Induction and molecular signature of pathogenic TH17 cells. Interleukin 17 (IL-17)-producing helper T cells (TH17 cells) are often present at the sites of tissue inflammation in autoimmune diseases, which has led to the conclusion that TH17 cells are main drivers of autoimmune tissue injury. However, not all TH17 cells are pathogenic; in fact, TH17 cells generated with transforming growth factor-β1 (TGF-β1) and IL-6 produce IL-17 but do not readily induce autoimmune disease without further exposure to IL-23. Here we found that the production of TGF-β3 by developing TH17 cells was dependent on IL-23, which together with IL-6 induced very pathogenic TH17 cells. Moreover, TGF-β3-induced TH17 cells were functionally and molecularly distinct from TGF-β1-induced TH17 cells and had a molecular signature that defined pathogenic effector TH17 cells in autoimmune disease.

Question: What is the difference between pathogenic and non-pathogenic TH17 cells?


The development of initial trust in an online company by new customers. Lack of trust in online companies is a primary reason why many web users do not shop online. This study proposes a model to explain how new customers of a web-based company develop initial trust in the company after their first visit. The model is empirically tested using a questionnaire-based field study. The results indicate that perceived company reputation and willingness to customize products and services can significantly affect initial trust. Perceived web site usefulness, ease of use, and security control are also significant antecedents of initial trust. Finally, we found no support for the hypothesized effect of individual customer trust propensity on initial trust. # 2003 Elsevier B.V. All rights reserved.

Question: How can we develop initial trust in an online company?


Leveraging social grouping for trust building in foreign electronic commerce firms: An exploratory study. Internet development has fueled e-commerce firms' globalization efforts, but many have met with only limited success. This often stems from the foreign firms' limited understanding of a focal country's local culture and idiosyncrasies. Foreign firms are usually viewed as out-group entities, which lowers consumers' trust in them. The extent of such a phenomenon varies. In locations where people are more skeptical of out-groups, a critical question is whether it is possible to transform such foreign out-group firms into in-groups, specifically with the support of popular social networking media. Based on Social Identity Theory and Trust Transference Process, five strategies leveraging social grouping and social ties to build trust for foreign electronic commerce firms were proposed. A survey was conducted to examine their effectiveness. The results suggest that social-grouping strategies are useful for in-grouping foreign out-group entities to build trust, and the effectiveness of strategies is determined by the social similarity and psychological distance between the consumer and the endorser. This has important implications for scholars and practitioners, both local and abroad, to leverage social grouping to boost Internet sales. © 2013 Elsevier Ltd. All rights reserved.

Question: How can we leverage social grouping to boost Internet sales?


Mining Multi-label Data. A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label. Textual data, such as documents and web pages, are frequently annotated with more than a single label. For example, a news article concerning the reactions of the Christian church to the release of the "Da Vinci Code" film can be labeled as both religion and movies. The categorization of textual data is perhaps the dominant multi-label application. Recently, the issue of learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of new applications, such as semantic annotation of images [1, 2, 3] and video [4, 5], functional genomics [6, 7, 8, 9, 10], music categorization into emotions [11, 12, 13, 14] and directed marketing [15]. Table 1 presents a variety of applications that are discussed in the literature. This chapter reviews past and recent work on the rapidly evolving research area of multi-label data mining. Section 2 defines the two major tasks in learning from multi-label data and presents a significant number of learning methods. Section 3 discusses dimensionality reduction methods for multi-label data. Sections 4 and 5 discuss two important research challenges, which, if successfully met, can significantly expand the real-world applications of multi-label learning methods: a) exploiting label structure and b) scaling up to domains with large number of labels. Section 6 introduces benchmark multi-label datasets and their statistics, while Section 7 presents the most frequently used evaluation measures for multi-label learn-

Question: What is the difference between single and multi-label learning?


Deep neural architectures for large scale android malware analysis. Android is arguably the most widely used mobile operating system in the world. Due to its widespead use and huge user base, it has attracted a lot of attention from the unsavory crowd of malware writers. Traditionally, techniques to counter such malicious software involved manually analyzing code and figuring out whether it was malicious or benign. However, due to the immense pace at which newer malware families are surfacing, such an approach is no longer feasible. Machine learning offers a way to tackle this issue of speed by automating the classification task. While several efforts have been made to use traditional machine learning techniques to Android malware detection, no reasonable effort has been made to utilize the newer, deep learning models in this domain. In this paper, we apply several deep learning models including fully connected, convolutional and recurrent neural networks as well as autoencoders and deep belief networks to detect Android malware from a large scale dataset of more than 55 GBs of Android malware. Further, we apply Bayesian machine learning to this problem domain to see how it fares with the deep learning based models while also providing insights into the dataset. We show that we are able to achieve better results using these models as compared to the state-of-the-art approaches. Our best model gets an F1 score of 0.986 with an AUC of 0.983 as compared to the existing best F1 score of 0.875 and AUC of 0.953.

Question: What is the best model for detecting Android malware?


Probabilistic Inference Using Markov Chain Monte Carlo Methods. Probabilistic inference is an attractive approach to uncertain reasoning and em pirical learning in arti cial intelligence Computational di culties arise however because probabilistic models with the necessary realism and exibility lead to com plex distributions over high dimensional spaces Related problems in other elds have been tackled using Monte Carlo methods based on sampling using Markov chains providing a rich array of techniques that can be applied to problems in arti cial intelligence The Metropolis algorithm has been used to solve di cult problems in statistical physics for over forty years and in the last few years the related method of Gibbs sampling has been applied to problems of statistical inference Concurrently an alternative method for solving problems in statistical physics by means of dynamical simulation has been developed as well and has recently been uni ed with the Metropolis algorithm to produce the hybrid Monte Carlo method In computer science Markov chain sampling is the basis of the heuristic optimization technique of simulated annealing and has recently been used in randomized algorithms for approximate counting of large sets In this review I outline the role of probabilistic inference in arti cial intelligence present the theory of Markov chains and describe various Markov chain Monte Carlo algorithms along with a number of supporting techniques I try to present a comprehensive picture of the range of methods that have been developed including techniques from the varied literature that have not yet seen wide application in arti cial intelligence but which appear relevant As illustrative examples I use the problems of probabilistic inference in expert systems discovery of latent classes from data and Bayesian learning for neural networks

Question: What is the difference between Markov chain Monte Carlo and Gibbs sampling?


Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System. An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.

Question: How to classify arrhythmia types from a lead ECG?


A new generation of microwave bandpass filters using multilayer ring resonators. A new generation of compact microwave bandpass filters (BPF) is proposed. The filters are based on multilayer ring resonators (MRR) technique which is applicable to both narrow band and ultra-wideband (UWB) bandpass filters. This class of filters has very compact circuit size and simple structure for fabrication. Multiple attenuation poles exist and can be simply placed to the left or right of the passband. The dimensions of the filters can be designed to around 2 mm × 2 mm × 1 mm. Out of the five filters presented, two of them are fabricated and measured. Good agreement between the simulation and measurements confirmed the design approach.

Question: What is a microwave bandpass filter?


Assessment of lexical semantic judgment abilities in alcohol-dependent subjects: An fMRI study. Neuropsychological studies have shown that alcohol dependence is associated with neurocognitive deficits in tasks requiring memory, perceptual motor skills, abstraction and problem solving, whereas language skills are relatively spared in alcoholics despite structural abnormalities in the language-related brain regions. To investigate the preserved mechanisms of language processing in alcohol-dependents, functional brain imaging was undertaken in healthy controls (n=18) and alcohol-dependents (n=16) while completing a lexical semantic judgment task in a 3 T MR scanner. Behavioural data indicated that alcohol-dependents took more time than controls for performing the task but there was no significant difference in their response accuracy. fMRI data analysis revealed that while performing the task, the alcoholics showed enhanced activations in left supramarginal gyrus, precuneus bilaterally, left angular gyrus, and left middle temporal gyrus as compared to control subjects. The extensive activations observed in alcoholics as compared to controls suggest that alcoholics recruit additional brain areas to meet the behavioural demands for equivalent task performance. The results are consistent with previous fMRI studies suggesting compensatory mechanisms for the execution of task for showing an equivalent performance or decreased neural efficiency of relevant brain networks. However, on direct comparison of the two groups, the results did not survive correction for multiple comparisons; therefore, the present findings need further exploration.

Question: What is the difference between alcoholics and controls?


PixelNet: Representation of the pixels, by the pixels, and for the pixels. We explore design principles for general pixel-level prediction problems, from low-level edge detection to midlevel surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fullyconvolutional network (FCN), have achieved remarkable success by exploiting the spatial redundancy of neighboring pixels through convolutional processing. Though computationally efficient, we point out that such approaches are not statistically efficient during learning precisely because spatial redundancy limits the information learned from neighboring pixels. We demonstrate that stratified sampling of pixels allows one to (1) add diversity during batch updates, speeding up learning; (2) explore complex nonlinear predictors, improving accuracy; and (3) efficiently train state-of-the-art models tabula rasa (i.e., "from scratch") for diverse pixel-labeling tasks. Our single architecture produces state-of-the-art results for semantic segmentation on PASCAL-Context dataset, surface normal estimation on NYUDv2 depth dataset, and edge detection on BSDS.

Question: How to train a convolutional neural network from scratch?


A Self-Affine Fractal Multiband Antenna. The design of a new fractal multiband antenna based on the self-affinity property is presented in this letter. Experimental return loss has been compared with that obtained using the method of moments and the fractal antenna is found to possess predictable multiband characteristics

Question: What is a fractal antenna?


Epidermal electronics: Skin sweat patch. An ultrathin, stretchable, and conformal sensor system for skin-mounted sweat measurement is characterized and demonstrated in this paper. As an epidermal device, the sweat sensor is mechanically designed for comfortable wear on the skin by employing interdigitated electrodes connected via stretchable serpentine-shaped conductors. Experimental results show that the sensor is sensitive to measuring frequency, sweat level and stretching deformation. It was found that 20kHz signals provide the most sensitive performance: electrical impedance changes 50% while sweat level increases from 20 to 80. In addition, sensor elongation from 15 up to 50% affected the measurement sensitivity of both electrical impedance and capacitance.

Question: What is the sweat sensor?


Analysis and modelling of a bidirectional push-pull converter with PWM plus phase-shift control. This paper presents analysis and modeling for a bidirectional push-pull dc-dc converter. In particular, attention is paid to leakage current reduction with PWM plus phase-shift control. The leakage current in different operating modes has been compared to identify the efficient modes for various operation conditions. The state space averaging method is adopted to derive the small signal and large signal models of the most efficient mode. A 30~70V/300V prototype was built. Both simulation results and experimental results prove the validity of the model.

Question: What is the purpose of this paper?


Highly sensitive sensor for detection of initial slip and its application in a multi-fingered robot hand. Tactile sensors for slip detection are essential for implementing human-like gripping in a robot hand. In previous studies, we proposed flexible, thin and lightweight slip detection sensors utilizing the characteristics of pressure-sensitive conductive rubber. This was achieved by using the high-frequency vibration component generated in the process of slipping of the gripped object in order to distinguish between slipping of the object and changes in the normal force. In this paper, we design a slip detection sensor for a multi-fingered robot hand and examine the influence of noise caused by the operation of such a hand. Finally, we describe an experiment focusing on the adjustment of the gripping force of a multi-fingered robot hand equipped with the developed sensors

Question: How to detect slip in a multi-fingered robot hand?


Dynamic surface self-reconstruction is the key of highly active perovskite nano-electrocatalysts for water splitting.. The growing need to store increasing amounts of renewable energy has recently triggered substantial R&D efforts towards efficient and stable water electrolysis technologies. The oxygen evolution reaction (OER) occurring at the electrolyser anode is central to the development of a clean, reliable and emission-free hydrogen economy. The development of robust and highly active anode materials for OER is therefore a great challenge and has been the main focus of research. Among potential candidates, perovskites have emerged as promising OER electrocatalysts. In this study, by combining a scalable cutting-edge synthesis method with time-resolved X-ray absorption spectroscopy measurements, we were able to capture the dynamic local electronic and geometric structure during realistic operando conditions for highly active OER perovskite nanocatalysts. Ba0.5Sr0.5Co0.8Fe0.2O3-δ as nano-powder displays unique features that allow a dynamic self-reconstruction of the material's surface during OER, that is, the growth of a self-assembled metal oxy(hydroxide) active layer. Therefore, besides showing outstanding performance at both the laboratory and industrial scale, we provide a fundamental understanding of the operando OER mechanism for highly active perovskite catalysts. This understanding significantly differs from design principles based on ex situ characterization techniques.

Question: What is the operando mechanism of perovskite catalysts for water splitting?


Destination bonding : Hybrid cognition using Instagram. Article history: Received August 28, 2015 Received in revised format November 28, 2015 Accepted December 1, 2015 Available online December 1, 2015 Empirical research has identified the phenomenon of destination bonding as a result of summated physical and emotional values associated with the destination. Physical values, namely natural landscape & other physical settings and emotional values, namely the enculturation processes, have a significant role to play in portraying visitors' cognitive framework for destination preference. The physical values seemed to be the stimulator for bonding that embodies action or behavior tendencies in imagery. The emotional values were the conditions that lead to affective bonding and are reflected in attitudes for a place which were evident in text narratives. Social networking on virtual platforms offers the scope for hybrid cognitive expression using imagery and text to the visitors. Instagram has emerged as an application-window to capture these hybrid cognitions of visitors. This study focuses on assessing the relationship between hybrid cognition of visitors expressed via Instagram and their bond with the destination. Further to this, the study attempts to examine the impact of hybrid cognition of visitors on the behavioral pattern of prospective visitors to the destination. The study revealed that sharing of visual imageries and related text by the visitors is an expression of the physico-emotional bonding with the destination. It was further established that hybrid cognition strongly asserts destination bonding and has been also found to have moderating impact on the link between destination bonding and electronic-word-of-mouth. © 2016 Growing Science Ltd. All rights reserved.

Question: What is destination bonding?


Low-degree Graph Partitioning via Local Search with Applications to Constraint Satisfaction, Max Cut, and Coloring. We present practical algorithms for constructing partitions of graphs into a fixed number of vertex-disjoint subgraphs that satisfy particular degree constraints. We use this in particular to find k-cuts of graphs of maximum degree ∆ that cut at least a k−1 k (1 + 1 2∆+k−1 ) fraction of the edges, improving previous bounds known. The partitions also apply to constraint networks, for which we give a tight analysis of natural local search heuristics for the maximum constraint satisfaction problem. These partitions also imply efficient approximations for several problems on weighted bounded-degree graphs. In particular, we improve the best performance ratio for the weighted independent set problem to 3 ∆+2 , and obtain an efficient algorithm for coloring 3-colorable graphs with at most 3∆+2 4 colors. Communicated by M. Fürer: submitted February 1996; revised March 1997. Halldórsson, Lau, Low-degree Graph Partitioning, JGAA, 1(3) 1–13 (1997) 2

Question: What is the best performance ratio for the weighted independent set problem?


Amorphous silicon thin film transistor circuit integration for organic LED displays on glass and plastic. This paper presents design considerations along with measurement results pertinent to hydrogenated amorphous silicon (a-Si:H) thin film transistor (TFT) drive circuits for active matrix organic light emitting diode (AMOLED) displays. We describe both pixel architectures and TFT circuit topologies that are amenable for vertically integrated, high aperture ratio pixels. Here, the OLED layer is integrated directly above the TFT circuit layer, to provide an active pixel area that is at least 90% of the total pixel area with an aperture ratio that remains virtually independent of scaling. Both voltage-programmed and current-programmed drive circuits are considered. The latter provides compensation for shifts in device characteristics due to metastable shifts in the threshold voltage of the TFT. Various drive circuits on glass and plastic were fabricated and tested. Integration of on-panel gate drivers is also discussed where we present the architecture of an a-Si:H based gate de-multiplexer that is threshold voltage shift invariant. In addition, a programmable current mirror with good linearity and stability is presented. Programmable current sources are an essential requirement in the design of source driver output stages.

Question: What is the difference between a-Si:H and a-Si:H?


RNN-based sequence prediction as an alternative or complement to traditional recommender systems. The recurrent neural networks have the ability to grasp the temporal patterns within the data. This is a property that can be used in order to help a recommender system better taking into account the user past history. Still the dimensionality problem that raises within the recommender system field also raises here as the number of items the system have to be aware of is susceptibility high. Recent research have studied the use of such neural networks at a user's session level. This thesis rather examines the use of this technique at a whole user's past history level associated with techniques such as embeddings and softmax sampling in order to accommodate with the high dimensionality. The proposed method results in a sequence prediction model that can be used as is for the recommender task or as a feature within a more complex system.

Question: What is the recommender system?


Pixel Based Off-line Signature Verification System. The verification of handwritten signatures is one of the oldest and the most popular authentication methods all around the world. As technology improved, different ways of comparing and analyzing signatures become more and more sophisticated. Since the early seventies, people have been exploring how computers can fully take over the task of signature verification and tried different methods. However, none of them is satisfactory enough and time consuming too. Therefore, our proposed pixel based offline signature verification system is one of the fastest and easiest ways to authenticate any handwritten signature we have ever found. For signature acquisition, we have used scanner. Then we have divided the signature image into 2D array and calculated the hexadecimal RGB value of each pixel. After that, we have calculated the total percentage of matching. If the percentage of matching is more than 90, the signature is considered as valid otherwise invalid. We have experimented on more than 35 signatures and the result of our experiment is quite impressive. We have made the whole system web based so that the signature can be verified from anywhere. The average execution time for signature verification is only 0.00003545 second only. Keywords– off-line signature, signature verification, pixel, execution time, pre-processing

Question: What is the average execution time for signature verification?


Design, measurement and equivalent circuit synthesis of high power HF transformer for three-phase composite dual active bridge topology. High voltage high frequency (HF) transformer provides the isolation between high and low dc link voltages in dual active bridge (DAB) converters. Such DAB converters are finding wide applications as an intermediate DC-DC converter in transformerless intelligent power substation (TIPS), which is proposed as an alternative for conventional distribution-transformer connecting 13.8 kV and 480 V grids. The design of HF transformer used in DAB stage of such application is very challenging considering the required isolation, size and cost. In this paper, the specification generation, design, characterization, test and measurement results on a 10kHz HF transformer are presented, highlighting the above challenges.

Question: What is a high voltage high frequency transformer?


Photographic tone reproduction for digital images. A classic photographic task is the mapping of the potentially high dynamic range of real world luminances to the low dynamic range of the photographic print. This tone reproduction problem is also faced by computer graphics practitioners who must map digital images to a low dynamic range print or screen. The work presented in this paper leverages the time-tested techniques of photographic practice to develop a new tone reproduction operator. In particular, we use and extend the techniques developed by Ansel Adams to deal with digital images. The resulting algorithm is simple and is shown to produce good results for the wide variety of images that we have tested. Photographic Tone Reproduction for Digital Images Erik Reinhard University of Utah Michael Stark University of Utah Peter Shirley University of Utah Jim Ferwerda Cornell University

Question: How to reproduce a digital image on a print?


Interleaved active clamp flyback inverter using a synchronous rectifier for a photovoltaic AC module system. An interleaved active clamp flyback inverter using a synchronous rectifier for a photovoltaic AC module system is proposed. In a conventional single flyback inverter for the photovoltaic AC module system, this inverter has drawbacks of a conduction losses of each switch, transformer copper and high ripple current of capacitor. To overcome these problems, two flyback converter modules with interleaved pulse-width modulation (PWM) are connected in parallel at the input and output sides reducing the ripple current on the input and output capacitors and decrease the current stress on the transformer windings. Thus, the transformer copper losses and the conduction losses on the switches are reduced. Also, reliability and life of capacitor are increased due to reducing the ripple current of input and output capacitors. In order to verify these, theoretical analysis and simulation are performed.

Question: How to reduce the ripple current of input and output capacitors?


An alignment based similarity measure for hand detection in cluttered sign language video. Locating hands in sign language video is challenging due to a number of factors. Hand appearance varies widely across signers due to anthropometric variations and varying levels of signer proficiency. Video can be captured under varying illumination, camera resolutions, and levels of scene clutter, e.g., high-res video captured in a studio vs. low-res video gathered by a Web cam in a user's home. Moreover, the signers' clothing varies, e.g., skin-toned clothing vs. contrasting clothing, short-sleeved vs. long-sleeved shirts, etc. In this work, the hand detection problem is addressed in an appearance matching framework. The histogram of oriented gradient (HOG) based matching score function is reformulated to allow non-rigid alignment between pairs of images to account for hand shape variation. The resulting alignment score is used within a support vector machine hand/not-hand classifier for hand detection. The new matching score function yields improved performance (in ROC area and hand detection rate) over the vocabulary guided pyramid match kernel (VGPMK) and the traditional, rigid HOG distance on American Sign Language video gestured by expert signers. The proposed match score function is computationally less expensive (for training and testing), has fewer parameters and is less sensitive to parameter settings than VGPMK. The proposed detector works well on test sequences from an inexpert signer in a non-studio setting with cluttered background.

Question: What is the difference between a hand detector and a hand recognizer?


On board eco-driving system for varying road-traffic environments using model predictive control. This paper presents model predictive control of a vehicle in a varying road-traffic environment for ecological (eco) driving. The vehicle control input is derived by rigorous reasoning approach of model based anticipation of road, traffic and fuel consumption in a crowded road network regulated by traffic signals. Model predictive control with Continuation and generalized minimum residual method for optimization is used to calculate the sequence of control inputs aiming at long run fuel economy maintaining a safe driving. Performance of the proposed eco-driving system is evaluated through simulations in AIMSUN microscopic transport simulator. In spite of nonlinearity and discontinuous movement of other traffic and signals, the proposed system is robust enough to control the vehicle safely. The driving behavior with fuel saving aspects is graphically illustrated, compared and analyzed to signify the prospect of the proposed eco-driving of a vehicle.

Question: How to control a vehicle in a varying road-traffic environment?


Technology Insight: noninvasive brain stimulation in neurology—perspectives on the therapeutic potential of rTMS and tDCS. In neurology, as in all branches of medicine, symptoms of disease and the resulting burden of illness and disability are not simply the consequence of the injury, inflammation or dysfunction of a given organ; they also reflect the consequences of the nervous system's attempt to adapt to the insult. This plastic response includes compensatory changes that prove adaptive for the individual, as well as changes that contribute to functional disability and are, therefore, maladaptive. In this context, brain stimulation techniques tailored to modulate individual plastic changes associated with neurological diseases might enhance clinical benefits and minimize adverse effects. In this Review, we discuss the use of two noninvasive brain stimulation techniques—repetitive transcranial magnetic stimulation and transcranial direct current stimulation—to modulate activity in the targeted cortex or in a dysfunctional network, to restore an adaptive equilibrium in a disrupted network for best behavioral outcome, and to suppress plastic changes for functional advantage. We review randomized controlled studies, in focal epilepsy, Parkinson's disease, recovery from stroke, and chronic pain, to illustrate these principles, and we present evidence for the clinical effects of these two techniques.

Question: What is the difference between repetitive transcranial magnetic stimulation and transcranial direct current stimulation?


The GRASP Multiple Micro-UAV Testbed. In the last five years, advances in materials, electronics, sensors, and batteries have fueled a growth in the development of microunmanned aerial vehicles (MAVs) that are between 0.1 and 0.5 m in length and 0.1-0.5 kg in mass [1]. A few groups have built and analyzed MAVs in the 10-cm range [2], [3]. One of the smallest MAV is the Picoftyer with a 60-mmpropellor diameter and a mass of 3.3 g [4]. Platforms in the 50-cm range are more prevalent with several groups having built and flown systems of this size [5]-[7]. In fact, there are severalcommercially available radiocontrolled (PvC) helicopters and research-grade helicopters in this size range [8].

Question: What is the smallest micro-UAV?


IGCTs vs. IGBTs for circuit breakers in advanced ship electrical systems. Solid-State Circuit Breakers (SSCBs) are being considered for several of the new Navy ships' power distribution platforms. This is because SSCBs offer many advantages to electrical systems that conventional Electro-Mechanical Circuit Breakers (EMCBs) cannot. These advantages include much faster interruption of overcurrents, based on lower current thresholds and/or di/dt rate of current rise; reduced energy flowing into the electrical system during faults; and reduced system ringing during fault interruptions. Additional advantages of SSCBs are the elimination of electrical arcing, which is particularly useful for DC and higher voltage distribution systems and systems in environments with volatile gases or explosives; reduced acoustic noise and Electro-Magnetic Interference (EMI) levels during circuit breaker switching; and reduced maintenance and lifecycle costs during the typical 40-year lifetime of the ship. This paper describes the advantages and disadvantages of SSCBs using IGCTs, SSCBs using IGBTs and conventional EMCBs. It will also describe the differences between IGCTs and IGBTs and will consider the design of the bus voltage snubber. This paper will also show how redesigning the ship's electrical system, while considering these important issues, can result in a more reliable, cost-effective power distribution system for the ship.

Question: What are the advantages and disadvantages of solid-state circuit breakers?


Millimeter wave cavity backed aperture coupled microstrip patch antenna. In this paper, a new single element broad band cavity backed multi-layer microstrip patch antenna is presented. This antenna is designed to be used in 79 GHz MIMO radar applications, and is fabricated with advanced high resolution multi-layer PCB technology. It has a wide bandwidth of 11.43 GHz, which is 14.5% of the center frequency. The beam widths in both E and H-planes are very wide, and are 144 degrees and 80 degrees respectively. Also, the antenna gain and efficiency are respectively 4.7 dB and 87%. Furthermore, measurements have been done, and show a good agreement with simulations.

Question: What is a cavity backed aperture coupled microstrip patch antenna?


Science and society. Social media and the elections.. C R E D IT : J O E S U T IL F F /J O E S U T L IF F .C O M I n the United States, social media sites—such as Facebook, Twitter, and YouTube—are currently being used by two out of three people ( 1), and search engines are used daily ( 2). Monitoring what users share or search for in social media and on the Web has led to greater insights into what people care about or pay attention to at any moment in time. Furthermore, it is also helping segments of the world population to be informed, to organize, and to react rapidly. However, social media and search results can be readily manipulated, which is something that has been underappreciated by the press and the general public. In times of political elections, the stakes are high, and advocates may try to support their cause by active manipulation of social media. For example, altering the number of followers can affect a viewer's conclusion about candidate popularity. Recently, it was noted that the number of followers for a presidential candidate in the United States surged by over 110 thousand within one single day, and analysis showed that most of these followers are unlikely to be real people ( 3). We can model propaganda efforts in graph-theoretic terms, as attempts to alter our "trust network": Each of us keeps a mental trust network that helps us decide what and what not to believe ( 4). The nodes in this weighted network are entities that we are already familiar with (people, institutions, and ideas), and the arcs are our perceived connections between these entities. The weights on the nodes are values of trust and distrust that we implicitly assign to every entity we know. A propagandist is trying to make us alter connections and values in our trust network, i.e., trying to infl uence our perception about the candidates for the coming elections, and thus "help us" decide on candidates of their choice. The Web, as seen by search engines ( 5), is similarly a w

Question: How many followers does a presidential candidate have?


Dual methods for nonconvex spectrum optimization of multicarrier systems. The design and optimization of multicarrier communications systems often involve a maximization of the total throughput subject to system resource constraints. The optimization problem is numerically difficult to solve when the problem does not have a convexity structure. This paper makes progress toward solving optimization problems of this type by showing that under a certain condition called the time-sharing condition, the duality gap of the optimization problem is always zero, regardless of the convexity of the objective function. Further, we show that the time-sharing condition is satisfied for practical multiuser spectrum optimization problems in multicarrier systems in the limit as the number of carriers goes to infinity. This result leads to efficient numerical algorithms that solve the nonconvex problem in the dual domain. We show that the recently proposed optimal spectrum balancing algorithm for digital subscriber lines can be interpreted as a dual algorithm. This new interpretation gives rise to more efficient dual update methods. It also suggests ways in which the dual objective may be evaluated approximately, further improving the numerical efficiency of the algorithm. We propose a low-complexity iterative spectrum balancing algorithm based on these ideas, and show that the new algorithm achieves near-optimal performance in many practical situations

Question: What is the time-sharing condition?


Mining frequent closed graphs on evolving data streams. Graph mining is a challenging task by itself, and even more so when processing data streams which evolve in real-time. Data stream mining faces hard constraints regarding time and space for processing, and also needs to provide for concept drift detection. In this paper we present a framework for studying graph pattern mining on time-varying streams. Three new methods for mining frequent closed subgraphs are presented. All methods work on coresets of closed subgraphs, compressed representations of graph sets, and maintain these sets in a batch-incremental manner, but use different approaches to address potential concept drift. An evaluation study on datasets comprising up to four million graphs explores the strength and limitations of the proposed methods. To the best of our knowledge this is the first work on mining frequent closed subgraphs in non-stationary data streams.

Question: Mining frequent closed subgraphs on evolving data streams


RN to BSN Transition: A Concept Analysis.. Over 670,000 ADN- and diploma-prepared nurses will need to complete their BSN degrees to meet the Institute of Medicine's recommendation that at least 80% of registered nurses (RNs) be BSN-prepared by year 2020. Understanding motivators, barriers, and the transition experience for RNs to advance their degree will help educators and nurse leaders understand the importance of a partnership to educate and mentor RNs to pursue a BSN degree.

Question: What is the difference between an RN and a BSN?


3D Kinematic Simulation for PA10-7C Robot Arm Based on VRML. In this paper, a graphical, flexible, interactive, and systematic 3D simulation helps facilitate analyzing and previewing kinematics of PA10-7C robot arm in terms of forward kinematics, inverse kinematics, and the Denavit-Hartenberg convention. Modeling and control are of critical importance when the robot arm is used for practical applications. In the paper, the D-H model of PA10-7C robot arm is given first to describe the relationship between two consecutive frames of joints. Based on this D-H model, forward kinematics is calculated efficiently. To describe the relationship between joint angular velocities in the joint space and the end effector's velocities in Cartesian space, Jacobian matrix for PA10-7C robot arm is derived. By Jacobian matrix, inverse kinematics approach is obtained in the paper. VRML-based model of PA10-7C is built in the paper for the simulation. Finally, simulation results are discussed and the paper is concluded.

Question: How to calculate the Jacobian matrix for a robot arm?


Big Data : New Tricks for Econometrics. 5 Nowadays computers are in the middle of most economic transactions. 6 These "computer-mediated transactions" generate huge amounts of 7 data, and new tools can be used to manipulate and analyze this data. 8 This essay offers a brief introduction to some of these tools and meth9 ods. 10 Computers are now involved in many economic transactions and can cap11 ture data associated with these transactions, which can then be manipulated 12 and analyzed. Conventional statistical and econometric techniques such as 13 regression often work well but there are issues unique to big data sets that 14 may require different tools. 15 First, the sheer size of the data involved may require more powerful data 16 manipulation tools. Second, we may have more potential predictors than 17 appropriate for estimation, so we need to do some kind of variable selection. 18 Third, large data sets may allow for more flexible relationships than simple 19 ∗Hal Varian is Chief Economist, Google Inc., Mountain View, California, and Emeritus Professor of Economics, University of California, Berkeley, California. Thanks to Jeffrey Oldham, Tom Zhang, Rob On, Pierre Grinspan, Jerry Friedman, Art Owen, Steve Scott, Bo Cowgill, Brock Noland, Daniel Stonehill, Robert Snedegar, Gary King, the editors of this journal for comments on earlier versions of this paper. 1 linear models. Machine learning techniques such as decision trees, support 20 vector machines, neural nets, deep learning and so on may allow for more 21 effective ways to model complex relationships. 22 In this essay I will describe a few of these tools for manipulating and an23 alyzing big data. I believe that these methods have a lot to offer and should 24 be more widely known and used by economists. In fact, my standard advice 25 to graduate students these days is "go to the computer science department 26 and take a class in machine learning." There have been very fruitful collabo27 rations between co

Question: What is the difference between linear models and machine learning techniques?


Learning preferences for manipulation tasks from online coactive feedback. We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than simple geometric constraints on trajectories; they are rather governed by the surrounding context of various objects and human interactions in the environment. We propose a coactive online learning framework for teaching preferences in contextually rich environments. The key novelty of our approach lies in the type of feedback expected from the user: the human user does not need to demonstrate optimal trajectories as training data, but merely needs to iteratively provide trajectories that slightly improve over the trajectory currently proposed by the system. We argue that this coactive preference feedback can be more easily elicited than demonstrations of optimal trajectories. Nevertheless, theoretical regret bounds of our algorithm match the asymptotic rates of optimal trajectory algorithms. We implement our algorithm on two high-degree-of-freedom robots, PR2 and Baxter, and present three intuitive mechanisms for providing such incremental feedback. In our experimental evaluation we consider two context rich settings, household chores and grocery store checkout, and show that users are able to train the robot with just a few feedbacks (taking only a few minutes).

Question: What is the difference between a coactive and a demonstration?


A Taxonomy of Deep Convolutional Neural Nets for Computer Vision. Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative – that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e., deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision – convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.

Question: What is a convolutional neural network?


OpenARC: open accelerator research compiler for directive-based, efficient heterogeneous computing. This paper presents Open Accelerator Research Compiler (OpenARC): an open-source framework that supports the full feature set of OpenACC V1.0 and performs source-to-source transformations, targeting heterogeneous devices, such as NVIDIA GPUs. Combined with its high-level, extensible Intermediate Representation (IR) and rich semantic annotations, OpenARC serves as a powerful research vehicle for prototyping optimization, source-to-source transformations, and instrumentation for debugging, performance analysis, and autotuning. In fact, OpenARC is equipped with various capabilities for advanced analyses and transformations, as well as built-in performance and debugging tools. We explain the overall design and implementation of OpenARC, and we present key analysis techniques necessary to efficiently port OpenACC applications. Porting various OpenACC applications to CUDA GPUs using OpenARC demonstrates that OpenARC performs similarly to a commercial compiler, while serving as a general research framework.

Question: What is open accelerator research compiler?


What Is Seen Is Who You Are: Are Cues in Selfie Pictures Related to Personality Characteristics?. Developments and innovation in the areas of mobile information technology, digital media and social networks foster new reflections on computer-mediated communication research, especially in the field of self-presentation. In this context, the selfie as a self-portrait photo is interesting, because as a meaningful gesture, it actively and directly relates the content of the photo to the author of the picture. From the perspective of the selfie as an image and the impression it forms, in the first part of the research we explored the distinctive characteristics of selfie pictures; moreover, from the perspective of the potential reflection of a selfie image on the personality of its author, in the second part we related the characteristics of selfie pictures to various personality constructs (e.g., Big Five personality traits narcissism and femininity-masculinity). Important aspects of selfies especially in relation to gender include the tilt of the head, the side of the face exhibited, mood and head position, later related also to the context of the selfie picture. We found no significant relations between selfie cues and personality constructs. The face-ism index was related to entitlement, and selfie availability to neuroticism.

Question: What is the face-ism index?


Bank Customer Credit Scoring by Using Fuzzy Expert System. Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts. For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.

Question: What is the difference between neural network and fuzzy expert system?


Hybrid CMOS/memristor circuits. This is a brief review of recent work on the prospective hybrid CMOS/memristor circuits. Such hybrids combine the flexibility, reliability and high functionality of the CMOS subsystem with very high density of nanoscale thin film resistance switching devices operating on different physical principles. Simulation and initial experimental results demonstrate that performance of CMOS/memristor circuits for several important applications is well beyond scaling limits of conventional VLSI paradigm.

Question: What is a hybrid CMOS/memristor circuit?


Self-Compassion and Body Dissatisfaction in Women : A Randomized Controlled Trial of a Brief Meditation Intervention. Body dissatisfaction is a major source of suffering among women of all ages. One potential factor that could mitigate body dissatisfaction is self-compassion, a construct that is garnering increasing research attention due to its strong association with psychological health. This study investigated whether a brief 3-week period of self-compassion meditation training would improve body satisfaction in a multigenerational group of women. Participants were randomized either to the meditation intervention group (N=98; M age=38.42) or to a waitlist control group (N=130; M age=36.42). Results suggested that compared to the control group, intervention participants experienced significantly greater reductions in body dissatisfaction, body shame, and contingent self-worth based on appearance, as well as greater gains in self-compassion and body appreciation. All improvements were maintained when assessed 3 months later. Self-compassion meditation may be a useful and cost-effective means of improving body image in

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


Scalable Hashing-Based Network Discovery. Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, energy, economics, and more. In these domains, networks are often constructed out of multiple time series by computing measures of association or similarity between pairs of series. The nodes in a discovered graph correspond to time series, which are linked via edges weighted by the association scores of their endpoints. After graph construction, the network may be thresholded such that only the edges with stronger weights remain and the desired sparsity level is achieved. While this approach is feasible for small datasets, its quadratic time complexity does not scale as the individual time series length and the number of compared series increase. Thus, to avoid the costly step of building a fully-connected graph before sparsification, we propose a fast network discovery approach based on probabilistic hashing of randomly selected time series subsequences. Evaluation on real data shows that our methods construct graphs nearly 15 times as fast as baseline methods, while achieving both network structure and accuracy comparable to baselines in task-based evaluation.

Question: What is the name of the algorithm?


Developing Citizens ' Trust towards Successful Adoption of E-Government : an Empirical Study from Saudi Arabia. Despite the significant influence of citizens' trust towards successful adoption of e-government, the majority of research studies investigating trust in e-government focus only on technology and government agencies. This research provides a deep understanding of the concept of trust in e-government by investigating the influence of technology, government agencies, risk, citizens' characteristic. Structure Equation Modelling PLS-SEM is utilized to analyse the collected data and to test the proposed hypotheses. The findings of the study reveals the positive and significant impacts of both technical factors and citizens' characteristic while factors related to government agencies and risk provide negative impacts in trust in egovernment. Also, this paper identifies effective strategies that government need to develop citizens' trust on their online services. Research contributions and limitations are also discussed in the end of the paper.

Question: What is the difference between a government agency and a government?


Edinburgh's Phrase-based Machine Translation Systems for WMT-14. This paper describes the University of Edinburgh's (UEDIN) phrase-based submissions to the translation and medical translation shared tasks of the 2014 Workshop on Statistical Machine Translation (WMT). We participated in all language pairs. We have improved upon our 2013 system by i) using generalized representations, specifically automatic word clusters for translations out of English, ii) using unsupervised character-based models to translate unknown words in RussianEnglish and Hindi-English pairs, iii) synthesizing Hindi data from closely-related Urdu data, and iv) building huge language on the common crawl corpus.

Question: What is the size of the Edinburgh's phrase-based submission to the WMT-14 shared task?


Sentiment Analysis of Citations Using Word2vec. Citation sentiment analysis is an important task in scientific paper analysis. Existing machine learning techniques for citation sentiment analysis are focusing on labor-intensive feature engineering, which requires large annotated corpus. As an automatic feature extraction tool, word2vec has been successfully applied to sentiment analysis of short texts. In this work, I conducted empirical research with the question: how well does word2vec work on the sentiment analysis of citations? The proposed method constructed sentence vectors (sent2vec) by averaging the word embeddings, which were learned from Anthology Collections (ACL-Embeddings). I also investigated polarity-specific word embeddings (PS-Embeddings) for classifying positive and negative citations. The sentence vectors formed a feature space, to which the examined citation sentence was mapped to. Those features were input into classifiers (support vector machines) for supervised classification. Using 10-cross-validation scheme, evaluation was conducted on a set of annotated citations. The results showed that word embeddings are effective on classifying positive and negative citations. However, hand-crafted features performed better for the overall classification.

Question: How well does word2vec work on the sentiment analysis of citations?


Manifold surface reconstruction of an environment from sparse Structure-from-Motion data. The majority of methods for the automatic surface reconstruction of an environment from an image sequence have two steps: Structure-from-Motion and dense stereo. From the computational standpoint, it would be interesting to avoid dense stereo and to generate a surface directly from the sparse cloud of 3D points and their visibility information provided by Structure-fromMotion. The previous attempts to solve this problem are currently very limited: the surface is non-manifold or has zero genus, the experiments are done on small scenes or objects using a few dozens of images. Our solution does not have these limitations. Furthermore, we experiment with hand-held or helmet-held catadioptric cameras moving in a city and generate 3D models such that the camera trajectory can be longer than one kilometer.

Question: How to reconstruct a surface from sparse 3D points?


FRIP: a region-based image retrieval tool using automatic image segmentation and stepwise Boolean AND matching. We present our region-based image retrieval tool, finding region in the picture (FRIP), that is able to accommodate, to the extent possible, region scaling, rotation, and translation. Our goal is to develop an effective retrieval system to overcome a few limitations associated with existing systems. To do this, we propose adaptive circular filters used for semantic image segmentation, which are based on both Bayes' theorem and texture distribution of image. In addition, to decrease the computational complexity without losing the accuracy of the search results, we extract optimal feature vectors from segmented regions and apply them to our stepwise Boolean AND matching scheme. The experimental results using real world images show that our system can indeed improve retrieval performance compared to other global property-based or region-of-interest-based image retrieval methods.

Question: What is the region-based image retrieval tool?


Revealing the structure of the world airline network. Resilience of most critical infrastructures against failure of elements that appear insignificant is usually taken for granted. The World Airline Network (WAN) is an infrastructure that reduces the geographical gap between societies, both small and large, and brings forth economic gains. With the extensive use of a publicly maintained data set that contains information about airports and alternative connections between these airports, we empirically reveal that the WAN is a redundant and resilient network for long distance air travel, but otherwise breaks down completely due to removal of short and apparently insignificant connections. These short range connections with moderate number of passengers and alternate flights are the connections that keep remote parts of the world accessible. It is surprising, insofar as there exists a highly resilient and strongly connected core consisting of a small fraction of airports (around 2.3%) together with an extremely fragile star-like periphery. Yet, in spite of their relevance, more than 90% of the world airports are still interconnected upon removal of this core. With standard and unconventional removal measures we compare both empirical and topological perceptions for the fragmentation of the world. We identify how the WAN is organized into different classes of clusters based on the physical proximity of airports and analyze the consequence of this fragmentation.

Question: How does the world airline network look like?


Concreteness ratings for 40 thousand generally known English word lemmas.. Concreteness ratings are presented for 37,058 English words and 2,896 two-word expressions (such as zebra crossing and zoom in), obtained from over 4,000 participants by means of a norming study using Internet crowdsourcing for data collection. Although the instructions stressed that the assessment of word concreteness would be based on experiences involving all senses and motor responses, a comparison with the existing concreteness norms indicates that participants, as before, largely focused on visual and haptic experiences. The reported data set is a subset of a comprehensive list of English lemmas and contains all lemmas known by at least 85 % of the raters. It can be used in future research as a reference list of generally known English lemmas.

Question: What is the concreteness of the word "zebra"?


Predicting good probabilities with supervised learning. We examine the relationship between the predictions made by different learning algorithms and true posterior probabilities. We show that maximum margin methods such as boosted trees and boosted stumps push probability mass away from 0 and 1 yielding a characteristic sigmoid shaped distortion in the predicted probabilities. Models such as Naive Bayes, which make unrealistic independence assumptions, push probabilities toward 0 and 1. Other models such as neural nets and bagged trees do not have these biases and predict well calibrated probabilities. We experiment with two ways of correcting the biased probabilities predicted by some learning methods: Platt Scaling and Isotonic Regression. We qualitatively examine what kinds of distortions these calibration methods are suitable for and quantitatively examine how much data they need to be effective. The empirical results show that after calibration boosted trees, random forests, and SVMs predict the best probabilities.

Question: What is the relationship between the predictions made by different learning algorithms and true posterior probabilities?


Focus group interviews in nursing research: part 1.. Focus groups are used by researchers in the social and behavioural sciences to explore phenomena and are accepted as a legitimate qualitative methodology. The primary goal of focus groups is to use interaction data resulting from discussion among participants to increase the depth of the enquiry and reveal aspects of the phenomenon assumed to be otherwise less accessible. This article, the first of three articles on focus groups, examines the nature of focus groups, issues regarding planning focus groups, selecting participants and the size of the groups. This article is aimed at students who are undertaking research modules as part of their academic studies or writing a research proposal as well as at novice researchers who intend to use focus groups as a means of data collection.

Question: What is a focus group?


Faster discovery of faster system configurations with spectral learning. Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to finding performance-optimal configurations. Prior work on predicting the performance of software configurations suffered from either (a) requiring far too many sample configurations or (b) large variances in their predictions. Both these problems can be avoided using the WHAT spectral learner. WHAT's innovation is the use of the spectrum (eigenvalues) of the distance matrix between the configurations of a configurable software system, to perform dimensionality reduction. Within that reduced configuration space, many closely associated configurations can be studied by executing only a few sample configurations. For the subject systems studied here, a few dozen samples yield accurate and stable predictors—less than 10% prediction error, with a standard deviation of less than 2%. When compared to the state of the art, WHAT (a) requires 2–10 times fewer samples to achieve similar prediction accuracies, and (b) its predictions are more stable (i.e., have lower standard deviation). Furthermore, we demonstrate that predictive models generated by WHAT can be used by optimizers to discover system configurations that closely approach the optimal performance.

Question: What is the subject of the paper?


Controlling Physically Based Virtual Musical Instruments Using The Gloves. In this paper we propose an empirical method to develop mapping strategies between a gestural-based interface (the Gloves) and physically based sound synthesis models. An experiment was conducted to investigate which gestures listeners associate with sounds synthesised using physical models, corresponding to three categories of sound: sustained, iterative and impulsive. The results of the experiment show that listeners perform similar gestures when controlling sounds from the different categories. We used such gestures in order to create the mapping strategy between the Gloves and the physically based synthesis engine.

Question: How to control a virtual instrument using the gloves?


Analysis of the Co-purchase Network of Products to Predict Amazon Sales-Rank. Amazon sales-rank gives a relative estimate of a product item's popularity among other items in the same category. An early prediction of the Amazon sales-rank of a product would imply an early guess of its sales-popularity relative to the other products on Amazon, which is one of the largest e-commerce hub across the globe. Traditional methods suggest use of product review related features, e.g., volume of reviews, text content of the reviews etc. for the purpose of prediction. In contrast, we propose in this paper for the first time a network-assisted approach to construct suitable features for prediction. In particular, we build a co-purchase network treating the individual products as nodes, with edges in between if two products are bought with one another. The way a product is positioned in this network (e.g., its centrality, clustering coefficient etc.) turns out to be a strong indicator of its sales-rank. This network-assisted approach has two distinct advantages over the traditional baseline method based on review analysis – (i) it works even if the product has no reviews (relevant especially in the early stages of the product launch) and (ii) it is notably more discriminative in classifying a popular (i.e., low sales-rank) product from an unpopular (i.e., high sales-rank) one. Based on this observation, we build a supervised model to early classify a popular product from an unpopular one. We report our results on two different product categories (CDs and cell phones) and obtain remarkably better classification accuracy compared to the baseline scheme. When the top 100 (700) products based on sales-rank are labelled as popular and the bottom 100 (700) are labelled as unpopular, the classification accuracy of our method is 89.85% (82.1%) for CDs and 84.11% (84.8%) for cell phones compared to 46.37% (68.75%) and 83.17% (71.95%) respectively from the baseline method.

Question: What is the co-purchase network of products?


A Taxi Order Dispatch Model based On Combinatorial Optimization. Taxi-booking apps have been very popular all over the world as they provide convenience such as fast response time to the users. The key component of a taxi-booking app is the dispatch system which aims to provide optimal matches between drivers and riders. Traditional dispatch systems sequentially dispatch taxis to riders and aim to maximize the driver acceptance rate for each individual order. However, the traditional systems may lead to a low global success rate, which degrades the rider experience when using the app. In this paper, we propose a novel system that attempts to optimally dispatch taxis to serve multiple bookings. The proposed system aims to maximize the global success rate, thus it optimizes the overall travel efficiency, leading to enhanced user experience. To further enhance users' experience, we also propose a method to predict destinations of a user once the taxi-booking APP is started. The proposed method employs the Bayesian framework to model the distribution of a user's destination based on his/her travel histories.  We use rigorous A/B tests to compare our new taxi dispatch method with state-of-the-art models using data collected in Beijing. Experimental results show that the proposed method is significantly better than other state-of-the art models in terms of global success rate (increased from 80% to 84%). Moreover, we have also achieved significant improvement on other metrics such as user's waiting-time and pick-up distance. For our destination prediction algorithm, we show that our proposed model is superior to the baseline model by improving the top-3 accuracy from 89% to 93%. The proposed taxi dispatch and destination prediction algorithms are both deployed in our online systems and serve tens of millions of users everyday.

Question: What is the difference between a taxi dispatch model and a taxi booking app?


Development and field test of novel two-wheeled UAV for bridge inspections. This paper presents the development and field test of a novel unmanned aerial vehicle (UAV) for bridge inspections. The proposed UAV, which consists of a quadrotor, a cylindrical cage installed in it, and two spokeless wheels freely rotating around the cage, can climb and run on the bridge surface. Its structure improves durability and reduces air resistance compared with conventional UAVs with a cage or wheels. These advantages expand the uses of the UAV in various fields. This paper evaluates the effectiveness of the proposed UAV in real-world bridge inspection scenarios. We tested basic locomotion and measured air resistances. Experimental results on bridges demonstrated the ability to inspect various locations on bridges that are difficult for human inspectors to access.

Question: What is the purpose of the UAV?


Introduction to Space-Time Wireless Communications. published by the press syndicate of the university of cambridge A catalog record for this book is available from the British Library ISBN 0 521 82615 2 hardback Contents List of figures page xiv List of tables xxii Preface xxiii List of abbreviations xxvi List of symbols xxix vii viii Contents 2.6 Polarization and field diverse channels 27 2.7 Antenna array topology 28 2.8 Degenerate channels 29 2.9 Reciprocity and its implications 31 3 ST channel and signal models

Question: What is the difference between a channel and a signal?


Network-Based Intrusion Detection Using Unsupervised Adaptive Resonance Theory ( ART ). This paper introduces the Unsupervised Neural Net based Intrusion Detector (UNNID) system, which detects network-based intrusions and attacks using unsupervised neural networks. The system has facilities for training, testing, and tunning of unsupervised nets to be used in intrusion detection. Using the system, we tested two types of unsupervised Adaptive Resonance Theory (ART) nets (ART-1 and ART-2). Based on the results, such nets can efficiently classify network traffic into normal and intrusive. The system uses a hybrid of misuse and anomaly detection approaches, so is capable of detecting known attack types as well as new attack types as anomalies.

Question: What is the unsupervised neural net based intrusion detector?


Forensic carving of network packets and associated data structures. Using validated carving techniques, we show that popular operating systems (e.g. Windows, Linux, and OSX) frequently have residual IP packets, Ethernet frames, and associated data structures present in system memory from long-terminated network traffic. Such information is useful for many forensic purposes including establishment of prior connection activity and services used; identification of other systems present on the system's LAN or WLAN; geolocation of the host computer system; and cross-drive analysis. We show that network structures can also be recovered from memory that is persisted onto a mass storage medium during the course of system swapping or hibernation. We present our network carving techniques, algorithms and tools, and validate these against both purpose-built memory images and a readily available forensic corpora. These techniques are valuable to both forensics tasks, particularly in analyzing mobile devices, and to cyber-security objectives such as malware analysis. Published by Elsevier Ltd.

Question: How to carve network packets?


Content-based retrieval of logo and trademarks in unconstrained color image databases using Color Edge Gradient Co-occurrence Histograms. In this paper, we present an algorithm that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme on compound color objects, for the retrieval of logos and trademarks in unconstrained color image databases. We introduce more accurate information to the CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, as compared to the simple color pixel difference classification of edges seen with the CECH. Our proposed method is thus reliant on edge gradient information, and so we call it the Color Edge Gradient Co-occurrence Histogram (CEGCH). We also introduce a color quantization method based in the hue–saturation–value (HSV) color space, illustrating that it is a more suitable scheme of quantization for image retrieval, compared to the color quantization scheme introduced with the CECH. Experimental results demonstrate that the CEGCH and the HSV color quantization scheme is insensitive to scaling, rotation, and partial deformations, and outperforms the use of the CECH in image retrieval, with higher precision and recall. We also perform experiments on a closely related algorithm based on the Color Co-occurrence Histogram (CCH) and demonstrate that our algorithm is also superior in comparison, with higher precision and recall. 2009 Elsevier Inc. All rights reserved.

Question: What is the difference between the color edge gradient co-occurrence histogram and the color co-occurrence histogram?


An Algorithm for image stitching and blending. In many clinical studies, including those of cancer, it is highly desirable to acquire images of whole tumour sections whilst retaining a microscopic resolution. A usual approach to this is to create a composite image by appropriately overlapping individual images acquired at high magnification under a microscope. A mosaic of these images can be accurately formed by applying image registration, overlap removal and blending techniques. We describe an optimised, automated, fast and reliable method for both image joining and blending. These algorithms can be applied to most types of light microscopy imaging. Examples from histology, from in vivo vascular imaging and from fluorescence applications are shown, both in 2D and 3D. The algorithms are robust to the varying image overlap of a manually moved stage, though examples of composite images acquired both with manually-driven and computer-controlled stages are presented. The overlap-removal algorithm is based on the cross-correlation method; this is used to determine and select the best correlation point between any new image and the previous composite image. A complementary image blending algorithm, based on a gradient method, is used to eliminate sharp intensity changes at the image joins, thus gradually blending one image onto the adjacent 'composite'. The details of the algorithm to overcome both intensity discrepancies and geometric misalignments between the stitched images will be presented and illustrated with several examples.

Question: How to stitch images?


{31 () Unifying Instance-based and Rule-based Induction Pedro Domingos. Several well-developed approaches to inductive learning now exist, but each has speci c limitations that are hard to overcome. Multi-strategy learning attempts to tackle this problem by combining multiple methods in one algorithm. This article describes a uni cation of two widely-used empirical approaches: rule induction and instance-based learning. In the new algorithm, instances are treated as maximally speci c rules, and classi cation is performed using a best-match strategy. Rules are learned by gradually generalizing instances until no improvement in apparent accuracy is obtained. Theoretical analysis shows this approach to be e cient. It is implemented in the RISE 3.1 system. In an extensive empirical study, RISE consistently achieves higher accuracies than state-of-the-art representatives of both its parent approaches (PEBLS and CN2), as well as a decision tree learner (C4.5). Lesion studies show that each of RISE's components is essential to this performance. Most signi cantly, in 14 of the 30 domains studied, RISE is more accurate than the best of PEBLS and CN2, showing that a signi cant synergy can be obtained by combining multiple empirical methods.

Question: What is the difference between instance-based learning and rule-based induction?


A generic abstract syntax model for embedded languages. Representing a syntax tree using a data type often involves having many similar-looking constructors. Functions operating on such types often end up having many similar-looking cases. Different languages often make use of similar-looking constructions. We propose a generic model of abstract syntax trees capable of representing a wide range of typed languages. Syntactic constructs can be composed in a modular fashion enabling reuse of abstract syntax and syntactic processing within and across languages. Building on previous methods of encoding extensible data types in Haskell, our model is a pragmatic solution to Wadler's "expression problem". Its practicality has been confirmed by its use in the implementation of the embedded language Feldspar.

Question: What is a generic abstract syntax model for embedded languages?


Characterization of silicone rubber based soft pneumatic actuators. Conventional pneumatic actuators have been a popular choice due to their decent force/torque output. Nowadays, new generation of pneumatic actuator made out of highly compliant elastomers, which we call soft pneumatic actuators (SPA), are drawing increasing attention due to their ease of fabrication, high customizability and innately softness. However, there is no effective method presented to characterize and understand these actuators, such as to measure the force and torque output, range of motion and the speed of actuation. In this work, we present two types of SPAs: bending and rotary actuators. In addition, we have developed two measurement setups to characterize actuators of different geometries. The measured force/torque outputs of different actuators are presented and analyzed. Step responses to certain pressure input are presented and discussed. A simple model is presented to provide physical insight to the observed behavior of the soft actuators. This work provides the basis for designing customized SPAs with application-specific requirements.

Question: What is a soft pneumatic actuator?


Question Type Classification Using a Part-of-Speech Hierarchy. Question type (or answer type) classification is the task of determining the correct type of the answer expected to a given query. This is often done by defining or discovering syntactic patterns that represent the structure of typical queries of each type, and classify a given query according to which pattern they satisfy. In this paper, we combine the idea of using informer spans as patterns with our own part-of-speech hierarchy in order to propose both a new approach to pattern-based question type classification and a new way of discovering the informers to be used as patterns. We show experimentally that using our part-of-speech hierarchy greatly improves type classification results, and allows our system to learn valid new informers.

Question: What is the part-of-speech hierarchy?


Visual exploration and evaluation of climate-related simulation data. Large, heterogeneous volumes of simulation data are calculated and stored in many disciplines, e.g. in climate and climate impact research. To gain insight, current climate analysis applies statistical methods and model sensitivity analyzes in combination with standard visualization techniques. However, there are some obstacles for researchers in applying the full functionality of sophisticated visualization, exploiting the available interaction and visualization functionality in order to go beyond data presentation tasks. In particular, there is a gap between available and actually applied multi-variate visualization techniques. Furthermore, visual data comparison of simulation (and measured) data is still a challenging task. Consequently, this paper introduces a library of visualization techniques, tailored to support exploration and evaluation of climate simulation data. These techniques are integrated into the easy-to-use visualization framework SimEnvVis - designed as a front-end user interface to a simulation environment - which provides a high level of user support generating visual representations.

Question: What is the size of the climate simulation data?


A study on intelligent personalized push notification with user history. In mobile application, the push notification is an important function to make users consume an item (eg. movie, novel). If a mobile application sends too many notifications to users without considering their preferences, it can cause them to turn off the notification or even deleting the mobile application. Thus, we develop a framework that selects a set of users who are likely to react to the push notification. To the best of our knowledge, we first apply recommendation techniques (i.e., collaborative filtering and content-based models) to the problem of discovering the target of push notification.

Question: What is the target of push notification?


Mobile monitoring with wearable photoplethysmographic biosensors.. earable biosensors (WBS) will permit continuous cardiovascular (CV) monitoring in a number of novel settings. Benefits may be realized in the diagnosis and treatment of a number of major diseases. WBS, in conjunction with appropriate alarm algorithms, can increase surveillance capabilities for CV catastrophe for high-risk subjects. WBS could also play a role in the treatment of chronic diseases, by providing information that enables precise titration of therapy or detecting lapses in patient compliance. WBS could play an important role in the wireless surveillance of people during hazardous operations (military, fire-fighting, etc.), or such sensors could be dispensed during a mass civilian casualty occurrence. Given that CV physio-logic parameters make up the " vital signs " that are the most important information in emergency medical situations, WBS might enable a wireless monitoring system for large numbers of at-risk subjects. This same approach may also have utility in monitoring the waiting room of today's overcrowded emergency departments. For hospital inpatients who require CV monitoring, current biosensor technology typically tethers patients in a tangle of cables, whereas wearable CV sensors could increase inpatient comfort and may even reduce the risk of tripping and falling, a perennial problem for hospital patients who are ill, medicated, and in an unfamiliar setting. On a daily basis, wearable CV sensors could detect a missed dose of medication by sensing untreated elevated blood pressure and could trigger an automated reminder for the patient to take the medication. Moreover, it is important for doctors to titrate the treatment of high blood pressure, since both insufficient therapy as well as excessive therapy (leading to abnormally low blood pressures) increase mortality. However, healthcare providers have only intermittent values of blood pressure on which to base therapy decisions;

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


Historical vignettes of the thyroid gland.. Although "glands" in the neck corresponding to the thyroid were known for thousands of years, they were mainly considered pathological when encountered. Recognition of the thyroid gland as an anatomical and physiological entity required human dissection, which began in earnest in the 16th century. Leonardo Da Vinci is generally credited as the first to draw the thyroid gland as an anatomical organ. The drawings were subsequently "lost" to medicine for nearly 260 years. The drawings were probably of a nonhuman specimen. Da Vinci vowed to produce an anatomical atlas, but it was never completed. Michelangelo Buonarroti promised to complete drawings for the anatomical work of Realdus Columbus, De Re Anatomica, but these were also never completed. Andreas Vesalius established the thyroid gland as an anatomical organ with his description and drawings in the Fabrica. The thyroid was still depicted in a nonhuman form during this time. The copper etchings of Bartholomew Eustachius made in the 1560s were obviously of humans, but were not actually published until 1714 with a description by Johannes Maria Lancisius. These etchings also depicted some interesting anatomy, which we describe. The Adenographia by Thomas Wharton in 1656 named the thyroid gland for the first time and more fully described it. The book also attempted to assign a function to the gland. The thyroid gland's interesting history thus touches a number of famous men from diverse backgrounds.

Question: What is the history of the thyroid gland?


Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams. Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention. Three technical challenges are central to this problem: (1) identification of salient medical keywords in (noisy) tweets, (2) mapping drug-effect relationships, and (3) classification of such relationships as adverse or non-adverse. We use a bipartite graph-theoretic representation called a drug-effect graph (DEG) for modeling drug and side effect relationships by representing the drugs and side effects as vertices. We construct individual DEGs on two data sources. The first DEG is constructed from the drug-effect relationships found in FDA package inserts as recorded in the SIDER database. The second DEG is constructed by mining the history of Twitter users. We use dictionary-based information extraction to identify medically-relevant concepts in tweets. Drugs, along with co-occurring symptoms are connected with edges weighted by temporal distance and frequency. Finally, information from the SIDER DEG is integrate with the Twitter DEG and edges are classified as either adverse or non-adverse using supervised machine learning. We examine both graph-theoretic and semantic features for the classification task. The proposed approach can identify adverse drug effects with high accuracy with precision exceeding 85 % and F1 exceeding 81 %. When compared with leading methods at the state-of-the-art, which employ un-enriched graph-theoretic analysis alone, our method lea

Question: What is the difference between a bipartite graph and a drug-effect graph?


Home, habits, and energy: examining domestic interactions and energy consumption. This paper presents findings from a qualitative study of people's everyday interactions with energy-consuming products and systems in the home. Initial results from a large online survey are also considered. This research focuses not only on "conservation behavior" but importantly investigates interactions with technology that may be characterized as "normal consumption" or "over-consumption." A novel vocabulary for analyzing and designing energy-conserving interactions is proposed based on our findings, including: cutting, trimming, switching, upgrading, and shifting. Using the proposed vocabulary, and informed by theoretical developments from various literatures, this paper demonstrates ways in which everyday interactions with technology in the home are performed without conscious consideration of energy consumption but rather are unconscious, habitual, and irrational. Implications for the design of energy-conserving interactions with technology and broader challenges for HCI research are proposed.

Question: What is the difference between "normal consumption" and "over-consumption"?


Sentiment-specific word embedding for Indonesian sentiment analysis. Word embedding has been proven to improve performance of sentiment classification systems. A new proposed word embedding called sentiment-specific word embedding exploits sentiment information, in order to reach better performance of sentiment analysis systems using word embedding. In this paper, we build Indonesian sentiment-specific word embedding and apply it for sentiment analysis. We compare performance of sentiment classification based on sentiment-specific word embedding with other popular feature representations such as bag of words, Term Frequency-Inverse Document Frequency (TF-IDF), and also generic word embedding. Although our sentiment-specific word embedding models achieve a higher score than the Word2Vec embedding, the lexical representations, bag of words and TF-IDF still gained better performance.

Question: What is sentiment-specific word embedding?


A Provably-Secure ECC-Based Authentication Scheme for Wireless Sensor Networks. A smart-card-based user authentication scheme for wireless sensor networks (in short, a SUA-WSN scheme) is designed to restrict access to the sensor data only to users who are in possession of both a smart card and the corresponding password. While a significant number of SUA-WSN schemes have been suggested in recent years, their intended security properties lack formal definitions and proofs in a widely-accepted model. One consequence is that SUA-WSN schemes insecure against various attacks have proliferated. In this paper, we devise a security model for the analysis of SUA-WSN schemes by extending the widely-accepted model of Bellare, Pointcheval and Rogaway (2000). Our model provides formal definitions of authenticated key exchange and user anonymity while capturing side-channel attacks, as well as other common attacks. We also propose a new SUA-WSN scheme based on elliptic curve cryptography (ECC), and prove its security properties in our extended model. To the best of our knowledge, our proposed scheme is the first SUA-WSN scheme that provably achieves both authenticated key exchange and user anonymity. Our scheme is also computationally competitive with other ECC-based (non-provably secure) schemes.

Question: What is a provably secure ECC-based authentication scheme for wireless sensor networks?


Continuous Glucose Monitoring Systems: A Review.. There have been continuous advances in the field of glucose monitoring during the last four decades, which have led to the development of highly evolved blood glucose meters, non-invasive glucose monitoring (NGM) devices and continuous glucose monitoring systems (CGMS). Glucose monitoring is an integral part of diabetes management, and the maintenance of physiological blood glucose concentration is the only way for a diabetic to avoid life-threatening diabetic complications. CGMS have led to tremendous improvements in diabetic management, as shown by the significant lowering of glycated hemoglobin (HbA1c) in adults with type I diabetes. Most of the CGMS have been minimally-invasive, although the more recent ones are based on NGM techniques. This manuscript reviews the advances in CGMS for diabetes management along with the future prospects and the challenges involved.

Question: What is the future of CGMS?


Simurg: An Extendable Multilingual Corpus for Abstractive Single Document Summarization. Abstractive single document summarization is considered as a challenging problem in the field of artificial intelligence and natural language processing. Meanwhile and specifically in the last two years, several deep learning summarization approaches were proposed that once again attracted the attention of researchers to this field.  It is a well-known issue that deep learning approaches do not work well with small amounts of data. With some exceptions, this is, unfortunately, the case for most of the datasets available for the summarization task. Besides this problem, it should be considered that phonetic, morphological, semantic and syntactic features of the language are constantly changing over the time and unfortunately most of the summarization corpora are constructed from old resources. Another problem is the language of the corpora. Not only in the summarization field, but also in other fields of natural language processing, most of the corpora are only available in English. In addition to the above problems, license terms, and fees of the corpora are obstacles that prevent many academics and specifically non-academics from accessing these data.  This work describes an open source framework to create an extendable multilingual corpus for abstractive single document summarization that addresses the above-mentioned problems. We describe a tool consisted of a scalable crawler and a centralized key-value store database to construct a corpus of an arbitrary size using a news aggregator service.

Question: What is an extendable multilingual corpus for abstractive single document summarization?


A Method for Obtaining Digital Signatures and Public-Key Cryptosystems. An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences: (1) Couriers or other secure means are not needed to transmit keys, since a message can be enciphered using an encryption key publicly revealed by the intented recipient. Only he can decipher the message, since only he knows the corresponding decryption key. (2) A message can be "signed" using a privately held decryption key. Anyone can verify this signature using the corresponding publicly revealed encryption key. Signatures cannot be forged, and a signer cannot later deny the validity of his signature. This has obvious applications in "electronic mail" and "electronic funds transfer" systems. A message is encrypted by representing it as a number M, raising M to a publicly specified power e, and then taking the remainder when the result is divided by the publicly specified product, n, of two large secret primer numbers p and q. Decryption is similar; only a different, secret, power d is used, where e * d ≡ 1(mod (p - 1) * (q - 1)). The security of the system rests in part on the difficulty of factoring the published divisor, n.

Question: What is the security of this system?


A multiscale retinex for bridging the gap between color images and the human observation of scenes. Direct observation and recorded color images of the same scenes are often strikingly different because human visual perception computes the conscious representation with vivid color and detail in shadows, and with resistance to spectral shifts in the scene illuminant. A computation for color images that approaches fidelity to scene observation must combine dynamic range compression, color consistency-a computational analog for human vision color constancy-and color and lightness tonal rendition. In this paper, we extend a previously designed single-scale center/surround retinex to a multiscale version that achieves simultaneous dynamic range compression/color consistency/lightness rendition. This extension fails to produce good color rendition for a class of images that contain violations of the gray-world assumption implicit to the theoretical foundation of the retinex. Therefore, we define a method of color restoration that corrects for this deficiency at the cost of a modest dilution in color consistency. Extensive testing of the multiscale retinex with color restoration on several test scenes and over a hundred images did not reveal any pathological behaviour.

Question: What is the multiscale retinex?


Using Features From Pre-trained TimeNET For Clinical Predictions. Predictive models based on Recurrent Neural Networks (RNNs) for clinical time series have been successfully used for various tasks such as phenotyping, in-hospital mortality prediction, and diagnostics. However, RNNs require large labeled data for training and are computationally expensive to train. Pre-training a network for some supervised or unsupervised tasks on a dataset, and then fine-tuning via transfer learning for a related end-task can be an efficient way to leverage deep models for scenarios that lack in either computational resources or labeled data, or both. In this work, we consider an approach to leverage a deep RNN – namely TimeNet [Malhotra et al., 2017] – that is pre-trained on a large number of diverse publicly available time-series from UCR Repository [Chen et al., 2015]. TimeNet maps varyinglength time series to fixed-dimensional feature vectors and acts as an off-the-shelf feature extractor. TimeNet-based approach overcome the need for hand-crafted features, and allows for use of traditional easy-to-train and interpretable linear models for the end-task, while still leveraging the features from a deep neural network. Empirical evaluation of the proposed approach on MIMIC-III1 data suggests promising direction for future exploration: our results are comparable to existing benchmarks while our models require lesser training and hyperparameter tuning effort.

Question: What is the difference between a deep neural network and a recurrent neural network?


Tree-Based Batch Mode Reinforcement Learning. Reinforcement learning aims to determine an optimal contro l policy from interaction with a system or from observations gathered from a system. In batch mode, i t can be achieved by approximating the so-calledQ-function based on a set of four-tuples (xt ,ut , rt ,xt+1) wherext denotes the system state at timet, ut the control action taken, rt the instantaneous reward obtained and xt+1 the successor state of the system, and by determining the contro l policy from this Q-function. The Q-function approximation may be obtained from the limit of a s equence of (batch mode) supervised learning problems. Within this framework we describe the use of several classical tree-based supervised learning methods (CART, Kd-tree, tree bagging) a d two newly proposed ensemble algorithms, namelyextremelyandtotally randomized trees. We study their performances on several examples and find that the ensemble methods based on regressi on trees perform well in extracting relevant information about the optimal control policy from sets of four-tuples. In particular, the totally randomized trees give good results while ensuring the convergence of the sequence, whereas by relaxing the convergence constraint even better accurac y results are provided by the extremely randomized trees.

Question: What is tree-based reinforcement learning?