title: Role of power grid in side channel attack and power-grid-aware secure design

abstract: Side-channel attack (SCA) is a method in which an attacker aims at extracting secret information from crypto chips by analyzing physical parameters (e.g. power). SCA has emerged as a serious threat to many mathematically unbreakable cryptography systems. From an attacker's point of view, the difficulty of mounting SCA largely depends on Signal-to-Noise Ratio (SNR) of the side-channel information. It has been shown that SNR primarily depends on algorithmic and circuit-level implementation, measurement noise, as well as device thermal noise. However, to the best of our knowledge, there has not been any study on the effect of power delivery network (PDN) on SCA resistance. We note that the PDN plays a significant role in SNR of measured supply current. Furthermore, SCA resistance strongly depends on the operating frequency due to RLC structure of a power grid. In this paper, we analyze the effect of power grid on SCA and provide quantitative results to demonstrate the frequency-dependent SCA resistance due to PDN-induced noise. This property can potentially be exploited by an attacker to facilitate the attack by operating a device at favorable frequency points. On the other hand, from a designer's perspective, one can explore countermeasures to secure the device at all operating frequencies while minimizing the design overhead. Based on this observation, we propose a frequency-dependent noise-injection based compensation technique to efficiently protect against SCA. Simulation results using realistic PDN model as well as experimental measurements using FPGA test board validate the observations on role of PDN in SCA and the efficacy of the proposed compensation approach.


title: Understanding ‘smart cities’: Intertwining development drivers with desired outcomes in a multidimensional framework

abstract: The convergence of technology and the city is commonly referred to as the ‘smart city’. It is seen as a possible remedy for the challenges that urbanisation creates in the age of global climate change, and as an enabler of a sustainable and liveable urban future. A review of the abundant but fragmented literature on smart city theories and practices, nevertheless, reveals that there is a limited effort to capture a comprehensive understanding on how the complex and multidimensional nature of the drivers of smart cities are linked to desired outcomes. The paper aims to develop a clearer understanding on this new city model by identifying and linking the key drivers to desired outcomes, and then intertwining them in a multidimensional framework. The methodological approach of this research includes a systematic review of the literature on smart cities, focusing on those aimed at conceptual development and provide empirical evidence base. The review identifies that the literature reveals three types of drivers of smart cities— community, technology, policy—which are linked to five desired outcomes—productivity, sustainability, accessibility, wellbeing, liveability, governance. These drivers and outcomes altogether assemble a smart city framework, where each of them represents a distinctive dimension of the smart cities notion. This paper helps in expanding our understanding beyond a monocentric technology focus of the current common smart city practice.


title: Multidimensional data classification with chordal distance based kernel and Support Vector Machines

abstract: In contemporary machine learning multidimensional rather than pure vector like data are frequently encountered. Traditionally, such multidimensional objects, such as color images or video sequences, are first transformed to a vector representation, and then processed by the classical learning algorithms operating with vectors. However, such multi-to-one dimension transformations usually lead to loss of important information. Thus, proposing novel methods for representing and learning with complex and multidimensional data is in focus of current machine learning research. In this paper, we propose a new method for efficient classification of multidimensional data based on a tensor-based kernel applied to the Support Vector Machines. We represent data as tensors, in order to preserve data dimensionality and to allow for processing of complex structures. To allow for an effective classification, we augment a Support Vector Machine (SVM) trained with Sequential Minimal Optimization (SMO) procedure with a chordal distance-based kernel for efficient classification of tensor-like objects. We also discuss different optimization methods for SVM, as well as present implementation details with computational time analysis. The proposed method is evaluated in both binary and multi-class classification problems. Comprehensive experimental analysis carried on a number of multidimensional benchmarks shows high usefulness of the proposed approach.


title: Novel wideband orthomode transducer for 70–95GHz

abstract: This paper presents a novel orthomode transducer (OMT) with the dimension of WR-10 waveguide. The internal structure of the OMT is in the shape of Y so we named it a Y-junction OMT, it contain one square waveguide port with the dimension 2.54mm × 2.54mm and two WR-10 rectangular waveguide ports with the dimension of 1.27mm × 2.54mm. The operating frequency band of OMT is 70–95GHz (more than 30% bandwidth) with simulated insertion loss <−0.3dB and cross polarization better than −40dB throughout the band for both TE10 and TE01 modes.


CAUSES AND EFFECTS OF DRUG AND SUBSTANCE ABUSE AMONG SECONDARY SCHOOL STUDENTS IN DAGORETTI


Making Motion Musical: Gesture Mapping Strategies for Interactive Computer Music


title: Code churn: a measure for estimating the impact of code change

abstract: This study presents a methodology that will produce a viable fault surrogate. The focus of the effort is on the precise measurement of software development process and product outcomes. Tools and processes for the static measurement of the source code have been installed and made operational in a large embedded software system. Source code measurements have been gathered unobtrusively for each build in the software evolution process. The measurements are synthesized to obtain the fault surrogate. The complexity of sequential builds is compared and a new measure, code churn, is calculated. This paper demonstrates the effectiveness of code complexity churn by validating it against the testing problem reports.


title: Building Applied Natural Language Generation Systems

abstract: In this article, we give an overview of Natural Language Generation (NLG) from an applied system-building perspective. The article includes a discussion of when NLG techniques should be used; suggestions for carrying out requirements analyses; and a description of the basic NLG tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation. Throughout, the emphasis is on established techniques that can be used to build simple but practical working systems now. We also provide pointers to techniques in the literature that are appropriate for more complicated scenarios.


title: CloudProtect: Managing Data Privacy in Cloud Applications

abstract: This paper describes the CloudProtect middleware that empowers users to encrypt sensitive data stored within various cloud applications. However, most web applications require data in plaintext for implementing the various functionalities and in general, do not support encrypted data management. Therefore, CloudProtect strives to carry out the data transformations (encryption/decryption) in a manner that is transparent to the application, i.e., preserves all functionalities of the application, including those that require data to be in plaintext. Additionally, CloudProtect allows users flexibility in trading off performance for security in order to let them optimally balance their privacy needs and usage-experience.


title: DC powered atmospheric pressure micro-plasmajet for biomedical applications

abstract: Summary form only given. Nonthermal (cold) plasmas operated in air at atmospheric pressure offer an appealing method for the processing and decontamination of surfaces. Most existing devices are operated with radiofrequency high voltages. Microhollow cathode discharges (MHCDs), on the other hand, allow us to generate a direct current driven plasma jet in atmospheric pressure gases, including air. The discharge is sustained by a voltage of only several hundred volts applied to two plane metal electrodes which are separated by a dielectric insulator. The plasma is confined in a cylindrical channel drilled through all layers. With a thickness of the dielectric of 0.25 mm and a diameter of the channel of less than 1 mm a stable glow discharge can be sustained. By flowing air or nitrogen through the channel into atmospheric pressure air, a well-defined plasma (afterglow) jet is generated with a typical, visible length of 10-20 mm. The gas temperature of air and nitrogen plasmas inside the channel was measured by means of emission spectroscopy using the 0-0 band within the second positive system of nitrogen. Increasing the flow rate from 0 to 200 ml/min resulted in a decrease in gas temperature from 2000 K to 800 K in the channel. The lower temperature allows us to replace high temperature ceramics and metals, such as alumina and molybdenum, respectively, with less expensive and easier machinable materials. The turbulent gas flow effectively cools the plasma jet down further to temperatures close to room temperature at a distance of 5 mm from the nozzle. This allows using this micro-plasma jet for treatment of heat sensitive materials and surfaces. E.g., the wettability of polypropylene (melting point 170degC) was increased by 70% when treated with the plasma jet. Other applications include the gentle cleaning and decontamination of organic materials. Studies on the treatment of fungal, viral, and bacterial skin diseases with the plasma jet are underway


title: A Visual Representation of Clusters Characteristics Using Edge Bundling for Parallel Coordinates

abstract: This paper proposes an edge bundling approach applied on parallel coordinates to improve the visualization of cluster information directly from the overview. Lines belonging to a cluster are bundled into a single curve between axes, where the horizontal and vertical positioning of the bundling intersection (known as bundling control points) to encode pertinent information about the cluster in a given dimension, such as variance, standard deviation, mean, median, and so on. The hypothesis is that adding this information to the overview improves the visualization overview at the same that it does not prejudice the understanding in other aspects. We have performed tests with participants to compare our approach with classic parallel coordinates and other consolidated bundling technique. The results showed most of the initially proposed hypotheses to be confirmed at the end of the study, as the tasks were performed successfully in the majority of tasks maintaining a low response time in average, as well as having more aesthetic pleasing according to participants' opinion.


THE EFFECT OF SUPPLY CHAIN DECOUPLING POINTS ON DEMAND PLANNING . AN EMPIRICAL STUDY


title: SeaGlass: Enabling City-Wide IMSI-Catcher Detection

abstract: Abstract Cell-site simulators, also known as IMSI-catchers and stingrays, are used around the world by governments and criminals to track and eavesdrop on cell phones. Despite extensive public debate surrounding their use, few hard facts about them are available. For example, the richest sources of information on U.S. government cell-site simulator usage are from anonymous leaks, public records requests, and court proceedings. This lack of concrete information and the difficulty of independently obtaining such information hampers the public discussion. To address this deficiency, we build, deploy, and evaluate SeaGlass, a city-wide cellsite simulator detection network. SeaGlass consists of sensors that measure and upload data on the cellular environment to find the signatures of portable cell-site simulators. SeaGlass sensors are designed to be robust, low-maintenance, and deployable in vehicles for long durations. The data they generate is used to learn a city’s network properties to find anomalies consistent with cell-site simulators. We installed SeaGlass sensors into 15 ridesharing vehicles across two cities, collecting two months of data in each city. Using this data, we evaluate the system and show how SeaGlass can be used to detect signatures of portable cell-site simulators. Finally, we evaluate our signature detection methods and discuss anomalies discovered in the data.


title: Behavioral dynamics on the web: Learning, modeling, and prediction

abstract: The queries people issue to a search engine and the results clicked following a query change over time. For example, after the earthquake in Japan in March 2011, the query japan spiked in popularity and people issuing the query were more likely to click government-related results than they would prior to the earthquake. We explore the modeling and prediction of such temporal patterns in Web search behavior. We develop a temporal modeling framework adapted from physics and signal processing and harness it to predict temporal patterns in search behavior using smoothing, trends, periodicities, and surprises. Using current and past behavioral data, we develop a learning procedure that can be used to construct models of users' Web search activities. We also develop a novel methodology that learns to select the best prediction model from a family of predictive models for a given query or a class of queries. Experimental results indicate that the predictive models significantly outperform baseline models that weight historical evidence the same for all queries. We present two applications where new methods introduced for the temporal modeling of user behavior significantly improve upon the state of the art. Finally, we discuss opportunities for using models of temporal dynamics to enhance other areas of Web search and information retrieval.


title: Power cycling test bench for IGBT power modules used in wind applications

abstract: A power cycling test bench is realized for the lifetime analysis of IGBT power modules and for the development of a reliability management system predicting the deterioration of the devices. The focus is on converters for doubly-fed induction generators used in wind turbines. In these applications, due to the low frequent power losses under steady state conditions and due to changing wind conditions, the thermomechanical stress on IGBT power modules is high. The presented test bench offers accelerated ageing of the power semiconductor devices under real load conditions with the phase legs operating from a 1070V DC dc-link allowing sinusoidal load currents up to 800A amplitude at frequencies from 0.1Hz to 13Hz.


title: Integral Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space

abstract: Policy iteration (PI) is a recursive process of policy evaluation and improvement to solve an optimal decision-making, e.g., reinforcement learning (RL) or optimal control problem and has served as the fundamental to develop RL methods. Motivated by integral PI (IPI) schemes in optimal control and RL methods in continuous time and space (CTS), this paper proposes on-policy IPI to solve the general RL problem in CTS, with its environment modeled by an ordinary differential equation (ODE). In such continuous domain, we also propose four off-policy IPI methods---two are the ideal PI forms that use advantage and Q-functions, respectively, and the other two are natural extensions of the existing off-policy IPI schemes to our general RL framework. Compared to the IPI methods in optimal control, the proposed IPI schemes can be applied to more general situations and do not require an initial stabilizing policy to run; they are also strongly relevant to the RL algorithms in CTS such as advantage updating, Q-learning, and value-gradient based (VGB) greedy policy improvement. Our on-policy IPI is basically model-based but can be made partially model-free; each off-policy method is also either partially or completely model-free. The mathematical properties of the IPI methods---admissibility, monotone improvement, and convergence towards the optimal solution---are all rigorously proven, together with the equivalence of on- and off-policy IPI. Finally, the IPI methods are simulated with an inverted-pendulum model to support the theory and verify the performance.


Open Marriage and Marital Adjustment.


title: Environment perception for inner-city driver assistance and highly-automated driving

abstract: While driver assistance systems mainly targeted highway or parking scenarios in the past, systems assisting in inner-city traffic increasingly get into focus today. Since driving in urban areas is characterized by a larger variety of situations that have to be covered, finding an adequate representation of the vehicle surrounding is a challenging task for the perception of these systems. In this paper we present our perception system that has been specifically designed for the demands of inner-city driving. It first features a plugin-based architecture by which multiple sensor setups as well as different driver assistance functions can be supported. Second, it is characterized by a hybrid modeling approach that combines the well known model-based object tracking technique with model-free representations in terms of grids. We will present details on a specific implementation of the system using a 3D lidar sensor. Finally, it is shown how the system is used in the Narrow Road Assistant - a next-generation driver assistance system supporting the driver in safely passing narrow road passages in inner-city.


title: Accurate and quasi-automatic lip tracking

abstract: Lip segmentation is an essential stage in many multimedia systems such as videoconferencing, lip reading, or low-bit-rate coding communication systems. In this paper, we propose an accurate and robust quasi-automatic lip segmentation algorithm. First, the upper mouth boundary and several characteristic points are detected in the first frame by using a new kind of active contour: the "jumping snake." Unlike classic snakes, it can be initialized far from the final edge and the adjustment of its parameters is easy and intuitive. Then, to achieve the segmentation, we propose a parametric model composed of several cubic curves. Its high flexibility enables accurate lip contour extraction even in the challenging case of a very asymmetric mouth. Compared to existing models, it brings a significant improvement in accuracy and realism. The segmentation in the following frames is achieved by using an interframe tracking of the keypoints and the model parameters. However, we show that, with a usual tracking algorithm, the keypoints' positions become unreliable after a few frames. We therefore propose an adjustment process that enables an accurate tracking even after hundreds of frames. Finally, we show that the mean keypoints' tracking errors of our algorithm are comparable to manual points' selection errors.


title: Isomer specific measurement of polychlorinated dibenzodioxin and dibenzofuran isomers in human blood from American Vietnam veterans two decades after exposure to Agent Orange

abstract: Abstract In an attempt to develop a quantitative biological marker of intake of 2,3,7,8-TCDD from Agent Orange in American Vietnam veterans, 28 participants in a Commonwealth of Massachusetts study volunteered to submit one unit (450 mL) of blood for polychlorinated dibenzodioxin (PCDD) and polychlorinated dibenzofuran (PCDF) analysis. Analysis was performed for all the currently measurable PCDD/Fs in the plasma lipid. It is concluded that measurement of all detectable PCDD/Fs in body tissue is a preferable way to detect intake of a specific isomer some time in the past. It is also concluded that blood, in addition to adipose tissue, is a promising tissue to use for detecting such elevations.


title: A Novel Method for Banknote RecognitionSystem

abstract: The purpose of currency recognition system is to categorize the currencies accurately. In this work, banknotes are recognized using a novel feature extraction technique such as speeded up robust features (SURF) which is a combination of both interest point detector and descriptor. Speeded up robust features are used to extract the local image features of an image. The SURF features extracted are both scale and rotation invariant which makes it robust against various image transformations. The interest points are detected from the test and the template images followed by SURF feature extraction. Then the distance between the SURF descriptors for corresponding matched interest points is calculated and the average distance is taken to find the category of the banknote. The proposed system is evaluated on the dataset and achieves high recognition rate.


title: Reliability of Capacitors for DC-Link Applications in Power Electronic Converters—An Overview

abstract: DC-link capacitors are an important part in the majority of power electronic converters which contribute to cost, size and failure rate on a considerable scale. From capacitor users' viewpoint, this paper presents a review on the improvement of reliability of dc link in power electronic converters from two aspects: 1) reliability-oriented dc-link design solutions; 2) conditioning monitoring of dc-link capacitors during operation. Failure mechanisms, failure modes and lifetime models of capacitors suitable for the applications are also discussed as a basis to understand the physics-of-failure. This review serves to provide a clear picture of the state-of-the-art research in this area and to identify the corresponding challenges and future research directions for capacitors and their dc-link applications.


title: Interactive machine learning for health informatics: when do we need the human-in-the-loop?

abstract: Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.” This “human-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.


title: Fairly Allocating Many Goods with Few Queries

abstract: We investigate the query complexity of the fair allocation of indivisible goods. For two agents with arbitrary monotonic valuations, we design an algorithm that computes an allocation satisfying envy-freeness up to one good (EF1), a relaxation of envy-freeness, using a logarithmic number of queries. We show that the logarithmic query complexity bound also holds for three agents with additive valuations. These results suggest that it is possible to fairly allocate goods in practice even when the number of goods is extremely large. By contrast, we prove that computing an allocation satisfying envyfreeness and another of its relaxations, envy-freeness up to any good (EFX), requires a linear number of queries even when there are only two agents with identical additive valuations.


title: Policy Adaptation for Deep Reinforcement Learning-Based Dialogue Management

abstract: Policy optimization is the core part of statistical dialogue management. Deep reinforcement learning has been successfully used for dialogue policy optimization for a static pre-defined domain. However, when the domain changes dynamically, e.g. a new previously unseen concept (or slot) which can be then used as a database search constraint is added, or the policy for one domain is transferred to another domain, the dialogue state space and action sets both will change. Therefore, the model structures for different domains have to be different. This makes dialogue policy adaptation/transfer challenging. Here a multi -agent dialogue policy (MADP) is proposed to tackle these problems. MADP consists of some slot-dependent agents (S-Agents) and a slot-independent agent (G-Agent). S-Agents have shared parameters in addition to private parameters for each one. During policy transfer, the shared parameters in S-Agents and the parameters in G-Agent can be directly transferred to the agents in extended/new domain. Simulation experiments showed that MADP can significantly speed up the policy learning and facilitate policy adaptation.


title: The Influence of Own-Product's Online Reviews on the Sales of Market Substitute Products

abstract: Whether and how online reviews of one commodity's homogeneous goods affect the sales of itself. By using multiple linear regression model, We find the number of reviews, the score of products, the percentage of positive or negative reviews, the number of words of reviews, and the level of the commentators do have impacts on the sales of product. We also find the online review valence and volume of itself have a positive impact on the sales of its own products, but the online review valence and volume of their market substitute products have a negative impact on the sales of its own product.


title: A Wide Input Range Isolated Stacked Resonant Switched-Capacitor dc-dc Converter for High Conversion Ratios

abstract: This paper presents an isolated dc-dc converter architecture for high conversion ratios using the stacked resonant switched-capacitor topology. Capacitive isolation is adopted in place of transformer isolation for saving cost and platform size. With phase shift operation, the topology can support a wide input range with a simple regulation scheme while enabling zero-voltage switching. The topology can be adapted to high conversion ratios via addition of stacking levels while keeping the same magnetics. The experimental results demonstrate a 2-Stack example achieving a peak efficiency of 92.8% and a peak power density of 283 W/inch3 with 92% efficiency. The converter can also maintain within 3% efficiency drop over 25%–100% load range, and 50%–100% input range.


title: An Improved Multistage Switched Inductor Boost Converter (Improved M-SIBC) for Renewable Energy Applications: A key to Enhance Conversion Ratio

abstract: In this article, an improved Multistage Switched Inductor (M-SI) based power converter or Improved Multistage Switched Inductor Boost Converter (Improved M-SIBC) is proposed for renewable applications which provides a key to enhancing the voltage conversion ratio. In the last decades, Switched Inductor (SI) and M-SI are the popular network/technique employed in DC-DC converter to achieve a high voltage conversion ratio. An improved SI and M-SI network/technique is proposed to enhance the existing the voltage conversion capabilities of SI and M-SI by replacing central uncontrolled switches by polarized capacitor. The anticipated power converter configuration combines the feature of the conventional boost converter and improved M-SI. The voltage conversion a capability is depends on the number of stages of M-SI and ON time of the control switch. The operation modes and characteristics of the proposed converter with steady state mathematical analysis for N-stages are discussed in detail. Moreover, the proposed converter compared with the existing converter in terms of the voltage conversion ratio and the detail of the number of components is also provided. Matrix Laboratory R2016a simulation results of 100W proposed improved M-SIBC with considering three stages are provided and the results always show a good agreement with theoretical analysis and also validates the improved M-SI network concept.


title: Using System Dynamics to Model Student Interest in Science, Technology, Engineering, and Mathematics

abstract: This paper presents preliminary results of a Raytheon project that uses systems dynamics to understand the intricacies of the U.S. educational system and to assist in exploring the effects of policies and programs, with a goal of doubling the numbers of science, technology, engineering and mathematics (STEM) college graduates by 2015. Specifically, a system dynamics model has been developed, the initial version of which targets increasing the number of students both capable and interested in pursuing careers in STEM disciplines. A few scenarios have been analyzed that examine changes for improving student capabilities. Initial results provide insight into the value and viability of a few proposed policies and indicate that with continued research, model development, and analysis it will be possible to further assess proposed improvements in the U.S education system.


title: Low Voltage Performance of a Microwave CMOS Gilbert Cell Mixer

abstract: This paper demonstrates the low voltage operation of a doubly balanced Gilbert mixer fabricated in a 0.8-/spl mu/m CMOS process and operating as both a down-converter and an up-converter. As a down-converter with an RF input of 1.9 GHz, the mixer has a single sideband noise figure as low as 7.8 dB and achieved down-conversion gain for supply voltages as low as 1.8 V. As an up-converter, the mixer demonstrates 10 dB of conversion gain at an RF frequency of 2.4 GHz with an applied local oscillator (LO) power of -7 dBm and LO-RF/LO-IF isolation of at least 30 dB. Up-conversion gain was achieved over a 5-GHz bandwidth and at supply voltages as low as 1.5 V. The mixer presented demonstrates the lowest single side band noise figure for a CMOS doubly balanced down-converting mixer and the highest frequency of operation for a mixer fabricated in CMOS technology to date.


title: A novel tunable origami accordion antenna

abstract: A novel origami accordion antenna structure is presented, which can be expanded and collapsed. Based on this structure, one origami antenna is designed with operating frequency that can be changed based on its height. The return loss, far-field radiation pattern and peak gain of this antenna are examined.


title: On large deviations theory and asymptotically efficient Monte Carlo estimation

abstract: It is shown how large-deviations theory can be applied to construct an asymptotically efficient simulation distribution for Monte Carlo simulation using the importance sampling technique. A sufficient and necessary condition is given for the asymptotic efficiency of the candidate simulation distributions. This is done in the multidimensional setting that is required by many practical simulation problems. The result obtained is applied primarily in two areas. First, the generalization of previous work dealing with functionals of Markov chains is discussed. A second area of application is the simulation of nonlinear systems with Gaussian inputs. As an example of a system with Gaussian inputs, the simulation of a digital communication channel with nonlinear ISI (intersymbol interference) characteristic of satellite data links is considered. In the case of linear ISI, the asymptotically efficient exponentially twisted distribution turns out to agree with a method previously proposed by D. Lu and K. Yao (1988). The large-deviations point of view provides some useful insight on how to extent the Lu and Yao method to nonlinear channels. >


title: The Laplacian Pyramid as a Compact Image Code

abstract: We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space. Pixel-to-pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself. The result is a net data compression since the difference, or error, image has low variance and entropy, and the low-pass filtered image may represented at reduced sample density. Further data compression is achieved by quantizing the difference image. These steps are then repeated to compress the low-pass image. Iteration of the process at appropriately expanded scales generates a pyramid data structure. The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus, the code tends to enhance salient image features. A further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression. Fast algorithms are described for coding and decoding.


title: PROTEINS: Structure, Function, and Bioinformatics Suppl 7:3–7 (2005) Critical Assessment of Methods of Protein Structure Prediction (CASP)—Round 6

abstract: This article provides an introduction to the special issue of the journal Proteins dedicated to the fifth CASP experiment to assess the state of the art in protein structure prediction. The article describes the conduct, the categories of prediction, and the evaluation and assessment procedures of the experiment. A brief summary of progress over the five CASP experiments is provided. Related developments in the field are also described.


title: Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Faces

abstract: Lighting estimation from faces is an important task and has applications in many areas such as image editing, intrinsic image decomposition, and image forgery detection. We propose to train a deep Convolutional Neural Network (CNN) to regress lighting parameters from a single face image. Lacking massive ground truth lighting labels for face images in the wild, we use an existing method to estimate lighting parameters, which are treated as ground truth with noise. To alleviate the effect of such noise, we utilize the idea of Generative Adversarial Networks (GAN) and propose a Label Denoising Adversarial Network (LDAN). LDAN makes use of synthetic data with accurate ground truth to help train a deep CNN for lighting regression on real face images. Experiments show that our network outperforms existing methods in producing consistent lighting parameters of different faces under similar lighting conditions. To further evaluate the proposed method, we also apply it to regress object 2D key points where ground truth labels are available. Our experiments demonstrate its effectiveness on this application.


title: A case of body stalk anomaly at 10 weeks of gestation

abstract: We describe a case of body stalk anomaly which was diagnosed at 10 weeks of gestation on a dating scan. The fetus was visualized within the exocelomic cavity outside an apparently intact amniotic membrane. The fetus displayed multiple anomalies characteristic of body stalk syndrome including a skull and brain defect, abdominal wall defect, kyphoscoliosis and gross deformities of the lower limbs. These findings do not support early amniotic membrane rupture as the primary event in the pathogenesis of body stalk anomaly and indicate that the exocelomic location of the fetus may actually be one of the features of this complex developmental anomaly.


title: Review Large brains and cognition: Where do elephants fit in?

abstract: Abstract Among terrestrial mammals, elephants share the unique status, along with humans and great apes, of having large brains, being long-lived and having offspring that require long periods of dependency. Elephants have the largest brains of all terrestrial mammals, including the greatest volume of cerebral cortex. In contrast to what one might expect from such a large-brained species, the performance of elephants in cognitive feats, such as tool use, visual discrimination learning and tests of “insight” behavior, is unimpressive in comparison to the performance by chimpanzees and, of course, humans. Where elephants do seem to excel is in long-term, extensive spatial-temporal and social memory. In addition, elephants appear to be somewhat unique among non-human species in their reactions to disabled and deceased conspecifics, exhibiting behaviors that are mindful of “theory-of-mind” phenomena. Information gleaned from studies on the neural cytoarchitecture of large brains reveals that the neurons of the cerebral cortex of elephants are much less densely populated than in large-brained primates. The interactions between cortical neurons would appear to be more global and less compartmentalized into local areas, and cortical information processing slower, than in great apes and humans. Although focused neural cytoarchitecture studies on the elephant are needed, this comparative perspective on the cortical neural cytoarchitecture appears to relate to differences in behavior between elephants and their primate counterparts.


title: High-Speed Query Processing over High-Speed Networks

abstract: Modern database clusters entail two levels of networks: connecting CPUs and NUMA regions inside a single server in the small and multiple servers in the large. The huge performance gap between these two types of networks used to slow down distributed query processing to such an extent that a cluster of machines actually performed worse than a single many-core server. The increased main-memory capacity of the cluster remained the sole benefit of such a scale-out. ::: The economic viability of high-speed interconnects such as InfiniBand has narrowed this performance gap considerably. However, InfiniBand's higher network bandwidth alone does not improve query performance as expected when the distributed query engine is left unchanged. The scalability of distributed query processing is impaired by TCP overheads, switch contention due to uncoordinated communication, and load imbalances resulting from the inflexibility of the classic exchange operator model. This paper presents the blueprint for a distributed query engine that addresses these problems by considering both levels of networks holistically. It consists of two parts: First, hybrid parallelism that distinguishes local and distributed parallelism for better scalability in both the number of cores as well as servers. Second, a novel communication multiplexer tailored for analytical database workloads using remote direct memory access (RDMA) and low-latency network scheduling for high-speed communication with almost no CPU overhead. An extensive evaluation within the HyPer database system using the TPC-H benchmark shows that our holistic approach indeed enables high-speed query processing over high-speed networks.


title: Byzantine Agreement Given Partial Broadcast

abstract: This paper considers unconditionally secure protocols for reliable broadcast among a set of n players, where up to t of the players can be corrupted by a (Byzantine) adversary but the remaining h = n - t players remain honest. In the standard model with a complete, synchronous network of bilateral authenticated communication channels among the players, broadcast is achievable if and only if 2n/h < 3. We show that, by extending this model by the existence of partial broadcast channels among subsets of b players, global broadcast can be achieved if and only if the number h of honest players satisfies 2n/h < b + 1. Achievability is demonstrated by protocols with communication and computation complexities polynomial in the size of the network, i.e., in the number of partial broadcast channels. A respective characterization for the related consensus problem is also given.


title: Relay selection of relay-assisted Device-to-Device and uplink communication underlying cellular networks

abstract: Recently, an enormous increasing number of mobile devices have changed people's life and brought huge demands for services. D2D (Device-to-Device) communication is a brand-new fashion that allows mobile stations communicating directly with each other using existing licensed band without traversing through core network, and thereby offload traffic from base stations (BS). On the other hand, relays are indispensable to the next generations of mobile communication networks. Relays can remove the coverage holes, increase system capacity, expand the coverage area of base stations and enhance transmission rate for the devices in cell edge, and thereby it also provides uniform data rate coverage in the cellular networks. This paper proposes a model that integrates the advantages of relay and D2D and proposes a method that can improve spectrum efficiency and decrease path loss or shadow fading which makes worse communication in the system. This paper also proposes a way how to choose a better relay and it can increase the system throughput. Simulation results of uplink and D2D communication show that the proposed method can increase the system throughput.


Membrane Binding Properties of Bacillomycin-D Derivatives with Model Membranes Composed of Different Sterols


title: Emulating the Power Consumption Behavior of Server Workloads Using CPU Performance Counters

abstract: The accurate measurement of a server's power consumption when running realistic workloads enables characterization of its energy efficiency and helps to make better provisioning and workload placement decisions. Information on the energy efficiency of a server for a given target workload can greatly influence such decisions and thus the final energy efficiency of a cluster or data center. However, measuring energy efficiency and power consumption of server applications has become challenging as applications are often distributed or require work intensive configuration, setup, and specialized load drivers for reproducible testing. As a result, it may be not feasible to perform tests using the actual workload that is to be deployed. We introduce an approach to create small-scale workloads that emulate the power consumption-relevant behavior of an application by deliberately triggering specific power relevant performance counter events. These workloads can then be easily deployed on a target server for fast and efficient power characterization. We validate the proposed approach by approximating the power consumption behavior of different workloads at multiple load levels. We show that our approach is capable of producing small-scale workloads that reflect the power consumption behavior of their reference applications over multiple load levels with a minimum error of less than 1%.


title: The undervaluation of corporate reputation as a supplier selection factor: An analysis of ingredient branding of complex products in the manufacturing industry

abstract: Prior research studies on supplier selection factors assess vendor reputation as a low ranked criterion. Reputation in these articles, however, only refers to the position in the industry, without considering the role of the final customer. Our results from a survey with 565 individuals suggest that the end user as a stakeholder should be also considered when analyzing a vendor’s prestige. We demonstrate that a supplier’s standing has negative as well as positive reputational effects on the buyer. Depending on the relevance of the purchased good as well as the reputation of the supplier and the buyer, low/high-ranked ingredients significantly decrease/increase the final customer’s perception of the buyer.


title: Automatic discovery of groups of objects for scene understanding

abstract: Objects in scenes interact with each other in complex ways. A key observation is that these interactions manifest themselves as predictable visual patterns in the image. Discovering and detecting these structured patterns is an important step towards deeper scene understanding. It goes beyond using either individual objects or the scene as a whole as the semantic unit. In this work, we promote "groups of objects". They are high-order composites of objects that demonstrate consistent spatial, scale, and viewpoint interactions with each other. These groups of objects are likely to correspond to a specific layout of the scene. They can thus provide cues for the scene category and can also prime the likely locations of other objects in the scene. It is not feasible to manually generate a list of all possible groupings of objects we find in our visual world. Hence, we propose an algorithm that automatically discovers groups of arbitrary numbers of participating objects from a collection of images labeled with object categories. Our approach builds a 4-dimensional transform space of location, scale and viewpoint, and efficiently identifies all recurring compositions of objects across images. We then model the discovered groups of objects using the deformable parts-based model. Our experiments on a variety of datasets show that using groups of objects can significantly boost the performance of object detection and scene categorization.


title: A Low Power Consumption Algorithm for Efficient Energy Consumption in ZigBee Motes

abstract: Wireless Sensor Networks (WSNs) are becoming increasingly popular since they can gather information from different locations without wires. This advantage is exploited in applications such as robotic systems, telecare, domotic or smart cities, among others. To gain independence from the electricity grid, WSNs devices are equipped with batteries, therefore their operational time is determined by the time that the batteries can power on the device. As a consequence, engineers must consider low energy consumption as a critical objective to design WSNs. Several approaches can be taken to make efficient use of energy in WSNs, for instance low-duty-cycling sensor networks (LDC-WSN). Based on the LDC-WSNs, we present LOKA, a LOw power Konsumption Algorithm to minimize WSNs energy consumption using different power modes in a sensor mote. The contribution of the work is a novel algorithm called LOKA that implements two duty-cycling mechanisms using the end-device of the ZigBee protocol (of the Application Support Sublayer) and an external microcontroller (Cortex M0+) in order to minimize the energy consumption of a delay tolerant networking. Experiments show that using LOKA, the energy required by the sensor device is reduced to half with respect to the same sensor device without using LOKA.


title: Dietary effects on DNA methylation: do they account for the hepatocarcinogenic properties of lipotrope deficient diets?

abstract: The first evidence suggesting that dietary deprivation of sources of one-carbon units could influence development of tumors is now almost fifty years old. In 1946, Copeland and Salmon reported that long-term feeding of a choline deficient diet increased the incidence of tumors in the liver and other organs of the rat. Once it was demonstrated that the peanut meal used in the diet was contaminated with aflatoxin (Newberne, 1965), it was assumed that the diet had in some way increased the sensitivity of cells to low levels of carcinogens. This supposition was supported by the demonstration that a variety of different diets deficient in choline and/or other lipotropes (methionine, folate and vitamin 12) increased the tumorigenicity of a wide range of carcinogens in liver and other organs (Rogers, 1975, 1993; Rogers and Newberne, 1980; Rogers et al., 1974; Shinozuka et al., 1978a, b; Lombardi and Shinozuka, 1979). Unequivocal demonstration that deficiency of lipotropes alone was sufficient to cause liver tumors was obtained by feeding of an amino-acid defined (AAD) diet lacking methionine and choline but supplemented with folate, vitamin B12 and homocystine (Mikol et al., 1983). In the absence of added carcinogens, this diet produced a high incidence of hepatomas in rats, yet was reported to “afford a slight protection against spontaneous tumor formation in extra-hepatic tissues.” These studies raised a number of questions that are yet to be resolved. Why is deficiency of lipotropes carcinogenic? Why is the liver the primary target tissue? Does moderate lipotrope deficiency play a significant role in development of human cancers?


title: Environmental Psychology Matters

abstract: Environmental psychology examines transactions between individuals and their built and natural environments. This includes investigating behaviors that inhibit or foster sustainable, climate-healthy, and nature-enhancing choices, the antecedents and correlates of those behaviors, and interventions to increase proenvironmental behavior. It also includes transactions in which nature provides restoration or inflicts stress, and transactions that are more mutual, such as the development of place attachment and identity and the impacts on and from important physical settings such as home, workplaces, schools, and public spaces. As people spend more time in virtual environments, online transactions are coming under increasing research attention. Every aspect of human existence occurs in one environment or another, and the transactions with and within them have important consequences both for people and their natural and built worlds. Environmental psychology matters.


A hypercube-based indirect encoding for evolving large-scale neural networks


title: Video Based Reconstruction of 3D People Models

abstract: This paper describes how to obtain accurate 3D body models and texture of arbitrary people from a single, monocular video in which a person is moving. Based on a parametric body model, we present a robust processing pipeline achieving 3D model fits with 5mm accuracy also for clothed people. Our main contribution is a method to nonrigidly deform the silhouette cones corresponding to the dynamic human silhouettes, resulting in a visual hull in a common reference frame that enables surface reconstruction. This enables efficient estimation of a consensus 3D shape, texture and implanted animation skeleton based on a large number of frames. We present evaluation results for a number of test subjects and analyze overall performance. Requiring only a smartphone or webcam, our method enables everyone to create their own fully animatable digital double, e.g., for social VR applications or virtual try-on for online fashion shopping.


title: Elliptic curve cryptography for real time embedded systems in IoT networks

abstract: Internet of things (IoT) is a growing network of embedded devices that can interact with the external environment. With this increasing global network, electronic communication between devices is becoming wireless and ubiquitous. Also, most of the IoT services will be realized as real time embedded systems which rely heavily on security mechanisms. This makes security imperative to embedded devices in IoT. This paper presents security challenges faced by system designers while designing secure embedded systems. The most difficult task for embedded system security is implementation of Public Key Cryptography (PKC). PKC based on Elliptic Curve Cryptography (ECC) which uses smaller key sizes is efficient for both private and public operations. Moreover, with IoT several embedded devices are becoming interconnected which makes ECC advantageous when you need to embed security. The comparative study suggests that ECC is best suited for resource constrained real time embedded systems in IoT.


title: An energy efficient hierarchical clustering algorithm for wireless sensor networks

abstract: A wireless network consisting of a large number of small sensors with low-power transceivers can be an effective tool for gathering data in a variety of environments. The data collected by each sensor is communicated through the network to a single processing center that uses all reported data to determine characteristics of the environment or detect an event. The communication or message passing process must be designed to conserve the limited energy resources of the sensors. Clustering sensors into groups, so that sensors communicate information only to clusterheads and then the clusterheads communicate the aggregated information to the processing center, may save energy. In this paper, we propose a distributed, randomized clustering algorithm to organize the sensors in a wireless sensor network into clusters. We then extend this algorithm to generate a hierarchy of clusterheads and observe that the energy savings increase with the number of levels in the hierarchy. Results in stochastic geometry are used to derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the network when all sensors report data through the clusterheads to the processing center.


title: To enhance the quality of CPR performed by youth layman

abstract: Background ::: By educating laymen, survival after cardiac arrest can increase in society. It is difficult to reach the entire population with cardiopulmonary resuscitation (CPR) training. However, if 15% of the population knows how to perform CPR, an increase in short- and long-term survival in patients suffering a cardiac arrest could be seen. To educate youth is a way to reach parts of the population. This study aimed to investigate the effect of a 2-h CPR intervention for youth.


title: Better Evaluation for Grammatical Error Correction

abstract: We present a novel method for evaluating grammatical error correction. The core of our method, which we call MaxMatch (M2), is an algorithm for efficiently computing the sequence of phrase-level edits between a source sentence and a system hypothesis that achieves the highest overlap with the gold-standard annotation. This optimal edit sequence is subsequently scored using F1 measure. We test our M2 scorer on the Helping Our Own (HOO) shared task data and show that our method results in more accurate evaluation for grammatical error correction.


title: Is This a Bug or an Obsolete Test?

abstract: In software evolution, developers typically need to identify whether the failure of a test is due to a bug in the source code under test or the obsoleteness of the test code when they execute a test suite. Only after finding the cause of a failure can developers determine whether to fix the bug or repair the obsolete test. Researchers have proposed several techniques to automate test repair. However, test-repair techniques typically assume that test failures are always due to obsolete tests. Thus, such techniques may not be applicable in real world software evolution when developers do not know whether the failure is due to a bug or an obsolete test. To know whether the cause of a test failure lies in the source code under test or in the test code, we view this problem as a classification problem and propose an automatic approach based on machine learning. Specifically, we target Java software using the JUnit testing framework and collect a set of features that may be related to failures of tests. Using this set of features, we adopt the Best-first Decision Tree Learning algorithm to train a classifier with some existing regression test failures as training instances. Then, we use the classifier to classify future failed tests. Furthermore, we evaluated our approach using two Java programs in three scenarios (within the same version, within different versions of a program, and between different programs), and found that our approach can effectively classify the causes of failed tests.


title: Bilingualism: The good, the bad, and the indifferent

abstract: The present paper summarizes research showing that bilingualism affects linguistic and cognitive performance across the lifespan. The effect on linguistic performance is generally seen as a deficit in which bilingual children control a smaller vocabulary than their monolingual peers and bilingual adults perform more poorly on rapid lexical retrieval tasks. The effect on cognitive performance is to enhance executive functioning and to protect against the decline of executive control in aging. These effects interact to produce a complex pattern regarding the effect of bilingualism on memory performance. Memory tasks based primarily on verbal recall are performed more poorly by bilinguals but memory tasks based primarily on executive control are performed better by bilinguals. Speculations regarding the mechanism responsible for these effects are described.


1 SQLizer : Query Synthesis from Natural Language


title: Named entity disambiguation by leveraging wikipedia semantic knowledge

abstract: Name ambiguity problem has raised an urgent demand for efficient, high-quality named entity disambiguation methods. The key problem of named entity disambiguation is to measure the similarity between occurrences of names. The traditional methods measure the similarity using the bag of words (BOW) model. The BOW, however, ignores all the semantic relations such as social relatedness between named entities, associative relatedness between concepts, polysemy and synonymy between key terms. So the BOW cannot reflect the actual similarity. Some research has investigated social networks as background knowledge for disambiguation. Social networks, however, can only capture the social relatedness between named entities, and often suffer the limited coverage problem. To overcome the previous methods' deficiencies, this paper proposes to use Wikipedia as the background knowledge for disambiguation, which surpasses other knowledge bases by the coverage of concepts, rich semantic information and up-to-date content. By leveraging Wikipedia's semantic knowledge like social relatedness between named entities and associative relatedness between concepts, we can measure the similarity between occurrences of names more accurately. In particular, we construct a large-scale semantic network from Wikipedia, in order that the semantic knowledge can be used efficiently and effectively. Based on the constructed semantic network, a novel similarity measure is proposed to leverage Wikipedia semantic knowledge for disambiguation. The proposed method has been tested on the standard WePS data sets. Empirical results show that the disambiguation performance of our method gets 10.7% improvement over the traditional BOW based methods and 16.7% improvement over the traditional social network based methods.


title: RMediation: An R package for mediation analysis confidence intervals.

abstract: This article describes the RMediation package,which offers various methods for building confidence intervals (CIs) for mediated effects. The mediated effect is the product of two regression coefficients. The distribution-of-the-product method has the best statistical performance of existing methods for building CIs for the mediated effect. RMediation produces CIs using methods based on the distribution of product, Monte Carlo simulations, and an asymptotic normal distribution. Furthermore, RMediation generates percentiles, quantiles, and the plot of the distribution and CI for the mediated effect. An existing program, called PRODCLIN, published in Behavior Research Methods, has been widely cited and used by researchers to build accurate CIs. PRODCLIN has several limitations: The program is somewhat cumbersome to access and yields no result for several cases. RMediation described herein is based on the widely available R software, includes several capabilities not available in PRODCLIN, and provides accurate results that PRODCLIN could not.


title: Fact Checking in Large Knowledge Graphs - A Discriminative Predicate Path Mining Approach.

abstract: Traditional fact checking by experts and analysts cannot keep pace with the volume of newly created information. It is important and necessary, therefore, to enhance our ability to computationally determine whether some statement of fact is true or false. We view this problem as a link-prediction task in a knowledge graph, and show that a new model of the top discriminative predicate paths is able to understand the meaning of some statement and accurately determine its veracity. We evaluate our approach by examining thousands of claims related to history, geography, biology, and politics using a public, million node knowledge graph extracted from Wikipedia and PubMedDB. Not only does our approach significantly outperform related models, we also find that the discriminative predicate path model is easily interpretable and provides sensible reasons for the final determination.


title: Social exclusion decreases prosocial behavior.

abstract: In 7 experiments, the authors manipulated social exclusion by telling people that they would end up alone later in life or that other participants had rejected them. Social exclusion caused a substantial reduction in prosocial behavior. Socially excluded people donated less money to a student fund, were unwilling to volunteer for further lab experiments, were less helpful after a mishap, and cooperated less in a mixed-motive game with another student. The results did not vary by cost to the self or by recipient of the help, and results remained significant when the experimenter was unaware of condition. The effect was mediated by feelings of empathy for another person but was not mediated by mood, state self-esteem, belongingness, trust, control, or self-awareness. The implication is that rejection temporarily interferes with emotional responses, thereby impairing the capacity for empathic understanding of others, and as a result, any inclination to help or cooperate with them is undermined.


title: Safety in Vehicle Platooning: A Systematic Literature Review

abstract: Vehicle platooning has been studied for several decades, with objectives such as improved traffic throughput on existing infrastructure or reduced energy consumption. All the time, it has been apparent that safety is an important issue. However, there are no comprehensive analyses of what is needed to achieve safety in platooning, but only scattered pieces of information. This paper investigates, through a systematic literature review, what is known about safety for platooning, including what analysis methods have been used, what hazards and failures have been identified, and solution elements that have been proposed to improve safety. Based on this, a gap analysis is performed to identify outstanding questions that need to be addressed in future research. These include dealing with a business ecosystem of actors that cooperate and compete around platooning, refining safety analysis methods to make them suitable for systems-of-systems, dealing with variability in vehicles, and finding solutions to various human factors issues.


title: Learning Paraphrasing for Multiword Expressions

abstract: In this paper, we investigate the impact of context for the paraphrase ranking task, comparing and quantifying results for ::: multi-word expressions and single words. We focus on systematic integration of existing paraphrase resources to produce ::: paraphrase candidates and later ask human annotators to judge paraphrasability in context. ::: We first conduct a paraphrase-scoring annotation task with and without context for targets that are i) single- and multi-word ::: expressions ii) verbs and nouns. We quantify how differently annotators score paraphrases when context information is provided. ::: Furthermore, we report on experiments with automatic paraphrase ranking. If we regard the problem as a binary classification ::: task, we obtain an F1–score of 81.56% and 79.87% for multi-word expressions and single words resp. using kNN classifier. Approaching the problem as a learning-to-rank task, we attain MAP scores up to 87.14% and 91.58% for multiword expressions and single words resp. using LambdaMART, thus yielding highquality contextualized paraphrased selection. Further, we provide the first dataset with paraphrase judgments for multi-word targets in context.


title: Cerebellum of the Premature Infant: Rapidly Developing, Vulnerable, Clinically Important

abstract: Brain abnormality in surviving premature infants is associated with an enormous amount of neurodevelopmental disability, manifested principally by cognitive, behavioral, attentional, and socialization deficits, most commonly with only relatively modest motor deficits. The most recognized contributing neuropathology is cerebral white matter injury. The thesis of this review is that acquired cerebellar abnormality is a relatively less recognized but likely important cause of neurodevelopmental disability in small premature infants. The cerebellar disease may be primarily destructive (eg, hemorrhage, infarction) or primarily underdevelopment. The latter appears to be especially common and relates to a particular vulnerability of the cerebellum of the small premature infant. Central to this vulnerability are the extraordinarily rapid and complex developmental events occurring in the cerebellum. The disturbance of development can be caused either by direct adverse effects on the cerebellum, especially the distinctive transient external granular layer, or by indirect remote trans-synaptic effects. This review describes the fascinating details of cerebellar development, with an emphasis on events in the premature period, the major types of cerebellar abnormality acquired during the premature period, their likely mechanisms of occurrence, and new insights into the relation of cerebellar disease in early life to subsequent cognitive/behavioral/attentional/socialization deficits.


title: DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data

abstract: Accurate rainfall forecasting is critical because it has a great impact on people's social and economic activities. Recent trends on various literatures show that Deep Learning (Neural Network) is a promising methodology to tackle many challenging tasks. In this study, we introduce a brand-new data-driven precipitation prediction model called DeepRain. This model predicts the amount of rainfall from weather radar data, which is three-dimensional and four-channel data, using convolutional LSTM (ConvLSTM). ConvLSTM is a variant of LSTM (Long Short-Term Memory) containing a convolution operation inside the LSTM cell. For the experiment, we used radar reflectivity data for a two-year period whose input is in a time series format in units of 6 min divided into 15 records. The output is the predicted rainfall information for the input data. Experimental results show that two-stacked ConvLSTM reduced RMSE by 23.0% compared to linear regression.


title: Safety of catheter ablation of atrial fibrillation using fiber optic–based contact force sensing

abstract: Background While the body of evidence showing the benefit of contact force (CF)–sensing catheters in improving atrial fibrillation ablation success rate is growing, real-world safety data of this technology are limited. Objective The aim of this study was to report the complication rate in patients undergoing ablation using a specific CF-sensing catheter (TactiCath Quartz CF ablation catheter, Abbott, St. Paul, MN) in a large worldwide database. Methods User-reported adverse event data from January 1, 2014 to December 31, 2015 were reviewed for the ablation catheter. These adverse events included cardiac perforation, cerebrovascular accident, atrioesophageal (AE) fistula, steam pop, and death. Rates of these adverse events, including geographic and temporal relationships, were calculated. Results Adverse events were reported in 178 of 41,709 patients (adverse event rate 0.427%). Adverse events included 117 cardiac perforations (0.281%), 5 cerebrovascular accidents (0.012%), 10 AE fistulas (0.024%), 53 audible steam pops (0.127%), and 13 deaths (0.031%). The total event rate in European countries was 0.53% vs 0.38% in the United States ( P = .0082). The AE fistula event rate peaked in late 2014 and early 2015 at 0.06% and decreased over time to a steady-state 0.01%, corresponding to the initial US release, subsequent increased use, and training on the manufacturer recommended settings. Conclusion Complication rates while using the TactiCath CF ablation catheter appear to decrease with training and experience. The major complication rate associated with this technology is low in the context of similar reports of conventional radiofrequency catheters.


title: Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

abstract: Ant Colony Optimization ACO has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing SA and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.


title: VISER: Visual Self-Regularization

abstract: In this work, we propose the use of large set of unlabeled images as a source of regularization data for learning robust visual representation. Given a visual model trained by a labeled dataset in a supervised fashion, we augment our training samples by incorporating large number of unlabeled data and train a semi-supervised model. We demonstrate that our proposed learning approach leverages an abundance of unlabeled images and boosts the visual recognition performance which alleviates the need to rely on large labeled datasets for learning robust representation. To increment the number of image instances needed to learn robust visual models in our approach, each labeled image propagates its label to its nearest unlabeled image instances. These retrieved unlabeled images serve as local perturbations of each labeled image to perform Visual Self-Regularization (VISER). To retrieve such visual self regularizers, we compute the cosine similarity in a semantic space defined by the penultimate layer in a fully convolutional neural network. We use the publicly available Yahoo Flickr Creative Commons 100M dataset as the source of our unlabeled image set and propose a distributed approximate nearest neighbor algorithm to make retrieval practical at that scale. Using the labeled instances and their regularizer samples we show that we significantly improve object categorization and localization performance on the MS COCO and Visual Genome datasets where objects appear in context.


title: Learning Optimized Features for Hierarchical Models of Invariant Object Recognition

abstract: There is an ongoing debate over the capabilities of hierarchical neural feedforward architectures for performing real-world invariant object recognition. Although a variety of hierarchical models exists, appropriate supervised and unsupervised learning methods are still an issue of intense research. We propose a feedforward model for recognition that shares components like weight sharing, pooling stages, and competitive nonlinearities with earlier approaches but focuses on new methods for learning optimal feature-detecting cells in intermediate stages of the hierarchical network. We show that principles of sparse coding, which were previously mostly applied to the initial feature detection stages, can also be employed to obtain optimized intermediate complex features. We suggest a new approach to optimize the learning of sparse features under the constraints of a weight-sharing or convolutional architecture that uses pooling operations to achieve gradual invariance in the feature hierarchy. The approach explicitly enforces symmetry constraints like translation invariance on the feature set. This leads to a dimension reduction in the search space of optimal features and allows determining more efficiently the basis representatives, which achieve a sparse decomposition of the input. We analyze the quality of the learned feature representation by investigating the recognition performance of the resulting hierarchical network on object and face databases. We show that a hierarchy with features learned on a single object data set can also be applied to face recognition without parameter changes and is competitive with other recent machine learning recognition approaches. To investigate the effect of the interplay between sparse coding and processing nonlinearities, we also consider alternative feedforward pooling nonlinearities such as presynaptic maximum selection and sum-of-squares integration. The comparison shows that a combination of strong competitive nonlinearities with sparse coding offers the best recognition performance in the difficult scenario of segmentation-free recognition in cluttered surround. We demonstrate that for both learning and recognition, a precise segmentation of the objects is not necessary.


title: Emotion Recognition from EEG Using RASM and LSTM

abstract: In the field of human-computer interaction, automatic emotion recognition is an important and challenging task. As a physiological signal that directly reflects the brain activity, EEG has advantages in emotion recognition. However, previous studies seldom consider together the temporal, spatial, and frequency characteristics of EEG signals, and the reported emotion recognition accuracy is not adequate for applications. To address this issue, this study proposes a new approach which extracts RASM as the feature to describe the frequency-space domain characteristics of EEG signals and constructs a LSTM network as the classifier to explore the temporal correlations of EEG signals. It is implemented on the DEAP dataset for a trial-level emotion recognition task. In a comparison with a number of relevant studies on DEAP, its mean accuracy of 76.67% ranks the first, which approves the effectiveness of this new approach.


title: Study of the usefulness of moisturizers on adherence of acne patients treated with adapalene

abstract: The efficacy of adapalene for acne treatment has been established. However, because of local adverse reactions, a fair number of patients discontinue adapalene treatment before experiencing its effects. To examine the usefulness of moisturizers concomitant with adapalene on adherence to treatment and its therapeutic effects in patients, 100 patients with mild to severe acne vulgaris were randomly assigned to one of two groups: group A, in which patients received adapalene in combination with a moisturizer (heparinoid); or group B, in which treatment consisted of adapalene alone and was converted to combination therapy with a moisturizer if local adverse reactions occurred that made adherence to treatment difficult. After 4 weeks of treatment, we compared the proportion of patients who adhered to therapy until the end of week 4, the number of treatment dropouts, and changes in the number of inflammatory eruptions and comedones between the groups. All of the patients in group A and 70% in group B continued the initial therapy until the end of week 4. In group B, three patients withdrew their consent and one patient stopped attending follow-up visits. The treatment period with the initial therapy was significantly elongated by the concomitant use of a moisturizer. No adverse effect of the use of a moisturizer was observed on the number of comedones and inflammatory eruptions. The concomitant use of a moisturizer with adapalene from the beginning of treatment did not affect its therapeutic effects and helped to improve adherence to treatment with adapalene.


title: Peer-to-peer communication across network address translators

abstract: Network Address Translation (NAT) causes well-known difficulties for peer-to-peer (P2P) communication, since the peers involved may not be reachable at any globally valid IP address. Several NAT traversal techniques are known, but their documentation is slim, and data about their robustness or relative merits is slimmer. This paper documents and analyzes one of the simplest but most robust and practical NAT traversal techniques, commonly known as hole punching. Hole punching is moderately well-understood for UDP communication, but we show how it can be reliably used to set up peer-to-peer TCP streams as well. After gathering data on the reliability of this technique on a wide variety of deployed NATs, we nd that about 82% of the NATs tested support hole punching for UDP, and about 64% support hole punching for TCP streams. As NAT vendors become increasingly conscious of the needs of important P2P applications such as Voice over IP and online gaming protocols, support for hole punching is likely to increase in the future.


title: Efficient antialiased rendering of 3-D linear fractals

abstract: Object instancing is the efficient method of representing an hierarchical object with a directed graph instead of a tree. If this graph contains a cycle then the object it represents is a linear fractal. Linear fractals are difficult to render for three specific reasons: (1) ray-fractal intersection is not trivial, (2) surface normals are undefined and (3) the object aliases at all sampling resolutions.Ray-fractal intersections are efficiently approximated to sub-pixel accuracy using procedural bounding volumes and a careful determination of the size of a pixel, giving the perception that the surface is infinitely detailed. Furthermore, a surface normal for these non-differentiable surfaces is defined and analyzed. Finally, the concept of antialiasing "covers" is adapted and used to solve the problem of sampling fractal surfaces.An initial bounding volume estimation method is also described, allowing a linear fractal to be rendered given only its iterated, function system. A parallel implementation of these methods is described and applications of these results to the rendering of other fractal models are given.


title: Improved Image Captioning with Adversarial Semantic Alignment

abstract: We study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions. We empirically study the viability of two training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST). We show that, surprisingly, SCST (a policy gradient method) shows more stable gradient behavior and improved results over Gumbel ST, even without accessing the discriminator gradients directly. We also address the open question of automatic evaluation for these models and introduce a new semantic score and demonstrate its strong correlation to human judgement. As an evaluation paradigm, we suggest that an important criterion is the ability of a captioner to generalize to compositions between objects that do not usually occur together, for which we introduce a captioned Out of Context (OOC) test set. The OOC dataset combined with our semantic score is a new benchmark for the captioning community. Under this OOC benchmark, and the traditional MSCOCO dataset, we show that SCST has a strong performance in both semantic score and human evaluation.


title: DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

abstract: We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.


title: Fast Localization and Segmentation of Optic Disk in Retinal Images Using Directional Matched Filtering and Level Sets

abstract: The optic disk (OD) center and margin are typically requisite landmarks in establishing a frame of reference for classifying retinal and optic nerve pathology. Reliable and efficient OD localization and segmentation are important tasks in automatic eye disease screening. This paper presents a new, fast, and fully automatic OD localization and segmentation algorithm developed for retinal disease screening. First, OD location candidates are identified using template matching. The template is designed to adapt to different image resolutions. Then, vessel characteristics (patterns) on the OD are used to determine OD location. Initialized by the detected OD center and estimated OD radius, a fast, hybrid level-set model, which combines region and local gradient information, is applied to the segmentation of the disk boundary. Morphological filtering is used to remove blood vessels and bright regions other than the OD that affect segmentation in the peripapillary region. Optimization of the model parameters and their effect on the model performance are considered. Evaluation was based on 1200 images from the publicly available MESSIDOR database. The OD location methodology succeeded in 1189 out of 1200 images (99% success). The average mean absolute distance between the segmented boundary and the reference standard is 10% of the estimated OD radius for all image sizes. Its efficiency, robustness, and accuracy make the OD localization and segmentation scheme described herein suitable for automatic retinal disease screening in a variety of clinical settings.


title: A Surround View Camera Solution for Embedded Systems

abstract: Automotive surround view camera system is an emerging automotive ADAS (Advanced Driver Assistance System) technology that assists the driver in parking the vehicle safely by allowing him/her to see a top-down view of the 360 degree surroundings of the vehicle. Such a system normally consists of four to six wide-angle (fish-eye lens) cameras mounted around the vehicle, each facing a different direction. From these camera inputs, a composite bird-eye view of the vehicle is synthesized and shown to the driver in real-time during parking. In this paper, we present a surround view camera solution that consists of three key algorithm components: geometric alignment, photometric alignment, and composite view synthesis. Our solution produces a seamlessly stitched bird-eye view of the vehicle from four cameras. It runs real-time on DSP C66x producing an 880x1080 output video at 30 fps.


title: Modeling and synthesizing task placement constraints in Google compute clusters

abstract: Evaluating the performance of large compute clusters requires benchmarks with representative workloads. At Google, performance benchmarks are used to obtain performance metrics such as task scheduling delays and machine resource utilizations to assess changes in application codes, machine configurations, and scheduling algorithms. Existing approaches to workload characterization for high performance computing and grids focus on task resource requirements for CPU, memory, disk, I/O, network, etc. Such resource requirements address how much resource is consumed by a task. However, in addition to resource requirements, Google workloads commonly include task placement constraints that determine which machine resources are consumed by tasks. Task placement constraints arise because of task dependencies such as those related to hardware architecture and kernel version. This paper develops methodologies for incorporating task placement constraints and machine properties into performance benchmarks of large compute clusters. Our studies of Google compute clusters show that constraints increase average task scheduling delays by a factor of 2 to 6, which often results in tens of minutes of additional task wait time. To understand why, we extend the concept of resource utilization to include constraints by introducing a new metric, the Utilization Multiplier (UM). UM is the ratio of the resource utilization seen by tasks with a constraint to the average utilization of the resource. UM provides a simple model of the performance impact of constraints in that task scheduling delays increase with UM. Last, we describe how to synthesize representative task constraints and machine properties, and how to incorporate this synthesis into existing performance benchmarks. Using synthetic task constraints and machine properties generated by our methodology, we accurately reproduce performance metrics for benchmarks of Google compute clusters with a discrepancy of only 13% in task scheduling delay and 5% in resource utilization.


title: A novel approach for SEMG signal classification with adaptive local binary patterns

abstract: Feature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.


title: Interactive Spatial Augmented Reality in Collaborative Robot Programming: User Experience Evaluation

abstract: This paper presents a novel approach to interaction between human workers and industrial collaborative robots. The proposed approach addresses problems introduced by existing solutions for robot programming. It aims to reduce the mental demands and attention switches by centering all interaction in a shared workspace, combining various modalities and enabling interaction with the system without any external devices. The concept allows simple programming in the form of setting program parameters using spatial augmented reality for visualization and a touch-enabled table and robotic arms as input devices. We evaluated the concept utilizing a user experience study with six participants (shop-floor workers). All participants were able to program the robot and to collaborate with it using the program they parametrized. The final goal is to create a distraction-free, usable and low-effort interface for effective human-robot collaboration, enabling any ordinary skilled worker to customize the robot's program to changes in production or to personal (e.g. ergonomic) needs.


title: Multi-labeled Relation Extraction with Attentive Capsule Network

abstract: To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence. To better cluster the features and precisely extract the relations, we further devise attention-based routing algorithm and sliding-margin loss function, and embed them into our capsule network. The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works.


title: Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers

abstract: The fundamental learning theory behind neural networks remains largely open. What classes of functions can neural networks actually learn? Why doesn't the trained network overfit when it is overparameterized? In this work, we prove that overparameterized neural networks can learn some notable concept classes, including two and three-layer networks with fewer parameters and smooth activations. Moreover, the learning can be simply done by SGD (stochastic gradient descent) or its variants in polynomial time using polynomially many samples. The sample complexity can also be almost independent of the number of parameters in the network. On the technique side, our analysis goes beyond the so-called NTK (neural tangent kernel) linearization of neural networks in prior works. We establish a new notion of quadratic approximation of the neural network, and connect it to the SGD theory of escaping saddle points.


Turkish Natural Language Processing


A Survey of Data Center Network Architectures


title: Architectural Decision Management for Digital Transformation of Products and Services

abstract: The digitization of our society changes the way we live, work, learn, communicate, and collaborate. The Internet of Things, Enterprise Social Networks, Adaptive Case Management, Mobility systems, A ...


title: A database synchronization algorithm for mobile devices

abstract: This paper proposes an SAMD (Synchronization Algorithms based on Message Digest) algorithm based on message digest in order to facilitate data synchronization between a server-side database and a mobile database. The SAMD algorithm makes the images at the server-side database and the mobile database uses message digest tables to compare two images in order to select the rows needed for synchronization. If the two images are different, the synchronization progresses according to synchronization policy. The SAMD algorithm does not use techniques that are dependent on specific database venders; neither does it use triggers, stored procedures or timestamps. The SAMD uses only the standard SQL functions for the synchronization. Therefore the SAMD algorithm can be used in any combinations of server-side database and mobile database because of its independence of database vender. This feature is important in order to build efficient mobile business systems because the upcoming mobile business environment has heterogeneous characteristics in which diverse mobile devices, mobile databases, and RDBMS exist.


Estimation of some Biochemical Parameters in Lycopersiconlycopersicum (L.) cv. Damyanti in Response to Acid Rain


title: Wordnet and Ontology Based Query Expansion for Semantic Information Retrieval in Sports Domain

abstract: Semantic Search has been a major longing factor from the envisage state of Semantic Web. Information on the Web is growing at a very rapid pace and has become quite voluminous over the past few years. Semantics of the query is not considered in Traditional Search system since it is a mere Keyword based Search. To increase the number of relevant documents retrieved, queries need to be disambiguated by looking at their context. A query expansion algorithm for Semantic Information Retrieval in Sports Domain (SIRSD) is proposed to do Semantic Search to improve search over large document repositories. This algorithm reformulates user queries by using Word Net and domain ontology to improve the returned results. Our proposal is illustrated with sample experiments showing improvements with respect to Traditional Search and providing ground for further research and discussion. SIRSD reduces the issue of semantic interoperability during the user query search. It has been inferred that there will be a higher value of average precision and recall for the Semantic Search system when compared to Traditional Search. The results show its effectiveness in generating a suitable number of query search with an accuracy of 87.1% compared to other competitors of generic search engines.


title: A neural predictor of cultural popularity

abstract: How can we predict popularity? Although superficially a trivial question, the desire for popularity consumes a great portion of the lives of many youths and adults. Being popular is a marker for social status, and consequently, would seem to confer a reproductive advantage in the evolution of the human species, thus explaining the importance of popularity to humans. Such importance extends to economic success as well because goods and services that are popular command higher prices. Here, we are interested in predicting cultural popularity – something that is popular in the broadest sense and appeals to a large number of individuals. Neuroeconomic research suggests that activity in reward-related regions of the brain, notably the orbitofrontal cortex and ventral striatum 1-4, is predictive of future purchasing decisions, but it is unknown whether the neural signals of a small group of individuals are predictive of the purchasing decisions of the population at large. For neuroimaging to be useful as a measure of widespread popularity, these neural responses would have to generalize to a much larger population that is not the direct subject of the brain imaging itself. Moreover, to be useful as a predictor, such a test would need to be done prospectively. Here, we test the possibility of using functional magnetic resonance imaging (fMRI) to predict the relative popularity of a common good: music. We used fMRI to measure the brain responses of a focus group of adolescents while listening to songs of relatively unknown artists 5. As a measure of popularity, the sales of these songs were totaled for the three years following scanning, and brain responses were then correlated with these “future” earnings. Although subjective likability of the songs was not predictive of sales, activity within the ventral striatum was significantly correlated with the number of units sold. These results suggest that the neural responses to goods are not only predictive of purchase decisions for those individuals actually scanned, but such responses generalize to the population at large and may be used to predict cultural popularity.


title: Detection and summation of pus cells and epithelial cells for sputum quality grading using K-Means clustering and color thresholding

abstract: Sputum with good quality is important in the diagnosis of many diseases. Some factor like the number of neutrophils (pus cell), squamous epithelial cell and the appearance of macroscopic should be considered to determine the sputum quality based on Bartlett's Criteria. Hence, a vision system which is able to detect and count the existence of pus cell and epithelial cell for sputum quality testing is developed. This vision system process four sputum image for each sputum sample and determine the average number of pus cells and epithelial cells in that sample. This system also able to determine either the sputum sample is positive or negative. The percentage of error for average number of pus cell is 26.3% whereas error for epithelial cell is 27.78%. However, the final grading of either the sputum sample is positive or negative is 100% accurate.


title: Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation.

abstract: Human mitochondrial DNA is widely used as tool in many fields including evolutionary anthropology and population history, medical genetics, genetic genealogy, and forensic science. Many applications require detailed knowledge about the phylogenetic relationship of mtDNA variants. Although the phylogenetic resolution of global human mtDNA diversity has greatly improved as a result of increasing sequencing efforts of complete mtDNA genomes, an updated overall mtDNA tree is currently not available. In order to facilitate a better use of known mtDNA variation, we have constructed an updated comprehensive phylogeny of global human mtDNA variation, based on both coding- and control region mutations. This complete mtDNA tree includes previously published as well as newly identified haplogroups, is easily navigable, will be continuously and regularly updated in the future, and is online available at http://www.phylotree.org. © 2008 Wiley-Liss, Inc.