Distributed Auction Algorithms for the Assignment Problem with Partial Information. operations centers (MOC), in which multiple DMs with partial information and partial control over assets are involved in the development of operational level plans. The MOC emphasizes standardized processes and methods, centralized assessment and guidance, networked distributed planning capabilities, and decentralized execution for assessing, planning and executing missions across a range of military operations  Abstract—Task-asset assignment is a fundamental problem paradigm in a wide variety of applications. Typical problem setting involves a single decision maker (DM) who has complete knowledge of the weight (reward, benefit, accuracy) matrix and who can control any of the assets to execute the tasks. Motivated by planning problems arising in distributed organizations, this paper introduces a novel variation of the assignment problem, wherein there are multiple DMs and each DM knows only a part of the weight matrix and/or controls a subset of the assets. We extend the auction algorithm to such realistic settings with various partial information structures using a blackboard coordination structure. We show that by communicating the bid, the best and the second best profits among DMs and with a coordinator, the DMs can reconstruct the centralized assignment solution. The auction setup provides a nice analytical framework for formalizing how team members build internal models of other DMs and achieve team cohesiveness over time. [1]. In this vein, we are developing analytical and computational models for multi-level coordinated mission planning and monitoring processes associated with MOCs, so that they can function effectively in highly dynamic, asymmetric, and unpredictable mission environments. Two key problem areas are: 1) realistic modeling of multi-level coordination structures that link tactical, operational and strategic levels of decision making; and 2) collaborative planni

Question: What is the difference between a task and an asset?


Programmatically Interpretable Reinforcement Learning. We present a reinforcement learning framework, called Programmatically Interpretable Reinforcement Learning (PIRL), that is designed to generate interpretable and verifiable agent policies. Unlike the popular Deep Reinforcement Learning (DRL) paradigm, which represents policies by neural networks, PIRL represents policies using a high-level, domain-specific programming language. Such programmatic policies have the benefits of being more easily interpreted than neural networks, and being amenable to verification by symbolic methods. We propose a new method, called Neurally Directed Program Search (NDPS), for solving the challenging nonsmooth optimization problem of finding a programmatic policy with maximal reward. NDPS works by first learning a neural policy network using DRL, and then performing a local search over programmatic policies that seeks to minimize a distance from this neural "oracle". We evaluate NDPS on the task of learning to drive a simulated car in the TORCS carracing environment. We demonstrate that NDPS is able to discover human-readable policies that pass some significant performance bars. We also show that PIRL policies can have smoother trajectories, and can be more easily transferred to environments not encountered during training, than corresponding policies discovered by DRL.

Question: What is the difference between a neural network and a programmatic policy?


Optimization Criteria, Sensitivity and Robustness of Motion and Structure Estimation. The prevailing efforts to study the standard formulation of motion and structure recovery have been recently focused on issues of sensitivity and and robustness of existing techniques. While many cogent observations have been made and verified experimentally, many statements do not hold in general settings and make a comparison of existing techniques difficult. With an ultimate goal of clarifying these issues we study the main aspects of the problem: the choice of objective functions, optimization techniques and the sensitivity and robustness issues in the presence of noise. We clearly reveal the relationship among different objective functions, such as "(normalized) epipolar constraints", "reprojection error" or "triangulation", which can all be be unified in a new " optimal triangulation" procedure formulated as a constrained optimization problem. Regardless of various choices of the objective function, the optimization problems all inherit the same unknown parameter space, the so called "essential manifold", making the new optimization techniques on Riemanian manifolds directly applicable. Using these analytical results we provide a clear account of sensitivity and robustness of the proposed linear and nonlinear optimization techniques and study the analytical and practical equivalence of different objective functions. The geometric characterization of critical points of a function defined on essential manifold and the simulation results clarify the difference between the effect of bas relief ambiguity and other types of local minima leading to a consistent interpretations of simulation results over large range of signal-to-noise ratio and variety of configurations. Further more we justify the choice of the linear techniques for (re)initialization and for detection of incorrect local minima.

Question: What is the difference between the epipolar constraint and the reprojection error?


The See-Through System: From implementation to test-drive. Cooperative awareness in vehicular networks is probably the killer application for vehicle-to-vehicle (V2V) communications that cannot be matched by infrastructure-based alternatives even when disregarding communication costs. New and improved driver assistance systems can be introduced by extending their reach to sensors residing in neighboring vehicles, such as windshield-installed cameras. In previous work, we defined theoretical foundations for a driver assistance system that leverages on V2V communication and windshield-installed cameras to transform vision-obstructing vehicles into transparent tubular objects. We now present an implementation of the actual See-Through System (STS), where we combine the communication aspects with the control and augmented reality components of the system. We present a validation methodology and test the system with multiple vehicles on a closed road segment. This evaluation shows that the STS is able to increase the visibility of drivers intending to overtake, thus increasing the safety of such critical maneuvers. It also shows that Dedicated Short Range Communication (DSRC) provides the required latency for this delay-critical inter-vehicle communication, which could hardly be guaranteed with infrastructure-based communication technologies.

Question: How does the see-through system work?


Bandit Structured Prediction for Learning from Partial Feedback in Statistical Machine Translation. We present an approach to structured prediction from bandit feedback, called Bandit Structured Prediction, where only the value of a task loss function at a single predicted point, instead of a correct structure, is observed in learning. We present an application to discriminative reranking in Statistical Machine Translation (SMT) where the learning algorithm only has access to a 1 − BLEU loss evaluation of a predicted translation instead of obtaining a gold standard reference translation. In our experiment bandit feedback is obtained by evaluating BLEU on reference translations without revealing them to the algorithm. This can be thought of as a simulation of interactive machine translation where an SMT system is personalized by a user who provides single point feedback to predicted translations. Our experiments show that our approach improves translation quality and is comparable to approaches that employ more informative feedback in learning.

Question: What is the bandit structured prediction for learning from partial feedback in statistical machine translation?


Discriminant kernel and regularization parameter learning via semidefinite programming. Regularized Kernel Discriminant Analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. The performance of RKDA depends on the selection of kernels. In this paper, we consider the problem of learning an optimal kernel over a convex set of kernels. We show that the kernel learning problem can be formulated as a semidefinite program (SDP) in the binary-class case. We further extend the SDP formulation to the multi-class case. It is based on a key result established in this paper, that is, the multi-class kernel learning problem can be decomposed into a set of binary-class kernel learning problems. In addition, we propose an approximation scheme to reduce the computational complexity of the multi-class SDP formulation. The performance of RKDA also depends on the value of the regularization parameter. We show that this value can be learned automatically in the framework. Experimental results on benchmark data sets demonstrate the efficacy of the proposed SDP formulations.

Question: What is the kernel trick?


Internet of Things: A review to support IoT architecture's design. Internet of Things (IoT) is a state-of-the-art field that is affecting every aspect of human life. This paper presents a state-of-the-art overview of IoT challenges, available standards, and popular architectures. The aim is to guide future researchers in the field of IoT by highlighting the main research gaps. Moreover, it provides research recommendations to help in designing future IoT architectures that fit IoT environment characteristics.

Question: What is the state-of-the-art overview of IoT challenges, available standards, and popular architectures?


Impact of a workplace stress reduction program on blood pressure and emotional health in hypertensive employees.. OBJECTIVES This study examined the impact of a workplace-based stress management program on blood pressure (BP), emotional health, and workplace-related measures in hypertensive employees of a global information technology company.   DESIGN Thirty-eight (38) employees with hypertension were randomly assigned to a treatment group that received the stress-reduction intervention or a waiting control group that received no intervention during the study period. The treatment group participated in a 16-hour program, which included instruction in positive emotion refocusing and emotional restructuring techniques intended to reduce sympathetic nervous system arousal, stress, and negative affect, increase positive affect, and improve performance. Learning and practice of the techniques was enhanced by heart rate variability feedback, which helped participants learn to self-generate physiological coherence, a beneficial physiologic mode associated with increased heart rhythm coherence, physiologic entrainment, parasympathetic activity, and vascular resonance. BP, emotional health, and workplace-related measures were assessed before and 3 months after the program.   RESULTS Three months post-intervention, the treatment group exhibited a mean adjusted reduction of 10.6 mm Hg in systolic BP and of 6.3 mm Hg in diastolic BP. The reduction in systolic BP was significant in relation to the control group. The treatment group also demonstrated improvements in emotional health, including significant reductions in stress symptoms, depression, and global psychological distress and significant increases in peacefulness and positive outlook. Reduced systolic BP was correlated with reduced stress symptoms. Furthermore, the trained employees demonstrated significant increases in the work-related scales of workplace satisfaction and value of contribution.   CONCLUSIONS Results su

Question: What is the impact of a workplace stress reduction program on blood pressure and emotional health in hypertensive employees?


Monte Carlo Strength Evaluation: Fast and Reliable Password Checking. Modern password guessing attacks adopt sophisticated probabilistic techniques that allow for orders of magnitude less guesses to succeed compared to brute force. Unfortunately, best practices and password strength evaluators failed to keep up: they are generally based on heuristic rules designed to defend against obsolete brute force attacks. Many passwords can only be guessed with significant effort, and motivated attackers may be willing to invest resources to obtain valuable passwords. However, it is eminently impractical for the defender to simulate expensive attacks against each user to accurately characterize their password strength. This paper proposes a novel method to estimate the number of guesses needed to find a password using modern attacks. The proposed method requires little resources, applies to a wide set of probabilistic models, and is characterised by highly desirable convergence properties.  The experiments demonstrate the scalability and generality of the proposal. In particular, the experimental analysis reports evaluations on a wide range of password strengths, and of state-of-the-art attacks on very large datasets, including attacks that would have been prohibitively expensive to handle with existing simulation-based approaches.

Question: How many guesses are needed to find a password?


Power Attack on Small RSA Public Exponent. In this paper, we present a new attack on RSA when the public exponent is short, for instance 3 or 2 +1, and when the classical exponent randomization is used. This attack works even if blinding is used on the messages. From a Simple Power Analysis (SPA) we study the problem of recovering the RSA private key when non consecutive bits of it leak from the implementation. We also show that such information can be gained from sliding window implementations not protected against SPA.

Question: How to recover the private key from a power attack?


Scrutinizing WPA2 Password Generating Algorithms in Wireless Routers. A wireless router is a networking device that enables a user to set up a wireless connection to the Internet. A router can offer a secure channel by cryptographic means which provides authenticity and confidentiality. Nowadays, almost all routers use a secure channel by default that is based on Wi-Fi Protected Access II (WPA2). This is a security protocol which is believed not to be susceptible to practical key recovery attacks. However, the passwords should have sufficient entropy to avert bruteforce attacks. In this paper, we compose a strategy on how to reverse-engineer embedded routers. Furthermore, we describe a procedure that can instantly gather a complete wireless authentication trace which enables an offline password recovery attack. Finally, we present a number of use cases where we identify extremely weak password generating algorithms in various routers which are massively deployed in The Netherlands. The algorithms are used to generate the default WPA2 password. Such a password is loaded during device initialization and hardware reset. Users that did not explicitly change their wireless password are most likely vulnerable to practical attacks which can recover their password within minutes. A stolen password allows an adversary to abuse someone else's internet connection, for instance compromising the firewall, making a fraudulent transaction or performing other criminal activities. Together with the Dutch National Cyber Security Centre we have initiated a responsible disclosure procedure. However, since these routers are also used by many other companies in various countries, our findings seem to relate an international industry wide security issue.

Question: How to generate a secure password?


Cross-Modal Correlation Learning by Adaptive Hierarchical Semantic Aggregation. With the explosive growth of web data, effective and efficient technologies are in urgent need for retrieving semantically relevant contents of heterogeneous modalities. Previous studies devote efforts to modeling simple cross-modal statistical dependencies, and globally projecting the heterogeneous modalities into a measurable subspace. However, global projections cannot appropriately adapt to diverse contents, and the naturally existing multilevel semantic relation in web data is ignored. We study the problem of semantic coherent retrieval, where documents from different modalities should be ranked by the semantic relevance to the query. Accordingly, we propose TINA, a correlation learning method by adaptive hierarchical semantic aggregation. First, by joint modeling of content and ontology similarities, we build a semantic hierarchy to measure multilevel semantic relevance. Second, with a set of local linear projections and probabilistic membership functions, we propose two paradigms for local expert aggregation, i.e., local projection aggregation and local distance aggregation. To learn the cross-modal projections, we optimize the structure risk objective function that involves semantic coherence measurement, local projection consistency, and the complexity penalty of local projections. Compared to existing approaches, a better bias-variance tradeoff is achieved by TINA in real-world cross-modal correlation learning tasks. Extensive experiments on widely used NUS-WIDE and ICML-Challenge for image-text retrieval demonstrate that TINA better adapts to the multilevel semantic relation and content divergence, and, thus, outperforms state of the art with better semantic coherence.

Question: What is the cross-modal correlation learning?


A Machine Learning Approach to Twitter User Classification. This paper addresses the task of user classification in social media, with an application to Twitter. We automatically infer the values of user attributes such as political orientation or ethnicity by leveraging observable information such as the user behavior, network structure and the linguistic content of the user's Twitter feed. We employ a machine learning approach which relies on a comprehensive set of features derived from such user information. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business. Finally, our analysis shows that rich linguistic features prove consistently valuable across the 3 tasks and show great promise for additional user classification needs.

Question: What is a machine learning approach to twitter user classification?


Stochastic policy gradient reinforcement learning on a simple 3D biped. We present a learning system which is able to quickly and reliably acquire a robust feedback control policy for 3D dynamic walking from a blank-slate using only trials implemented on our physical robot. The robot begins walking within a minute and learning converges in approximately 20 minutes. This success can be attributed to the mechanics of our robot, which are modeled after a passive dynamic walker, and to a dramatic reduction in the dimensionality of the learning problem. We reduce the dimensionality by designing a robot with only 6 internal degrees of freedom and 4 actuators, by decomposing the control system in the frontal and sagittal planes, and by formulating the learning problem on the discrete return map dynamics. We apply a stochastic policy gradient algorithm to this reduced problem and decrease the variance of the update using a state-based estimate of the expected cost. This optimized learning system works quickly enough that the robot is able to continually adapt to the terrain as it walks.

Question: How to learn to walk?


Analysis and design of compact two-way Wilkinson power dividers using coupled lines. This paper analyzes the two-way Wilkinson power dividers using coupled lines as λ/4 impedance transformer to have more compact layout. The analysis shows that the input port matching is only influenced by the even mode impedance of the coupled lines. Once the even mode impedance is fixed, all other specifications of the power divider are determined by the odd mode impedance of the coupled lines. Design tradeoffs among the output matching, isolation and the odd mode impedance are shown. By using smaller odd mode impedance, layout constraint can be relaxed. To validate the analysis, Wilkinson power dividers using coupled lines with areas of 1.5 cm2 and 1.25 cm2 are designed together with a conventional divider with an area of 3.8 cm2. The experimental results of the three designs are comparable. When coupled lines are used, the layout is more compact with a reduction in size of more than 50% compared to the conventional design.

Question: What is the area of a power divider?


AXIAL-FLUX PERMANENT-MAGNET MOTOR DESIGN FOR ELECTRIC VEHICLE DIRECT DRIVE USING SIZING EQUATION AND FINITE ELEMENT ANALYSIS. The design process of a double-sided slotted TORUS axialflux permanent-magnet (AFPM) motor suitable for direct drive of electric vehicle (EV) is presented. It used sizing equation and Finite Element Analysis (FEA). AFPM motor is a high-torque-density motor easily mounted compactly onto a vehicle wheel, fitting the wheel rim perfectly. A preliminary design is a double-sided slotted AFPM motor with 6 rotor poles for high torque-density and stable rotation. In determining the design requirements, a simple vehicle-dynamics model that evaluates vehicle performance through the typical cruising trip of an automobile was considered. To obtain, with the highest possible torque, the initial design parameters of the motor, AFPM's fundamental theory and sizing equation were applied. Vector Field Opera-3D 14.0 commercial software ran the FEA of the motor design, evaluating and enhancing accuracy of the design parameters. Results of the FEA simulation were compared with those obtained from the sizing equation; at no-load condition, the flux density at every part of the motor agreed. The motor's design meets all the requirements and limits of EV, and fits the shape and size of a classical-vehicle wheel rim. The design process is comprehensive and can be used for an arbitrary EV with an arbitrary cruising scenario.

Question: How to design a double-sided slotted torus axial flux permanent magnet motor for electric vehicle direct drive?


Question: What is a topic model?


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

Question: What is the difference between asynchronous and synchronous SGD?


A Scientific Methodology for MIS Case Studies.

Question: What is a scientific methodology for MIS case studies?


DNA Sequence Compression Using the Burrows-Wheeler Transform. We investigate off-line dictionary oriented approaches to DNA sequence compression, based on the Burrows-Wheeler Transform (BWT). The preponderance of short repeating patterns is an important phenomenon in biological sequences. Here, we propose off-line methods to compress DNA sequences that exploit the different repetition structures inherent in such sequences. Repetition analysis is performed based on the relationship between the BWT and important pattern matching data structures, such as the suffix tree and suffix array. We discuss how the proposed approach can be incorporated in the BWT compression pipeline.

Question: What is the relationship between the BWT and the suffix tree?


Design and implementation of a 120A resonant inverter for induction furnace. This paper presents the design and implementation of a series resonant inverter operating at high frequency for induction heating applications. The main advantage of this work is focused in applying power electronics, particularly a series resonant inverter based on an IGBT's full bridge and a series RLC Load matched with a high frequency coupling transformer. The series resonant inverter is designed to deliver a current load up to 120A with a one phase AC source. Due to the inherent high speed operation and high efficiency of switching converters, other advantages of the induction furnace presented in this paper are that it require less time to start melting, and require less physical space than other traditional furnaces.

Question: How to design a 120A resonant inverter for induction furnace?


Enterprise-wide Optimization : A New Frontier in Process Systems Engineering. Enterprise-wide optimization (EWO) is a new emerging area that lies at the interface of chemical engineering and operations research, and has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the operations of supply, manufacturing and distribution activities of a company to reduce costs and inventories. A major focus in EWO is the optimal operation of manufacturing facilities, which often requires the use of nonlinear process models. Major operational items include planning, scheduling, realtime optimization and inventory control. One of the key features of EWO is integration of the information and the decision-making among the various functions that comprise the supply chain of the company. This can be achieved with modern IT tools, which together with the internet, have promoted e-commerce. However, as will be discussed, to fully realize the potential of transactional IT tools, the development of sophisticated deterministic and stochastic linear/nonlinear optimization models and algorithms (analytical IT tools) is needed to explore and analyze alternatives of the supply chain to yield overall optimum economic performance, as well as high levels of customer satisfaction. An additional challenge is the integrated and coordinated decision-making across the various functions in a company (purchasing, manufacturing, distribution, sales), across various geographically distributed organizations (vendors, facilities and markets), and across various levels of decision-making (strategic, tactical and operational). © 2005 American Institute of Chemical Engineers AIChE J, 51: 1846–1857, 2005

Question: What is enterprise-wide optimization?


LARS: A Location-Aware Recommender System. This paper proposes LARS, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items, LARS, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS can apply these techniques separately, or in concert, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the Movie Lens movie recommendation system reveals that LARS is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.

Question: What is a location-aware recommender system?


Face Detection using Digital Image Processing. This paper presents a technique for automatically detecting human faces in digital color images. This is two-step process which first detects regions contain human skin in the color image and then extracts information from these regions which might indicate the location of a face in the image. The skin detection is performed using a skin filter which relies on color and texture information. The face detection is performed on a grayscale image containing only the detected skin areas. A combination of thresh holding and mathematical morphology are used to extract object features that would indicate the presence of a face. The face detection process works predictably and fairly reliably, as test results show.

Question: How does face detection work?


GENETIC ALGORITHM APPLIED TO OPTIMIZATION OF THE SHIP HULL FORM WITH RESPECT TO SEAKEEPING PERFORMANCE. Hull form optimization from a hydrodynamic performance point of view is an important aspect in preliminary ship design. This study presents a computational method to estimate the ship seakeeping in regular head waves. In the optimization process, the genetic algorithm (GA) is linked to the computational method to obtain an optimum hull form by taking into account the displacement as a design constraint. New hull forms are obtained from the wellknown S60 hull and the classical Wigley hull taken as initial hulls in the optimization process at two Froude numbers (Fn=0.2 and Fn=0.3). The optimization variables are a combination of ship hull offsets and main dimensions. The objective function of the optimization procedure includes the peak values for vertical absolute motion at the centre of gravity (CG) and the bow point (0.15Lwl) behind the forward perpendicular (FP).

Question: What is the genetic algorithm?


If Only my Leader Would just Do Something! Passive Leadership Undermines Employee Well-being Through Role Stressors and Psychological Resource Depletion.. The goal of this study was to develop and test a sequential mediational model explaining the negative relationship of passive leadership to employee well-being. Based on role stress theory, we posit that passive leadership will predict higher levels of role ambiguity, role conflict and role overload. Invoking Conservation of Resources theory, we further hypothesize that these role stressors will indirectly and negatively influence two aspects of employee well-being, namely overall mental health and overall work attitude, through psychological work fatigue. Using a probability sample of 2467 US workers, structural equation modelling supported the model by showing that role stressors and psychological work fatigue partially mediated the negative relationship between passive leadership and both aspects of employee well-being. The hypothesized, sequential indirect relationships explained 47.9% of the overall relationship between passive leadership and mental health and 26.6% of the overall relationship between passive leadership and overall work attitude. Copyright © 2016 John Wiley & Sons, Ltd.

Question: What is the goal of this study?


Omnidirectional planar dipole array antenna for WLAN access point. In this paper, a low-cost planar dipole array antenna with an omnidirectional radiation characteristic is presented. The proposed omnidirectional planar dipole array antenna comprises two 1/spl times/2 dipole arrays arranged back to back, and can be easily constructed by printing on both sides of a dielectric substrate. By selecting the central ground plane to have a small width (about 6 mm for operating at about 2.4 GHz), the proposed array antenna can have a good omnidirectional radiation pattern in the azimuthal plane (gain variations less than 2 dBi). Prototypes of the proposed antenna for WLAN operation in the 2.4 GHz band (2400-2484 MHz) were constructed and studied.

Question: What is a planar dipole array antenna?


A four-band dual-polarized cavity-backed antenna on LTCC technology for 60GHz applications. In this work, we present a 4-band dual-polarized antenna designed for 8-chanel applications. Based on LTCC technology, the antenna is a patch coupled aperture with a backed cavity. Each antenna element of a designated band contains two channels through two orthogonally polarized ports. Combining four dual-polarized antenna elements under different frequency, an 8-channel antenna for 60GHz applications can be achieved. The array antenna contains 8 feeding ports, which correspond to 8 independent channels. The isolation between each port can reach 20dB in most of the frequency band.

Question: What is the frequency band of the antenna?


Analysis of digital bang-bang clock and data recovery for multi-gigabit/s serial transceivers. A Harmonic Balance method for analyzing digital bang-bang clock and data recovery (CDR) is proposed in this paper. The jitter tolerance performance of the CDR is predicted by a function with variables that can be easily correlated to design parameters. A 6.25Gb/s serial transceiver was fabricated in 90nm CMOS technology. Measurements show that the jitter tolerance performance can be accurately predicted by the proposed method.

Question: What is the jitter tolerance performance of a 6.25Gb/s serial transceiver?


Soft start-up for high frequency LLC resonant converter with optimal trajectory control. This paper investigates the soft start-up for high frequency LLC resonant converter with optimal trajectory control. Two methods are proposed to realize soft start-up for high frequency LLC converter by commercial low-cost microcontrollers (MCU). Both methods can achieve soft start-up with minimum stress and optimal energy delivery. One method is mixed-signal implementation by sensing resonant tank to minimize the digital delay. Another method is digital implementation with look-up table. Experimental results are demonstrated on 500kHz 1kW 400V/12V LLC converter.

Question: Soft start-up for high frequency LLC resonant converter with optimal trajectory control.


Detecting topic evolution in scientific literature: how can citations help?. Understanding how topics in scientific literature evolve is an interesting and important problem. Previous work simply models each paper as a bag of words and also considers the impact of authors. However, the impact of one document on another as captured by citations, one important inherent element in scientific literature, has not been considered. In this paper, we address the problem of understanding topic evolution by leveraging citations, and develop citation-aware approaches. We propose an iterative topic evolution learning framework by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model. We evaluate the effectiveness and efficiency of our approaches and compare with the state of the art approaches on a large collection of more than 650,000 research papers in the last 16 years and the citation network enabled by CiteSeerX. The results clearly show that citations can help to understand topic evolution better.

Question: How can citations help to understand topic evolution?


Actinomycetes from the South China Sea sponges: isolation, diversity, and potential for aromatic polyketides discovery. Marine sponges often harbor dense and diverse microbial communities including actinobacteria. To date no comprehensive investigation has been performed on the culturable diversity of the actinomycetes associated with South China Sea sponges. Structurally novel aromatic polyketides were recently discovered from marine sponge-derived Streptomyces and Saccharopolyspora strains, suggesting that sponge-associated actinomycetes can serve as a new source of aromatic polyketides. In this study, a total of 77 actinomycete strains were isolated from 15 South China Sea sponge species. Phylogenetic characterization of the isolates based on 16S rRNA gene sequencing supported their assignment to 12 families and 20 genera, among which three rare genera (Marihabitans, Polymorphospora, and Streptomonospora) were isolated from marine sponges for the first time. Subsequently, β-ketoacyl synthase (KSα) gene was used as marker for evaluating the potential of the actinomycete strains to produce aromatic polyketides. As a result, KSα gene was detected in 35 isolates related to seven genera (Kocuria, Micromonospora, Nocardia, Nocardiopsis, Saccharopolyspora, Salinispora, and Streptomyces). Finally, 10 strains were selected for small-scale fermentation, and one angucycline compound was detected from the culture extract of Streptomyces anulatus strain S71. This study advanced our knowledge of the sponge-associated actinomycetes regarding their diversity and potential in producing aromatic polyketides.

Question: What is the diversity of the actinomycetes in the South China Sea?


A review of facial nerve anatomy.. An intimate knowledge of facial nerve anatomy is critical to avoid its inadvertent injury during rhytidectomy, parotidectomy, maxillofacial fracture reduction, and almost any surgery of the head and neck. Injury to the frontal and marginal mandibular branches of the facial nerve in particular can lead to obvious clinical deficits, and areas where these nerves are particularly susceptible to injury have been designated danger zones by previous authors. Assessment of facial nerve function is not limited to its extratemporal anatomy, however, as many clinical deficits originate within its intratemporal and intracranial components. Similarly, the facial nerve cannot be considered an exclusively motor nerve given its contributions to taste, auricular sensation, sympathetic input to the middle meningeal artery, and parasympathetic innervation to the lacrimal, submandibular, and sublingual glands. The constellation of deficits resulting from facial nerve injury is correlated with its complex anatomy to help establish the level of injury, predict recovery, and guide surgical management.

Question: What is the anatomy of the facial nerve?


Shedding Light on the Dark Triad of Personality : Narcissism , Machiavellianism , and Psychopathy. Of the offensive yet non-pathological personalities in the literature, three are especially prominent: Machiavellianism, subclinical narcissism, and subclinical psychopathy. We evaluated the recent contention that, in normal samples, this Dark Triad of constructs are one and the same. In a sample of 245 students, we measured the three constructs with standard measures and examined a variety of laboratory and self-report correlates. The measures were moderately inter-correlated, but certainly were not equivalent. Their only common Big Five correlate was disagreeableness. Subclinical psychopaths were distinguished by low neuroticism; Machiavellians, and psychopaths were low in conscientiousness; narcissism showed small positive associations with cognitive ability. Narcissists and, to a lesser extent, psychopaths exhibited self-enhancement on two objectively scored indexes. We conclude that the Dark Triad of personalities, as currently measured, are overlapping but distinct constructs. 2002 Elsevier Science (USA). All rights reserved.

Question: What is the dark triad of personality?


Growers versus showers: a meaningful (or useful) distinction?. Yafi et al. have conducted a study that will be of great interest to the lay community and also of import to practicing urologists who routinely encounter patients with concerns about the appearance of their phallus [1]. In one study 14% of men expressed dissatisfaction with their genitals with flaccid penile length being the most common source of dissatisfaction [2]. The concerns of such men tend to be the perception that their penis is small, or at least smaller than the penises of other men.

Question: What is the difference between a phallus and a penis?


Learning Bayesian Networks: The Combination of Knowledge and Statistical Data.

Question: What is the combination of knowledge and statistical data?


UI on the Fly: Generating a Multimodal User Interface. UI on the Fly is a system that dynamically presents coordinated multimodal content through natural language and a small-screen graphical user interface. It adapts to the user's preferences and situation. Multimodal Functional Unification Grammar (MUG) is a unification-based formalism that uses rules to generate content that is coordinated across several communication modes. Faithful variants are scored with a heuristic function.

Question: What is a unification-based formalism?


peepHole : Securing your homes with Raspberry Pi and Amazon Echo. In recent years, the field of Internet of Things (IoT) has seen significant investments made by the research community and the industry. Specifically, the Smart Home space has been a prime focus with the introduction of devices such as Amazon Echo, Google Home, Samsung Smart Things among others. The growth of an industry results in innovative, economic, and advanced solutions. In this paper, we focus on smart home security and how to build a robust, cost-effective system that can be widely used. We power our system using Amazon Echo, Amazon's cloud services, its speech services. A Raspberry Pi with a camera module is used as the hardware component for providing home security. We describe the different components of our product and we show that our system works effectively to identify the person at the doorstep, and have thus named our system peepHole. Keywords—Intenet of Things, Smart Homes, Home Security, Amazon Web Services, Amazon Echo, Raspberry Pi, Face Verification

Question: What is the cost of a raspberry pi?


Practical Bayesian Optimization of Machine Learning Algorithms. The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a "black art" requiring expert experience, rules of thumb, or sometimes bruteforce search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. In this work, we consider this problem through the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP). We show that certain choices for the nature of the GP, such as the type of kernel and the treatment of its hyperparameters, can play a crucial role in obtaining a good optimizer that can achieve expertlevel performance. We describe new algorithms that take into account the variable cost (duration) of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks.

Question: What is the difference between a GP and a Bayesian Optimization?


An AAL system based on IoT technologies and linked open data for elderly monitoring in smart cities. The average age growing of the urban population, with an increasing number of 65+ citizens, is calling for the cities to provide global services specifically geared to elderly people. In this context, collecting data from the elderly's environment and his/her habits and making them available in a structured way to third parties for analysis, is the first step towards the realization of innovative user-centric services. This paper presents a city-wide general IoT-based sensing infrastructure and a data management layer providing some REST and Linked Open Data Application Programming Interfaces (APIs) that collect and present data related to elderly people. In particular, this architecture is used by the H2020 City4Age project to help geriatricians in identifying the onset of Mild Cognitive Impairment (MCI) disease.

Question: What is the average age of the urban population?


Keeping up to date: An academic researcher's information journey. Keeping up to date with research developments is a central activity of academic researchers, but researchers face difficulties in managing the rapid growth of available scientific information. This study examined how researchers stay up to date, using the information journey model as a framework for analysis and investigating which dimensions influence information behaviors. We designed a 2-round study involving semistructured interviews and prototype testing with 61 researchers with 3 levels of seniority (PhD student to professor). Data were analyzed following a semistructured qualitative approach. Five key dimensions that influence information behaviors were identified: level of seniority, information sources, state of the project, level of familiarity, and how well defined the relevant community is. These dimensions are interrelated and their values determine the flow of the information journey. Across all levels of professional expertise, researchers used similar hard (formal) sources to access content, while soft (interpersonal) sources were used to filter information. An important "pain point" that future information tools should address is helping researchers filter information at the point of need. Introduction

Question: How to keep up to date with research developments?


Wideband planar monopole antennas with dual band-notched characteristics. Wideband planar monopole antennas with dual band-notched characteristics are presented. The proposed antenna consists of a wideband planar monopole antenna and the multiple cup-, cap-, and inverted L-shaped slots, producing band-notched characteristics. In order to generate dual band-notched characteristic, we propose nine types of planar monopole antennas, which have two or three cap (cup or inverted L)-shaped slots in the radiator. This technique is suitable for creating ultra-wideband antenna with narrow frequency notches or for creating multiband antennas

Question: What is a wideband planar monopole antenna?


Methods of mapping from phase to sine amplitude in direct digital synthesis. There are many methods for performing functional mapping from phase to sine amplitude (e.g., ROM look-up, coarse/fine segmentation into multiple ROM's, Taylor series, CORDIC algorithm). The spectral purity of the conventional direct digital synthesizer (DDS) is also determined by the resolution of the values stored in the sine table ROM. Therefore, it is desirable to increase the resolution of the ROM. Unfortunately, larger ROM storage means higher power consumption, lower reliability, lower speed, and greatly increased costs. Different memory compression and algorithmic techniques and their effect on distortion and trade-offs are investigated in detail. A computer program has been created to simulate the effects of the memory compression and algorithmic techniques on the output spectrum of the DDS. For each memory compression and algorithmic technique, the worst case spurious response is calculated using the computer program.

Question: What is the effect of memory compression on distortion?


Gesture Recognition with a 3-D Accelerometer. Gesture-based interaction, as a natural way for human-computer interaction, has a wide range of applications in ubiquitous computing environment. This paper presents an acceleration-based gesture recognition approach, called FDSVM (Frame-based Descriptor and multi-class SVM), which needs only a wearable 3-dimensional accelerometer. With FDSVM, firstly, the acceleration data of a gesture is collected and represented by a frame-based descriptor, to extract the discriminative information. Then a SVM-based multi-class gesture classifier is built for recognition in the nonlinear gesture feature space. Extensive experimental results on a data set with 3360 gesture samples of 12 gestures over weeks demonstrate that the proposed FDSVM approach significantly outperforms other four methods: DTW, Naïve Bayes, C4.5 and HMM. In the user-dependent case, FDSVM achieves the recognition rate of 99.38% for the 4 direction gestures and 95.21% for all the 12 gestures. In the user-independent case, it obtains the recognition rate of 98.93% for 4 gestures and 89.29% for 12 gestures. Compared to other accelerometer-based gesture recognition approaches reported in literature FDSVM gives the best resulrs for both user-dependent and user-independent cases.

Question: How to recognize gestures with a 3-D accelerometer?


An Empirical Study on Android-Related Vulnerabilities. Mobile devices are used more and more in everyday life. They are our cameras, wallets, and keys. Basically, they embed most of our private information in our pocket. For this and other reasons, mobile devices, and in particular the software that runs on them, are considered first-class citizens in the software-vulnerabilities landscape. Several studies investigated the software-vulnerabilities phenomenon in the context of mobile apps and, more in general, mobile devices. Most of these studies focused on vulnerabilities that could affect mobile apps, while just few investigated vulnerabilities affecting the underlying platform on which mobile apps run: the Operating System (OS). Also, these studies have been run on a very limited set of vulnerabilities. In this paper we present the largest study at date investigating Android-related vulnerabilities, with a specific focus on the ones affecting the Android OS. In particular, we (i) define a detailed taxonomy of the types of Android-related vulnerability, (ii) investigate the layers and subsystems from the Android OS affected by vulnerabilities, and (iii) study the survivability of vulnerabilities (i.e., the number of days between the vulnerability introduction and its fixing). Our findings could help OS and apps developers in focusing their verification & validation activities, and researchers in building vulnerability detection tools tailored for the mobile world.

Question: What is the largest study at date investigating Android-related vulnerabilities?


Applying Graph theory to the Internet of Things. In the Internet of Things (IoT), we all are ``things''. Graph theory, a branch of discrete mathematics, has been proven to be useful and powerful in understanding complex networks in history. By means of graph theory, we define new concepts and terminology, and explore the definition of IoT, and then show that IoT is the union of a topological network, a data-functional network and a domi-functional network.

Question: What is the union of a topological network, a data-functional network and a domi-functional network?


Synchronous reluctance motor multi-static MEC model. High efficiency, low cost and high reliable electrical machines are designed to meet very demanding specifications for a wide range of applications. The SynchRM (Synchronous Reluctance Motor) has very interesting properties and features. One of the main advantages is that the motor can be designed without rare earth materials permanent magnets. In this article, a modelling method of such machines using the MEC (Magnetic Equivalent Circuit) is proposed. The main challenges are to obtain a very accurate parametric model in order to calculate torque variations and to make possible geometric optimizations in future work.

Question: What is a reluctance motor?


Texture Synthesis by Non-parametric Sampling. A non-parametric method for texture synthesis is proposed. The texture synthesis process grows a new image outward from an initial seed, one pixel at a time. A Markov random field model is assumed, and the conditional distribution of a pixel given all its neighbors synthesized so far is estimated by querying the sample image and finding all similar neighborhoods. The degree of randomness is controlled by a single perceptually intuitive parameter. The method aims at preserving as much local structure as possible and produces good results for a wide variety of synthetic and real-world textures.

Question: How to synthesize a texture?


Content delivery over TLS: a cryptographic analysis of keyless SSL. The Transport Layer Security (TLS) protocol is designed to allow two parties, a client and a server, to communicate securely over an insecure network. However, when TLS connections are proxied through an intermediate middlebox, like a Content Delivery Network (CDN), the standard endto- end security guarantees of the protocol no longer apply. In this paper, we investigate the security guarantees provided by Keyless SSL, a CDN architecture currently deployed by CloudFlare that composes two TLS 1.2 handshakes to obtain a proxied TLS connection. We demonstrate new attacks that show that Keyless SSL does not meet its intended security goals. These attacks have been reported to CloudFlare and we are in the process of discussing fixes. We argue that proxied TLS handshakes require a new, stronger, 3-party security definition. We present 3(S)ACCEsecurity, a generalization of the 2-party ACCE security definition that has been used in several previous proofs for TLS. We modify Keyless SSL and prove that our modifications guarantee 3(S)ACCE-security, assuming ACCE-security for the individual TLS 1.2 connections. We also propose a new design for Keyless TLS 1.3 and prove that it achieves 3(S)ACCEsecurity, assuming that the TLS 1.3 handshake implements an authenticated 2-party key exchange. Notably, we show that secure proxying in Keyless TLS 1.3 is computationally lighter and requires simpler assumptions on the certificate infrastructure than our proposed fix for Keyless SSL. Our results indicate that proxied TLS architectures, as currently used by a number of CDNs, may be vulnerable to subtle attacks and deserve close attention.

Question: What is the difference between a content delivery network and a content distribution network?


Geometry Guided Adversarial Facial Expression Synthesis. Facial expression synthesis has drawn much attention in the field of computer graphics and pattern recognition. It has been widely used in face animation and recognition. However, it is still challenging due to the high-level semantic presence of large and non-linear face geometry variations. This paper proposes a Geometry-Guided Generative Adversarial Network (G2-GAN) for continuously-adjusting and identity-preserving facial expression synthesis. We employ facial geometry (fiducial points) as a controllable condition to guide facial texture synthesis with specific expression. A pair of generative adversarial subnetworks is jointly trained towards opposite tasks: expression removal and expression synthesis. The paired networks form a mapping cycle between neutral expression and arbitrary expressions, with which the proposed approach can be conducted among unpaired data. The proposed paired networks also facilitate other applications such as face transfer, expression interpolation and expression-invariant face recognition. Experimental results on several facial expression databases show that our method can generate compelling perceptual results on different expression editing tasks.

Question: How to synthesize facial expressions?


SDN On-The-Go (OTG) physical testbed. An emerging field of research, Software Defined Networks (SDN) promises to change the landscape of traditional network topology and management. Researchers and early adopters alike need adequate SDN testing facilities for their experiments but their options are limited. Industry is responding slowly with embedded support for SDN in their enterprise grade network hardware but it is cost prohibitive for many test environments with a single SDN switch costing thousands of dollars. There are a few emerging community SDN test networks that are fantastic for testing large topologies with production grade traffic but there is a cost associated with membership and some controlled experiments are difficult. A free and indispensible alternative to a dedicated hardware SDN is to use network emulation tools. These software tools are widely used and invaluable to SDN research. They provide an amazingly precise representation of physical network nodes and behavior but are inherently limited by their aggregation with other virtual devices on the same compute node. Some of our research requires a higher precision than software emulation can provide. Our solution is to build a low cost, portable, standalone SDN testbed. Called SDN On-The-Go (OTG), it is a complete, self-contained testbed that consists of four dedicated ZodiacFX SDN switches, four RaspberryPi3 hosts, a dedicated Kangaroo+ controller with 4GB RAM and a couple of routers to form the network isolation. The testbed supports many configurations for pseudo real-world SDN experiments that produce reliable and repeatable results. It can be used as a standalone research tool or as part of a larger network with production quality traffic. SDN OTG is designed to be used as a portable teaching device, moved from classroom to classroom or taken home for private research. We achieved our repeatability factor of an order of magnitude greater than emulation based testing. Our SDN OTG physical te

Question: What is a SDN testbed?


Photoshopping the selfie: Self photo editing and photo investment are associated with body dissatisfaction in adolescent girls.. OBJECTIVE Social media engagement by adolescent girls is high. Despite its appeal, there are potential negative consequences for body dissatisfaction and disordered eating from social media use. This study aimed to examine, in a cross-sectional design, the relationship between social media use in general, and social media activities related to taking "selfies" and sharing specifically, with overvaluation of shape and weight, body dissatisfaction, and dietary restraint.   METHOD Participants were 101 grade seven girls (M(age)  = 13.1, SD = 0.3), who completed self-report questionnaires of social media use and body-related and eating concerns measures.   RESULTS Results showed that girls who regularly shared self-images on social media, relative to those who did not, reported significantly higher overvaluation of shape and weight, body dissatisfaction, dietary restraint, and internalization of the thin ideal. In addition, among girls who shared photos of themselves on social media, higher engagement in manipulation of and investment in these photos, but not higher media exposure, were associated with greater body-related and eating concerns, including after accounting for media use and internalization of the thin ideal.   DISCUSSION Although cross-sectional, these findings suggest the importance of social media activities for body-related and eating concerns as well as potential avenues for targeted social-media-based intervention.

Question: How to photoshop a selfie


Out-of-domain FrameNet Semantic Role Labeling. Domain dependence of NLP systems is one of the major obstacles to their application in large-scale text analysis, also restricting the applicability of FrameNet semantic role labeling (SRL) systems. Yet, current FrameNet SRL systems are still only evaluated on a single in-domain test set. For the first time, we study the domain dependence of FrameNet SRL on a wide range of benchmark sets. We create a novel test set for FrameNet SRL based on user-generated web text and find that the major bottleneck for out-of-domain FrameNet SRL is the frame identification step. To address this problem, we develop a simple, yet efficient system based on distributed word representations. Our system closely approaches the state-of-the-art in-domain while outperforming the best available frame identification system out-of-domain. We publish our system and test data for research purposes.1

Question: What is the frame identification step?


Performance of Two 18-Story Steel Moment-Frame Buildings in Southern California During Two Large Simulated San Andreas Earthquakes. Using state-of-the-art computational tools in seismology and structural engineering, validated using data from the Mw=6.7 January 1994 Northridge earthquake, we determine the damage to two 18-story steel moment-frame buildings, one existing and one new, located in southern California due to ground motions from two hypothetical magnitude 7.9 earthquakes on the San Andreas Fault. The new building has the same configuration as the existing building but has been redesigned to current building code standards. Two cases are considered: rupture initiating at Parkfield and propagating from north to south, and rupture propagating from south to north and terminating at Parkfield. Severe damage occurs in these buildings at many locations in the region in the north-to-south rupture scenario. Peak velocities of 1 m.s−1 and 2 m.s−1 occur in the Los Angeles Basin and San Fernando Valley, respectively, while the corresponding peak displacements are about 1 m and 2 m, respectively. Peak interstory drifts in the two buildings exceed 0.10 and 0.06 in many areas of the San Fernando Valley and the Los Angeles Basin, respectively. The redesigned building performs significantly better than the existing building; however, its improved design based on the 1997 Uniform Building Code is still not adequate to prevent serious damage. The results from the south-to-north scenario are not as alarming, although damage is serious enough to cause significant business interruption and compromise life safety. DOI: 10.1193/1.2360698

Question: What is the difference between the existing and the new building?


A Computational Model for the Dynamical Learning of Event Taxonomies. We present a computational model that can learn event taxonomies online from the continuous sensorimotor information flow perceived by an agent while interacting with its environment. Our model implements two fundamental learning biases. First, it learns probabilistic event models as temporal sensorimotor forward models and event transition models, which predict event model transitions given particular perceptual circumstances. Second, learning is based on the principle of minimizing free energy, which is further biased towards the detection of free energy transients. As a result, the algorithm forms conceptual structures that encode events and event boundaries. We show that event taxonomies can emerge when the algorithm is run on multiple levels of precision. Moreover, we show that generally any type of forward model can be used, as long as it learns sufficiently fast. Finally, we show that the developed structures can be used to hierarchically plan goaldirected behavior by means of active inference.

Question: What is a computational model for the dynamical learning of event taxonomies?


Raimond: Quantitative Data Extraction from Twitter to Describe Events. Social media play a decisive role in communicating and spreading information during global events. In particular, real-time microblogging platforms such as Twitter have become prevalent. Researchers have used microblogging for a number of tasks, including past events analysis, predictions, and information retrieval. Nevertheless, little attention has been given to quantitative data extraction. In this paper, we address two questions: can we develop a mechanism to extract quantitative data from a collection of tweets, and can we use the salient findings to describe an event? To answer the first question, we introduce Raimond, a virtual text curator, specialized in quantitative data extraction from Twitter. To address the second question, we use our system on three events and evaluate its output using a crowdsourcing strategy. We demonstrate the e↵ectiveness of our approach with a number of real world examples.

Question: How to extract quantitative data from twitter?


Policy ratification. It is not sufficient to merely check the syntax of new policies before they are deployed in a system; policies need to be analyzed for their interactions with each other and with their local environment. That is, policies need to go through a ratification process. We believe policy ratification becomes an essential part of system management as the number of policies in the system increases and as the system administration becomes more decentralized. In this paper, we focus on the basic tasks involved in policy ratification. To a large degree, these basic tasks can be performed independent of policy model and language and require little domain-specific knowledge. We present algorithms from constraint, linear, and logic programming disciplines to help perform ratification tasks. We provide an algorithm to efficiently assign priorities to the policies based on relative policy preferences indicated by policy administrators. Finally, with an example, we show how these algorithms have been integrated with our policy system to provide feedback to a policy administrator regarding potential interactions of policies with each other and with their deployment environment.

Question: What is policy ratification?


A New Approach to Quantify Network Security by Ranking of Security Metrics and Considering Their Relationships. There are several characteristics in computer networks, which play important roles in determining the level of network security. These characteristics known as security metrics can be applied for security quantification in computer networks. Most of the researches on this area has focused on defining the new security metrics to improve the quantification process. In this paper, we present a new approach to analyze and quantify the network security by ranking of security metrics with considering the relationships between them. Our ranking method reveals the importance of each security metric to quantify security in the network under surveillance. The proposed approach helps the network administrators to have a better insight on the level of network security

Question: What is the new approach to quantify network security?


Collaborative detection of cyberbullying behavior in Twitter data. As the size of Twitter© data is increasing, so are undesirable behaviors of its users. One of such undesirable behavior is cyberbullying, which may even lead to catastrophic consequences. Hence, it is critical to efficiently detect cyberbullying behavior by analyzing tweets, if possible in realtime. Prevalent approaches to identify cyberbullying are mainly stand-alone and thus, are time-consuming. This research improves detection task using the principles of collaborative computing. Different collaborative paradigms are suggested and discussed in this paper. Preliminary results indicate an improvement in time and accuracy of the detection mechanism over the stand-alone paradigm.

Question: What is cyberbullying?


Learning Recurrent Span Representations for Extractive Question Answering. The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have typically focused on answer selection from a set of candidates pre-defined manually or through the use of an external NLP pipeline. However, Rajpurkar et al. (2016) recently released the SQUAD dataset in which the answers can be arbitrary strings from the supplied text. In this paper, we focus on this answer extraction task, presenting a novel model architecture that efficiently builds fixed length representations of all spans in the evidence document with a recurrent network. We show that scoring explicit span representations significantly improves performance over other approaches that factor the prediction into separate predictions about words or start and end markers. Our approach improves upon the best published results of Wang & Jiang (2016) by 5% and decreases the error of Rajpurkar et al.'s baseline by > 50%.

Question: What is the best published result for the answer extraction task?


UAVS FOR THE DOCUMENTATION OF ARCHAEOLOGICAL EXCAVATIONS. UAV photogrammetry experienced a growing variety of diverse applications in different scientific disciplines. Comparable early, UAVs were deployed in the Cultural Heritage and Archaeology domains, mainly for the purpose of monument, building and landscape modelling. In this paper, we will focus on the investigation of UAV application for documenting archaeological excavations. Due to the fact, that excavations are dynamic processes and therefore the objects to be acquired change significantly within few hours, UAVs can provide a suitable alternative to traditional measurement methods such as measuring tape and tachymeter in some cases. Nevertheless, the image processing steps have to be automated to a large amount as results, usually sketches, maps, orthophotos and 3D models, should be available temporally close to the related excavation event. In order to accelerate the processing workflow, an interface between the UAV ground control software and various photogrammetric software packages was developed at ETH Zurich which allows for an efficient management and transfer of orientation, trajectory and sensor data for fast project setup. The capabilities of photogrammetric methods using UAVs as a sensor platform will be demonstrated in 3 case studies: The documentation of a large archaeological site in Bhutan, an excavation of a smaller site containing ancient tombs which include several uncovered objects in the Nasca region in Peru and the Maya site of Copán in Honduras. The first and the third case study deal with the 3D modelling of buildings and their remains by means of photogrammetry, which means that accurate flight planning had to be applied and followed during the flights. In the second case study, we acquired various aerial images over the excavation area Pernil Alto near Palpa in a more simple way for quick documentation of the area of interest. In a third part, we will present our results from compar

Question: What is the difference between photogrammetry and photogrammetry?


Transient effects in application of PWM inverters to induction motors. Standard squirrel cage induction motors are subjected to nonsinusoidal wave shapes, when supplied from adjustable frequency inverters. In addition to causing increased heating, these wave patterns can be destructive to the insulation. Pulse width modulated (PWM) inverter output amplitudes and risetimes are investigated, and motor insulation capabilities are discussed. Voltage reflections are simulated for various cable lengths and risetimes and are presented graphically. Simulations confirm potential problems with long cables and short risetimes. Application precautions are also discussed.<<ETX>>

Question: What is the ETX?


Estimating surface normals in noisy point cloud data. In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal estimation when the PCD is sampled from a smooth curve in R2 or a smooth surface in R3 and noise is added. The analysis allows us to find the optimal neighborhood size using other local information from the PCD. Experimental results are also provided.

Question: How to estimate surface normals in noisy point cloud data?


Exemplar-Based 3D Shape Segmentation in Point Clouds. This paper addresses the problem of automatic 3D shape segmentation in point cloud representation. Of particular interest are segmentations of noisy real scans, which is a difficult problem in previous works. To guide segmentation of target shape, a small set of pre-segmented exemplar shapes in the same category is adopted. The main idea is to register the target shape with exemplar shapes in a piece-wise rigid manner, so that pieces under the same rigid transformation are more likely to be in the same segment. To achieve this goal, an over-complete set of candidate transformations is generated in the first stage. Then, each transformation is treated as a label and an assignment is optimized over all points. The transformation labels, together with nearest-neighbor transferred segment labels, constitute final labels of target shapes. The method is not dependent on high-order features, and thus robust to noise as can be shown in the experiments on challenging datasets.

Question: How to segment a point cloud?


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

Question: What is the difference between the Asian and Caucasian facial features?


Social Norms and Community Enforcement. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Question: How do I use JSTOR?


Counting Graphlets: Space vs Time. Counting graphlets is a well-studied problem in graph mining and social network analysis. Recently, several papers explored very simple and natural approaches based on Monte Carlo sampling of Markov Chains (MC), and reported encouraging results. We show, perhaps surprisingly, that this approach is outperformed by a carefully engineered version of color coding (CC) [1], a sophisticated algorithmic technique that we extend to the case of graphlet sampling and for which we prove strong statistical guarantees. Our computational experiments on graphs with millions of nodes show CC to be more accurate than MC. Furthermore, we formally show that the mixing time of the MC approach is too high in general, even when the input graph has high conductance. All this comes at a price however. While MC is very efficient in terms of space, CC's memory requirements become demanding when the size of the input graph and that of the graphlets grow. And yet, our experiments show that a careful implementation of CC can push the limits of the state of the art, both in terms of the size of the input graph and of that of the graphlets.

Question: What is the mixing time of the MC approach?


Periodic Leaky-Wave Antenna for Millimeter Wave Applications Based on Substrate Integrated Waveguide. Substrate integrated waveguides (SIW) are built up of periodically arranged metallic via-holes or via-slots. The leakage loss of SIW structures increases with the distance between the via-holes or via-slots. An open periodic waveguide with a large via distance supports the propagation of leaky-wave modes and can thus be used for the design of a leaky-wave antenna. In this paper, this leakage loss is studied in detail and used to design a periodic leaky-wave antenna. The proposed concept represents an excellent choice for applications in the millimeter-wave band. Due to its versatility, the finite difference frequency domain method for periodic guided-wave or leaky-wave structures is used to analyze the characteristics of the proposed periodic leaky-wave antenna. Two modes (TE10 and TE20) are investigated and their different leaky-wave properties are analyzed. Based on the proposed leaky-mode analysis method, a novel periodic leaky-wave antenna at 28-34 GHz is designed and fabricated.

Question: What is the difference between a leaky-wave antenna and a guided-wave antenna?


Enhancing representation learning with tensor decompositions for knowledge graphs and high dimensional sequence modeling. The capability of processing and digesting raw data is one of the key features of a humanlike artificial intelligence system. For instance, real-time machine translation should be able to process and understand spoken natural language, and autonomous driving relies on the comprehension of visual inputs. Representation learning is a class of machine learning techniques that autonomously learn to derive latent features from raw data. These new features are expected to represent the data instances in a vector space that facilitates the machine learning task. This thesis studies two specific data situations that require efficient representation learning: knowledge graph data and high dimensional sequences. In the first part of this thesis, we first review multiple relational learning models based on tensor decomposition for knowledge graphs. We point out that relational learning is in fact a means of learning representations through one-hot mapping of entities. Furthermore, we generalize this mapping function to consume a feature vector that encodes all known facts about each entity. It enables the relational model to derive the latent representation instantly for a new entity, without having to re-train the tensor decomposition. In the second part, we focus on learning representations from high dimensional sequential data. Sequential data often pose the challenge that they are of variable lengths. Electronic health records, for instance, could consist of clinical event data that have been collected at subsequent time steps. But each patient may have a medical history of variable length. We apply recurrent neural networks to produce fixed-size latent representations from the raw feature sequences of various lengths. By exposing a prediction model to these learned representations instead of the raw features, we can predict the therapy prescriptions mo

Question: What is the difference between a knowledge graph and a knowledge base?


A Comparison of String Metrics for Matching Names and Records. We describe an open-source Java toolkit of methods for matching names and records. We summarize results obtained from using various string distance metrics on the task of matching entity names: these metrics include distance functions proposed by several different communities, including edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. We then describe an extension to the toolkit which allows records to be compared. We discuss some issues involved in performing a similar comparison for record-matching techniques, and finally present results for some baseline record-matching algorithms which are based on string comparisons between fields.

Question: What is a record?


The diverse CB1 and CB2 receptor pharmacology of three plant cannabinoids: delta9-tetrahydrocannabinol, cannabidiol and delta9-tetrahydrocannabivarin.. Cannabis sativa is the source of a unique set of compounds known collectively as plant cannabinoids or phytocannabinoids. This review focuses on the manner with which three of these compounds, (-)-trans-delta9-tetrahydrocannabinol (delta9-THC), (-)-cannabidiol (CBD) and (-)-trans-delta9-tetrahydrocannabivarin (delta9-THCV), interact with cannabinoid CB1 and CB2 receptors. Delta9-THC, the main psychotropic constituent of cannabis, is a CB1 and CB2 receptor partial agonist and in line with classical pharmacology, the responses it elicits appear to be strongly influenced both by the expression level and signalling efficiency of cannabinoid receptors and by ongoing endogenous cannabinoid release. CBD displays unexpectedly high potency as an antagonist of CB1/CB2 receptor agonists in CB1- and CB2-expressing cells or tissues, the manner with which it interacts with CB2 receptors providing a possible explanation for its ability to inhibit evoked immune cell migration. Delta9-THCV behaves as a potent CB2 receptor partial agonist in vitro. In contrast, it antagonizes cannabinoid receptor agonists in CB1-expressing tissues. This it does with relatively high potency and in a manner that is both tissue and ligand dependent. Delta9-THCV also interacts with CB1 receptors when administered in vivo, behaving either as a CB1 antagonist or, at higher doses, as a CB1 receptor agonist. Brief mention is also made in this review, first of the production by delta9-THC of pharmacodynamic tolerance, second of current knowledge about the extent to which delta9-THC, CBD and delta9-THCV interact with pharmacological targets other than CB1 or CB2 receptors, and third of actual and potential therapeutic applications for each of these cannabinoids.

Question: What is the difference between CB1 and CB2 receptors?


Development of support system for forward tilting of the upper body. We have been developing a wearable robot that directly and physically supports human movement. A ldquomuscle suitrdquo for the arm that will provide muscular support for the paralyzed or those otherwise unable to move unaided is one example. Whereas low back pain is one of the most serious issue for the manual worker and in this paper, we focus on developing the support system for forward tilting of the upper body. In this paper, the concept and mechanical structure of the support system is shown. From the experimental result to keep forward tilting posture with load indicates our system is very effective for keeping forward tilting posture and it becomes clear that more than 60% reduction of muscular power is possible.

Question: How can we support the upper body?


Floor Layout Planning Using Artificial Intelligence Technique. In the era of e-commerce while buying furniture online the customers obviously feel the need for visual representation of the arrangement of their furniture. Even when doing interiors of the house it's difficult to just rely on assumptions about best layouts possible and professional help may become quite expensive. In this project, we make use of Genetic Algorithm (GA) which is an Artificial Intelligence technique to display various optimal arrangements of furniture. The basic idea behind using GA is developing an evolutionary design model. This is done by generating chromosomes for each possible solution and then performing a crossover between them in each generation until an optimum fitness function is reached. Modification in chromosome representation may also be done for better results. The proposed system will generate different layout designs for the furniture keeping in consideration the structure of a master bedroom.

Question: How to design a floor layout?


Machine Learning Approach for Intrusion Detection on Cloud Virtual Machines. Development of the cloud computing in recent years is increasing rapidly and gained great success, its security issues have got more and more attention. Many challenges in cloud computation increase the threat of data and service availability. There is need of many security services in order to improve cloud security for users as well as providers. In this paper, we propose a Anomaly Intrusion Detection System using machine learning approach for virtual machines on cloud computing. Our proposal is feature selection over events from Virtual Machine Monitor to detect anomaly in parallel to training the system so it will learn new threats and update the model. The experiment has been carried out on NSL-KDD'99 datasets using Naïve Bayes Tree (NB Tree) Classifier and hybrid approach of NB Tree and Random Forest.

Question: What is the machine learning approach for intrusion detection on cloud virtual machines?


Faster reinforcement learning after pretraining deep networks to predict state dynamics. Deep learning algorithms have recently appeared that pretrain hidden layers of neural networks in unsupervised ways, leading to state-of-the-art performance on large classification problems. These methods can also pretrain networks used for reinforcement learning. However, this ignores the additional information that exists in a reinforcement learning paradigm via the ongoing sequence of state, action, new state tuples. This paper demonstrates that learning a predictive model of state dynamics can result in a pretrained hidden layer structure that reduces the time needed to solve reinforcement learning problems.

Question: What is reinforcement learning?


Theory of Credit Card Networks : A Survey of the Literature. Credit cards provide benefits to consumers and merchants not provided by other payment instruments as evidenced by their explosive growth in the number and value of transactions over the last 20 years. Recently, credit card networks have come under scrutiny from regulators and antitrust authorities around the world. The costs and benefits of credit cards to network participants are discussed. Focusing on interrelated bilateral transactions, several theoretical models have been constructed to study the implications of several business practices of credit card networks. The results and implications of these economic models along with future research topics are discussed.

Question: What is the theory of credit card networks?


The Influence of Background Music on the Behavior of Restaurant Patrons Author ( s ) :. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. .

Question: How to find a journal article?


The tear trough and lid/cheek junction: anatomy and implications for surgical correction.. BACKGROUND The tear trough and the lid/cheek junction become more visible with age. These landmarks are adjacent, forming in some patients a continuous indentation or groove below the infraorbital rim. Numerous, often conflicting procedures have been described to improve the appearance of the region. The purpose of this study was to evaluate the anatomy underlying the tear trough and the lid/cheek junction and to evaluate the procedures designed to correct them.   METHODS Twelve fresh cadaver lower lid and midface dissections were performed (six heads). The orbital regions were dissected in layers, and medical photography was performed.   RESULTS In the subcutaneous plane, the tear trough and lid/cheek junction overlie the junction of the palpebral and orbital portions of the orbicularis oculi muscle and the cephalic border of the malar fat pad. In the submuscular plane, these landmarks differ. Along the tear trough, the orbicularis muscle is attached directly to the bone. Along the lid/cheek junction, the attachment is ligamentous by means of the orbicularis retaining ligament.   CONCLUSIONS The tear trough and lid/cheek junction are primarily explained by superficial (subcutaneous) anatomical features. Atrophy of skin and fat is the most likely explanation for age-related visibility of these landmarks. "Descent" of this region with age is unlikely (the structures are fixed to bone). Bulging orbital fat accentuates these landmarks. Interventions must extend significantly below the infraorbital rim. Fat or synthetic filler may be best placed in the intraorbicularis plane (tear trough) and in the suborbicularis plane (lid/cheek junction).

Question: What is the tear trough and lid/cheek junction?


Recurrent neural networks for time-series prediction.. Recurrent neural networks have been used for time-s eri s prediction with good results. In this dissertation we compare recurrent neural networks with time-delayed feed forward networks, feed forward networks and li near regression models to see which architecture that can make the most accurate predictions. The data used in all experiments is real-world sales data containing two kinds of segments: campaign segments and non-campaign segments. The task is to make predictions of sales under campaigns and with this in mind we evaluate if we c an get more accurate predictions if we only use the campaign segments when modeling the data. Throughout the entire project the KDD process have been used to give a st ructured work-process. The results showed that recurrent network is not better han the other evaluated algorithms, in fact, the time-delayed feed forward neural network showed to give the best predictions. The results also showed that we c ould get more accurate predictions when only using information from campaign segments.

Question: What is the difference between recurrent neural networks and feed forward neural networks?


Relay Attacks on Passive Keyless Entry and Start Systems in Modern Cars. We demonstrate relay attacks on Passive Keyless Entry and Start (PKES) systems used in modern cars. We build two efficient and inexpensive attack realizations, wired and wireless physical-layer relays, that allow the attacker to enter and start a car by relaying messages between the car and the smart key. Our relays are completely independent of the modulation, protocol, or presence of strong authentication and encryption. We perform an extensive evaluation on 10 car models from 8 manufacturers. Our results show that relaying the signal in one direction only (from the car to the key) is sufficient to perform the attack while the true distance between the key and car remains large (tested up to 50 meters, non line-of-sight). We also show that, with our setup, the smart key can be excited from up to 8 meters. This removes the need for the attacker to get close to the key in order to establish the relay. We further analyze and discuss critical system characteristics. Given the generality of the relay attack and the number of evaluated systems, it is likely that all PKES systems based on similar designs are also vulnerable to the same attack. Finally, we propose immediate mitigation measures that minimize the risk of relay attacks as well as recent solutions that may prevent relay attacks while preserving the convenience of use, for which PKES systems were initially introduced.

Question: How does the relay attack work?


Analysis Operator Learning and its Application to Image Reconstruction. Exploiting a priori known structural information lies at the core of many image reconstruction methods that can be stated as inverse problems. The synthesis model, which assumes that images can be decomposed into a linear combination of very few atoms of some dictionary, is now a well established tool for the design of image reconstruction algorithms. An interesting alternative is the analysis model, where the signal is multiplied by an analysis operator and the outcome is assumed to be sparse. This approach has only recently gained increasing interest. The quality of reconstruction methods based on an analysis model severely depends on the right choice of the suitable operator. In this paper, we present an algorithm for learning an analysis operator from training images. Our method is based on lp-norm minimization on the set of full rank matrices with normalized columns. We carefully introduce the employed conjugate gradient method on manifolds, and explain the underlying geometry of the constraints. Moreover, we compare our approach to state-of-the-art methods for image denoising, inpainting, and single image super-resolution. Our numerical results show competitive performance of our general approach in all presented applications compared to the specialized state-of-the-art techniques.

Question: What is the difference between the synthesis and analysis model?


DataSite: Proactive visual data exploration with computation of insight-based recommendations. Effective data analysis ideally requires the analyst to have high expertise as well as high knowledge of the data. Even with such familiarity, manually pursuing all potential hypotheses and exploring all possible views is impractical. We present DataSite, a proactive visual analytics system where the burden of selecting and executing appropriate computations is shared by an automatic server-side computation engine. Salient features identified by these automatic background processes are surfaced as notifications in a feed timeline. DataSite effectively turns data analysis into a conversation between analyst and computer, thereby reducing the cognitive load and domain knowledge requirements. We validate the system with a user study comparing it to a recent visualization recommendation system, yielding significant improvement, particularly for complex analyses that existing analytics systems do not support well.

Question: What is a proactive visual analytics system?


Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image. We develop an inverse graphics approach to the problem of scene understanding, obtaining a rich representation that includes descriptions of the objects in the scene and their spatial layout, as well as global latent variables like the camera parameters and lighting. The framework's stages include object detection, the prediction of the camera and lighting variables, and prediction of object-specific variables (shape, appearance and pose). This acts like the encoder of an autoencoder, with graphics rendering as the decoder Importantly the scene representation is interpretable and is of variable dimension to match the detected number of objects plus the global variables. For the prediction of the camera latent variables we introduce a novel architecture termed Probabilistic HoughNets (PHNs), which provides a principled approach to combining information from multiple detections. We demonstrate the quality of the reconstructions obtained quantitatively on synthetic data, and qualitatively on real scenes.

Question: How to obtain a rich 3d explanation of a scene from a single image?


High Performance Methods for Linked Open Data Connectivity Analytics. The main objective of Linked Data is linking and integration, and a major step for evaluating whether this target has been reached, is to find all the connections among the Linked Open Data (LOD) Cloud datasets. Connectivity among two or more datasets can be achieved through common Entities, Triples, Literals, and Schema Elements, while more connections can occur due to equivalence relationships between URIs, such as owl:sameAs, owl:equivalentProperty and owl:equivalentClass, since many publishers use such equivalence relationships, for declaring that their URIs are equivalent with URIs of other datasets. However, there are not available connectivity measurements (and indexes) involving more than two datasets, that cover the whole content (e.g., entities, schema, triples) or "slices" (e.g., triples for a specific entity) of datasets, although they can be of primary importance for several real world tasks, such as Information Enrichment, Dataset Discovery and others. Generally, it is not an easy task to find the connections among the datasets, since there exists a big number of LOD datasets and the transitive and symmetric closure of equivalence relationships should be computed for not missing connections. For this reason, we introduce scalable methods and algorithms, (a) for performing the computation of transitive and symmetric closure for equivalence relationships (since they can produce more connections between the datasets); (b) for constructing dedicated global semantics-aware indexes that cover the whole content of datasets; and (c) for measuring the connectivity among two or more datasets. Finally, we evaluate the speedup of the proposed approach, while we report comparative results for over two billion triples.

Question: What is the canadian military?


Estimating software based on use case points. It is well documented that software product cost estimates are notoriously inaccurate across the software industry. Creating accurate cost estimates for software product development projects early in the product development lifecycle has always been a challenge for the industry. This article describes how a large multi-team software engineering organization (over 450 engineers) estimates project cost accurately and early in the software development lifecycle using Use Case Points, and the process of evaluating metrics to ensure the accuracy of the model.The engineering teams of Agilis Solutions in partnership with FPT Software, provide our customers with accurate estimates for software product projects early in the product lifecycle. The bases for these estimates are initial definitions of Use Cases, given point factors and modified for technical and environmental factors according to the Use Case Point method defined within the Rational Unified Process. After applying the process across hundreds of sizable (60 man-months average) software projects, we have demonstrated metrics that prove an estimating accuracy of less than 9% deviation from actual to estimated cost on 95% of our projects. Our process and this success factor is documented over a period of five years, and across more than 200 projects.

Question: How do you estimate software based on use case points?


Nonexistence of Certain Symmetrical Spherical Codes. A spherical 1-code Wis any finite subset of the unit sphere in n dimensions S n-1 for which d(u, v) > I for every u, v from W, u ~ v. A spherical 1-code is symmetric ifu ~ Wimplies u fi W. The best upper bounds on the size of symmetric spherical codes on S n-~ were obtained in [1]. Here we obtain the same bounds by a similar method and improve these bounds for n = 5, 10, 14 and 22.

Question: How large is the size of a spherical code?