintained less than 2.6 cm under 0 ~ 100 klx.


Design of Orthogonal Uplink Pilot Sequences for TDD Massive MIMO under Pilot Contamination


title: A survey of channel models for underwater optical wireless communication

abstract: This paper describes and assesses underwater channel models for optical wireless communication. Models considered are: inherent optical properties; vector radiative transfer theory with the small-angle analytical solution and numerical solutions of the vector radiative transfer equation (Monte Carlo, discrete ordinates and invariant imbedding). Variable composition and refractive index, in addition to background light, are highlighted as aspects of the channel which advanced models must represent effectively. Models are assessed against these aspects in terms of their ability to predict transmitted power and spatial and temporal distributions of light a specified distance from a transmitter. Monte Carlo numerical methods are found to be the most versatile but are compromised by long computational time and greater errors than other methods.


title: Triple-Band Microstrip-Fed Monopole Antenna Loaded With CRLH Unit Cell

abstract: This letter presents a novel metamaterial-inspired planar monopole antenna. The proposed structure consists of a monopole loaded with a composite right/left-handed (CRLH) unit cell. It operates at two narrow bands, 0.925 and 1.227 GHz, and one wide band, 1.56-2.7 GHz, i.e., it covers several communication standards. The CRLH-loaded monopole occupies the same Chu's sphere as a conventional monopole that operates at 2.4 GHz. The radiation patterns at the different operating frequencies are still quasi-omnidirectional. Measurements and EM simulations are in a good agreement with the theoretical predictions.


title: MINERVA: a second-generation museum tour-guide robot

abstract: This paper describes an interactive tour-guide robot, which was successfully exhibited in a Smithsonian museum. During its two weeks of operation, the robot interacted with thousands of people, traversing more than 44 km at speeds of up to 163 cm/sec. Our approach specifically addresses issues such as safe navigation in unmodified and dynamic environments, and short-term human-robot interaction. It uses learning pervasively at all levels of the software architecture.


title: Challenges in the Transition from Waterfall to Scrum - a Casestudy at Portbase

abstract: upcoming trend in software development is the Scrum- framework. Old sequential methods like Waterfall have proven ineffective. Many companies are making the switch from Wa- terfall to Scrum and there are a number of challenges in this transition. In this paper, known challenges are researched based on existing literature and case-study research is performed. Known challenges are identified, previously unknown chal- lenges might surface and advice is given on how to deal with these challenges. These results are relevant to other companies making the transition to Scrum. KeywordsScrum, agile, transition Many companies are making the transition from a Waterfall based development process to the Scrum methodology. In chapter 2 the characteristics of Waterfall and Scrum are ex- plained. The positive and negative aspects of both methodolo- gies are summarized. This gives us an overview of the reasons to adopt Scrum. Chapter 3 gives the problem statement and the relevance of this work. Chapter 4 defines the research approach taken. In chapter 5 literature research is performed to identify already know challenges in the transition from Waterfall to Scrum. Chapter 6 describes the case-study at Portbase, a logis- tics information hub, who has made the transition from Water- fall to Scrum in 2012. The reasons for adopting Scrum, changes in the development process, the effectiveness of Scrum and les- sons learned in the transition will be researched. The literature research helps at identifying potential problems. In chapter 7 the results will be discussed, comparing them to findings in lit- erature and possibly identifying previously unknown chal- lenges. These findings are relevant for other companies transi- tioning from Waterfall to Scrum.


title: Revisiting Trends in Augmented Reality Research: A Review of the 2nd Decade of ISMAR (2008–2017)

abstract: In 2008, Zhou et al. presented a survey paper summarizing the previous ten years of ISMAR publications, which provided invaluable insights into the research challenges and trends associated with that time period. Ten years later, we review the research that has been presented at ISMAR conferences since the survey of Zhou et al., at a time when both academia and the AR industry are enjoying dramatic technological changes. Here we consider the research results and trends of the last decade of ISMAR by carefully reviewing the ISMAR publications from the period of 2008-2017, in the context of the first ten years. The numbers of papers for different research topics and their impacts by citations were analyzed while reviewing them-which reveals that there is a sharp increase in AR evaluation and rendering research. Based on this review we offer some observations related to potential future research areas or trends, which could be helpful to AR researchers and industry members looking ahead.


title: Pathological laughter and crying: a link to the cerebellum.

abstract: Patients with pathological laughter and crying (PLC) are subject to relatively uncontrollable episodes of laughter, crying or both. The episodes occur either without an apparent triggering stimulus or following a stimulus that would not have led the subject to laugh or cry prior to the onset of the condition. PLC is a disorder of emotional expression rather than a primary disturbance of feelings, and is thus distinct from mood disorders in which laughter and crying are associated with feelings of happiness or sadness. The traditional and currently accepted view is that PLC is due to the damage of pathways that arise in the motor areas of the cerebral cortex and descend to the brainstem to inhibit a putative centre for laughter and crying. In that view, the lesions \`disinhibit' or \`release' the laughter and crying centre. The neuroanatomical findings in a recently studied patient with PLC, along with new knowledge on the neurobiology of emotion and feeling, gave us an opportunity to revisit the traditional view and propose an alternative. Here we suggest that the critical PLC lesions occur in the cerebro-ponto-cerebellar pathways and that, as a consequence, the cerebellar structures that automatically adjust the execution of laughter or crying to the cognitive and situational context of a potential stimulus, operate on the basis of incomplete information about that context, resulting in inadequate and even chaotic behaviour.


title: Empirical evidence on the link between object-oriented measures and external quality attributes: a systematic literature review

abstract: There is a plethora of studies investigating object-oriented measures and their link with external quality attributes, but usefulness of the measures may differ across empirical studies. This study aims to aggregate and identify useful object-oriented measures, specifically those obtainable from the source code of object-oriented systems that have gone through such empirical evaluation. By conducting a systematic literature review, 99 primary studies were identified and traced to four external quality attributes: reliability, maintainability, effectiveness and functionality. A vote-counting approach was used to investigate the link between object-oriented measures and the attributes, and to also assess the consistency of the relation reported across empirical studies. Most of the studies investigate links between object-oriented measures and proxies for reliability attributes, followed by proxies for maintainability. The least investigated attributes were: effectiveness and functionality. Measures from the CK their link with reliability and maintainability could be context dependent. There were too few studies traced to effectiveness and functionality attributes; thus a meaningful vote-counting analysis could not be conducted for these attributes. Thus, there is a need for diversification of quality attributes investigated in empirical studies. This would help with identifying useful measures during quality assessment initiatives, and not just for reliability and maintainability aspects.


title: MixTrain: Scalable Training of Formally Robust Neural Networks.

abstract: Making neural networks robust against adversarial inputs has resulted in an arms race between new defenses and attacks. The most promising defenses, adversarially robust training and verifiably robust training, have limitations that restrict their practical applications. The adversarially robust training only makes the networks robust against a subclass of attackers and we reveal such weaknesses by developing a new attack based on interval gradients. By contrast, verifiably robust training provides protection against any L-p norm-bounded attacker but incurs orders of magnitude more computational and memory overhead than adversarially robust training. ::: We propose two novel techniques, stochastic robust approximation and dynamic mixed training, to drastically improve the efficiency of verifiably robust training without sacrificing verified robustness. We leverage two critical insights: (1) instead of over the entire training set, sound over-approximations over randomly subsampled training data points are sufficient for efficiently guiding the robust training process; and (2) We observe that the test accuracy and verifiable robustness often conflict after certain training epochs. Therefore, we use a dynamic loss function to adaptively balance them for each epoch. ::: We designed and implemented our techniques as part of MixTrain and evaluated it on six networks trained on three popular datasets including MNIST, CIFAR, and ImageNet-200. Our evaluations show that MixTrain can achieve up to $95.2\%$ verified robust accuracy against $L_\infty$ norm-bounded attackers while taking $15$ and $3$ times less training time than state-of-the-art verifiably robust training and adversarially robust training schemes, respectively. Furthermore, MixTrain easily scales to larger networks like the one trained on ImageNet-200, significantly outperforming the existing verifiably robust training methods.


title: A 4-channel 12.5Gb/s Common-Gate Transimpedance Amplifier Array for DVI/HDMI Applications

abstract: In this paper, a four-channel 12.5-Gb/s differential transimpedance amplifier (TIA) array is realized in a 0.18-mum standard CMOS technology for the applications of digital visual interface (DVI) and high-definition multimedia interface (HDMI) cables. By exploiting the common-gate configuration as input stage, the TIA relaxes the design tradeoffs between the bandwidth and the large photodiode capacitance. Post-layout simulations demonstrate that a single channel TIA achieves 64-dBH transimpedance gain, 3.125-Gb/s operations with 2.1-GHz bandwidth for 2-pF input parasitic capacitance including photodiode capacitance and ESD protection pad capacitance, and 1.52-muA average input noise current that corresponds to -18dBm sensitivity for 10-12 BER. The 4-channel TIA array consumes 200-mW from a single 1.8-V supply.


title: Matheuristics for the irregular bin packing problem with free rotations

abstract: We present a number of variants of a constructive algorithm able to solve a wide variety of variants of the Two-Dimensional Irregular Bin Packing Problem (2DIBPP). The aim of the 2DIBPP is to pack a set of irregular pieces, which may have concavities, into stock sheets (bins) with fixed dimensions in such a way that the utilization is maximized. This problem is inspired by a real application from a ceramic company in Spain. In addition, this problem arises in other industries such as the garment industry or ship building. The constructive procedure presented in this paper allows both free orientation for the pieces, as in the case of the ceramic industry, or a finite set of orientations as in the case of the garment industry. We explicitly model the assignment of pieces to bins and compare with the more common strategy of packing bins sequentially. There are very few papers in the literature that address the bin packing problem with irregular pieces and to our knowledge this is the first to additionally consider free rotation of pieces with bin packing. We propose several Integer Programing models to determine the association between pieces and bins and then we use a Mixed Integer Programing model for placing the pieces into the bins. The computational results show that the algorithm obtains high quality results in sets of instances with different properties. We have used both industry data and the available data in the literature of 2D irregular strip packing and bin packing problems.


title: HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks

abstract: Similarity search is an important function in many applications, which usually focuses on measuring the similarity between objects with the same type. However, in many scenarios, we need to measure the relatedness between objects with different types. With the surge of study on heterogeneous networks, the relevance measure on objects with different types becomes increasingly important. In this paper, we study the relevance search problem in heterogeneous networks, where the task is to measure the relatedness of heterogeneous objects (including objects with the same type or different types). A novel measure HeteSim is proposed, which has the following attributes: (1) a uniform measure: it can measure the relatedness of objects with the same or different types in a uniform framework; (2) a path-constrained measure: the relatedness of object pairs are defined based on the search path that connects two objects through following a sequence of node types; (3) a semi-metric measure: HeteSim has some good properties (e.g., selfmaximum and symmetric), which are crucial to many data mining tasks. Moreover, we analyze the computation characteristics of HeteSim and propose the corresponding quick computation strategies. Empirical studies show that HeteSim can effectively and efficiently evaluate the relatedness of heterogeneous objects.


title: Action recognition in the premotor cortex.

abstract: We recorded electrical activity from 532 neurons in the rostral part of inferior area 6 (area F5) of two macaque monkeys. Previous data had shown that neurons of this area discharge during goal-directed hand and mouth movements. We describe here the properties of a newly discovered set of F5 neurons (‘mirror neurons’, n = 92) all of which became active both when the monkey performed a given action and when it observed a similar action performed by the experimenter. Mirror neurons, in order to be visually triggered, required an interaction between the agent of the action and the object of it. The sight of the agent alone or of the object alone (three-dimensional objects, food) were ineffective. Hand and the mouth were by far the most effective agents. The actions most represented among those activating mirror neurons were grasping, manipulating and placing. In most mirror neurons (92%) there was a clear relation between the visual action they responded to and the motor response they coded. In ∼30% of mirror neurons the congruence was very strict and the effective observed and executed actions corresponded both in terms of general action (e.g. grasping) and in terms of the way in which that action was executed (e.g. precision grip). We conclude by proposing that mirror neurons form a system for matching observation and execution of motor actions. We discuss the possible role of this system in action recognition and, given the proposed homology between F5 and human Brocca’s region, we posit that a matching system, similar to that of mirror neurons exists in humans and could be involved in recognition of actions as well as phonetic gestures.


title: Multicamera People Tracking with a Probabilistic Occupancy Map

abstract: Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes. In addition, we also derive metrically accurate trajectories for each of them. Our contribution is twofold. First, we demonstrate that our generative model can effectively handle occlusions in each time frame independently, even when the only data available comes from the output of a simple background subtraction algorithm and when the number of individuals is unknown a priori. Second, we show that multiperson tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and that we avoid confusing them with one another.


CXCL13 expression in the gut promotes accumulation of IL-22-producing lymphoid tissue-inducer cells, and formation of isolated lymphoid follicles


title: Deep CNN-LSTM with combined kernels from multiple branches for IMDb review sentiment analysis

abstract: Deep learning neural networks have made significant progress in the area of image and video analysis. This success of neural networks can be directed towards improvements in textual sentiment classification. In this paper, we describe a novel approach to sentiment analysis through the use of combined kernel from multiple branches of convolutional neural network (CNN) with Long Short-term Memory (LSTM) layers. Our combination of CNN and LSTM schemes produces a model with the highest reported accuracy on the Internet Movie Database (IMDb) review sentiment dataset. Additionally, we present multiple architecture variations of our proposed model to illustrate our attempts to increase accuracy while minimizing overfitting. We experiment with numerous regularization techniques, network structures, and kernel sizes to create five high-performing models for comparison. These models are capable of predicting the sentiment polarity of reviews from the IMDb dataset with accuracy above 89%. Firstly, the accuracy of our best performing proposed model surpasses the previously published models and secondly it vastly improves upon the baseline CNN+LSTM model. The capability of the combined kernel from multiple branches of CNN based LSTM architecture could also be lucrative towards other datasets for sentiment analysis or simply text classification. Furthermore, the proposed model has the potential in machine learning in video and audio.


title: Fully Integrated Passive UHF RFID Transponder IC With 16.7-"W Minimum RF Input Power

abstract: This paper presents a novel fully integrated passive transponder IC with 4.5- or 9.25-m reading distance at 500-mW ERP or 4-W EIRP base-station transmit power, respectively, operating in the 868/915-MHz ISM band with an antenna gain less than -0.5 dB. Apart from the printed antenna, there are no external components. The IC is implemented in a 0.5-/spl mu/m digital two-poly two-metal digital CMOS technology with EEPROM and Schottky diodes. The IC's power supply is taken from the energy of the received RF electromagnetic field with help of a Schottky diode voltage multiplier. The IC includes dc power supply generation, phase shift keying backscatter modulator, pulse width modulation demodulator, EEPROM, and logic circuitry including some finite state machines handling the protocol used for wireless write and read access to the IC's EEPROM and for the anticollision procedure. The IC outperforms other reported radio-frequency identification ICs by a factor of three in terms of required receive power level for a given base-station transmit power and tag antenna gain.


title: A model for verifiable grounding and execution of complex natural language instructions

abstract: Current methods of grounding natural language instructions do not include reactive or temporal components, making these methods unsuitable for instructions describing tasks as sets of conditional instructions. We introduce the Verifiable Distributed Correspondence Graph (V-DCG) model, which enables the validation of natural language instructions by using Linear Temporal Logic (LTL) specifications together with physical world groundings. We demonstrate the V-DCG model on a physical robot and provide examples of the output our system produces for natural language instructions.


Semantic Dependency Parsing via Book Embedding


title: Extinction risk from climate change

abstract: Climate change over the past ∼30 years has produced numerous shifts in the distributions and abundances of species1,2 and has been implicated in one species-level extinction3. Using projections of species' distributions for future climate scenarios, we assess extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface. Exploring three approaches in which the estimated probability of extinction shows a power-law relationship with geographical range size, we predict, on the basis of mid-range climate-warming scenarios for 2050, that 15–37% of species in our sample of regions and taxa will be ‘committed to extinction’. When the average of the three methods and two dispersal scenarios is taken, minimal climate-warming scenarios produce lower projections of species committed to extinction (∼18%) than mid-range (∼24%) and maximum-change (∼35%) scenarios. These estimates show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.


title: Phase-based frame interpolation for video

abstract: Standard approaches to computing interpolated (in-between) frames in a video sequence require accurate pixel correspondences between images e.g. using optical flow. We present an efficient alternative by leveraging recent developments in phase-based methods that represent motion in the phase shift of individual pixels. This concept allows in-between images to be generated by simple per-pixel phase modification, without the need for any form of explicit correspondence estimation. Up until now, such methods have been limited in the range of motion that can be interpolated, which fundamentally restricts their usefulness. In order to reduce these limitations, we introduce a novel, bounded phase shift correction method that combines phase information across the levels of a multi-scale pyramid. Additionally, we propose extensions for phase-based image synthesis that yield smoother transitions between the interpolated images. Our approach avoids expensive global optimization typical of optical flow methods, and is both simple to implement and easy to parallelize. This allows us to interpolate frames at a fraction of the computational cost of traditional optical flow-based solutions, while achieving similar quality and in some cases even superior results. Our method fails gracefully in difficult interpolation settings, e.g., significant appearance changes, where flow-based methods often introduce serious visual artifacts. Due to its efficiency, our method is especially well suited for frame interpolation and retiming of high resolution, high frame rate video.


title: Designing Large Scale REST APIs Based on REST Chart

abstract: REST Chart is a Petri-Net based XML modeling framework for REST API. This paper presents two important enhancements and extensions to REST Chart modeling - Hyperlink Decoration and Hierarchical REST Chart. In particular, the proposed Hyperlink Decoration decomposes resource connections from resource representation, such that hyperlinks can be defined independently of schemas. This allows a Navigation-First Design by which the important global connections of a REST API can be designed first and reused before the local resource representations are implemented and specified. Hierarchical REST Chart is a powerful mechanism to rapidly decompose and extend a REST API in several dimensions based on Hyperlink Decoration. These new mechanisms can be used to manage the complexities in large scale REST APIs that undergo frequent changes as in some large scale open source development projects. This paper shows that these new capabilities can fit nicely in the REST Chart XML with very minor syntax changes. These enhancements to REST Chart are applied successfully in designing and verifying REST APIs for software-defined-networking (SDN) and Cloud computing.


title: Modeling anisotropic surface reflectance with example-based microfacet synthesis

abstract: We present a new technique for the visual modeling of spatiallyvarying anisotropic reflectance using data captured from a single view. Reflectance is represented using a microfacet-based BRDF which tabulates the facets' normal distribution (NDF) as a function of surface location. Data from a single view provides a 2D slice of the 4D BRDF at each surface point from which we fit a partial NDF. The fitted NDF is partial because the single view direction coupled with the set of light directions covers only a portion of the "half-angle" hemisphere. We complete the NDF at each point by applying a novel variant of texture synthesis using similar, overlapping partial NDFs from other points. Our similarity measure allows azimuthal rotation of partial NDFs, under the assumption that reflectance is spatially redundant but the local frame may be arbitrarily oriented. Our system includes a simple acquisition device that collects images over a 2D set of light directions by scanning a linear array of LEDs over a flat sample. Results demonstrate that our approach preserves spatial and directional BRDF details and generates a visually compelling match to measured materials.


title: Dynamic controller for lane keeping and obstacle avoidance assistance system

abstract: This paper presents the design and simulation tests of a steering assistance for passenger vehicles based on a dynamic state feedback controller. Its main purpose is to avoid unintended lane departure and collisions. The design of the proposed lane keeping system takes into account the road curvature, considered as an exogenous input, into its internal model. The computation of the control law has been achieved by linking Lyapunov theory of stability to Bilinear Matrix Inequalities which considers bounds in the control input and minimises the reachable set of the vehicle after activation. This control strategy ensures convergence of the lateral offset to zero, even in curvy roads. Simulations show the performance of the controller and an extended application for collision avoidance.


title: Portfolio Selection Using Fuzzy Analytic Hierarchy Process (FAHP)

abstract: Investors participate in stock markets for the exclusive purposes of earning highest return and sustaining and enhancing profitability. With this in mind, identifying effective determining factors regarding the selection of both the volume and type of invested securities (portfolio) is a matter of great importance. The selection of an optimal portfolio is dependent upon different factors, including extrinsic and intrinsic criteria that are associated with investors’ objectives.The present study first attempts to identify the most effective factors utilized in the selection of a portfolio; then, taking advantage of the Fuzzy Analytical Hierarchy Process (FAHP) as one of the multi-criteria decision-making (MCDM) models, the study proceeds to demonstrate the selection of an adequate stock portfolio. In this paper, in order to investigate the criteria that are most effective in the selection of stocks, a hierarchy of criteria is first presented. Using the Delphi methodology which recognizes the value of experts’ opinions, the best available advice from experts is selected and analyzed, and the weight of each identified criterion is measured applying the Analytic Hierarchy Process (AHP) method. The findings of this research suggest that the most significant criteria in the decisions made by investors regarding the selection of stocks are international risk, international economy, competitiveness (as extrinsic factors), and profit, management quality, information transparency, efficiency (as the criterion associated with investors’ objectives and as intrinsic factors). Finally, taking such criteria into consideration and using the FAHP method, we proceed to select a stock portfolio include the top seven companies listed in Tehran Stock Exchange during the period of 2007-2011.


title: Design procedure of class-E amplifier for off-nominal operation at 50% duty ratio

abstract: Design equations for satisfying the off-nominal operating condition [i.e., only the zero-voltage switching (ZVS) condition] of the Class-E amplifier with a linear shunt capacitance at a duty ratio D=0.5 are derived. A new parameter s (V/s), called the slope of switch voltage when the switch turns on is introduced to obtain an image of the distance from the nominal conditions. By examining off-nominal Class-E operation degree of the design freedom of the Class-E amplifier increases by one. In addition various amplifier parameters such as operating frequency, output power, and load resistance range can be set as design specifications. For example, the peak switch voltage and switch current can be taken into account in the design procedure. Examples of a design procedure of the Class-E amplifier for off-nominal operation are given. The theoretical results were verified with PSpice simulation and experiments.


title: An adaptive PMU based fault detection/location technique for transmission lines. I. Theory and algorithms

abstract: An adaptive fault detection/location technique based on a phasor measurement unit (PMU) for an EHV/UHV transmission line is presented. A fault detection/location index in terms of Clarke components of the synchronized voltage and current phasors is derived. The line parameter estimation algorithm is also developed to solve the uncertainty of parameters caused by aging of transmission lines. This paper also proposes a new discrete Fourier transform (DFT) based algorithm (termed the smart discrete Fourier transform, SDFT) to eliminate system noise and measurement errors such that extremely accurate fundamental frequency components can be extracted for calculation of fault detection/location index. The EMTP was used to simulate a high voltage transmission line with faults at various locations. To simulate errors involved in measurements, Gaussian-type noise has been added to the raw output data generated by EMTP. Results have shown that the new DFT based method can extract exact phasors in the presence of frequency deviation and harmonics. The parameter estimation algorithm can also trace exact parameters very well. The accuracy of both new DFT based method and parameter estimation algorithm can achieve even up to 99.999% and 99.99% respectively, and is presented in Part II. The accuracy of fault location estimation by the proposed technique can achieve even up to 99.9% in the performance evaluation, which is also presented in Part II.


title: Knowledge-based textual inference via parse-tree transformations

abstract: Textual inference is an important component in many applications for understanding natural language. Classical approaches to textual inference rely on logical representations for meaning, which may be regarded as "external" to the natural language itself. However, practical applications usually adopt shallower lexical or lexical-syntactic representations, which correspond closely to language structure. In many cases, such approaches lack a principled meaning representation and inference framework. We describe an inference formalism that operates directly on language-based structures, particularly syntactic parse trees. New trees are generated by applying inference rules, which provide a unified representation for varying types of inferences. We use manual and automatic methods to generate these rules, which cover generic linguistic structures as well as specific lexical-based inferences. We also present a novel packed data-structure and a corresponding inference algorithm that allows efficient implementation of this formalism. We proved the correctness of the new algorithm and established its efficiency analytically and empirically. The utility of our approach was illustrated on two tasks: unsupervised relation extraction from a large corpus, and the Recognizing Textual Entailment (RTE) benchmarks.


title: Modular Smart Transformer architectures: An overview and proposal of a interphase architecture

abstract: Several Smart Transformer (ST) architectures have been discussed in literature and the modular approach proved to be interesting because of its advantage of fault tolerance associated to the possibility to use low voltage/current rated devices. In this context, this paper presents an overview and classification of the modular ST architectures, in which the topologies, connections and building block converters are discussed. Furthermore, a ST architectures named “interphase ST architecture” is proposed and described in detail. The main characteristic is that the phases of the three-phase system are connected to the same building block converter, in such way that a single module controls the power exchange among the phases. Therefore, this solution offers higher power control capability. Finally, simulation results are provided and the power transfer capability among the phases of the three phase system is demonstrated.


Refinements to Indices for Perceived Humanness, Attractiveness, and Eeriness


title: Combining the real-time wavelet denoising and long-short-term-memory neural network for predicting stock indexes

abstract: A stock market index can be a valuable indicator to describe the performance of a stock market in a particular region. Nevertheless, it is very difficult to forecast its future values or trends since the index data often demonstrate a high degree of fluctuations. Intrinsically, the wavelet denoising is a useful method to separate the signals from noise in many practical multi-media applications while the long-short-term-memory neural network (LSTM) is a powerful recurrent neutral network (RNN) architecture of learning and prediction models used in the field of computational intelligence. Nevertheless, few research studies have ever considered their combination for the prediction of stock or index movement. More importantly, some existing proposals trying to combine wavelet denoising with other artificial neural network architectures suffer from two major drawbacks. First, when applying the conventional one-time wavelet transform for denoising the stock data, this approach has made a serious logical flaw to include future stock data in its training phase, thus leading to impressive results in the backtesting yet actually impractical in real-world applications. In addition, the wavelet functions and decomposition levels are typically fixed in those studies for which they will not be able to produce optimal results in terms of the prediction accuracies. Hence, we propose in this paper a novel model to combine real-time wavelet denoising functions with the LSTM to predict the East Asian stock indexes in which the wavelet denoising adopts a sliding window mechanism to exclude the future data while its system configuration is flexibly optimized based on some predefined criteria. The empirical results reveal that the performance of our proposed prediction model shows significant improvements when compared to those of the original LSTM model without utilizing the wavelet denoising function. Furthermore, there are many interesting and possible directions including the integration with other deep learning networks for the future investigation of this work.


title: Adaptive Color Attributes for Real-Time Visual Tracking

abstract: Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power. This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attribute-based evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24 % in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second.


title: Toward a Unified Model of Information Security Policy Compliance

abstract: Information systems security (ISS) behavioral research has produced different models to explain security policy compliance. This paper (1) reviews 11 theories that have served the majority of previous information security behavior models, (2) empirically compares these theories (Study 1), (3) proposes a unified model, called the unified model of information security policy compliance (UMISPC), which integrates elements across these extant theories, and (4) empirically tests the UMISPC in a new study (Study 2), which provided preliminary empirical support for the model. The 11 theories reviewed are (1) the theory of reasoned action, (2) neutralization techniques, (3) the health belief model, (4) the theory of planned behavior, (5) the theory of interpersonal behavior, (6) the protection motivation theory, (7) the extended protection motivation theory, (8) deterrence theory and rational choice theory, (9) the theory of self-regulation, (10) the extended parallel processing model, and (11) the control balance theory. The UMISPC is an initial step toward empirically examining the extent to which the existing models have similar and different constructs. Future research is needed to examine to what extent the UMISPC can explain different types of ISS behaviors (or intentions thereof). Such studies will determine the extent to which the UMISPC needs to be revised to account for different types of ISS policy violations and the extent to which the UMISPC is generalizable beyond the three types of ISS violations we examined. Finally, the UMISPC is intended to inspire future ISS research to further theorize and empirically demonstrate the important differences between rival theories in the ISS context that are not captured by current measures.


title: NaCl on 8-bit AVR Microcontrollers

abstract: This paper presents first results of the Networking and Cryptography library (NaCl) on the 8-bit AVR family of microcontrollers. We show that NaCl, which has so far been optimized mainly for different desktop and server platforms, is feasible on resource-constrained devices while being very fast and memory efficient. Our implementation shows that encryption using Salsa20 requires 268 cycles/byte, authentication using Poly1305 needs 195 cycles/byte, a Curve25519 scalar multiplication needs 22 791 579 cycles, signing of data using Ed25519 needs 23 216 241 cycles, and verification can be done within 32 634 713 cycles. All implemented primitives provide at least 128-bit security, run in constant time, do not use secret-data-dependent branch conditions, and are open to the public domain (no usage restrictions).


title: VSL#3 probiotics exerts the anti-inflammatory activity via PI3k/Akt and NF-κB pathway in rat model of DSS-induced colitis

abstract: VSL#3 probiotics can be effective on induction and maintenance of the remission of clinical ulcerative colitis. However, the mechanisms are not fully understood. The aim of this study was to examine the effects of VSL#3 probiotics on dextran sulfate sodium (DSS)-induced colitis in rats. Acute colitis was induced by administration of DSS 3.5 % for 7 days in rats. Rats in two groups were treated with either 15 mg VSL#3 or placebo via gastric tube once daily after induction of colitis; rats in other two groups were treated with either the wortmannin (1 mg/kg) via intraperitoneal injection or the wortmannin + VSL#3 after induction of colitis. Anti-inflammatory activity was assessed by myeloperoxidase (MPO) activity. Expression of inflammatory related mediators (iNOS, COX-2, NF-κB, Akt, and p-Akt) and cytokines (TNF-α, IL-6, and IL-10) in colonic tissue were assessed. TNF-α, IL-6, and IL-10 serum levels were also measured. Our results demonstrated that VSL#3 and wortmannin have anti-inflammatory properties by the reduced disease activity index and MPO activity. In addition, administration of VSL#3 and wortmannin for 7 days resulted in a decrease of iNOS, COX-2, NF-κB, TNF-α, IL-6, and p-Akt and an increase of IL-10 expression in colonic tissue. At the same time, administration of VSL#3 and wortmannin resulted in a decrease of TNF-α and IL-6 and an increase of IL-10 serum levels. VSL#3 probiotics therapy exerts the anti-inflammatory activity in rat model of DSS-induced colitis by inhibiting PI3K/Akt and NF-κB pathway.


title: Modeling Relation Paths for Representation Learning of Knowledge Bases

abstract: Representation learning of knowledge bases (KBs) aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text.


title: A machine learning approach for prediction of on-time performance of flights

abstract: One of the major business problems that airlines face is the significant costs that are associated with flights being delayed due to natural occurrences and operational shortcomings, which is an expensive affair for the airlines, creating problems in scheduling and operations for the end-users thus causing bad reputation and customer dissatisfaction. In our paper, a two-stage predictive model was developed employing supervised machine learning algorithms for the prediction of flight on-time performance. The first stage of the model performs binary classification to predict the occurrence of flight delays and the second stage does regression to predict the value of the delay in minutes. The dataset used for evaluating the model was obtained from historical data which contains flight schedules and weather data for 5 years. It was observed that, in the classification stage, Gradient Boosting Classifier performed the best and in the regression stage, Extra-Trees Regressor performed the best. The performance of the other algorithms is also extensively documented in the paper. Furthermore, a real-time Decision Support Tool was built using the model which utilizes features that are readily available before the departure of an airplane and can inform passengers and airlines about flight delays in advance, helping them reduce possible monetary losses.


title: Fire-fighting mobile robotics and interdisciplinary design-comparative perspectives

abstract: The objective of this paper is to illustrate the benefits of an autonomous fire-fighting robot design competition as an effective tool for undergraduate education. It presents experiences at the United States Air Force Academy, USAF Academy, CO; Pennsylvania State University-Abington; and Trinity College, Hartford, CT, together with the results of the contest surveys conducted in collaboration with The Technion*Israel Institute of Technology, Haifa. The primary goal of the design project is to create an autonomous mobile robot that navigates through a maze searching for a fire (simulated by a burning candle), detects the candle's flame, extinguishes the flame, and returns to a designated starting location in the maze. The fire-fighting design contest promotes interdisciplinary design and teamwork. To accomplish the stated goal, students must integrate knowledge gained from such classes as engineering design, circuits, controls, signals and systems, computer programming, mathematics, and engineering mechanics. Within the three institutions, the contest has been successfully utilized as a foundation for a wide range of educational goals. These activities include freshman design, robotics courses, K-12 outreach, senior design projects, and undergraduate research.


Teaching Diversity: Using a Multifaceted Approach to Engage Students


title: Preliminary studies with a mechanical model for the evaluation of spinal motion palpation.

abstract: This study was designed to investigate the ability of eight graduate chiropractors and 19 final year chiropractic students to detect the presence or absence of lumbar spine intersegmental motion restriction by palpating a spinal model equipped with artificial fixators. There was little difference between the two groups of palpators with regard to the successful identification of the presence or absence of fixation. Typically, 43 per cent of reported fixations were correctly identified in contrast to the 16 per cent expected by chance alone. Similarly, 13 per cent of levels which were actually free were identified as fixed compared with the expected 19 per cent.


title: Towards Autonomic DDoS Mitigation using Software Defined Networking

abstract: Distributed Denial of Service attacks (DDoS) have remained as one of the most destructive attacks in the Internet for over two decades. Despite tremendous efforts on the design of DDoS defense strategies, few of them have been considered for widespread deployment due to strong design assumptions on the Internet infrastructure, prohibitive operational costs and complexity. Recently, the emergence of Software Defined Networking (SDN) has offered a solution to reduce network management complexity. It is also believed to facilitate security management thanks to its programmability. To explore the advantages of using SDN to mitigate DDoS attacks, we propose a distributed collaborative framework that allows the customers to request DDoS mitigation service from ISPs. Upon request, ISPs can change the label of the anomalous traffic and redirect them to security middleboxes, while attack detection and analysis modules are deployed at customer side, avoiding privacy leakage and other legal concerns. Our preliminary analysis demonstrates that SDN has promising potential to enable autonomic mitigation of DDoS attacks, as well as other large-scale attacks


title: Fundamental Tensor Operations for Large-Scale Data Analysis in Tensor Train Formats

abstract: We discuss extended definitions of linear and multilinear operations such as Kronecker, Hadamard, and contracted products, and establish links between them for tensor calculus. Then we introduce effective low-rank tensor approximation techniques including Candecomp/Parafac (CP), Tucker, and tensor train (TT) decompositions with a number of mathematical and graphical representations. We also provide a brief review of mathematical properties of the TT decomposition as a low-rank approximation technique. With the aim of breaking the curse-of-dimensionality in large-scale numerical analysis, we describe basic operations on large-scale vectors, matrices, and high-order tensors represented by TT decomposition. The proposed representations can be used for describing numerical methods based on TT decomposition for solving large-scale optimization problems such as systems of linear equations and symmetric eigenvalue problems.


title: Efficient parallel-pipelined GHASH for message authentication

abstract: AES/GCM is a common mode for authenticated encryption. One of its components is Galois HASH (GHASH) which achieves the authentication task. In this work, we present an efficient key independent hardware implementation for parallel-pipelined GHASH. Karatsuba Ofman Algorithm (KOA) is used for Galois Field (GF) multiplication. Unlike previous parallel hardware architectures based on KOA, we use only one reduction array for all parallel KOA multipliers. Therefore, an area optimized design is achieved. In addition, pipelined KOA is adopted to get higher clock frequency. 4-Parallel pipelined GHASH is evaluated using Xilinx Virtex5. It occupies 7.128k Slices and achieves 113.8 Gbps as an authentication throughput. Higher hardware efficiency (throughput/slice) in comparison with prior art (Key independent GHASH) is achieved.


title: Multi-view Metric Learning for Multi-view Video Summarization

abstract: Traditional methods on video summarization are designed to generate summaries for single-view video records; and thus they cannot fully exploit the redundancy in multi-view video records. In this paper, we present a multi-view metric learning framework for multi-view video summarization that combines the advantages of maximum margin clustering with the disagreement minimization criterion. The learning framework thus has the ability to find a metric that best separates the data, and meanwhile to force the learned metric to maintain original intrinsic information between data points, for example geometric information. Facilitated by such a framework, a systematic solution to the multi-view video summarization problem is developed. To the best of our knowledge, it is the first time to address multi-view video summarization from the viewpoint of metric learning. The effectiveness of the proposed method is demonstrated by experiments.


title: Digital image integrity - a survey of protection and verification techniques

abstract: Abstract We are currently on a verge of a revolution in digital photography. Developments in computational imaging and adoption of artificial intelligence have spawned new editing techniques that give impressive results in astonishingly short time-frames. The advent of multi-sensor and multi-lens cameras will further challenge many existing integrity verification techniques. As a result, it will be necessary to re-evaluate our notion of image authenticity and look for new techniques that could work efficiently in this new reality. The goal of this paper is to thoroughly review existing techniques for protection and verification of digital image integrity. In contrast to other recent surveys, the discussion covers the most important developments both in active protection and in passive forensic analysis techniques. Existing approaches are analyzed with respect to their capabilities, fundamental limitations, and prospective attack vectors. Whenever possible, the discussion is supplemented with real operation examples and a list of available implementations. Finally, the paper reviews resources available in the research community, including public data-sets and commercial or open-source software. The paper concludes by discussing relevant developments in computational imaging and highlighting future challenges and open research problems.


Automatic Calibration of Lidar and Camera Images using Normalized Mutual Information


title: Modeling total crime and the affecting factors in Central Java using geographically weighted regression

abstract: Analysing the relationship between number of crime cases and affecting factors became an interesting research topic over the last ten years. The total number of crime in Indonesia did not show a consistent decrease. In order to upgrade people safeness quality, the government need to know the factors influence people committing crime acts. Rather than using classical regression analysis, geographically weighted regression (GWR) was preferable since it gave a better representative model by effectively resolve spatial non-stationary problem which is generally exist in spatial data of social phenomenon. Spatial non-stationary is a situation when the relationship between variables are significantly different in each location of observation point, so that classic regression analysis will result a misleading interpretation in some location. GWR handled the spatial non-stationary problem by generating a single model in each observation point which allow different relationship to exist at different point in space. This study used number of crime cases (y) as the dependent variable and the factors which affect the number of crime cases as independent variables that consist of the number of illiterates (X1), the number of unemployed (X2), the number of poor population (X3), population density (X4), the number of victims of drug (X5). This study used secondary data collected by POLRI, BPS and Indonesian Ministry of Social Affairs in Central Java during 2015. GWR generated model for 35 city/regency in Central Java.


title: Marketing metrics' usage: Its predictors and implications for customer relationship management

abstract: article i nfo Marketing metrics Customer relationship management Value-based organizational culture and processes Cross-functional conflict Innovative value proposition This research aims to determine the conditions that foster use of marketing metrics in customer relationship management (CRM) and identify the organizational factors that strengthen/weaken the impact of usage of marketing metrics on CRM performance. Based on the customer value-based theory of the firm and the contingency perspective, a research framework was developed to shed light on the predictor roles of customer value-based organizational culture and processes in determining usage of marketing metrics, and foster an understanding of the moderating roles of marketing-supply chain conflict, and innovative value proposition on the marketing metric-performance relationships. Empirical evidence from a sample of 209 business firms confirmed the main effect that customer value-based organizational culture and processes support a firm's use of marketing metrics that in turn enhance its CRM performance. Notable moderating effects were also identified. Although marketing-supply chain conflict weakens the impact of marketing metrics usage in achieving superior CRM performance, innovative value proposition strengthens the


title: Application of Monte-Carlo tree search in a fighting game AI

abstract: This paper describes an application of Monte-Carlo Tree Search (MCTS) in a fighting game AI. MCTS is a best-first search technique that uses stochastic simulations. In this paper, we evaluate its effectiveness on FightingICE, a game AI competition platform at Computational Intelligence and Games Conferences. Our results confirm that MCTS is an effective search for controlling a game AI in the aforementioned platform.