Algorithmic Trading Using Intelligent Agents. Trading in financial markets is undergoing a radical transformation, one in which algorithmic methods are becoming increasingly more important. The development of intelligent agents that can act as autonomous traders seems like a logical step forward in this "algorithms arms race". In this paper we describe an infrastructure for implementing hybrid intelligent agents with the ability to trade financial instruments without requiring human supervision. We used this infrastructure, which relies heavily on artificial intelligence models and problem solving methodologies, to implement two currency trading agents. So far these agents have been able to profit from inefficiencies in the currency market, which lead us to believe our infrastructure can be of practical interest to the investment industry.

Question: What is an algorithm?


Novel Wideband Transition Between Coplanar Waveguide and Microstrip Line. A novel wideband vertical transition for connecting the coplanar waveguide (CPW) to the microstrip line is proposed. This transition can be very useful for millimeter-wave packaging and vertical interconnects. It is multilayered, partly tapered, and consists of only one via interconnect. Two different transitions are designed. The first transition allows connectivity of a CPW with Zc=50 Ω to a microstrip line with Zc=16 Ω with a bandwidth of 10-60 GHz. The second transition has the same characteristic impedance, Zc=50 Ω, at the two ports. In this case, the operating frequency is from 40 MHz to 60 GHz. The return losses of both transitions are generally lower than -10 dB over their indicated frequency ranges, while the maximum measured insertion losses are 1.8 and 2.4 dB for the first and second transition, respectively. To extract the S-parameters of the transitions, a new thru-line technique, based on the standard thru-reflect-line two-tier calibration is introduced. Simulation and experimental results, showing good agreement, are presented and discussed.

Question: What is a thru-line technique?


A survey of automated material handling systems in 300-mm SemiconductorFabs. The fast-paced developments and technological breakthroughs in the semiconductor manufacturing industry elevates the importance of optimum utilization of resources. The newer 300-mm wafers fabs place a high level of emphasis on increasing yield and reducing cycle times. Automated material handling systems are importanttools that help us achieve these objectives. In addition, due to the increased weight and size of 300-mm wafers, an automated material handling system isa must for a 300-mm manufacturing facility. This paper discusses various approaches for automated materials handling in semiconductor manufacturing industries.

Question: What is the importance of automated material handling systems in semiconductor manufacturing industries?


Predicting Song Popularity. Future Work Dataset and Features Music has been an integral part of our culture all throughout human history. In 2012 alone, the U.S. music industry generated $15 billion. Having a fundamental understanding of what makes a song popular has major implications to businesses that thrive on popular music, namely radio stations, record labels, and digital and physical music market places. Many private companies in these industries have solutions for this problem, but details have been kept private for competitive reasons. For our project, we will predict song popularity based on an song's audio features and metadata. Methods & Results

Question: What is the future of music?


Multi-scale CNN Stereo and Pattern Removal Technique for Underwater Active Stereo System. Demands on capturing dynamic scenes of underwater environments are rapidly growing. Passive stereo is applicable to capture dynamic scenes, however the shape with textureless surfaces or irregular reflections cannot be recovered by the technique. In our system, we add a pattern projector to the stereo camera pair so that artificial textures are augmented on the objects. To use the system at underwater environments, several problems should be compensated, i.e., refraction, disturbance by fluctuation and bubbles. Further, since surface of the objects are interfered by the bubbles, projected patterns, etc., those noises and patterns should be removed from captured images to recover original texture. To solve these problems, we propose three approaches; a depth-dependent calibration, Convolutional Neural Network(CNN)-stereo method and CNN-based texture recovery method. A depth-dependent calibration I sour analysis to find the acceptable depth range for approximation by center projection to find the certain target depth for calibration. In terms of CNN stereo, unlike common CNN based stereo methods which do not consider strong disturbances like refraction or bubbles, we designed a novel CNN architecture for stereo matching using multi-scale information, which is intended to be robust against such disturbances. Finally, we propose a multi-scale method for bubble and a projected-pattern removal method using CNNs to recover original textures. Experimental results are shown to prove the effectiveness of our method compared with the state of the art techniques. Furthermore, reconstruction of a live swimming fish is demonstrated to confirm the feasibility of our techniques.

Question: How to remove bubbles and patterns from images?


PLINK: a tool set for whole-genome association and population-based linkage analyses.. Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

Question: What is PLINK?


Do Better ImageNet Models Transfer Better?. Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy (r = 0.99 and 0.96, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield penultimate layer features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested.

Question: What is the relationship between ImageNet accuracy and transfer accuracy?


Alignment by Agreement. We present an unsupervised approach to symmetric word alignment in which two simple asymmetric models are trained jointly to maximize a combination of data likelihood and agreement between the models. Compared to the standard practice of intersecting predictions of independently-trained models, joint training provides a 32% reduction in AER. Moreover, a simple and efficient pair of HMM aligners provides a 29% reduction in AER over symmetrized IBM model 4 predictions. Disciplines Computer Sciences Comments Percy Liang, Ben Taskar, and Dan Klein. 2006. Alignment by agreement. In Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL '06). Association for Computational Linguistics, Stroudsburg, PA, USA, 104-111. DOI=10.3115/1220835.1220849 http://dx.doi.org/10.3115/1220835.1220849 © ACM, 2006. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, {(2006)} http://doi.acm.org/10.3115/1220835.1220849" Email permissions@acm.org This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/533 Alignment by Agreement Percy Liang UC Berkeley Berkeley, CA 94720 pliang@cs.berkeley.edu Ben Taskar UC Berkeley Berkeley, CA 94720 taskar@cs.berkeley.edu Dan Klein UC Berkeley Berkeley, CA 94720 klein@cs.berkeley.edu

Question: How large is the canadian military?


Multi-connectivity in 5G mmWave cellular networks. The millimeter wave (mmWave) frequencies offer the potential of orders of magnitude increases in capacity for next-generation cellular wireless systems. However, links in mmWave networks are highly susceptible to blocking and may suffer from rapid variations in quality. Connectivity to multiple cells - both in the mmWave and in the traditional lower frequencies - is thus considered essential for robust connectivity. However, one of the challenges in supporting multi-connectivity in the mmWave space is the requirement for the network to track the direction of each link in addition to its power and timing. With highly directional beams and fast varying channels, this directional tracking may be the main bottleneck in realizing robust mmWave networks. To address this challenge, this paper proposes a novel measurement system based on (i) the UE transmitting sounding signals in directions that sweep the angular space, (ii) the mmWave cells measuring the instantaneous received signal strength along with its variance to better capture the dynamics and, consequently, the reliability of a channel/direction and, finally, (iii) a centralized controller making handover and scheduling decisions based on the mmWave cell reports and transmitting the decisions either via a mmWave cell or conventional microwave cell (when control signaling paths are not available). We argue that the proposed scheme enables efficient and highly adaptive cell selection in the presence of the channel variability expected at mmWave frequencies.

Question: How to measure the direction of a mmWave link?


Towards value-based pricing — An integrative framework for decision making. Despite a recent surge of interest, the subject of pricing in general and value-based pricing in particular has received little academic investigation. Yet, pricing has a huge impact on financial results, both in absolute terms and relative to other instruments of the marketing mix. The objective of this paper is to present a comprehensive framework for pricing decisions which considers all relevant dimensions and elements for profitable and sustainable pricing decisions. The theoretical framework is useful for guiding new product pricing decisions as well as for implementing price-repositioning strategies for existing products. The practical application of this framework is illustrated by a case study involving the pricing decision for a major product launch at a global chemical company. D 2003 Elsevier Inc. All rights reserved.

Question: What is the value of a chemical company?


Improving Interaction in HMD-Based Vehicle Simulators through Real Time Object Reconstruction. Bringing real objects into the virtual world has been shown to increase usability and presence in virtual reality applications. This paper presents a system to generate a real time virtual reconstruction of real world user interface elements for use in a head mounted display based driving simulator. Our system uses sensor fusion algorithms to combine data from depth and color cameras to generate an accurate, detailed, and fast rendering of the user's hands while using the simulator. We tested our system and show in our results that the inclusion of the participants real hands, the wheel, and the shifter in the virtual environment increases the immersion, presence, and usability of the simulation. Our system can also be used to bring other real objects into the virtual world, especially when accuracy, detail, and real time updates are desired.

Question: How do you improve immersion in a virtual reality application?


DeepTag: inferring diagnoses from veterinary clinical notes. Large scale veterinary clinical records can become a powerful resource for patient care and research. However, clinicians lack the time and resource to annotate patient records with standard medical diagnostic codes and most veterinary visits are captured in free-text notes. The lack of standard coding makes it challenging to use the clinical data to improve patient care. It is also a major impediment to cross-species translational research, which relies on the ability to accurately identify patient cohorts with specific diagnostic criteria in humans and animals. In order to reduce the coding burden for veterinary clinical practice and aid translational research, we have developed a deep learning algorithm, DeepTag, which automatically infers diagnostic codes from veterinary free-text notes. DeepTag is trained on a newly curated dataset of 112,558 veterinary notes manually annotated by experts. DeepTag extends multitask LSTM with an improved hierarchical objective that captures the semantic structures between diseases. To foster human-machine collaboration, DeepTag also learns to abstain in examples when it is uncertain and defers them to human experts, resulting in improved performance. DeepTag accurately infers disease codes from free-text even in challenging cross-hospital settings where the text comes from different clinical settings than the ones used for training. It enables automated disease annotation across a broad range of clinical diagnoses with minimal preprocessing. The technical framework in this work can be applied in other medical domains that currently lack medical coding resources.

Question: What is the name of the algorithm?


Inverse Kinematics for a Point-Foot Quadruped Robot with Dynamic Redundancy Resolution. In this work we examine the control of center of mass and swing leg trajectories in LittleDog, a point-foot quadruped robot. It is not clear how to formulate a function to compute forward kinematics of the center of mass of the robot as a function of actuated joint angles because point-foot walkers have no direct actuation between the feet and the ground. Nevertheless, we show that a whole-body Jacobian exists and is well defined when at least three of the feet are on the ground. Also, the typical approach of work-space centering for redundancy resolution causes destabilizing motions when executing fast motions. An alternative redundancy resolution optimization is proposed which projects single-leg inverse kinematic solutions into the nullspace. This hybrid approach seems to minimize 1) unnecessary rotation of the body, 2) twisting of the stance legs, and 3) whole-body involvement in achieving a step leg trajectory. In simulation, this control allows the robot to perform significantly more dynamic behaviors while maintaining stability.

Question: How do you control a quadruped robot?


Robust real-time underwater digital video streaming using optical communication. We present a real-time video delivery solution based on free-space optical communication for underwater applications. This solution comprises of AquaOptical II, a high-bandwidth wireless optical communication device, and a two-layer digital encoding scheme designed for error-resistant communication of high resolution images. Our system can transmit digital video reliably through a unidirectional underwater channel, with minimal infrastructural overhead. We present empirical evaluation of this system's performance for various system configurations, and demonstrate that it can deliver high quality video at up to 15 Hz, with near-negligible communication latencies of 100 ms. We further characterize the corresponding end-to-end latencies, i.e. from time of image acquisition until time of display, and reveal optimized results of under 200 ms, which facilitates a wide range of applications such as underwater robot tele-operation and interactive remote seabed monitoring.

Question: How can we transmit video reliably through a unidirectional underwater channel?


Blood Exosomes Endowed with Magnetic and Targeting Properties for Cancer Therapy.. Exosomes are a class of naturally occurring nanoparticles that are secreted endogenously by mammalian cells. Clinical applications for exosomes remain a challenge because of their unsuitable donors, low scalability, and insufficient targeting ability. In this study, we developed a dual-functional exosome-based superparamagnetic nanoparticle cluster as a targeted drug delivery vehicle for cancer therapy. The resulting exosome-based drug delivery vehicle exhibits superparamagnetic behavior at room temperature, with a stronger response to an external magnetic field than individual superparamagnetic nanoparticles. These properties enable exosomes to be separated from the blood and to target diseased cells. In vivo studies using murine hepatoma 22 subcutaneous cancer cells showed that drug-loaded exosome-based vehicle delivery enhanced cancer targeting under an external magnetic field and suppressed tumor growth. Our developments overcome major barriers to the utility of exosomes for cancer application.

Question: What is the difference between a superparamagnetic nanoparticle and a superparamagnetic nanoparticle cluster?


A mobile-based home automation system. The rapidly advancing mobile communication technology and the decrease in costs make it possible to incorporate mobile technology into home automation systems. We propose a mobile-based home automation system that consists of a mobile phone with Java capabilities, a cellular modem, and a home server. The home appliances are controlled by the home server, which operates according to the user commands received from the mobile phone via the cellular modem. In our proposed system the home server is built upon an SMS/GPRS (short message service/general packet radio service) mobile cell module and a microcontroller, allowing a user to control and monitor any variables related to the home by using any Java capable cell phone. This paper presents the design and implementation of AT modem driver, text based command processing software, and power failure resilient output for a microcontroller to facilitate in sending and receiving data via the cell module, together with the design of Java application to enable the cell phone to send commands and receive alerts through the cell module

Question: What is a mobile-based home automation system?


Direct type-specific conic fitting and eigenvalue bias correction. A new method to fit specific types of conics to scattered data points is introduced. Direct, specific fitting of ellipses and hyperbolae is achieved by imposing a quadratic constraint on the conic coefficients, whereby an improved partitioning of the design matrix is devised so as to improve computational efficiency and numerical stability by eliminating redundant aspects of the fitting procedure. Fitting of parabolas is achieved by determining an orthogonal basis vector set in the Grassmannian space of the quadratic terms' coefficients. The linear combination of the basis vectors that fulfills the parabolic condition and has a minimum residual norm is determined using Lagrange multipliers. This is the first known direct solution for parabola specific fitting. Furthermore, the inherent bias of a linear conic fit is addressed. We propose a linear method of correcting this bias, producing better geometric fits which are still constrained to specific conic type. 2007 Published by Elsevier B.V.

Question: What is the first known direct solution for parabola specific fitting?


Deep Learning for Multi-label Classification. In multi-label classification, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classification time. Developing better feature-space representations has been predominantly employed to reduce complexity, e.g., by eliminating non-helpful feature attributes from the input space prior to (or during) training. This is an important task, since many multilabel methods typically create many different copies or views of the same input data as they transform it, and considerable memory can be saved by taking advantage of redundancy. In this paper, we show that a proper development of the feature space can make labels less interdependent and easier to model and predict at inference time. For this task we use a deep learning approach with restricted Boltzmann machines. We present a deep network that, in an empirical evaluation, outperforms a number of competitive methods from the literature.

Question: What is a deep learning approach?


A signal detection method for temporal variation of adverse effect with vaccine adverse event reporting system data. BACKGROUND To identify safety signals by manual review of individual report in large surveillance databases is time consuming; such an approach is very unlikely to reveal complex relationships between medications and adverse events. Since the late 1990s, efforts have been made to develop data mining tools to systematically and automatically search for safety signals in surveillance databases. Influenza vaccines present special challenges to safety surveillance because the vaccine changes every year in response to the influenza strains predicted to be prevalent that year. Therefore, it may be expected that reporting rates of adverse events following flu vaccines (number of reports for a specific vaccine-event combination/number of reports for all vaccine-event combinations) may vary substantially across reporting years. Current surveillance methods seldom consider these variations in signal detection, and reports from different years are typically collapsed together to conduct safety analyses. However, merging reports from different years ignores the potential heterogeneity of reporting rates across years and may miss important safety signals.   METHOD Reports of adverse events between years 1990 to 2013 were extracted from the Vaccine Adverse Event Reporting System (VAERS) database and formatted into a three-dimensional data array with types of vaccine, groups of adverse events and reporting time as the three dimensions. We propose a random effects model to test the heterogeneity of reporting rates for a given vaccine-event combination across reporting years. The proposed method provides a rigorous statistical procedure to detect differences of reporting rates among years. We also introduce a new visualization tool to summarize the result of the proposed method when applied to multiple vaccine-adverse event combinations.   RESULT We applied the propos

Question: How to detect safety signals in large surveillance databases?


Self-compassion versus global self-esteem: two different ways of relating to oneself.. This research examined self-compassion and self-esteem as they relate to various aspects of psychological functioning. Self-compassion entails treating oneself with kindness, recognizing one's shared humanity, and being mindful when considering negative aspects of oneself. Study 1 (N=2,187) compared self-compassion and global self-esteem as they relate to ego-focused reactivity. It was found that self-compassion predicted more stable feelings of self-worth than self-esteem and was less contingent on particular outcomes. Self-compassion also had a stronger negative association with social comparison, public self-consciousness, self-rumination, anger, and need for cognitive closure. Self-esteem (but not self-compassion) was positively associated with narcissism. Study 2 (N=165) compared global self-esteem and self-compassion with regard to positive mood states. It was found that the two constructs were statistically equivalent predictors of happiness, optimism, and positive affect. Results from these two studies suggest that self-compassion may be a useful alternative to global self-esteem when considering what constitutes a healthy self-stance.

Question: What is the difference between self-compassion and self-esteem?


Mobility Management for Femtocells in LTE-Advanced: Key Aspects and Survey of Handover Decision Algorithms. Support of femtocells is an integral part of the Long Term Evolution - Advanced (LTE-A) system and a key enabler for its wide adoption in a broad scale. Femtocells are short-range, low-power and low-cost cellular stations which are installed by the consumers in an unplanned manner. Even though current literature includes various studies towards understanding the main challenges of interference management in the presence of femtocells, little light has been shed on the open issues of mobility management (MM) in the two-tier macrocell-femtocell network. In this paper, we provide a comprehensive discussion on the key aspects and research challenges of MM support in the presence of femtocells, with the emphasis given on the phases of a) cell identification, b) access control, c) cell search, d) cell selection/reselection, e) handover (HO) decision, and f) HO execution. A detailed overview of the respective MM procedures in the LTE-A system is also provided to better comprehend the solutions and open issues posed in real-life systems. Based on the discussion for the HO decision phase, we subsequently survey and classify existing HO decision algorithms for the two-tier macrocell-femtocell network, depending on the primary HO decision criterion used. For each class, we overview up to three representative algorithms and provide detailed flowcharts to describe their fundamental operation. A comparative summary of the main decision parameters and key features of selected HO decision algorithms concludes this work, providing insights for future algorithmic design and standardization activities.

Question: What is the mobility management for femtocells in LTE-A?


Introducing the Occupational Balance Questionnaire (OBQ).. OBJECTIVE The concept of occupational balance is frequently used in occupational therapy but the fact that it has been defined and measured differently is a limitation. This article introduces the Occupational Balance Questionnaire (OBQ), which focuses on satisfaction with the amount and variation of occupations. It consists of 13 items measured on six-step ordinal scales. It has shown good content validity in a sample of 21 occupational therapists but other psychometric properties have not been investigated. The aim was to investigate the OBQ regarding internal consistency, test-retest reliability, and floor/ceiling effects.   METHODS The OBQ was administered twice to a sample selected through convenience sampling. Internal consistency was investigated by Cronbach's alpha and test-retest reliability analysed with Spearman's Rho correlation for the total score and weighted kappa on each item. Potential floor/ceiling effects were explored by checking for the percentage of participants who scored lowest and highest.   RESULTS The results demonstrated that the OBQ has good internal consistency (Cronbach's alpha 0.936) and sufficient test-retest reliability (Spearman's Rho for the total score was 0.926) and, thus, seems stable over time. No floor or ceiling effect was detected.   CONCLUSIONS The OBQ therefore showed promising reliability, although further instrument development studies to examine its construct validity are required.

Question: What is the difference between internal consistency and test-retest reliability?


Sustainability and ICT – An overview of the field. Sustainable development requires the decoupling of economic growth from environmental impacts and from the use of natural resources. This article gives an overview of existing approaches to using Information and Communication Technology (ICT) in the service of sustainability: Environmental Informatics, Green ICT, and Sustainable Human-Computer Interaction (HCI). These approaches are then discussed in the context of the Jevons paradox, an economic argument implying that technological efficiency alone will not produce sustainability. This consideration leads to the conclusion that a combination of efficiency and sufficiency strategies is the most effective way to stimulate innovations which will unleash ICT's potential to support sustainability.

Question: What is the Jevons paradox?


Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem. 0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.02.075 ⇑ Corresponding author. Tel.: +4773597119. E-mail addresses: hanhong@stud.ntnu.no (H (Y. Wang), Kesheng.wang@ntnu.no (K. Wang), (Y. Chen). One of the most studied problems in the financial investment expert system is the intractability of portfolios. The non-linear constrained portfolio optimization problem with multi-objective functions cannot be efficiently solved using traditionally approaches. This paper presents a meta-heuristic approach to portfolio optimization problem using Particle Swarm Optimization (PSO) technique. The model is tested on various restricted and unrestricted risky investment portfolios and a comparative study with Genetic Algorithms is implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers. 2011 Elsevier Ltd. All rights reserved.

Question: What is the difference between a meta-heuristic and a heuristic?


An ant system based on moderate search for TSP. Ant Colony Optimization (ACO) algorithms often suffer from criticism for the local optimum and premature convergence. In order to overcome these inherent shortcomings shared by most ACO algorithms, we divide the ordinary ants into two types: the utilizationoriented ants and the exploration-oriented ants. The utilization-oriented ants focus on constructing solutions based on the learned experience like ants in many other ACO algorithms. On the other hand, inspired by the adaptive behaviors of some real-world Monomorium ant species who tend to select paths with moderate pheromone concentration, a novel search strategy, that is, a completely new transition rule is designed for the exploration-oriented ants to explore more unknown solutions. In addition, a new corresponding update strategy is also employed. Moreover, applying the new search strategy and update strategy, we propose an improved version of ACO algorithm— Moderate Ant System. This improved algorithm is experimentally turned out to be effective and competitive.

Question: What is the difference between the utilization-oriented ants and the exploration-oriented ants?


Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. In recent years, the interest in unsupervised learning of disentangled representations has significantly increased. The key assumption is that real-world data is generated by a few explanatory factors of variation and that these factors can be recovered by unsupervised learning algorithms. A large number of unsupervised learning approaches based on auto-encoding and quantitative evaluation metrics of disentanglement have been proposed; yet, the efficacy of the proposed approaches and utility of proposed notions of disentanglement has not been challenged in prior work. In this paper, we provide a sober look on recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train more than 12 000 models covering the six most prominent methods, and evaluate them across six disentanglement metrics in a reproducible large-scale experimental study on seven different data sets. On the positive side, we observe that different methods successfully enforce properties "encouraged" by the corresponding losses. On the negative side, we observe that in our study (1) "good" hyperparameters seemingly cannot be identified without access to ground-truth labels, (2) good hyperparameters neither transfer across data sets nor across disentanglement metrics, and (3) that increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. These results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.

Question: How large is the canadian military?


Closed loop speed control of chopper fed DC motor for industrial drive application. In this paper a comparative transient and robustness analysis of closed loop speed control employing different linear controllers for the same dc motor using 4 quadrant chopper is investigated. The controller configurations can be broadly classified under (i) Integer order PID controller (ii) linear state space observer and (iii) fractional order PID controller. All of them exhibit superior performances, the first one is the conventional controller used in industries but the later two are modern controllers that have rich potential for industry use and have large advantage over the conventional controllers. The closed loop control of chopper fed DC motor is shown with the 1st quadrant operation of chopper circuit.

Question: What is the difference between integer and fractional order controllers?


Will 5G See its Blind Side? Evolving 5G for Universal Internet Access. Internet has shown itself to be a catalyst for economic growth and social equity but its potency is thwarted by the fact that the Internet is off limits for the vast majority of human beings. Mobile phones—the fastest growing technology in the world that now reaches around 80% of humanity—can enable universal Internet access if it can resolve coverage problems that have historically plagued previous cellular architectures (2G, 3G, and 4G). These conventional architectures have not been able to sustain universal service provisioning since these architectures depend on having enough users per cell for their economic viability and thus are not well suited to rural areas (which are by definition sparsely populated). The new generation of mobile cellular technology (5G), currently in a formative phase and expected to be finalized around 2020, is aimed at orders of magnitude performance enhancement. 5G offers a clean slate to network designers and can be molded into an architecture also amenable to universal Internet provisioning. Keeping in mind the great social benefits of democratizing Internet and connectivity, we believe that the time is ripe for emphasizing universal Internet provisioning as an important goal on the 5G research agenda. In this paper, we investigate the opportunities and challenges in utilizing 5G for global access to the Internet for all (GAIA). We have also identified the major technical issues involved in a 5G-based GAIA solution and have set up a future research agenda by defining open research problems.

Question: What is the future of 5G?


Creating a Labeled Dataset for Medical Misinformation in Health Forums. The dissemination of medical misinformation online presents a challenge to human health. Machine learning techniques provide a unique opportunity for decreasing the cognitive load associated with deciding upon whether any given user comment is likely to contain misinformation, but a paucity of labeled data of medical misinformation makes supervised approaches a challenge. In order to ameliorate this condition, we present a new labeled dataset of misinformative and non-misinformative comments developed over posted questions and comments on a health discussion forum. This required extraction of candidate misinformative entries from the corpus using information retrieval techniques, development of a codex and labeling strategy for the dataset, and the creation of features for use in machine learning tasks. By identifying the nine most descriptive features with regard to classification as misinformative or non-misinformative through the use of Recursive Feature Elimination, we achieved a classification accuracy of 90.1%, where the dataset is comprised 85.8% of non-misinformative comments. In our opinion, this dataset and analysis will aid the machine learning community in the development of an online misinformation classification system over user-generated content such as medical forum posts.

Question: What is the purpose of this dataset?


On the Logic and Purpose of Significance Testing. There has been much recent attention given to the problems involved with the traditional approach to null hypothesis significance testing (NHST). Many have suggested that, perhaps, NHST should be abandoned altogether in favor of other bases for conclusions such as confidence intervals and effect size estimates (e.g., Schmidt, 1996). The purposes of this article are to (a) review the function that data analysis is supposed to serve in the social sciences, (b) examine the ways in which these functions are performed by NHST, (c) examine the case against NHST, and (d) evaluate interval-based estimation as an alternative to NHST.

Question: What is the purpose of data analysis?


Image Segmentation Techniques Overview. The technology of image segmentation is widely used in medical image processing, face recognition pedestrian detection, etc. The current image segmentation techniques include region-based segmentation, edge detection segmentation, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc. This paper analyzes and summarizes these algorithms of image segmentation, and compares the advantages and disadvantages of different algorithms. Finally, we make a prediction of the development trend of image segmentation with the combination of these algorithms.

Question: What is the difference between region-based segmentation and edge detection segmentation?


Are effective teachers like good parents? Teaching styles and student adjustment in early adolescence.. This study examined the utility of parent socialization models for understanding teachers' influence on student adjustment in middle school. Teachers were assessed with respect to their modeling of motivation and to Baumrind's parenting dimensions of control, maturity demands, democratic communication, and nurturance. Student adjustment was defined in terms of their social and academic goals and interest in class, classroom behavior, and academic performance. Based on information from 452 sixth graders from two suburban middle schools, results of multiple regressions indicated that the five teaching dimensions explained significant amounts of variance in student motivation, social behavior, and achievement. High expectations (maturity demands) was a consistent positive predictor of students' goals and interests, and negative feedback (lack of nurturance) was the most consistent negative predictor of academic performance and social behavior. The role of motivation in mediating relations between teaching dimensions and social behavior and academic achievement also was examined; evidence for mediation was not found. Relations of teaching dimensions to student outcomes were the same for African American and European American students, and for boys and girls. The implications of parent socialization models for understanding effective teaching are discussed.

Question: What is the difference between a teacher and a parent?


Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT. Latest results indicate that features learned via convolutional neural networks outperform previous descriptors on classification tasks by a large margin. It has been shown that these networks still work well when they are applied to datasets or recognition tasks different from those they were trained on. However, descriptors like SIFT are not only used in recognition but also for many correspondence problems that rely on descriptor matching. In this paper we compare features from various layers of convolutional neural nets to standard SIFT descriptors. We consider a network that was trained on ImageNet and another one that was trained without supervision. Surprisingly, convolutional neural networks clearly outperform SIFT on descriptor matching.

Question: What is the difference between convolutional neural networks and SIFT?


No Free Lunch Theorems for Search. We show that all algorithms that search for an extremum of a cost function perform exactly the same when averaged over all possible cost functions In particular if algorithm A outperforms algorithm B on some cost functions then loosely speaking there must exist exactly as many other functions where B outperforms A Starting from this we analyze a number of the other a priori characteristics of the search problem like its geometry and its information theoretic aspects This analysis allows us to derive mathematical benchmarks for assessing a particular search algo rithm s performance We also investigate minimax aspects of the search problem the validity of using characteristics of a partial search over a cost function to predict future behavior of the search algorithm on that cost function and time varying cost functions We conclude with some discussion of the justi ability of biologically inspired search methods

Question: What is the average cost function?


The subepithelial connective tissue graft palatal donor site: anatomic considerations for surgeons.. Surgeons must become completely familiar with the anatomy of the palatal donor site to feel confident in providing the subepithelial connective tissue graft procedure. Variations in the size and shape of the hard palate affect the dimensions of donor tissue harvested, as well as the location of the greater palatine neurovascular bundle. This article classifies palatal vaults according to height as high, average, and shallow. Illustrations and cadaver dissection are utilized to demonstrate that surgeons can gain substantial donor tissue specimens without encountering the neurovascular bundle. Actions to be followed in the unlikely event that the neurovasculature is encountered are reviewed.

Question: What is the anatomy of the palatal donor site?


A contextual-bandit approach to personalized news article recommendation. Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation.  In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks.  The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.

Question: What is a contextual bandit?


AMPds: A public dataset for load disaggregation and eco-feedback research. A home-based intelligent energy conservation system needs to know what appliances (or loads) are being used in the home and when they are being used in order to provide intelligent feedback or to make intelligent decisions. This analysis task is known as load disaggregation or non-intrusive load monitoring (NILM). The datasets used for NILM research generally contain real power readings, with the data often being too coarse for more sophisticated analysis algorithms, and often covering too short a time period. We present the Almanac of Minutely Power dataset (AMPds) for load disaggregation research; it contains one year of data that includes 11 measurements at one minute intervals for 21 sub-meters. AMPds also includes natural gas and water consumption data. Finally, we use AMPds to present findings from our own load disaggregation algorithm to show that current, rather than real power, is a more effective measure for NILM.

Question: What is the AMPds dataset?


Word Attention for Sequence to Sequence Text Understanding. Attention mechanism has been a key component in Recurrent Neural Networks (RNNs) based sequence to sequence learning framework, which has been adopted in many text understanding tasks, such as neural machine translation and abstractive summarization. In these tasks, the attention mechanism models how important each part of the source sentence is to generate a target side word. To compute such importance scores, the attention mechanism summarizes the source side information in the encoder RNN hidden states (i.e., ht), and then builds a context vector for a target side word upon a subsequence representation of the source sentence, since ht actually summarizes the information of the subsequence containing the first t-th words in the source sentence. We in this paper, show that an additional attention mechanism called word attention, that builds itself upon word level representations, significantly enhances the performance of sequence to sequence learning. Our word attention can enrich the source side contextual representation by directly promoting the clean word level information in each step. Furthermore, we propose to use contextual gates to dynamically combine the subsequence level and word level contextual information. Experimental results on abstractive summarization and neural machine translation show that word attention significantly improve over strong baselines. In particular, we achieve the state-of-the-art result on WMT'14 English-French translation task with 12M training data.

Question: What is the state-of-the-art result on WMT'14 English-French translation task with 12M training data?


A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution. We address the problem of image upscaling in the form of single image super-resolution based on a dictionary of lowand highresolution exemplars. Two recently proposed methods, Anchored Neighborhood Regression (ANR) and Simple Functions (SF), provide state-ofthe-art quality performance. Moreover, ANR is among the fastest known super-resolution methods. ANR learns sparse dictionaries and regressors anchored to the dictionary atoms. SF relies on clusters and corresponding learned functions. We propose A+, an improved variant of ANR, which combines the best qualities of ANR and SF. A+ builds on the features and anchored regressors from ANR but instead of learning the regressors on the dictionary it uses the full training material, similar to SF. We validate our method on standard images and compare with state-of-the-art methods. We obtain improved quality (i.e. 0.2-0.7dB PSNR better than ANR) and excellent time complexity, rendering A+ the most efficient dictionary-based super-resolution method to date.

Question: What is the most efficient dictionary-based super-resolution method to date?


Retrieval of Song Lyrics from Sung Queries. Retrieving the lyrics of a sung recording from a database of text documents is a research topic that has not received much attention so far. Such a retrieval system has many practical applications, e.g. for karaoke applications or for indexing large song databases by their lyric content. We present a new method for lyrics retrieval. An acoustic model trained on singing is used to obtain phoneme probabilities from sung queries, which are then mapped to phoneme sequences. These are compared against lines of textual lyrics in a large corpus in order to retrieve the best-matching song. The approach is tested on three sung datasets. Lyrics are retrieved from a set of 300 possible songs (12,000 lines of lyrics). The results are highly encouraging and could be used further to perform automatic lyrics alignment and keyword spotting for large databases of songs, or for retrieving lyrics from the internet.

Question: How to retrieve lyrics from a database of songs?


Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a margin-based binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class is compared against all others, or in which all pairs of classes are compared to each other, or in which output codes with error-correcting properties are used. We propose a general method for combining the classifiers generated on the binary problems, and we prove a general empirical multiclass loss bound given the empirical loss of the individual binary learning algorithms. The scheme and the corresponding bounds apply to many popular classification learning algorithms including support-vector machines, AdaBoost, regression, logistic regression and decision-tree algorithms. We also give a multiclass generalization error analysis for general output codes with AdaBoost as the binary learner. Experimental results with SVM and AdaBoost show that our scheme provides a viable alternative to the most commonly used multiclass algorithms.

Question: How to reduce multiclass to binary?


Attribute-based encryption with non-monotonic access structures. We construct an Attribute-Based Encryption (ABE) scheme that allows a user's private key to be expressed in terms of any access formula over attributes. Previous ABE schemes were limited to expressing only monotonic access structures. We provide a proof of security for our scheme based on the Decisional Bilinear Diffie-Hellman (BDH) assumption. Furthermore, the performance of our new scheme compares favorably with existing, less-expressive schemes.

Question: What is attribute-based encryption?


A simple real-word error detection and correction using local word bigram and trigram. Spelling error is broadly classified in two categories namely non word error and real word error. In this paper a localized real word error detection and correction method is proposed where the scores of bigrams generated by immediate left and right neighbour of the candidate word and the trigram of these three words are combined. A single character position error model is assumed so that if a word W is erroneous then the correct word belongs to the set of real words S generated by single character edit operation on W. The above combined score is calculated also on all members of S. These words are ranked in the decreasing order of the score. By observing the rank and using a rule based approach, the error decision and correction candidates are simultaneously selected. The approach gives comparable accuracy with other existing approaches but is computationally attractive. Since only left and right neighbor are involved, multiple errors in a sentence can also be detected ( if the error occurs in every alternate words ).

Question: What is a real word error?


Hypertrophy of labia minora: experience with 163 reductions.. OBJECTIVE Our purpose was to describe the surgical procedure, its results, and its complications and to determine whether patients are satisfied with surgical reduction of labia minora in cases of hypertrophy.   STUDY DESIGN The records of 163 patients who underwent reduction of the labia minora during a 9-year period were reviewed. The ages of the patients ranged from 12 to 67 years (median, 26). Motives for requesting surgery were aesthetic concerns in 87% of the cases, discomfort in clothing in 64%, discomfort with exercise in 26%, and entry dyspareunia in 43%. Anatomic results were assessed 1 month postoperatively. Patient satisfaction was assessed by means of a mailed questionnaire.   RESULTS No surgery-related significant complications were noticed. Anatomic results were satisfactory for 151 patients (93%). Ninety-eight completed questionnaires were returned. Eighty-one patients (83%) found that the results after surgery were satisfactory. Eighty-seven (89%) were satisfied with the aesthetic result, and 91 (93%) approved the functional outcome. Four patients (4%) would not undergo the same procedure again.   CONCLUSION Labia minora reduction is a simple surgical procedure associated with a high degree of patient satisfaction.

Question: What is the labia minora?


Association of polymorphisms in the cytochrome P450 CYP2C9 with warfarin dose requirement and risk of bleeding complications. BACKGROUND The cytochrome P450 CYP2C9 is responsible for the metabolism of S-warfarin. Two known allelic variants CYP2C9*2 and CYP2C9*3 differ from the wild type CYP2C9*1 by a single aminoacid substitution in each case. The allelic variants are associated with impaired hydroxylation of S-warfarin in in-vitro expression systems. We have studied the effect of CYP2C9 polymorphism on the in-vivo warfarin dose requirement.   METHODS Patients with a daily warfarin dose requirement of 1.5 mg or less (low-dose group, n=36), randomly selected patients with a wide range of dose requirements from an anticoagulant clinic in north-east England (clinic control group, n=52), and 100 healthy controls from the community in the same region were studied. Genotyping for the CYP2C9*2 and CYP2C9*3 alleles was done by PCR analysis. Case notes were reviewed to assess the difficulties encountered during the induction of warfarin therapy and bleeding complications in the low-dose and clinic control groups.   FINDINGS The odds ratio for individuals with a low warfarin dose requirement having one or more CYP2C9 variant alleles compared with the normal population was 6.21 (95% CI 2.48-15.6). Patients in the low-dose group were more likely to have difficulties at the time of induction of warfarin therapy (5.97 [2.26-15.82]) and have increased risk of major bleeding complications (rate ratio 3.68 [1.43-9.50]) when compared with randomly selected clinic controls.   INTERPRETATION We have shown that there is a strong association between CYP2C9 variant alleles and low warfarin dose requirement. CYP2C9 genotyping may identify a subgroup of patients who have difficulty at induction of warfarin therapy and are potentially at a higher risk of bleeding complications.

Question: What is the difference between a polymorphism and a mutation?


Automatic recognition and analysis of human faces and facial expressions: a survey. Humans detect and identify faces in a scene with little or no effort . However, building an automated system that accomplishes this task is very difficult . There are several related Subproblems : detection of a pattern as a face . identification of the face, analysis of facial expressions, and classification based on physical features of the face . A system that performs these operations will find many applications, e .g . criminal identification, authentication in secure systems, etc . Most of the work to date has been in identification . This paper surveys the past work in solving these problems . The capability of the human visual system with respect to these problems is also discussed . It is meant to serve as a guide for an automated system . Some new approaches to these problems are also briefly discussed . Face detection Face identification Facial expressions Classification Facial features

Question: What is the difference between a face and a face?


The future of seawater desalination: energy, technology, and the environment.. In recent years, numerous large-scale seawater desalination plants have been built in water-stressed countries to augment available water resources, and construction of new desalination plants is expected to increase in the near future. Despite major advancements in desalination technologies, seawater desalination is still more energy intensive compared to conventional technologies for the treatment of fresh water. There are also concerns about the potential environmental impacts of large-scale seawater desalination plants. Here, we review the possible reductions in energy demand by state-of-the-art seawater desalination technologies, the potential role of advanced materials and innovative technologies in improving performance, and the sustainability of desalination as a technological solution to global water shortages.

Question: What is the future of seawater desalination?


Design of Broadband Linear and Efficient Power Amplifier for Long-Term Evolution Applications. In this letter, a broadband linear and efficient power amplifier for base-stations in long-term evolution (LTE) applications is designed, fabricated, and experimentally verified, employing Cree's CGH40010F GaN HEMT. A novel method of baseband, fundamental and harmonic impedances control is used to provide high linearity and efficiency across a broad bandwidth. When producing a two-tone carrier-to-intermodulation ratio (C/I) of 30 dBc, the circuit has demonstrated a two-tone power added efficiency (PAE) between 45%-60% across the frequency range from 1.6 to 2.6 GHz while delivering 36.0-38.5 dBm average output power. For a single-carrier 20 MHz LTE signal with a peak-to-average ratio (PAR) of 6.5 dB, a measured high PAE of 40%-55% can be achieved at an average output power of 35.3-37.5 dBm with an adjacent channel leakage ratio (ACLR) of about -30 dBc from 1.6 to 2.6 GHz.

Question: What is the design of a broadband linear and efficient power amplifier for long-term evolution applications?


Double Pole Four Throw switch design with CMOS inverter. To avoid the uses of multiple RF chain associated with the multiple antennas, RF switch is most essential component. Multiple antennas systems are used to replace traditional single antennas circuitry in the radio transceiver system in order to improve the transmission capability and reliability. The desired switching system must have low cost and simple structure, yet still can capture all the advantage of Multiple Inputs Multiple Output (MIMO) systems. In this paper, we have proposed a Double Pole Four Throw (DP4T) RF switch using Complementary Metal Oxide Semiconductor (CMOS) technology with inverter property. This switch is low in cost, and capable of selecting data streams to or from the two antennas for transmitting or receiving processes, respectively.

Question: What is the difference between a double pole four throw switch and a double pole four throw switch?


Feature Analysis for Fake Review Detection through Supervised Classification. Nowadays, review sites are more and more confronted with the spread of misinformation, i.e., opinion spam, which aims at promoting or damaging some target businesses, by misleading either human readers, or automated opinion mining and sentiment analysis systems. For this reason, in the last years, several data-driven approaches have been proposed to assess the credibility of user-generated content diffused through social media in the form of on-line reviews. Distinct approaches often consider different subsets of characteristics, i.e., features, connected to both reviews and reviewers, as well as to the network structure linking distinct entities on the review-site in exam. This article aims at providing an analysis of the main review- and reviewer-centric features that have been proposed up to now in the literature to detect fake reviews, in particular from those approaches that employ supervised machine learning techniques. These solutions provide in general better results with respect to purely unsupervised approaches, which are often based on graph-based methods that consider relational ties in review sites. Furthermore, this work proposes and evaluates some additional new features that can be suitable to classify genuine and fake reviews. For this purpose, a supervised classifier based on Random Forests have been implemented, by considering both well-known and new features, and a large-scale labeled dataset from which all these features have been extracted. The good results obtained show the effectiveness of new features to detect in particular singleton fake reviews, and in general the utility of this study.

Question: What is the difference between supervised and unsupervised machine learning?


Low cost Ka-band transmitter for CubeSat systems. CubeSat platforms grow increasingly popular in commercial ventures as alternative solutions for global Internet networks deep space exploration, and aerospace research endeavors. Many technology companies and system engineers plan to implement small satellite systems as part of global Low Earth Orbit (LEO) inter-satellite constellations. High performing low cost hardware is of key importance in driving these efforts. This paper presents the heterodyne architecture and performance of Ka-Band Integrated Transmitter Assembly (ITA) Module, which could be implemented in nano/microsatellite or other satellite systems as a low-cost solution for high data rate space communication systems. The module converts a 0.9 to 1.1 GHz IF input signal to deliver linear transmission of +29 dBm at 26.7 to 26.9 GHz frequency range with a built-in phase locked oscillator, integrated transmitter, polarizer, and lens corrected antenna.

Question: What is a heterodyne architecture?


Pisotriquetral joint disorders: an under-recognized cause of ulnar side wrist pain. Pisotriquetral joint disorders are often under-recognized in routine clinical practice. They nevertheless represent a significant cause of ulnar side wrist pain. The aim of this article is to present the main disorders of this joint and discuss the different imaging modalities that can be useful for its assessment.

Question: What is the main cause of ulnar side wrist pain?


Alana: Social Dialogue using an Ensemble Model and a Ranker trained on User Feedback. We describe our Alexa prize system (called 'Alana') which consists of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose system responses. This paper reports on the version of the system developed and evaluated in the semi-finals of the competition (i.e. up to 15 August 2017), but not on subsequent enhancements. The ranker for this system was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition. In order to avoid initial problems of inappropriate and boring utterances coming from big datasets such as Reddit and Twitter, we later focussed on 'clean' data sources such as news and facts. We report on experiments with different ranking functions and versions of our NewsBot. We find that a multiturn news strategy is beneficial, and that a ranker trained on the ratings feedback from users is also effective. Our system continuously improved using the data gathered over the course over the competition (1 July – 15 August) . Our final user score (averaged user rating over the whole semi-finals period) was 3.12, and we achieved 3.3 for the averaged user rating over the last week of the semi-finals (8-15 August 2017). We were also able to achieve long dialogues (average 10.7 turns) during the competition period. In subsequent weeks, after the end of the semi-final competition, we have achieved our highest scores of 3.52 (daily average, 18th October), 3.45 (weekly average on 23 and 24 October), and average dialogue lengths of 14.6 turns (1 October), and median dialogue length of 2.25 minutes (average for 7 days on 10th October).

Question: What is the average length of a conversation with alana?


Best Keyword Cover Search. It is common that the objects in a spatial database (e.g., restaurants/hotels) are associated with keyword(s) to indicate their businesses/services/features. An interesting problem known as Closest Keywords search is to query objects, called keyword cover, which together cover a set of query keywords and have the minimum inter-objects distance. In recent years, we observe the increasing availability and importance of keyword rating in object evaluation for the better decision making. This motivates us to investigate a generic version of Closest Keywords search called Best Keyword Cover which considers inter-objects distance as well as the keyword rating of objects. The baseline algorithm is inspired by the methods of Closest Keywords search which is based on exhaustively combining objects from different query keywords to generate candidate keyword covers. When the number of query keywords increases, the performance of the baseline algorithm drops dramatically as a result of massive candidate keyword covers generated. To attack this drawback, this work proposes a much more scalable algorithm called keyword nearest neighbor expansion (keyword-NNE). Compared to the baseline algorithm, keyword-NNE algorithm significantly reduces the number of candidate keyword covers generated. The in-depth analysis and extensive experiments on real data sets have justified the superiority of our keyword-NNE algorithm.

Question: What is the best keyword cover search?


Data Approximation Using Biarcs. . An algorithm for data approximation with biarcs is presented. The method uses a specific formulation of biarcs appropriate for parametric curves in Bézier or NURBS formulation. A base curve is applied to obtain tangents and anchor points for the individual arcs joining in G 1 continuity. Data sampled from circular arcs or straight line segments is represented precisely by one biarc. The method is most useful in numerical control to drive the cutter along straight line or circular paths.

Question: What is a biarc?


Statistics and Social Network of YouTube Videos. YouTube has become the most successful Internet website providing a new generation of short video sharing service since its establishment in early 2005. YouTube has a great impact on Internet traffic nowadays, yet itself is suffering from a severe problem of scalability. Therefore, understanding the characteristics of YouTube and similar sites is essential to network traffic engineering and to their sustainable development. To this end, we have crawled the YouTube site for four months, collecting more than 3 million YouTube videos' data. In this paper, we present a systematic and in-depth measurement study on the statistics of YouTube videos. We have found that YouTube videos have noticeably different statistics compared to traditional streaming videos, ranging from length and access pattern, to their growth trend and active life span. We investigate the social networking in YouTube videos, as this is a key driving force toward its success. In particular, we find that the links to related videos generated by uploaders' choices have clear small-world characteristics. This indicates that the videos have strong correlations with each other, and creates opportunities for developing novel techniques to enhance the service quality.

Question: What is the size of the youtube video?


Plagiarism Meets Paraphrasing: Insights for the Next Generation in Automatic Plagiarism Detection. Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyze the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagiarism detection systems. With this aim in mind, we created the P4P corpus, a new resource that uses a paraphrase typology to annotate a subset of the PAN-PC-10 corpus for automatic plagiarism detection. The results of the Second International Competition on Plagiarism Detection were analyzed in the light of this annotation.The presented experiments show that (i) more complex paraphrase phenomena and a high density of paraphrase mechanisms make plagiarism detection more difficult, (ii) lexical substitutions are the paraphrase mechanisms used the most when plagiarizing, and (iii) paraphrase mechanisms tend to shorten the plagiarized text. For the first time, the paraphrase mechanisms behind plagiarism have been analyzed, providing critical insights for the improvement of automatic plagiarism detection systems.

Question: What is the relationship between paraphrasing and plagiarism?


The complex dielectric constant of pure and sea water from microwave satellite observations. We provide a new fit for the microwave complex dielectric constant of water in the salinity range between 0-40 ppt using two Debye relaxation wavelengths. For pure water, the fit is based on laboratory measurements in the temperature range between -20/spl deg/C and +40/spl deg/C including supercooled water and for frequencies up to 500 GHz. For sea water, our fit is valid for temperatures between -2/spl deg/C and +29/spl deg/C and for frequencies up to at least 90 GHz. At low frequencies, our new model is a modified version of the Klein-Swift model. We compare the results of the new fit with various other models and provide a validation using an extensive analysis of brightness temperatures from the Special Sensor Microwave Imager.

Question: What is the complex dielectric constant of water?


Putting Egocentric and Allocentric into Perspective. In the last decade many studies examined egocentric and allocentric spatial relations. For various tasks, navigators profit from both kinds of relations. However, their interrelation seems to be underspecified. We present four elementary representations of allocentric and egocentric relations (sensorimotor contingencies, egocentric coordinate systems, allocentric coordinate systems, and perspective-free representations) and discuss them with respect to their encoding and retrieval. Elementary representations are problematic for capturing large spaces and situations which encompass both allocentric and egocentric relations at the same time. Complex spatial representations provide a solution to this problem. They combine elementary coordinate representations either by pair-wise connections or by hierarchical embedding. We discuss complex spatial representations with respect to computational requirements and their plausibility regarding behavioral and neural findings. This work is meant to clarify concepts of egocentric and allocentric, to show their limitations, benefits and empirical plausibility and to point out new directions for future research.

Question: What is the difference between egocentric and allocentric?


Decision strategies and susceptibility to phishing. Phishing emails are semantic attacks that con people into divulging sensitive information using techniques to make the user believe that information is being requested by a legitimate source. In order to develop tools that will be effective in combating these schemes, we first must know how and why people fall for them. This study reports preliminary analysis of interviews with 20 non-expert computer users to reveal their strategies and understand their decisions when encountering possibly suspicious emails. One of the reasons that people may be vulnerable to phishing schemes is that awareness of the risks is not linked to perceived vulnerability or to useful strategies in identifying phishing emails. Rather, our data suggest that people can manage the risks that they are most familiar with, but don't appear to extrapolate to be wary of unfamiliar risks. We explore several strategies that people use, with varying degrees of success, in evaluating emails and in making sense of warnings offered by browsers attempting to help users navigate the web.

Question: What strategies do people use to evaluate emails?


Investigating performance of students: a longitudinal study. This paper, investigates how academic performance of students evolves over the years in their studies during a study programme. To determine typical progression patterns over the years, students are described by a 4 tuple (e.g. x1, x2, x3, x4), these being the clusters' mean to which a student belongs to in each year of the degree. For this purpose, two consecutive cohorts have been analyzed using X-means clustering. Interestingly the patterns found in both cohorts show that a substantial number of students stay in the same kind of groups during their studies.

Question: How do students perform over the years?


Evaluation of cross-language voice conversion based on GMM and straight. Voice conversion is a technique for producing utterances using any target speakers' voice from a single source speaker's utterance. In this paper, we apply cross-language voice conversion between Japanese and English to a system based on a Gaussian Mixture Model (GMM) method and STRAIGHT, a high quality vocoder. To investigate the effects of this conversion system across different languages, we recorded two sets of bilingual utterances and performed voice conversion experiments using a mapping function which converts parameters of acoustic features for a source speaker to those of a target speaker. The mapping functions were trained using bilingual databases of both Japanese and English speech. In an objective evaluation using Mel cepstrum distortion (Mel CD), it was confirmed that the system can perform cross-language voice conversion with the same performance as that within a single-language.

Question: How does the system perform cross-language voice conversion?


Breath acetone monitoring by portable Si:WO3 gas sensors.. Breath analysis has the potential for early stage detection and monitoring of illnesses to drastically reduce the corresponding medical diagnostic costs and improve the quality of life of patients suffering from chronic illnesses. In particular, the detection of acetone in the human breath is promising for non-invasive diagnosis and painless monitoring of diabetes (no finger pricking). Here, a portable acetone sensor consisting of flame-deposited and in situ annealed, Si-doped epsilon-WO(3) nanostructured films was developed. The chamber volume was miniaturized while reaction-limited and transport-limited gas flow rates were identified and sensing temperatures were optimized resulting in a low detection limit of acetone (∼20ppb) with short response (10-15s) and recovery times (35-70s). Furthermore, the sensor signal (response) was robust against variations of the exhaled breath flow rate facilitating application of these sensors at realistic relative humidities (80-90%) as in the human breath. The acetone content in the breath of test persons was monitored continuously and compared to that of state-of-the-art proton transfer reaction mass spectrometry (PTR-MS). Such portable devices can accurately track breath acetone concentration to become an alternative to more elaborate breath analysis techniques.

Question: What is the difference between flame-deposited and in situ annealed, Si-doped epsilon-WO(3) nanostructured films?


Gravity-independent mobility and drilling on natural rock using microspines. To grip rocks on the surfaces of asteroids and comets, and to grip the cliff faces and lava tubes of Mars, a 250 mm diameter omni-directional anchor is presented that utilizes a hierarchical array of claws with suspension flexures, called microspines, to create fast, strong attachment. Prototypes have been demonstrated on vesicular basalt and a'a lava rock supporting forces in all directions away from the rock. Each anchor can support >;160 N tangent, >;150 N at 45°, and >;180 N normal to the surface of the rock. A two-actuator selectively-compliant ankle interfaces these anchors to the Lemur IIB robot for climbing trials. A rotary percussive drill was also integrated into the anchor, demonstrating self-contained rock coring regardless of gravitational orientation. As a harder-than-zero-g proof of concept, 20mm diameter boreholes were drilled 83 mm deep in vesicular basalt samples, retaining a 12 mm diameter rock core in 3-6 pieces while in an inverted configuration, literally drilling into the ceiling.

Question: How does the anchor work?


Deep Recurrent Neural Networks for Acoustic Modelling. We present a novel deep Recurrent Neural Network (RNN) model for acoustic modelling in Automatic Speech Recognition (ASR). We term our contribution as a TC-DNN-BLSTM-DNN model, the model combines a Deep Neural Network (DNN) with Time Convolution (TC), followed by a Bidirectional LongShort Term Memory (BLSTM), and a final DNN. The first DNN acts as a feature processor to our model, the BLSTM then generates a context from the sequence acoustic signal, and the final DNN takes the context and models the posterior probabilities of the acoustic states. We achieve a 3.47 WER on the Wall Street Journal (WSJ) eval92 task or more than 8% relative improvement over the baseline DNN models

Question: What is a bidirectional long short term memory?


Graph-based term weighting for information retrieval. A standard approach to Information Retrieval (IR) is to model text as a bag of words. Alternatively, text can be modelled as a graph, whose vertices represent words, and whose edges represent relations between the words, defined on the basis of any meaningful statistical or linguistic relation. Given such a text graph, graph theoretic computations can be applied to measure various properties of the graph, and hence of the text. This work explores the usefulness of such graph-based text representations for IR. Specifically, we propose a principled graph-theoretic approach of (1) computing term weights and (2) integrating discourse aspects into retrieval. Given a text graph, whose vertices denote terms linked by co-occurrence and grammatical modification, we use graph ranking computations (e.g. PageRank Page et al. in The pagerank citation ranking: Bringing order to the Web. Technical report, Stanford Digital Library Technologies Project, 1998) to derive weights for each vertex, i.e. term weights, which we use to rank documents against queries. We reason that our graph-based term weights do not necessarily need to be normalised by document length (unlike existing term weights) because they are already scaled by their graph-ranking computation. This is a departure from existing IR ranking functions, and we experimentally show that it performs comparably to a tuned ranking baseline, such as BM25 (Robertson et al. in NIST Special Publication 500-236: TREC-4, 1995). In addition, we integrate into ranking graph properties, such as the average path length, or clustering coefficient, which represent different aspects of the topology of the graph, and by extension of the document represented as a graph. Integrating such properties into ranking allows us to consider issues such as discourse coherence, flow and density during retrieval. We experimentally show that this type of ranking performs comparably to BM25, and can eve

Question: What is the average path length of a graph?


Usability evaluation for history educational games. The potential for integration of digital games and learning becomes ever more significant recently. One of the goals in educational game design is to create engaging and immersive learning experiences for delivering specified learning goals, outcomes and experiences. However, there is limited number of research done on game usability or quality of game user interface. Failure to design usable game interfaces can interfere with the larger goal of creating a compelling experience for users and can have a negative effect on the overall quality and success of a game. In this paper, we review usability problems identified by previous researchers and propose a history educational game design which includes pedagogical and game design component. Some snapshots of our game module are also presented. Finally we present a usability evaluation method for history educational game design. From our critical literature reviews, we also proposed six constructs which are interface, mechanics, gameplay, playability, feedback and immersion for usability evaluation.

Question: What is the history of the game?


A Character Model with Moral Emotions: Preliminary Evaluation. In literary and drama criticism, emotions, and moral emotions in particular, have been pointed out as one of characterizing features of stories. In this paper, we propose to model story characters as value-based emotional agents, who appraise their own and others' actions based on their desires and values, and feel the appropriate moral emotions in response to narrative situations that challenge their goals and values. In order to validate the appropriateness of the agent model for narrative characters, we ran an experiment with human participants aimed at comparing their expectations about characters' emotions with the predictions of the value-based model of emotional agent. The results of the experiment show that the participants' expectations meet the predictions of the model. 1998 ACM Subject Classification I.2.11 Intelligent Agents, I.2.0 Cognitive Simulation

Question: What is the difference between a character and an agent?


Misplaced Trust ? Exploring the Structure of the E-Government-Citizen Trust Relationship. A growing body of research focuses on the relationship between e-government, the relatively new mode of citizen-to-government contact founded in information and communications technologies, and citizen trust in government. For many, including both academics and policy makers, e-government is seen as a potentially transformational medium, a mode of contact that could dramatically improve citizen perceptions of government service delivery and possibly reverse the long-running decline in citizen trust in government. To date, however, the literature has left significant gaps in our understanding of the e-government-citizen trust relationship. This study intends to fill some of these gaps. Using a cross-sectional sample of 787 end users of US federal government services, data from the American Customer Satisfaction Index study, and structural equation modeling statistical techniques, this study explores the structure of the e-government-citizen trust relationship. Included in the model are factors influencing the decision to adopt e-government, as well as prior expectations, overall satisfaction, and outcomes including both confidence in the particular agency experienced and trust in the federal government overall. The findings suggest that although e-government The authors would like to thank Professor Claes Fornell of the University ofMichigan for granting access to the ACSI data that made this study possible. Thanks also to Jason Joyner of CFI Group for his extensive and helpful comments on an early version of the article. Forrest V. Morgeson III is a Research Scientist at the ACSI in Ann Arbor, MI. He has a PhD in Political Science from the University of Pittsburgh. His recent research focuses on the financial impact of consumer satisfaction and citizen satisfaction with federal government services—both online and offline—and has recently been published or is forthcoming in jour

Question: What is the structure of the e-government-citizen trust relationship?


Efficient Self-Interpretation in Lambda Calculus. We start by giving a compact representation schema for λ-terms and show how this leads to an exceedingly small and elegant self-interpreter. We then define the notion of a self-reducer, and show how this too can be written as a small λ-term. Both the self-interpreter and the self-reducer are proved correct. We finally give a constructive proof for the second fixed point theorem for the representation schema. All the constructions have been implemented on a computer, and experiments verify their correctness. Timings show that the self-interpreter and self-reducer are quite efficient, being about 35 and 50 times slower than direct execution using a call-byneed reduction strategy.

Question: What is the second fixed point theorem for the representation schema?


Mapping gray matter development: Implications for typical development and vulnerability to psychopathology. Recent studies with brain magnetic resonance imaging (MRI) have scanned large numbers of children and adolescents repeatedly over time, as their brains develop, tracking volumetric changes in gray and white matter in remarkable detail. Focusing on gray matter changes specifically, here we explain how earlier studies using lobar volumes of specific anatomical regions showed how different lobes of the brain matured at different rates. With the advent of more sophisticated brain mapping methods, it became possible to chart the dynamic trajectory of cortical maturation using detailed 3D and 4D (dynamic) models, showing spreading waves of changes evolving through the cortex. This led to a variety of time-lapse films revealing characteristic deviations from normal development in schizophrenia, bipolar illness, and even in siblings at genetic risk for these disorders. We describe how these methods have helped clarify how cortical development relates to cognitive performance, functional recovery or decline in illness, and ongoing myelination processes. These time-lapse maps have also been used to study effects of genotype and medication on cortical maturation, presenting a powerful framework to study factors that influence the developing brain.

Question: How does the brain develop?


Designing Anthropomorphic Robot Hand With Active Dual-Mode Twisted String Actuation Mechanism and Tiny Tension Sensors. In this letter, using the active dual-mode twisted string actuation (TSA) mechanism and tiny tension sensors on the tendon strings, an anthropomorphic robot hand is newly designed in a compact manner. Thanks to the active dual-mode TSA mechanism, which is a miniaturized transmission, the proposed robot hand dose has a wide range of operation in terms of the grasping force and speed. It experimentally produces maximally the fingertip force 31.3 N and minimally the closing time of 0.5 s in average. Also, tiny tension sensor with the dimension of 4.7 (width) × 4.0 (height) × 10.75 (length) mm is newly presented and embedded at the fingertips in order to measure the tension on the tendon strings, which would allow the grasping force control. The kinetic and kinematic analyses are performed and the performance is verified by experiments.

Question: How to design an anthropomorphic robot hand?


Research Paper: Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports. OBJECTIVE To evaluate the performance of a natural language processing system in extracting pneumonia-related concepts from chest x-ray reports.   METHODS    DESIGN Four physicians, three lay persons, a natural language processing system, and two keyword searches (designated AAKS and KS) detected the presence or absence of three pneumonia-related concepts and inferred the presence or absence of acute bacterial pneumonia from 292 chest x-ray reports. Gold standard: Majority vote of three independent physicians. Reliability of the gold standard was measured.   OUTCOME MEASURES Recall, precision, specificity, and agreement (using Finn's R: statistic) with respect to the gold standard. Differences between the physicians and the other subjects were tested using the McNemar test for each pneumonia concept and for the disease inference of acute bacterial pneumonia.   RESULTS Reliability of the reference standard ranged from 0.86 to 0.96. Recall, precision, specificity, and agreement (Finn R:) for the inference on acute bacterial pneumonia were, respectively, 0.94, 0.87, 0.91, and 0.84 for physicians; 0.95, 0.78, 0.85, and 0.75 for natural language processing system; 0.46, 0.89, 0.95, and 0.54 for lay persons; 0.79, 0.63, 0.71, and 0.49 for AAKS; and 0.87, 0.70, 0.77, and 0.62 for KS. The McNemar pairwise comparisons showed differences between one physician and the natural language processing system for the infiltrate concept and between another physician and the natural language processing system for the inference on acute bacterial pneumonia. The comparisons also showed that most physicians were significantly different from the other subjects in all pneumonia concepts and the disease inference.   CONCLUSION In extracting pneumonia related concepts from chest x-ray reports, the performance of the natural language processing system was similar to that of physicians and better than tha

Question: What is the difference between the physicians and the natural language processing system?


Critical Success Factors for Effective IT Governance in the Public Sector Organizations in a Developing Country: The Case of Tanzania. Today in many organisations in the public sector, the use of IT has become pervasive in every facet of the organisations' endeavours in supporting and evolving public service delivery. This pervasive use of technology has caused a critical dependency on IT, which in this environment involves a complex mix of political, organisational, technical and cultural concerns that call for a specific focus on effective IT governance. Much research has been done on IT governance effectiveness in these organisations. However adequate focus has not been given to such organisations in a developing country environment like Tanzania, which on one hand is characterised by IT resources, knowledge, and culture constraints and on the other hand by the increase of IT investment and applications. In this paper specifically we address such a gap by identifying the critical success factors (CSFs) for effective IT governance in this environment. The study is based mainly on IT governance focus areas in analysing the relevant CSFs from the literature and five organizations in the Tanzanian public sector. Moreover we have taken into account the fact that the IT value to be achieved due to effective IT governance is related to efficient and cost effective IT delivery, innovation and business impact. As a result of this research we have identified eleven CSFs that should be considered for effective IT governance practice in Tanzanian public sector organizations.

Question: What are the critical success factors for effective IT governance in the public sector organizations in a developing country?


A Theoretical Framework for Structured Prediction using Factor Graph Complexity ∗. We present a general theoretical analysis of structured prediction. By introducing a new complexity measure that explicitly factors in the structure of the output space and the loss function, we are able to derive new data-dependent learning guarantees for a broad family of losses and for hypothesis sets with an arbitrary factor graph decomposition. To the best of our knowledge, these are both the most favorable and the most general guarantees for structured prediction (and multiclass classification) currently known. We also extend this theory by leveraging the principle of Voted Risk Minimization (VRM) and showing that learning is possible with complex factor graphs. We both present new learning bounds in this advanced setting as well as derive two new families of algorithms, Voted Conditional Random Field and Voted Structured Boosting, which can make use of very complex features and factor graphs without overfitting. Finally, we validate our theory through experiments on several datasets.

Question: What is the most general theoretical analysis of structured prediction?


Bistatic Synthetic Aperture Radar Imaging for Arbitrary Flight Trajectories. In this paper, we present an analytic, filtered backprojection (FBP) type inversion method for bistatic synthetic aperture radar (BISAR). We consider a BISAR system where a scene of interest is illuminated by electromagnetic waves that are transmitted, at known times, from positions along an arbitrary, but known, flight trajectory and the scattered waves are measured from positions along a different flight trajectory which is also arbitrary, but known. We assume a single-scattering model for the radar data, and we assume that the ground topography is known but not necessarily flat. We use microlocal analysis to develop the FBP-type reconstruction method. We analyze the computational complexity of the numerical implementation of the method and present numerical simulations to demonstrate its performance.

Question: What is the bistatic synthetic aperture radar?


WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming. The ability to automatically monitor agricultural fields is an important capability in precision farming, enabling steps towards more sustainable agriculture. Precise, high-resolution monitoring is a key prerequisite for targeted intervention and the selective application of agro-chemicals. In this paper, we present a novel weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Although a map can be generated by processing single segmented images incrementally, this requires additional complex information fusion techniques which struggle to handle high fidelity maps due to their computational costs and problems in ensuring global consistency. Moreover, computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.

Question: What is the area under the curve?


Forecasting emerging technologies : Use of bibliometrics and patent analysis. It is rather difficult to forecast emerging technologies as there is no historical data available. In such cases, the use of bibliometrics and patent analysis have provided useful data. This paper presents the forecasts for three emerging technology areas by integrating the use of bibliometrics and patent analysis into well-known technology forecasting tools such as scenario planning, growth curves and analogies. System dynamics is also used to be able to model the dynamic ecosystem of the technologies and their diffusion. Technologies being forecasted are fuel cell, food safety and optical storage technologies. Results from these three applications help us to validate the proposed methods as appropriate tools to forecast emerging technologies. © 2006 Elsevier Inc. All rights reserved.

Question: What is the use of bibliometrics and patent analysis in forecasting emerging technologies?


Real time automation of agricultural environment. The paper, "Real time automation of agricultural environment", using PIC16F877A and GSM SIM300 modem is focused on automating the irrigation system for social welfare of Indian agricultural system. This system will be useful for monitoring the soil moisture condition of the farm as well as controlling the soil moisture by monitoring the level of water in the water source and accordingly switching the motor ON/OFF for irrigation purposes. The system proposes a soil moisture sensor at each place where the moisture has to be monitored. Once the moisture reaches a particular level, the system takes appropriate steps to regulate or even stop the water flow. The circuit also monitors the water in the water source so that if the water level becomes very low, it switches off the motor to prevent damage to the motor due to dry run. The system also consists of a GSM modem through which the farmer can easily be notified about the critical conditions occurring during irrigation process.

Question: What is the soil moisture sensor?