Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions. This paper presents a novel traffic classification scheme to improve classification performance when few training data are available. In the proposed scheme, traffic flows are described using the discretized statistical features and flow correlation information is modeled by bag-of-flow (BoF). We solve the BoF-based traffic classification in a classifier combination framework and theoretically analyze the performance benefit. Furthermore, a new BoF-based traffic classification method is proposed to aggregate the naive Bayes (NB) predictions of the correlated flows. We also present an analysis on prediction error sensitivity of the aggregation strategies. Finally, a large number of experiments are carried out on two large-scale real-world traffic datasets to evaluate the proposed scheme. The experimental results show that the proposed scheme can achieve much better classification performance than existing state-of-the-art traffic classification methods.

Question: How to classify traffic?


Primate frontal eye fields. II. Physiological and anatomical correlates of electrically evoked eye movements.. We studied single neurons in the frontal eye fields of awake macaque monkeys and compared their activity with the saccadic eye movements elicited by microstimulation at the sites of these neurons. Saccades could be elicited from electrical stimulation in the cortical gray matter of the frontal eye fields with currents as small as 10 microA. Low thresholds for eliciting saccades were found at the sites of cells with presaccadic activity. Presaccadic neurons classified as visuomovement or movement were most associated with low (less than 50 microA) thresholds. High thresholds (greater than 100 microA) or no elicited saccades were associated with other classes of frontal eye field neurons, including neurons responding only after saccades and presaccadic neurons, classified as purely visual. Throughout the frontal eye fields, the optimal saccade for eliciting presaccadic neural activity at a given recording site predicted both the direction and amplitude of the saccades that were evoked by microstimulation at that site. In contrast, the movement fields of postsaccadic cells were usually different from the saccades evoked by stimulation at the sites of such cells. We defined the low-threshold frontal eye fields as cortex yielding saccades with stimulation currents less than or equal to 50 microA. It lies along the posterior portion of the arcuate sulcus and is largely contained in the anterior bank of that sulcus. It is smaller than Brodmann's area 8 but corresponds with the union of Walker's cytoarchitectonic areas 8A and 45. Saccade amplitude was topographically organized across the frontal eye fields. Amplitudes of elicited saccades ranged from less than 1 degree to greater than 30 degrees. Smaller saccades were evoked from the ventrolateral portion, and larger saccades were evoked from the dorsomedial portion. Within the arcuate sulcus, evoked saccades were

Question: What is the size of the canadian military?


Multi-Prediction Deep Boltzmann Machines. We introduce the multi-prediction deep Boltzmann machine (MP-DBM). The MPDBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. Prior methods of training DBMs either do not perform well on classification tasks or require an initial learning pass that trains the DBM greedily, one layer at a time. The MP-DBM does not require greedy layerwise pretraining, and outperforms the standard DBM at classification, classification with missing inputs, and mean field prediction tasks.1

Question: What is a deep Boltzmann machine?


Hilbert space embeddings of conditional distributions with applications to dynamical systems. In this paper, we extend the Hilbert space embedding approach to handle conditional distributions. We derive a kernel estimate for the conditional embedding, and show its connection to ordinary embeddings. Conditional embeddings largely extend our ability to manipulate distributions in Hilbert spaces, and as an example, we derive a nonparametric method for modeling dynamical systems where the belief state of the system is maintained as a conditional embedding. Our method is very general in terms of both the domains and the types of distributions that it can handle, and we demonstrate the effectiveness of our method in various dynamical systems. We expect that conditional embeddings will have wider applications beyond modeling dynamical systems.

Question: What is a Hilbert space embedding?


PALSAR Radiometric and Geometric Calibration. This paper summarizes the results obtained from geometric and radiometric calibrations of the Phased-Array L-Band Synthetic Aperture Radar (PALSAR) on the Advanced Land Observing Satellite, which has been in space for three years. All of the imaging modes of the PALSAR, i.e., single, dual, and full polarimetric strip modes and scanning synthetic aperture radar (SCANSAR), were calibrated and validated using a total of 572 calibration points collected worldwide and distributed targets selected primarily from the Amazon forest. Through raw-data characterization, antenna-pattern estimation using the distributed target data, and polarimetric calibration using the Faraday rotation-free area in the Amazon, we performed the PALSAR radiometric and geometric calibrations and confirmed that the geometric accuracy of the strip mode is 9.7-m root mean square (rms), the geometric accuracy of SCANSAR is 70 m, and the radiometric accuracy is 0.76 dB from a corner-reflector analysis and 0.22 dB from the Amazon data analysis (standard deviation). Polarimetric calibration was successful, resulting in a VV/HH amplitude balance of 1.013 (0.0561 dB) with a standard deviation of 0.062 and a phase balance of 0.612deg with a standard deviation of 2.66deg .

Question: What is the standard deviation of the radiometric accuracy?


Kinematics and Dynamics of a New 16 DOF Humanoid Biped Robot with Active Toe Joint. Humanoid biped  robots are  typically complex in design, having numerous Degrees‐of‐Freedom (DOF) due to the ambitious goal of mimicking the human gait. The paper proposes a new architecture for a biped robot with  seven  DOF  per  each  leg  and  one  DOF corresponding  to  the  toe  joint.  Furthermore,  this work presents  close  equations  for  the  forward  and  inverse kinematics by dividing the walking gait into the Sagittal and  Frontal  planes.  This  paper  explains  the mathematical model of  the dynamics  equations  for  the legs  into  the  Sagittal  and  Frontal  planes  by  further applying the principle of Lagrangian dynamics. Finally, a control approach using a PD control law with gravity compensation  was  recurred  in  order  to  control  the desired  trajectories and  finding  the  required  torque by the  joints.  The  paper  contains  several  simulations  and numerical  examples  to  prove  the  analytical  results, using  SimMechanics  of  MATLAB  toolbox  and SolidWorks to verify the analytical results.

Question: How to control a biped robot?


Compact UWB Bandpass Filter With Ultra Narrow Notched Band. A compact ultra-wideband (UWB) bandpass filter with an ultra narrow notched band is proposed using a hybrid microstrip and coplanar waveguide (CPW) structure. The CPW detached-mode resonator (DMR) composed of a quarter-wavelength (¿/4) nonuniform CPW resonator with a short-stub and a ¿/4 single-mode CPW resonator (SMCR) can allocate three split frequencies at the lower end, middle, higher end of the UWB passband. The conventional broadside-coupled microstrip/CPW structure is introduced to improve the bandwidth enhancement around the split frequencies, which leads to good UWB operation. To avoid the interferences such as WLAN signals, the ¿/4 meander slot-line structure embedded in the DMR is employed to obtain the notched band inside the UWB passband. The design is then verified by experiment. Good passband and stopband performances are achieved. Specifically, the fabricated filter has a 10 dB notched fractional bandwidth (FBW) of 2.06% at the notched center frequency of 5.80 GHz.

Question: What is a compact UWB bandpass filter with an ultra narrow notched band?


A Fully Projective Formulation to Improve the Accuracy of Lowe's Pose-Estimation Algorithm. Both the original version of David Lowe's innuential and classic algorithm for tracking known objects and a reformulation of it implemented by Ishii et al. rely on (diierent) approximated imaging models. Removing their simplifying assumptions yields a fully pro-jective solution with signiicantly improved accuracy and convergence, and arguably better computation{ time properties.

Question: What is a fully projective formulation?


Derivatives of Regular Expressions. Kleene's regular expressions, which can be used for describing sequential circuits, were defined using three operators (union, concatenation and iterate) on sets of sequences. Word descriptions of problems can be more easily put in the regular expression language if the language is enriched by the inclusion of other logical operations. However, il~ the problem of converting the regular expression description to a state diagram, the existing methods either cannot handle expressions with additional operators, or are made quite complicated by the presence of such operators. In this paper the notion of a derivative of a regular expression is introduced atld the properties of derivatives are discussed. This leads, in a very natural way, to the construction of a state diagram from a regular expression containing any number of logical operators.

Question: What is a derivative of a regular expression?


Hexahedral Meshing With Varying Element Sizes. Hexahedral (or Hex-) meshes are preferred in a number of scientific and engineering simulations and analyses due to their desired numerical properties. Recent state-of-the-art techniques can generate high quality hex-meshes. However, they typically produce hex-meshes with uniform element sizes and thus may fail to preserve small scale features on the boundary surface. In this work, we present a new framework that enables users to generate hex-meshes with varying element sizes so that small features will be filled with smaller and denser elements, while the transition from smaller elements to larger ones is smooth, compared to the octree-based approach. This is achieved by first detecting regions of interest (ROI) of small scale features. These ROIs are then magnified using the as-rigid-as-possible (ARAP) deformation with either an automatically determined or a user-specified scale factor. A hex-mesh is then generated from the deformed mesh using existing approaches that produce hex-meshes with uniform-sized elements. This initial hex-mesh is then mapped back to the original volume before magnification to adjust the element sizes in those ROIs. We have applied this framework to a variety of man-made and natural models to demonstrate its effectiveness.

Question: How to generate hexahedral meshes with varying element sizes?


How Personal Experience and Technical Knowledge Affect Using Conversational Agents. Conversational agents (CA) use dialogues to interact with users so as to offer an experience of naturalistic interaction. However, due to the low transparency and poor explanability of mechanism inside CA, individual's understanding of CA's capabilities may affect how the individual interacts with CA and the sustainability of CA use. To examine how users' understanding affect perceptions and experiences of using CA, we conducted a laboratory study asking 41 participants performed a set of tasks using Apple Siri. We independently manipulated two factors: (1) personal experience of using CA, and (2) technical knowledge about CA's system model. We conducted mixed-method analyses of post-task usability measures and interviews, and confirmed that use experience and technical knowledge affects perceived usability and mental models differently.

Question: How personal experience and technical knowledge affect using conversational agents?


New Developments in Space Syntax Software. The Spatial Positioning tool (SPOT) is an isovist-based spatial analysis software, and is written in Java working as a stand-alone program. SPOT differs from regular Space syntax software as it can produce integration graphs and intervisibility graphs from a selection of positions. The concept of the software originates from a series of field studies on building interiors highly influenced by organizations and social groups. We have developed SPOT as a prototype. Basic SPOT operations use selections of positions and creations of isovist sets. The sets are color-coded and layered; the layers can be activated and visible by being turned on or off. At this point, there are two graphs produced in SPOT, the isovist overlap graph that shows intervisibility between overlapping isovist fields and the network integration analysis built on visibility relations. The program aims to be used as a fast and interactive sketch tool as well as a precise analysis tool. Data, images, and diagrams can be exported for use in conjunction with other CAD or illustration programs. The first stage of development is to have a functioning prototype with the implementation of all the basic algorithms and a minimal basic functionality in respect to user interaction.

Question: What is the difference between isovist and space syntax?


A Training Algorithm for Optimal Margin Classifiers. A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.

Question: What is the training algorithm for optimal margin classifiers?


Workspace characterization for concentric tube continuum robots. Concentric tube robots exhibit complex workspaces due to the way their component tubes bend and twist as they interact with one another. This paper explores ways to compute and characterize their workspaces. We use Monte Carlo random samples of the robot's joint space and a discrete volumetric workspace representation, which can describe both reachability and redundancy. Experiments on two physical prototypes are provided to illustrate the proposed approach.

Question: How do concentric tube robots work?


Fast trajectory clustering using Hashing methods. There has been an explosion in the usage of trajectory data. Clustering is one of the simplest and most powerful approaches for knowledge discovery from trajectories. In order to produce meaningful clusters, well-defined metrics are required to capture the essence of similarity between trajectories. One such distance function is Dynamic Time Warping (DTW), which aligns two trajectories together in order to determine similarity. DTW has been widely accepted as a very good distance measure for trajectory data. However, trajectory clustering is very expensive due to the complexity of the similarity functions, for example, DTW has a high computational cost O(n2), where n is the average length of the trajectory, which makes the clustering process very expensive. In this paper, we propose the use of hashing techniques based on Distance-Based Hashing (DBH) and Locality Sensitive Hashing (LSH) to produce approximate clusters and speed up the clustering process.

Question: How to cluster trajectories?


A practical path loss model for indoor WiFi positioning enhancement. Positioning within a local area refers to technology whereby each node is self-aware of its position. Based on empirical study, this paper proposes an enhancement to the path loss model in the indoor environment for improved accuracy in the relationship between distance and received signal strength. We further demonstrate the potential of our model for the WiFi positioning system, where the mean errors in the distance estimation are 2.3 m and 2.9 m for line of sight and non line of sight environments, respectively.

Question: How accurate is the path loss model?


Polygon-Invariant Generalized Hough Transform for High-Speed Vision-Based Positioning. The generalized Hough transform (GHT) is widely used for detecting or locating objects under similarity transformation. However, a weakness of the traditional GHT is its large storage requirement and time-consuming computational complexity due to the 4-D parameter space voting strategy. In this paper, a polygon-invariant GHT (PI-GHT) algorithm, as a novel scale- and rotation-invariant template matching method, is presented for high-speed object vision-based positioning. To demonstrate the performance of PI-GHT, several experiments were carried out to compare this novel algorithm with the other five popular matching methods. Experimental results show that the computational effort required by PI-GHT is smaller than that of the common methods due to the similarity transformations applied to the scale- and rotation-invariant triangle features. Moreover, the proposed PI-GHT maintains inherent robustness against partial occlusion, noise, and nonlinear illumination changes, because the local triangle features are based on the gradient directions of edge points. Consequently, PI-GHT is implemented in packaging equipment for radio frequency identification devices at an average time of 4.13 ms and 97.06% matching rate, to solder paste printing at average time nearly 5 ms with 99.87%. PI-GHT is applied to LED manufacturing equipment to locate multiobjects at least five times improvement in speed with a 96% matching rate.

Question: What is the average time required to solder paste printing?


An Improving Deception Detection Method in Computer-Mediated Communication. Online deception is disrupting our daily life, organizational process, and even national security. Existing deception detection approaches followed a traditional paradigm by using a set of cues as antecedents, and used a variety of data sets and common classification models to detect deception, which were demonstrated to be an accurate technique, but the previous results also showed the necessity to expand the deception feature set in order to improve the accuracy. In our study, we propose a novel feature selection method of the combination of CHI statistics and hypothesis testing, and achieve the accuracy level of 86% and F-measure of 0.84 by using the novel feature sets and SVM classification models, which exceeds the previous experiment results.

Question: What is the accuracy of deception detection?


3D spatial pyramid: descriptors generation from point clouds for indoor scene classification. Traditionally, the indoor scene classification problem has been approached from a 2D image recognition point of view. In most visual scene classification systems, a descriptor for the input image is generated to obtain a suitable representation that includes features related to color, shape or spatial information. Techniques based on the use of a spatial pyramid have proven to be adequate to perform this step. In the past years, on the other hand, 3D sensors have become widely available, which allows to include new information sources to the framework previously described. In this work we rely on RGB-D data to extend the spatial pyramid approach, aimed at building descriptors that can lead to a more robust representation against changing lighting conditions. The proposed descriptors are evaluated on the RobotVision@ImageCLEF-2013 benchmark dataset, remarkably outperforming state-of-the-art 3D local and global descriptors.

Question: How to build a spatial pyramid?


Using BPMN to Model a BPEL Process. The Business Process Modeling Notation (BPMN) has been developed to enable business user to develop readily understandable graphical representations of business processes. BPMN is also supported with appropriate graphical object properties that will enable the generation of executable BPEL. Thus, BPMN creates a standardized bridge for the gap between the business process design and process implementation. This paper presents a simple, yet instructive example of how a BPMN diagram can be used to generate a BPEL process.

Question: How to use BPMN to model a BPEL process?


Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks. In this paper, we propose the novel principal backpropagation networks (PBNets) to revisit the backpropagation algorithms commonly used in training two-stream networks for video action recognition. We content that existing approaches always take all the frames/snippets for the backpropagation not optimal for video recognition since the desired actions only occur in a short period within a video. To remedy these drawbacks, we design a watch-and-choose mechanism. In particular, the watching stage exploits a dense snippet-wise temporal pooling strategy to discover the global characteristic for each input video, while the choosing phase only backpropagates a small number of representative snippets that are selected with two novel strategies, i.e., Max-rule and KL-rule. We prove that with the proposed selection strategies, performing the backpropagation on the selected subset is capable of decreasing the loss of the whole snippets as well. The proposed PBNets are evaluated on two standard video action recognition benchmarks UCF101 and HMDB51, where it surpasses the state of the arts consistently, but requiring less memory and computation to achieve high performance.

Question: How to select the snippets for backpropagation?


Distributed Secret Sharing Approach With Cheater Prevention Based on QR Code. QR barcodes are used extensively due to their beneficial properties, including small tag, large data capacity, reliability, and high-speed scanning. However, the private data of the QR barcode lacks adequate security protection. In this article, we design a secret QR sharing approach to protect the private QR data with a secure and reliable distributed system. The proposed approach differs from related QR code schemes in which it uses the QR characteristics to achieve secret sharing and can resist the print-and-scan operation. The secret can be split and conveyed with QR tags in the distribution application, and the system can retrieve the lossless secret when authorized participants cooperate. General browsers can read the original data from the marked QR tag via a barcode reader, and this helps reduce the security risk of the secret. Based on our experiments, the new approach is feasible and provides content readability, cheater detectability, and an adjustable secret payload of the QR barcode.

Question: How to protect the private data of QR barcode?


Distributed Hessian-Free Optimization for Deep Neural Network. Training deep neural network is a high dimensional and a highly non-convex optimization problem. In this paper, we revisit Hessian-free optimization method for deep networks with negative curvature direction detection. We also develop its distributed variant and demonstrate superior scaling potential to SGD, which allows more efficiently utilizing larger computing resources thus enabling large models and faster time to obtain desired solution. We show that these techniques accelerate the training process for both the standard MNIST dataset and also the TIMIT speech recognition problem, demonstrating robust performance with upto an order of magnitude larger batch sizes. This increased scaling potential is illustrated with near l inear speed-up on upto 32 CPU nodes for a simple 4-layer network.

Question: How to train a deep neural network?


Instant messaging in teen life. Instant Messaging (IM) is being widely adopted by teenagers. In a study of 16 teenage IM users, we explore IM as an emerging feature of teen life, focusing our questions on its support of interpersonal communication and its role and salience in everyday life. We qualitatively describe the teens' IM use interpersonally, as well as its place in the domestic ecology. We also identify technology adoption conditions and discuss behaviors around privacy management. In this initial investigation, we found differences in the nature of use between high school and college teens, differences we propose are accounted for by teens' degree of autonomy as a function of domestic and scholastic obligations, the development of independent work practices, Internet connectivity access, and even transportation access. Moreover, while teen IM use is in part characterized as an optimizing choice between multiple communications media, practice is also tied to concerns around peer pressure, peer group membership and creating additional opportunities to socialize.

Question: What is the difference between instant messaging and texting?


On the Parameter Extraction of a Five-Parameter Double-Diode Model of Photovoltaic Cells and Modules. The main contribution of this paper is to present a new set of approximate analytical solutions for the parameters of a photovoltaic (PV) five-parameter double-diode model that can be used as initial values for the numerical solutions based on the Newton-Raphson method. The proposed formulations are developed based on only the limited information given by the PV manufacturers, i.e., the open-circuit voltage ( Voc), the short circuit current ( Isc), and the current and voltage at the maximum power point (Im and Vm). Compared with the existing techniques that require the entire experimental I-V curve or additional information such as the slope of the I-V curves of the open circuit and the short circuit points, the proposed technique is quite independent of these additional data, and, it is therefore, a low cost and fast parameter extraction method. The accuracy of the theoretical I-V curves is evaluated through the comparison of the simulation results and experimental data. The results of the application of the proposed technique to different PV modules show the accuracy and validity of the proposed analytical-numerical method.

Question: What is the main contribution of this paper?


GhostVLAD for set-based face recognition. The objective of this paper is to learn a compact representation of image sets for template-based face recognition. We make the following contributions: first, we propose a network architecture which aggregates and embeds the face descriptors produced by deep convolutional neural networks into a compact fixed-length representation. This compact representation requires minimal memory storage and enables efficient similarity computation. Second, we propose a novel GhostVLAD layer that includes ghost clusters, that do not contribute to the aggregation. We show that a quality weighting on the input faces emerges automatically such that informative images contribute more than those with low quality, and that the ghost clusters enhance the network's ability to deal with poor quality images. Third, we explore how input feature dimension, number of clusters and different training techniques affect the recognition performance. Given this analysis, we train a network that far exceeds the state-of-the-art on the IJB-B face recognition dataset. This is currently one of the most challenging public benchmarks, and we surpass the state-of-the-art on both the identification and verification protocols.

Question: How does ghostvlad work?


A review of mangrove rehabilitation in the Philippines: successes, failures and future prospects. From half a million hectares at the turn of the century, Philippine mangroves have declined to only 120,000 ha while fish/shrimp culture ponds have increased to 232,000 ha. Mangrove replanting programs have thus been popular, from community initiatives (1930s–1950s) to government-sponsored projects (1970s) to large-scale international development assistance programs (1980s to present). Planting costs escalated from less than US$100 to over $500/ha, with half of the latter amount allocated to administration, supervision and project management. Despite heavy funds for massive rehabilitation of mangrove forests over the last two decades, the long-term survival rates of mangroves are generally low at 10–20%. Poor survival can be mainly traced to two factors: inappropriate species and site selection. The favored but unsuitable Rhizophora are planted in sandy substrates of exposed coastlines instead of the natural colonizers Avicennia and Sonneratia. More significantly, planting sites are generally in the lower intertidal to subtidal zones where mangroves do not thrive rather than the optimal middle to upper intertidal levels, for a simple reason. Such ideal sites have long been converted to brackishwater fishponds whereas the former are open access areas with no ownership problems. The issue of pond ownership may be complex and difficult, but such should not outweigh ecological requirements: mangroves should be planted where fishponds are, not on seagrass beds and tidal flats where they never existed. This paper reviews eight mangrove initiatives in the Philippines and evaluates the biophysical and institutional factors behind success or failure. The authors recommend specific protocols (among them pushing for a 4:1 mangrove to pond ratio recommended for a healthy ecosystem) and wider policy directions to make mangrove rehabilitation in the country more effective.

Question: What is the difference between a mangrove and a fishpond?


Distributional Semantic Models of Attribute Meaning in Adjectives and Nouns. Attributes such as size, weight or color are at the core of conceptualization, i.e., the formal representation of entities or events in the real world. In natural language, formal attributes find their counterpart in attribute nouns which can be used in order to generalize over individual properties (e.g., big or small in case of size, blue or red in case of color). In order to ascribe such properties to entities or events, adjectivenoun phrases are a very frequent linguistic pattern (e.g., a blue shirt, a big lion). In these constructions, attribute meaning is conveyed only implicitly, i.e., without being overtly realized at the phrasal surface. This thesis is about modeling attribute meaning in adjectives and nouns in a distributional semantics framework. This implies the acquisition of meaning representations for adjectives, nouns and their phrasal combination from corpora of natural language text in an unsupervised manner, without tedious handcrafting or manual annotation efforts. These phrase representations can be used to predict implicit attribute meaning from adjective-noun phrases – a problem which will be referred to as attribute selection throughout this thesis. The approach to attribute selection proposed in this thesis is framed in structured distributional models. We model adjective and noun meanings as distinct semantic vectors in the same semantic space spanned by attributes as dimensions of meaning. Based on these word representations, we make use of vector composition operations in order to construct a phrase representation from which the most prominent attribute(s) being expressed in the compositional semantics of the adjective-noun phrase can be selected by means of an unsupervised selection function. This approach not only accounts for the linguistic principle of compositionality that underlies adjective-noun phrases, but also avoids inherent sparsity issues that result

Question: What is the distributional semantics framework?


Differentially Private K-Means Clustering. There are two broad approaches for differentially private data analysis. The interactive approach aims at developing customized differentially private algorithms for various data mining tasks. The non-interactive approach aims at developing differentially private algorithms that can output a synopsis of the input dataset, which can then be used to support various data mining tasks. In this paper we study the effectiveness of the two approaches on differentially private k-means clustering. We develop techniques to analyze the empirical error behaviors of the existing interactive and non-interactive approaches. Based on the analysis, we propose an improvement of DPLloyd which is a differentially private version of the Lloyd algorithm. We also propose a non-interactive approach EUGkM which publishes a differentially private synopsis for k-means clustering. Results from extensive and systematic experiments support our analysis and demonstrate the effectiveness of our improvement on DPLloyd and the proposed EUGkM algorithm.

Question: What is the difference between the interactive and non-interactive approaches?


Privacy-Preserving Human Activity Recognition from Extreme Low Resolution. Privacy protection from unwanted video recordings is an important societal challenge. We desire a computer vision system (e.g., a robot) that can recognize human activities and assist our daily life, yet ensure that it is not recording video that may invade our privacy. This paper presents a fundamental approach to address such contradicting objectives: human activity recognition while only using extreme low-resolution (e.g., 16x12) anonymized videos. We introduce the paradigm of inverse super resolution (ISR), the concept of learning the optimal set of image transformations to generate multiple low-resolution (LR) training videos from a single video. Our ISR learns different types of sub-pixel transformations optimized for the activity classification, allowing the classifier to best take advantage of existing high-resolution videos (e.g., YouTube videos) by creating multiple LR training videos tailored for the problem. We experimentally confirm that the paradigm of inverse super resolution is able to benefit activity recognition from extreme low-resolution videos.

Question: How to protect privacy from unwanted video recordings?


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

Question: What is the JSTOR archive?


Conformal Surface Parameterization for Texture Mapping. In this paper , we give an explicit methodfor mappingany simply connectedsurfaceonto the spherein a mannerwhich preservesangles. This techniquerelieson certainconformalmappingsfrom differentialgeometry. Our methodprovidesa new way to automaticallyassigntexturecoordinates to complex undulatingsurfaces. We demonstratea finite elementmethod thatcanbeusedto applyour mappingtechniqueto a triangulatedgeometric descriptionof a surface.

Question: How to map a surface onto the sphere?


Manufacturing Analytics and Industrial Internet of Things. Over the last two decades, manufacturing across the globe has evolved to be more intel-ligent and data driven. In the age of industrial Internet of Things, a smart production unit can be perceived as a large connected industrial system of materials, parts, machines, tools, inventory, and logistics that can relay data and communicate with each other. While, traditionally, the focus has been on machine health and predictive maintenance, the manufacturing industry has also started focusing on analyzing data from the entire production line. These applications bring a new set of analytics challenges. Unlike tradi-tional data mining analysis, which consists of lean datasets (that is, datasets with few fea-tures), manufacturing has fat datasets. In addition, previous approaches to manufacturing analytics restricted themselves to small time periods of data. The latest advances in big data analytics allows researchers to do a deep dive into years of data. Bosch collects and utilizes all available information about its products to increase its understanding of complex linear and nonlinear relationships between parts, machines, and assembly lines. This helps in use cases such as the discovery of the root cause of internal defects. This article presents a case study and provides detail about challenges and approaches in data extraction, modeling, and visualization.

Question: What is the root cause of internal defects?


Discriminative Information Retrieval for Knowledge Discovery. We propose a framework for discriminative Information Retrieval (IR) atop linguistic features, trained to improve the recall of tasks such as answer candidate passage retrieval, the initial step in text-based Question Answering (QA). We formalize this as an instance of linear feature-based IR (Metzler and Croft, 2007), illustrating how a variety of knowledge discovery tasks are captured under this approach, leading to a 44% improvement in recall for candidate

Question: What is the name of the framework for discriminative IR?


Block Chain based Searchable Symmetric Encryption. The mechanism for traditional Searchable Symmetric Encryption is pay-then-use. That is to say, if a user wants to search some documents that contain special keywords, he needs to pay to the server firstly, then he can enjoy search service. Under this situation, these kinds of things will happen: After the user paying the service fees, the server may either disappear because of the poor management or returning nothing. As a result, the money that the user paid cannot be brought back quickly. Another case is that the server may return incorrect document sets to the user in order to save his own cost. Once such events happen, it needs the arbitration institution to mediate which will cost a long time. Besides, to settle the disputes the user has to pay to the arbitration institution. Ideally, we deeply hope that when the user realizes the server has a tendency to cheat in the task of searching, he can immediately and automatically withdraw his money to safeguard his right. However, the existing SSE protocols cannot satisfy this demand. To solve this dilemma, we find a compromised method by introducing the block chain into SSE. Our scheme achieves three goals stated below. Firstly, when the server does not return any thing to user after he gets the search token, the user can get some compensation from the server, because the server can infer some important information from the Index and this token. Besides, the user also doesn't pay the service charge. Secondly, if the documents that the server returns are false, the server cannot receive service fees, meanwhile, he will be punished. Lastly, when the user receives some bitcoin from server at the beginning, he may terminate the protocol. Under this situation, the server is a victim. In order to prevent such thing from happening, the server will broadcast a transaction to redeem his pledge after an appointed time. ∗Corresponding author Email addresses: tianhb@mail.sysu.ed

Question: What is the difference between block chain and blockchain?


Design considerations on low voltage synchronous power MOSFETs with monolithically integrated gate voltage pull-down circuitry. In this paper, a monolithically integrated gate voltage pull-down circuitry is presented to avoid the unintentional C·dV/dt induced turn-on. The concept of a low threshold voltage MOSFET with this integrated gate voltage pull-down circuitry is introduced as a contributing factor to the next generation high frequency DC-DC converter efficiency improvement. Design considerations on this new device and influences of critical design parameters on device/circuit performance will be fully discussed. In synchronous buck application, this integrated power module achieves more than 2% efficiency improvement over reference solution at high operation frequency (1MHz) under 19V input and 1.3V output condition.

Question: What is the purpose of this paper?


Organic food: buying more safety or just peace of mind? A critical review of the literature.. Consumer concern over the quality and safety of conventional food has intensified in recent years, and primarily drives the increasing demand for organically grown food, which is perceived as healthier and safer. Relevant scientific evidence, however, is scarce, while anecdotal reports abound. Although there is an urgent need for information related to health benefits and/or hazards of food products of both origins, generalized conclusions remain tentative in the absence of adequate comparative data. Organic fruits and vegetables can be expected to contain fewer agrochemical residues than conventionally grown alternatives; yet, the significance of this difference is questionable, inasmuch as actual levels of contamination in both types of food are generally well below acceptable limits. Also, some leafy, root, and tuber organic vegetables appear to have lower nitrate content compared with conventional ones, but whether or not dietary nitrate indeed constitutes a threat to human health is a matter of debate. On the other hand, no differences can be identified for environmental contaminants (e.g. cadmium and other heavy metals), which are likely to be present in food from both origins. With respect to other food hazards, such as endogenous plant toxins, biological pesticides and pathogenic microorganisms, available evidence is extremely limited preventing generalized statements. Also, results for mycotoxin contamination in cereal crops are variable and inconclusive; hence, no clear picture emerges. It is difficult, therefore, to weigh the risks, but what should be made clear is that 'organic' does not automatically equal 'safe.' Additional studies in this area of research are warranted. At our present state of knowledge, other factors rather than safety aspects seem to speak in favor of organic food.

Question: What is the difference between organic and conventional food?


A taxonomy of argumentation models used for knowledge representation. Understanding argumentation and its role in human reasoning has been a continuous subject of investigation for scholars from the ancient Greek philosophers to current researchers in philosophy, logic and artificial intelligence. In recent years, argumentation models have been used in different areas such as knowledge representation, explanation, proof elaboration, commonsense reasoning, logic programming, legal reasoning, decision making, and negotiation. However, these models address quite specific needs and there is need for a conceptual framework that would organize and compare existing argumentation-based models and methods. Such a framework would be very useful especially for researchers and practitioners who want to select appropriate argumentation models or techniques to be incorporated in new software systems with argumentation capabilities. In this paper, we propose such a conceptual framework, based on taxonomy of the most important argumentation models, approaches and systems found in the literature. This framework highlights the similarities and differences between these argumentation models. As an illustration of the practical use of this framework, we present a case study which shows how we used this framework to select and enrich an argumentation model in a knowledge acquisition project which aimed at representing argumentative knowledge contained in texts critiquing military courses of action.

Question: What is the difference between argumentation models?


Enhancing the trust-based recommendation process with explicit distrust. When a Web application with a built-in recommender offers a social networking component which enables its users to form a trust network, it can generate more personalized recommendations by combining user ratings with information from the trust network. These are the so-called trust-enhanced recommendation systems. While research on the incorporation of trust for recommendations is thriving, the potential of explicitly stated distrust remains almost unexplored. In this article, we introduce a distrust-enhanced recommendation algorithm which has its roots in Golbeck's trust-based weighted mean. Through experiments on a set of reviews from Epinions.com, we show that our new algorithm outperforms its standard trust-only counterpart with respect to accuracy, thereby demonstrating the positive effect that explicit distrust can have on trust-based recommendations.

Question: How to incorporate distrust into a recommender system?


A design and development of an intelligent jammer and jamming detection methodologies using machine learning approach. Nowadays, the utilization of mobile phones has increased rapidly. Due to this evolution schools, mosques, prisons, etc., require silence and security. This is achieved by using the mobile phone jammers. An intelligent mobile jammer is designed for allowing only the emergency calls by utilizing the microcontroller. Here, the jammer utilizes the successive approximation for reducing the transmission power. However, it requires few modifications. Therefore in this paper, an improved successive approximation based on divide and conquer algorithm is proposed in order to design the improved intelligent mobile jammer and reduce transmission power. Subsequently, the proposed jammer is analysed based on the different scenarios and frequency bands to illustrate their performance effectiveness. Furthermore, the normal activities are distinguished from jamming by using machine learning-based detection system according to the various parameters. Finally, the proposed algorithm is compared with conventional algorithms to demonstrate its performance efficiency in terms of detection accuracy.

Question: What is the difference between a jammer and a blocker?


Application of Gradient Boosting through SAS Enterprise Miner TM 12 . 3 to Classify Human Activities. Using smart clothing with wearable medical sensors integrated to keep track of human health is now attracting many researchers. However, body movement caused by daily human activities inserts artificial noise into physiological data signals, which affects the output of a health monitoring/alert system. To overcome this problem, recognizing human activities, determining relationship between activities and physiological signals, and removing noise from the collected signals are essential steps. This paper focuses on the first step, which is human activity recognition. Our research shows that no other study used SAS® for classifying human activities. For this study, two data sets were collected from an open repository. Both data sets have 561 input variables and one nominal target variable with four levels. Principal component analysis along with other variable reduction and selection techniques were applied to reduce dimensionality in the input space. Several modeling techniques with different optimization parameters were used to classify human activity. The gradient boosting model was selected as the best model based on a test misclassification rate of 0.1233. That is, 87.67% of total events were classified correctly.

Question: How to classify human activities using SAS?


Scene Text Synthesis for Efficient and Effective Deep Network Training. A large amount of annotated training images is critical for training accurate and robust deep network models but the collection of a large amount of annotated training images is often time-consuming and costly. Image synthesis alleviates this constraint by generating annotated training images automatically by machines which has attracted increasing interest in the recent deep learning research. We develop an innovative image synthesis technique that composes annotated training images by realistically embedding foreground objects of interest (OOI) into background images. The proposed technique consists of two key components that in principle boost the usefulness of the synthesized images in deep network training. The first is context-aware semantic coherence which ensures that the OOI are placed around semantically coherent regions within the background image. The second is harmonious appearance adaptation which ensures that the embedded OOI are agreeable to the surrounding background from both geometry alignment and appearance realism. The proposed technique has been evaluated over two related but very different computer vision challenges, namely, scene text detection and scene text recognition. Experiments over a number of public datasets demonstrate the effectiveness of our proposed image synthesis technique the use of our synthesized images in deep network training is capable of achieving similar or even better scene text detection and scene text recognition performance as compared with using real images.

Question: How to synthesize images for deep learning?


Detection of falls using accelerometers and mobile phone technology.. OBJECTIVES to study the sensitivity and specificity of fall detection using mobile phone technology.   DESIGN an experimental investigation using motion signals detected by the mobile phone.   SETTING AND PARTICIPANTS the research was conducted in a laboratory setting, and 18 healthy adults (12 males and 6 females; age = 29 ± 8.7 years) were recruited.   MEASUREMENT each participant was requested to perform three trials of four different types of simulated falls (forwards, backwards, lateral left and lateral right) and eight other everyday activities (sit-to-stand, stand-to-sit, level walking, walking up- and downstairs, answering the phone, picking up an object and getting up from supine). Acceleration was measured using two devices, a mobile phone and an independent accelerometer attached to the waist of the participants.   RESULTS Bland-Altman analysis shows a higher degree of agreement between the data recorded by the two devices. Using individual upper and lower detection thresholds, the specificity and sensitivity for mobile phone were 0.81 and 0.77, respectively, and for external accelerometer they were 0.82 and 0.96, respectively.   CONCLUSION fall detection using a mobile phone is a feasible and highly attractive technology for older adults, especially those living alone. It may be best achieved with an accelerometer attached to the waist, which transmits signals wirelessly to a phone.

Question: How do you detect falls using a mobile phone?


Chinese Word Segmentation and Named Entity Recognition Based on Conditional Random Fields. Chinese word segmentation (CWS), named entity recognition (NER) and part-ofspeech tagging is the lexical processing in Chinese language. This paper describes the work on these tasks done by France Telecom Team (Beijing) at the fourth International Chinese Language Processing Bakeoff. In particular, we employ Conditional Random Fields with different features for these tasks. In order to improve NER relatively low recall; we exploit non-local features and alleviate class imbalanced distribution on NER dataset to enhance the recall and keep its relatively high precision. Some other post-processing measures such as consistency checking and transformation-based error-driven learning are used to improve word segmentation performance. Our systems participated in most CWS and POS tagging evaluations and all the NER tracks. As a result, our NER system achieves the first ranks on MSRA open track and MSRA/CityU closed track. Our CWS system achieves the first rank on CityU open track, which means that our systems achieve state-of-the-art performance on Chinese lexical processing.

Question: What is the first rank on MSRA open track?


On Maximizing Sensor Network Lifetime by Energy Balancing. Many physical systems, such as water/electricity distribution networks, are monitored by battery-powered wireless-sensor networks (WSNs). Since battery replacement of sensor nodes is generally difficult, long-term monitoring can be only achieved if the operation of the WSN nodes contributes to long WSN lifetime. Two prominent techniques to long WSN lifetime are 1) optimal sensor activation and 2) efficient data gathering and forwarding based on compressive sensing. These techniques are feasible only if the activated sensor nodes establish a connected communication network (connectivity constraint), and satisfy a compressive sensing decoding constraint (cardinality constraint). These two constraints make the problem of maximizing network lifetime via sensor node activation and compressive sensing NP-hard. To overcome this difficulty, an alternative approach that iteratively solves energy balancing problems is proposed. However, understanding whether maximizing network lifetime and energy balancing problems are aligned objectives is a fundamental open issue. The analysis reveals that the two optimization problems give different solutions, but the difference between the lifetime achieved by the energy balancing approach and the maximum lifetime is small when the initial energy at sensor nodes is significantly larger than the energy consumed for a single transmission. The lifetime achieved by energy balancing is asymptotically optimal, and that the achievable network lifetime is at least 50% of the optimum. Analysis and numerical simulations quantify the efficiency of the proposed energy balancing approach.

Question: How to maximize sensor network lifetime?


Graphical Models and Belief Propagation-hierarchy for Optimal Physics-Constrained Network Flows. In this manuscript we review new ideas and first results on application of the Graphical Models approach, originated from Statistical Physics, Information Theory, Computer Science and Machine Learning, to optimization problems of network flow type with additional constraints related to the physics of the flow. We illustrate the general concepts on a number of enabling examples from power system and natural gas transmission (continental scale) and distribution (district scale) systems. 1.1 Introductory remarks In this chapter we discuss optimization problems which appears naturally in the classical settings describing flows over networks constrained by the physical nature Michael Chertkov Theoretical Division, T-4 & CNLS, Los Alamos National Laboratory Los Alamos, NM 87545, USA and Energy System Center, Skoltech, Moscow, 143026, Russia, e-mail: chertkov@lanl. gov Sidhant Misra Theoretical Division, T-5, Los Alamos National Laboratory Los Alamos, NM 87545, USA, e-mail: sidhant@lanl.gov Marc Vuffray Theoretical Division, T-4, Los Alamos National Laboratory Los Alamos, NM 87545, USA, e-mail: sidhant@lanl.gov Krishnamurthy Dvijotham Pacific Northwest National Laboratory, PO Box 999, Richland, WA 99352, USA e-mail: krishnamurthy.dvijotham@pnnl.gov Pascal Van Hentenryck University of Michigan, Department of Industrial & Operations Engineering Ann Arbor, MI 48109, USA, e-mail: pvanhent@umich.edu 1 ar X iv :1 70 2. 01 89 0v 1 [ cs .S Y ] 7 F eb 2 01 7 2 M. Chertkov, S. Misra, M. Vuffray, D. Krishnamurthy, and P. Van Hentenryck of the flows which appear in the context of electric power systems, see e.g. [27, 44], and natural gas application, see e.g. [13] and references there in. Other examples of physical flows where similar optimization problem arise include pipe-flow systems, such as district heating [75, 1] and water [54], as well as traffic systems [40]. We aim to show that t

Question: What is the difference between the graphical models approach and the belief propagation approach?


A novel approach to CAD system for the detection of lung nodules in CT images. Detection of pulmonary nodule plays a significant role in the diagnosis of lung cancer in early stage that improves the chances of survival of an individual. In this paper, a computer aided nodule detection method is proposed for the segmentation and detection of challenging nodules like juxtavascular and juxtapleural nodules. Lungs are segmented from computed tomography (CT) images using intensity thresholding; brief analysis of CT image histogram is done to select a suitable threshold value for better segmentation results. Simple morphological closing is used to include juxtapleural nodules in segmented lung regions. K-means clustering is applied for the initial detection and segmentation of potential nodules; shape specific morphological opening is implemented to refine segmentation outcomes. These segmented potential nodules are then divided into six groups on the basis of their thickness and percentage connectivity with lung walls. Grouping not only helped in improving system's efficiency but also reduced computational time, otherwise consumed in calculating and analyzing unnecessary features for all nodules. Different sets of 2D and 3D features are extracted from nodules in each group to eliminate false positives. Small size nodules are differentiated from false positives (FPs) on the basis of their salient features; sensitivity of the system for small nodules is 83.33%. SVM classifier is used for the classification of large nodules, for which the sensitivity of the proposed system is 93.8% applying 10-fold cross-validation. Receiver Operating Characteristic (ROC) curve is used for the analysis of CAD system. Overall sensitivity of the system is 91.65% with 3.19 FPs per case, and accuracy is 96.22%. The system took 3.8 seconds to analyze each image.

Question: What is a CAD system?


Fast Compact City Modeling for Navigation Pre-Visualization. Nowadays, GPS-based car navigation systems mainly use speech and aerial views of simplified road maps to guide drivers to their destination. However, drivers often experience difficulties in linking the simple 2D aerial map with the visual impression that they get from the real environment, which is inherently ground-level based. Therefore, supplying realistically textured 3D city models at ground-level proves very useful for pre-visualizing an upcoming traffic situation. Because this pre-visualization can be rendered from the expected future viewpoints of the driver, the latter will more easily understand the required maneuver. 3D city models can be reconstructed from the imagery recorded by surveying vehicles. The vastness of image material gathered by these vehicles, however, puts extreme demands on vision algorithms to ensure their practical usability. Algorithms need to be as fast as possible and should result in compact, memory efficient 3D city models for future ease of distribution and visualization. For the considered application, these are not contradictory demands. Simplified geometry assumptions can speed up vision algorithms while automatically guaranteeing compact geometry models. We present a novel city modeling framework which builds upon this philosophy to create 3D content at high speed which could allow for pre-visualization of any conceivable traffic situation by car navigation modules.

Question: How can I get a fast compact city model?


Fair Client Puzzles from the Bitcoin Blockchain. Client puzzles have been proposed as a mechanism for proving legitimate intentions by providing "proofs of work", which can be applied to discourage malicious usage of resources. A typical problem of puzzle constructions is the difference in expected solving time on different computing platforms. We call puzzles which can be solved independently of client computing resources fair client puzzles. We propose a construction for client puzzles requiring widely distributed computational effort for their solution. These puzzles can be solved using the mining process of Bitcoin, or similar cryptocurrencies. Adapting existing definitions, we show that our puzzle construction satisfies formal requirements of client puzzles under reasonable assumptions. We describe a way of transforming our client puzzles for use in denial of service scenarios and demonstrate a practical construction.

Question: What is a fair client puzzle?


A Modular Framework for Versatile Conversational Agent Building. This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible.

Question: What is a conversational agent?


Photo tourism: exploring photo collections in 3D. We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.

Question: How to build a photo tour of a scenic location?


Normal vulvovaginal, perineal, and pelvic anatomy with reconstructive considerations.. A thorough insight into the female genital anatomy is crucial for understanding and performing pelvic reconstructive procedures. The intimate relationship between the genitalia and the muscles, ligaments, and fascia that provide support is complex, but critical to restore during surgery for correction of prolapse or aesthetic reasons. The external female genitalia include the mons pubis, labia majora and minora, clitoris, vestibule with glands, perineal body, and the muscles and fascia surrounding these structures. Through the perineal membrane and the perineal body, these superficial vulvar structures are structurally related to the deep pelvic muscle levator ani with its fascia. The levator ani forms the pelvic floor with the coccygeus muscle and provides vital support to all the pelvic organs and stability to the perineum. The internal female genital organs include the vagina, cervix, uterus, tubes, and ovaries with their visceral fascia. The visceral fascia also called the endopelvic fascia, surrounds the pelvic organs and connects them to the pelvic walls. It is continuous with the paraurethral and paravaginal fascia, which is attached to the perineal membrane. Thus, the internal and external genitalia are closely related to the muscles and fascia, and work as one functioning unit.

Question: What is the anatomy of the female genitalia?


Money Laundering on the Internet. Electronic money (or e-money) is money that is represented digitally and can be exchanged by means of smart card from one party to another without the need for an intermediary. It is anticipated that emoney will work just like paper money. One of its potential key features is anonymity. The proceeds of crime that are in the form of e-money could be used, to buy foreign currency and high value goods to be resold. E-money may therefore be used to place dirty money without having to smuggle cash or conduct face to face transactions.

Question: What is money laundering?


Paraplay: Exploring Playfulness Around Physical Console Gaming. We present the concept of paraplay: playful activities that take place within the context of an interactive game or other play activity, but outside the activity itself. By critically examining work related to gaming and play goals and motivations we argue that the concept of playfulness should have a stronger role in our understanding of gaming sessions, and particularly social gaming sessions. In order to further understand the role of playfulness in social gaming we conducted an empirical field study of physical console gaming. Six families with a total of 32 participants were provided with an Xbox 360 console, Kinect sensor, and three casual physical video games to play together for a period of approximately two weeks. Participants were instructed to record their social gaming sessions. We conducted video analysis on these recordings as well as interviews with many of the participants. We found numerous types and examples of playfulness within the gaming session even from those who were not actively participating in the game. Drawing on the results of this study we present a taxonomy of paraplay and discuss the ways that playfulness can be exhibited in a social play session. We show that participants in a game situation act within a wider context of playfulness, according to a variety of significant roles ranging from active player through to audience member. We explore these roles and their attributes to provide a rich account of paraplay and its importance in understanding playful activities broadly.

Question: What is paraplay?


How Does He Saw Me ? A Recommendation Engine for Picking Heroes in Dota 2. In this paper, we present a hero recommendation engine for the popular computer game Dota 2. We detail previous efforts at hero recommendation, building on these to provide a more complete exploration of machine learning algorithms applied to this problem. In addition to discussing the details behind the machine learning algorithms we use, we also provide insight into our method of data collection and feature selection. In doing so, the outcome of our efforts is two-fold. First, we provide the first public survey of machine learning algorithms applied to Dota 2–in the process gaining further insight into the algorithms we explore. Second, we provide a tool that we believe will be useful for both the casual and competitive sides of Dota 2's 6 million unique player base.

Question: What is the first large-scale Canadian peacekeeping mission?


Factors affecting the Choice of High Tech Engineering majors for University women and Men in Bangladesh. In contrast to western countries which are experiencing declines in female enrollment in high tech engineering (HTE), Bangladesh has experienced a continuous increase in female enrollment in engineering. In order to better understand the factors driving the choice of HTE as a major, survey data was collected from 590 male and female students in HTE majors in Bangladesh. Hypotheses related to gender differences in the influence of various factors on major choice were examined using t-tests. Results revealed that, while several factors had a similar impact for women and men, gender differences in selfefficacy and socio-economic status persist and may represent barriers to women's participation.

Question: What factors influence the choice of high tech engineering majors for university women and men in Bangladesh?


Least Squares Temporal Difference Learning and Galerkin ' s Method. The problem of estimating the value function underlying a Markovian reward process is considered. As it is well known, the value function underlying a Markovian reward process satisfied a linear fixed point equation. One approach to learning the value function from finite data is to find a good approximation to the value function in a given (linear) subspace of the space of value functions. We review some of the issues that arise when following this approach, as well as some results that characterize the finite-sample performance of some of the algorithms. 1 Markovian Reward Processes Let X be a measurable space and consider a stochastic process (X0, R1, X1, R2, X2, . . .), where Xt ∈ X and Rt+1 ∈ R, t = 0, 1, 2, . . .. The process is called a Markovian Reward process if • (X0, X1, . . .) is a Markov process, and • for any t ≥ 0, given Xt, Xt+1 the distribution of Rt+1 is independent of the history of the process. Here, Xt is called the state of the system at time t, while Rt+1 is the reward associated to transitioning from Xt to Xt+1. We shall denote by P the Markovian kernel underlying the process: Thus, the distribution of (Xt+1, Rt+1) given Xt is given by P(·, ·|Xt), t = 0, 1, . . .. Fix the so-called discount factor 0 ≤ γ ≤ 1 and define the (total discounted) return associated to the process R = ∞ ∑

Question: What is the discount factor?


Antidepressant effects of sleep deprivation require astrocyte-dependent adenosine mediated signaling. Major depressive disorder is a debilitating condition with a lifetime risk of ten percent. Most treatments take several weeks to achieve clinical efficacy, limiting the ability to bring instant relief needed in psychiatric emergencies. One intervention that rapidly alleviates depressive symptoms is sleep deprivation; however, its mechanism of action is unknown. Astrocytes regulate responses to sleep deprivation, raising the possibility that glial signaling mediates antidepressive-like actions of sleep deprivation. Here, we found that astrocytic signaling to adenosine (A1) receptors was required for the robust reduction of depressive-like behaviors following 12 hours of sleep deprivation. As sleep deprivation activates synaptic A1 receptors, we mimicked the effect of sleep deprivation on depression phenotypes by administration of the A1 agonist CCPA. These results provide the first mechanistic insight into how sleep deprivation impacts mood, and provide a novel pathway for rapid antidepressant development by modulation of glial signaling in the brain.

Question: How does sleep deprivation affect depression?


Circulating mutant DNA to assess tumor dynamics. The measurement of circulating nucleic acids has transformed the management of chronic viral infections such as HIV. The development of analogous markers for individuals with cancer could similarly enhance the management of their disease. DNA containing somatic mutations is highly tumor specific and thus, in theory, can provide optimum markers. However, the number of circulating mutant gene fragments is small compared to the number of normal circulating DNA fragments, making it difficult to detect and quantify them with the sensitivity required for meaningful clinical use. In this study, we applied a highly sensitive approach to quantify circulating tumor DNA (ctDNA) in 162 plasma samples from 18 subjects undergoing multimodality therapy for colorectal cancer. We found that ctDNA measurements could be used to reliably monitor tumor dynamics in subjects with cancer who were undergoing surgery or chemotherapy. We suggest that this personalized genetic approach could be generally applied to individuals with other types of cancer (pages 914–915).

Question: How can we use circulating mutant DNA to assess tumor dynamics?


Impaired face processing in autism: fact or artifact?. Within the last 10 years, there has been an upsurge of interest in face processing abilities in autism which has generated a proliferation of new empirical demonstrations employing a variety of measuring techniques. Observably atypical social behaviors early in the development of children with autism have led to the contention that autism is a condition where the processing of social information, particularly faces, is impaired. While several empirical sources of evidence lend support to this hypothesis, others suggest that there are conditions under which autistic individuals do not differ from typically developing persons. The present paper reviews this bulk of empirical evidence, and concludes that the versatility and abilities of face processing in persons with autism have been underestimated.

Question: What is the difference between fact and artifact?


Sustainable, effective implementation of a surgical preprocedural checklist: an "attestation" format for all operating team members.. BACKGROUND Adoption ofa preprocedural pause (PPP) associated with a checklist and a team briefing has been shown to improve teamwork function in operating rooms (ORs) and has resulted in improved outcomes. The format of the World Health Organization Safe Surgery Saves Lives checklist has been used as a template for a PPP. Performing a PPP, described as a "time-out," is one of the three principal components, along with a preprocedure verification process and marking the procedure site, of the Joint Commission's Universal Protocol for Preventing Wrong Site, Wrong Procedure, Wrong Person Surgery. However, if the surgeon alone leads the pause, its effectiveness may be decreased by lack of input from other operating team members.   METHODS In this study, the PPP was assessed to measure participation and input from operating team members. On the basis of low participation levels, the pause was modified to include an attestation from each member of the team.   RESULTS Preliminary analysis of our surgeon-led pause revealed only 54% completion of all items, which increased to 97% after the intervention. With the new format, operating team members stopped for the pause in 96% of cases, compared with 78% before the change. Operating team members introduced themselves in 94% of cases, compared with 44% before the change. Follow-up analysis showed sustained performance at 18 months after implementation.   CONCLUSIONS A preprocedural checklist format in which each member of the operating team provides a personal attestation can improve pause compliance and may contribute to improvements in the culture of teamwork within an OR. Successful online implementation of a PPP, which includes participation by all operating team members, requires little or no additional expense and only minimal formal coaching outside working situations.

Question: What is the difference between a preprocedural pause and a preprocedural checklist?


PARS: A Uniform and Open-source Password Analysis and Research System. In this paper, we introduce an open-source and modular password analysis and research system, PARS, which provides a uniform, comprehensive and scalable research platform for password security. To the best of our knowledge, PARS is the first such system that enables researchers to conduct fair and comparable password security research. PARS contains 12 state-of-the-art cracking algorithms, 15 intra-site and cross-site password strength metrics, 8 academic password meters, and 15 of the 24 commercial password meters from the top-150 websites ranked by Alexa. Also, detailed taxonomies and large-scale evaluations of the PARS modules are presented in the paper.

Question: What is a password analysis and research system?


Class segmentation and object localization with superpixel neighborhoods. We propose a method to identify and localize object classes in images. Instead of operating at the pixel level, we advocate the use of superpixels as the basic unit of a class segmentation or pixel localization scheme. To this end, we construct a classifier on the histogram of local features found in each superpixel. We regularize this classifier by aggregating histograms in the neighborhood of each superpixel and then refine our results further by using the classifier in a conditional random field operating on the superpixel graph. Our proposed method exceeds the previously published state-of-the-art on two challenging datasets: Graz-02 and the PASCAL VOC 2007 Segmentation Challenge.

Question: What is the state-of-the-art on class segmentation and object localization?


Passive ultrasonic irrigation of the root canal: a review of the literature.. Ultrasonic irrigation of the root canal can be performed with or without simultaneous ultrasonic instrumentation. When canal shaping is not undertaken the term passive ultrasonic irrigation (PUI) can be used to describe the technique. In this paper the relevant literature on PUI is reviewed from a MEDLINE database search. Passive ultrasonic irrigation can be performed with a small file or smooth wire (size 10-20) oscillating freely in the root canal to induce powerful acoustic microstreaming. PUI can be an important supplement for cleaning the root canal system and, compared with traditional syringe irrigation, it removes more organic tissue, planktonic bacteria and dentine debris from the root canal. PUI is more efficient in cleaning canals than ultrasonic irrigation with simultaneous ultrasonic instrumentation. PUI can be effective in curved canals and a smooth wire can be as effective as a cutting K-file. The taper and the diameter of the root canal were found to be important parameters in determining the efficacies of dentine debris removal. Irrigation with sodium hypochlorite is more effective than with water and ultrasonic irrigation is more effective than sonic irrigation in the removal of dentine debris from the root canal. The role of cavitation during PUI remains inconclusive. No detailed information is available on the influence of the irrigation time, the volume of the irrigant, the penetration depth of the instrument and the shape and material properties of the instrument. The influence of irrigation frequency and intensity on the streaming pattern as well as the complicated interaction of acoustic streaming with the adherent biofilm needs to be clarified to reveal the underlying physical mechanisms of PUI.

Question: What is the role of cavitation during PUI?


MVC architecture driven restructuring to achieve client-side web page composition. This paper presents a restructuring approach to relocating web page composition from servers to browsers for Java web applications. The objective is to reduce redundant manipulation and transfer of code/data that are shared by web pages. The reduction is carried out through a restructuring algorithm, effectively keeping consistency between source and target applications from the perspective of the model-view-controller (MVC) architecture, because the problem requires the target application to preserve the observable behavior of its source application. Case studies show that our restructuring tool can efficiently support the restructuring process.

Question: What is the MVC architecture?


Quasar: resource-efficient and QoS-aware cluster management. Cloud computing promises flexibility and high performance for users and high cost-efficiency for operators. Nevertheless, most cloud facilities operate at very low utilization, hurting both cost effectiveness and future scalability.  We present Quasar, a cluster management system that increases resource utilization while providing consistently high application performance. Quasar employs three techniques. First, it does not rely on resource reservations, which lead to underutilization as users do not necessarily understand workload dynamics and physical resource requirements of complex codebases. Instead, users express performance constraints for each workload, letting Quasar determine the right amount of resources to meet these constraints at any point. Second, Quasar uses classification techniques to quickly and accurately determine the impact of the amount of resources (scale-out and scale-up), type of resources, and interference on performance for each workload and dataset. Third, it uses the classification results to jointly perform resource allocation and assignment, quickly exploring the large space of options for an efficient way to pack workloads on available resources. Quasar monitors workload performance and adjusts resource allocation and assignment when needed. We evaluate Quasar over a wide range of workload scenarios, including combinations of distributed analytics frameworks and low-latency, stateful services, both on a local cluster and a cluster of dedicated EC2 servers. At steady state, Quasar improves resource utilization by 47% in the 200-server EC2 cluster, while meeting performance constraints for workloads of all types.

Question: What is the difference between scale-out and scale-up?


Efficient Randomized Pattern-Matching Algorithms. We present randomized algorithms to solve the following string-matching problem and some of its generalizations: Given a string X of length n (the pattern) and a string Y (the text), find the first occurrence of X as a consecutive block within Y. The algorithms represent strings of length n by much shorter strings called fingerprints, and achieve their efficiency by manipulating fingerprints instead of longer strings. The algorithms require a constant number of storage locations, and essentially run in real time. They are conceptually simple and easy to implement. The method readily generalizes to higher-dimensional patternmatching problems.

Question: What is the first occurrence of a pattern in a text?


Scene Parsing with Object Instances and Occlusion Ordering. This work proposes a method to interpret a scene by assigning a semantic label at every pixel and inferring the spatial extent of individual object instances together with their occlusion relationships. Starting with an initial pixel labeling and a set of candidate object masks for a given test image, we select a subset of objects that explain the image well and have valid overlap relationships and occlusion ordering. This is done by minimizing an integer quadratic program either using a greedy method or a standard solver. Then we alternate between using the object predictions to refine the pixel labels and vice versa. The proposed system obtains promising results on two challenging subsets of the LabelMe and SUN datasets, the largest of which contains 45, 676 images and 232 classes.

Question: What is the largest dataset of images of objects?


Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. BACKGROUND Information about the distribution of causes of and time trends for child mortality should be periodically updated. We report the latest estimates of causes of child mortality in 2010 with time trends since 2000.   METHODS Updated total numbers of deaths in children aged 0-27 days and 1-59 months were applied to the corresponding country-specific distribution of deaths by cause. We did the following to derive the number of deaths in children aged 1-59 months: we used vital registration data for countries with an adequate vital registration system; we applied a multinomial logistic regression model to vital registration data for low-mortality countries without adequate vital registration; we used a similar multinomial logistic regression with verbal autopsy data for high-mortality countries; for India and China, we developed national models. We aggregated country results to generate regional and global estimates.   FINDINGS Of 7·6 million deaths in children younger than 5 years in 2010, 64·0% (4·879 million) were attributable to infectious causes and 40·3% (3·072 million) occurred in neonates. Preterm birth complications (14·1%; 1·078 million, uncertainty range [UR] 0·916-1·325), intrapartum-related complications (9·4%; 0·717 million, 0·610-0·876), and sepsis or meningitis (5·2%; 0·393 million, 0·252-0·552) were the leading causes of neonatal death. In older children, pneumonia (14·1%; 1·071 million, 0·977-1·176), diarrhoea (9·9%; 0·751 million, 0·538-1·031), and malaria (7·4%; 0·564 million, 0·432-0·709) claimed the most lives. Despite tremendous efforts to identify relevant data, the causes of only 2·7% (0·205 million) of deaths in children younger than 5 years were medically certified in 2010. Between 2000 and 2010, the global burden of deaths in children younger than 5 years decreased by 2 million, of which pneumonia, measles,

Question: What is the leading cause of death in children?


' SMART ' CANE FOR THE VISUALLY IMPAIRED : DESIGN AND CONTROLLED FIELD TESTING OF AN AFFORDABLE OBSTACLE DETECTION SYSTEM. (USA) respectively. Since graduation, they have been associated with this project in voluntary and individual capacity. Sandeep Singh Gujral, an occupational therapist was an intern at the IIT Delhi from August-November 2009 and conducted user training for the trials.

Question: What is the project name?


Circularly polarized array antenna with corporate-feed network and series-feed elements. In this paper, corporate-feed circularly polarized microstrip array antennas are studied. The antenna element is a series-feed slot-coupled structure. Series feeding causes sequential rotation effect at the element level. Antenna elements are then used to form the subarray by applying sequential rotation to their feeding. Arrays having 4, 16, and 64 elements were made. The maximum achieved gains are 15.3, 21, and 25.4 dBic, respectively. All arrays have less than 15 dB return loss and 3 dB axial ratio from 10 to 13 GHz. The patterns are all quite symmetrical.

Question: What is a circularly polarized array antenna?


Coping with child sexual abuse among college students and post-traumatic stress disorder: the role of continuity of abuse and relationship with the perpetrator.. OBJECTIVE The purpose of this study was to examine the effects of child sexual abuse (CSA) on the use of coping strategies and post-traumatic stress disorder (PTSD) scores in young adults, as well as the role of avoidance and approach coping strategies in those PTSD scores in CSA victims. The role of coping strategies was studied by considering their possible interactive effect with the continuity of abuse and the relationship with the perpetrator; the effect of coping strategies on PTSD was also compared between CSA victim and non-CSA victim participants.   METHOD The sample was comprised of 138 victims of CSA and another 138 participants selected as a comparison group. Data about child sexual abuse were obtained from a questionnaire developed for this purpose. Coping strategies were assessed with the How I Deal with Things Scale (Burt & Katz, 1987), while PTSD scores were assessed with the "Escala de Gravedad de Síntomas del Trastorno de Estrés Postraumático" (Severity of Symptoms of PTSD Scale; Echeburúa et al., 1997).   RESULTS Participants who had been victims of CSA showed significantly higher PTSD scores and lower approach coping strategies scores. However, differences in avoidance coping strategies between groups were not consistent and did not always follow the expected direction. Only the use of avoidance coping strategies was related to PTSD, participants who used these showing higher scores. The effects of avoidance strategies were stronger in continued than in isolated abuse, in intrafamilial than in extrafamilial abuse and in CSA victims than in non-victims.   CONCLUSIONS These results confirm the idea of CSA as a high-risk experience that can affect the victim's coping strategies and lead to PTSD to a lesser or greater extent depending on the coping strategy used. Moreover, the role of these

Question: What is the difference between coping strategies?


Living with Seal Robots in a Care House - Evaluations of Social and Physiological Influences. Robot therapy for elderly residents in a care house has been conducted since June 2005. Two therapeutic seal robots were introduced and activated for over 9 hours every day to interact with the residents. This paper presents the results of this experiment. In order to investigate the psychological and social effects of the robots, the activities of the residents in public areas were recorded by video cameras during daytime hours (8:30-18:00) for over 2 months. In addition, the hormones 17-Ketosteroid sulfate (17-KS-S) and 17-hydroxycorticosteroids (17-OHCS) in the residents' urine were obtained and analyzed. The results indicate that their social interaction was increased through interaction with the seal robots. Furthermore, the results of the urinary tests showed that the reactions of the subjects' vital organs to stress improved after the introduction of the robots

Question: What is the purpose of the experiment?


Clustering of time series data - a survey. Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters. The past researchs are organized into three groups depending upon whether they work directly with the raw data either in the time or frequency domain, indirectly with features extracted from the raw data, or indirectly with models built from the raw data. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. Moreover, the areas that time series clustering have been applied to are also summarized, including the sources of data used. It is hoped that this review will serve as the steppingstone for those interested in advancing this area of research. 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Question: What is the difference between clustering and classification?


PLEdestrians: A Least-Effort Approach to Crowd Simulation. We present a new algorithm for simulating large-scale crowds at interactive rates based on the Principle of Least Effort. Our approach uses an optimization method to compute a biomechanically energy-efficient, collision-free trajectory that minimizes the amount of effort for each heterogeneous agent in a large crowd. Moreover, the algorithm can automatically generate many emergent phenomena such as lane formation, crowd compression, edge and wake effects ant others. We compare the results from our simulations to data collected from prior studies in pedestrian and crowd dynamics, and provide visual comparisons with real-world video. In practice, our approach can interactively simulate large crowds with thousands of agents on a desktop PC and naturally generates a diverse set of emergent behaviors.

Question: How large is the crowd?


The Growing Relationship Between China and Sub-Saharan Africa : Macroeconomic , Trade , Investment , and Aid Links. China's economic ascendance over the past two decades has generated ripple effects in the world economy. Its search for natural resources to satisfy the demands of industrialization has led it to Sub-Saharan Africa. Trade between China and Africa in 2006 totaled more than $50 billion, with Chinese companies importing oil from Angola and Sudan, timber from Central Africa, and copper from Zambia. Demand from China has contributed to an upward swing in prices, particularly for oil and metals from Africa, and has given a boost to real GDP in Sub-Saharan Africa. Chinese aid and investment in infrastructure are bringing desperately needed capital to the continent. At the same time, however, strong Chinese demand for oil is contributing to an increase in the import bill for many oil-importing SubSaharan African countries, and its exports of low-cost textiles, while benefiting African consumers, is threatening to displace local production. China poses a challenge to good governance and macroeconomic management in Africa because of the potential Dutch disease implications of commodity booms. China presents both an opportunity for Africa to reduce its marginalization from the global economy and a challenge for it to effectively harness the influx of resources to promote poverty-reducing economic development at home. JEL codes: F01, F35, F41, N55, N57, Q33, Q43

Question: What is the relationship between China and Sub-Saharan Africa?


Internet of Things : Architectural Framework for eHealth Security. The Internet of Things (IoT) holds big promises for Health Care, especially in Proactive Personal eHealth. To fulfil the promises major challenges must be overcome, particularly regarding privacy and security. This paper explores these issues, discusses use case scenarios, and advances a secure architecture framework. We close the paper with a discussion of the state of various standard organizations, in the conviction of the critical role they will play in making eHealth bloom.

Question: What is the state of the art in eHealth security?


Serverless Computing: Design, Implementation, and Performance. We present the design of a novel performance-oriented serverless computing platform implemented in. NET, deployed in Microsoft Azure, and utilizing Windows containers as function execution environments. Implementation challenges such as function scaling and container discovery, lifecycle, and reuse are discussed in detail. We propose metrics to evaluate the execution performance of serverless platforms and conduct tests on our prototype as well as AWS Lambda, Azure Functions, Google Cloud Functions, and IBM's deployment of Apache OpenWhisk. Our measurements show the prototype achieving greater throughput than other platforms at most concurrency levels, and we examine the scaling and instance expiration trends in the implementations. Additionally, we discuss the gaps and limitations in our current design, propose possible solutions, and highlight future research.

Question: What is serverless computing?


Task-Driven Feature Pooling for Image Classification. Feature pooling is an important strategy to achieve high performance in image classification. However, most pooling methods are unsupervised and heuristic. In this paper, we propose a novel task-driven pooling (TDP) model to directly learn the pooled representation from data in a discriminative manner. Different from the traditional methods (e.g., average and max pooling), TDP is an implicit pooling method which elegantly integrates the learning of representations into the given classification task. The optimization of TDP can equalize the similarities between the descriptors and the learned representation, and maximize the classification accuracy. TDP can be combined with the traditional BoW models (coding vectors) or the recent state-of-the-art CNN models (feature maps) to achieve a much better pooled representation. Furthermore, a self-training mechanism is used to generate the TDP representation for a new test image. A multi-task extension of TDP is also proposed to further improve the performance. Experiments on three databases (Flower-17, Indoor-67 and Caltech-101) well validate the effectiveness of our models.

Question: What is the task-driven feature pooling?


A Comparison of String Distance Metrics for Name-Matching Tasks. Using an open-source, Java toolkit of name-matching methods, we experimentally compare string distance metrics on the task of matching entity names. We investigate a number of different metrics proposed by different communities, including edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. Overall, the best-performing method is a hybrid scheme combining a TFIDF weighting scheme, which is widely used in information retrieval, with the Jaro-Winkler string-distance scheme, which was developed in the probabilistic record linkage community.

Question: What is the best string distance metric for name matching?


Deep Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Imagery. A semantic segmentation algorithm must assign a label to every pixel in an image. Recently, semantic segmentation of RGB imagery has advanced significantly due to deep learning. Because creating datasets for semantic segmentation is laborious, these datasets tend to be significantly smaller than object recognition datasets. This makes it difficult to directly train a deep neural network for semantic segmentation, because it will be prone to overfitting. To cope with this, deep learning models typically use convolutional neural networks pre-trained on large-scale image classification datasets, which are then fine-tuned for semantic segmentation. For non-RGB imagery, this is currently not possible because large-scale labeled non-RGB datasets do not exist. In this paper, we developed two deep neural networks for semantic segmentation of multispectral remote sensing imagery. Prior to training on the target dataset, we initialize the networks with large amounts of synthetic multispectral imagery. We show that this significantly improves results on real-world remote sensing imagery, and we establish a new state-of-the-art result on the challenging Hamlin Beach State Park Dataset.

Question: What is the state of the art for semantic segmentation of multispectral remote sensing imagery?


Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification. Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.

Question: How to improve the generalization ability of the triplet loss?