Coalition Structure Generation: Dynamic Programming Meets Anytime Optimization. Coalition structure generation involves partitioning a set of agents into exhaustive and disjoint coalitions so as to maximize the social welfare. What makes this such a challenging problem is that the number of possible solutions grows exponentially as the number of agents increases. To date, two main approaches have been developed to solve this problem, each with its own strengths and weaknesses. The state of the art in the first approach is the Improved Dynamic Programming (IDP) algorithm, due to Rahwan and Jennings, that is guaranteed to find an optimal solution in O(3), but which cannot generate a solution until it has completed its entire execution. The state of the art in the second approach is an anytime algorithm called IP, due to Rahwan et al., that provides worst-case guarantees on the quality of the best solution found so far, but which is O(n). In this paper, we develop a novel algorithm that combines both IDP and IP, resulting in a hybrid performance that exploits the strength of both algorithms and, at the same, avoids their main weaknesses. Our approach is also significantly faster (e.g. given 25 agents, it takes only 28% of the time required by IP, and 0.3% of the time required by IDP).

Question: What is the state of the art in coalition structure generation?


Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks. In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.

Question: How old is the person in the image?


Classifying Objectionable Websites Based on Image Content. This paper describes IBCOW (Image-based Classi cation of Objectionable Websites), a system capable of classifying a website as objectionable or benign based on image content. The system uses WIPETM (Wavelet Image Pornography Elimination) and statistics to provide robust classi cation of on-line objectionable World Wide Web sites. Semantically-meaningful feature vector matching is carried out so that comparisons between a given on-line image and images marked as "objectionable" and "benign" in a training set can be performed efciently and e ectively in the WIPE module. If more than a certain number of images sampled from a site is found to be objectionable, then the site is considered to be objectionable. The statistical analysis for determining the size of the image sample and the threshold number of objectionable images is given in this paper. The system is practical for real-world applications, classifying a Web site at a speed of less than 2 minutes each, including the time to compute the feature vector for the images downloaded from the site, on a Pentium Pro PC. Besides its exceptional speed, it has demonstrated 97% sensitivity and 97% speci city in classifying a Web site based solely on images. Both the sensitivity and the speci city in real-world applications is expected to be higher because our performance evaluation is relatively conservative and surrounding text can be used to assist the classi cation process.

Question: What is the size of the image sample?


PHP FRAMEWORK FOR DATABASE MANAGEMENT BASED ON MVC PATTERN. PHP is a powerful language to develop dynamic and interactive web applications. One of the defining features of PHP is the ease for developers to connect and manipulate a database. PHP prepares the functions for database manipulation. However, database management is done by the Structure Query Language (SQL). Most novice programmers often have trouble with SQL syntax. In this paper, we present the PHP framework for database management based on the MVC pattern. The MVC pattern is very useful for the architecture of web applications, separating the model, view and controller of a web application. The PHP framework encapsulated, common database operations are INSERT, UPDATE, DELETE and SELECT. Developers will not be required to consider the specific SQL statement syntax, just to call it the method in the model module. In addition, we use White-Box testing for the code verification in the model module. Lastly, a web application example is shown to illustrate the process of the PHP framework.

Question: What is the MVC pattern?


Cues to deception.. Do people behave differently when they are lying compared with when they are telling the truth? The combined results of 1,338 estimates of 158 cues to deception are reported. Results show that in some ways, liars are less forthcoming than truth tellers, and they tell less compelling tales. They also make a more negative impression and are more tense. Their stories include fewer ordinary imperfections and unusual contents. However, many behaviors showed no discernible links, or only weak links, to deceit. Cues to deception were more pronounced when people were motivated to succeed, especially when the motivations were identity relevant rather than monetary or material. Cues to deception were also stronger when lies were about transgressions.

Question: What are the cues to deception?


Lightweight Monitoring of Distributed Streams. As data becomes dynamic, large, and distributed, there is increasing demand for what have become known as distributed stream algorithms. Since continuously collecting the data to a central server and processing it there is infeasible, a common approach is to define local conditions at the distributed nodes, such that—as long as they are maintained—some desirable global condition holds.  Previous methods derived local conditions focusing on communication efficiency. While proving very useful for reducing the communication volume, these local conditions often suffer from heavy computational burden at the nodes. The computational complexity of the local conditions affects both the runtime and the energy consumption. These are especially critical for resource-limited devices like smartphones and sensor nodes. Such devices are becoming more ubiquitous due to the recent trend toward smart cities and the Internet of Things. To accommodate for high data rates and limited resources of these devices, it is crucial that the local conditions be quickly and efficiently evaluated.  Here we propose a novel approach, designated CB (for Convex/Concave Bounds). CB defines local conditions using suitably chosen convex and concave functions. Lightweight and simple, these local conditions can be rapidly checked on the fly. CB's superiority over the state-of-the-art is demonstrated in its reduced runtime and power consumption, by up to six orders of magnitude in some cases. As an added bonus, CB also reduced communication overhead in all the tested application scenarios.

Question: What is the difference between convex and concave functions?


Detection of Atrial Fibrillation using model-based ECG analysis. Atrial fibrillation (AF) is an arrhythmia that can lead to several patient risks. This kind of arrhythmia affects mostly elderly people, in particular those who suffer from heart failure (one of the main causes of hospitalization). Thus, detection of AF becomes decisive in the prevention of cardiac threats. In this paper an algorithm for AF detection based on a novel algorithm architecture and feature extraction methods is proposed. The aforementioned architecture is based on the analysis of the three main physiological characteristics of AF: i) P wave absence ii) heart rate irregularity and iii) atrial activity (AA). Discriminative features are extracted using model-based statistic and frequency based approaches. Sensitivity and specificity results (respectively, 93.80% and 96.09% using the MIT-BIH AF database) show that the proposed algorithm is able to outperform state-of-the-art methods.

Question: How to detect atrial fibrillation?


Deception : The Role of Consequences. Deception is part of many economic interactions. Business people, politicians, diplomats, lawyers, and students in the experimental laboratory who make use of private information do not always do so honestly. This observation indicates that behavior often rejects the moral approach to deception. As St. Augustine wrote, "To me, however, it seems certain that every lie is a sin. . . " (St. Augustine, 421). Later, philosophers like Immanuel Kant (1787) again adopted this uncompromising moral stance when arguing against lying. At the other extreme, economic theory is built on the assumption of "homo economicus," a figure who acts selfishly and is unconcerned about the well-being of others. An implication of this assumption is that lies will be told whenever it is beneficial for the liar, regardless of their effect on the other party. Another implication is that there is no negative outcome associated with lying per se. This assumption is very useful in many economic models. Consider contract theory, where it is assumed that without an explicit contract, neither side will fulfill its respective obligations. For example, George Akerlof's (1970) paper on asymmetric information and the market for lemons assumes that sellers of used cars will always lie if it is in their benefit to do so. In the mechanism design literature (e.g., Bengt Holmstrom, 1979), the standard assumption is that people will tell the truth only if this is incentive-compatible given material outcomes. In the literature on tax evasion, the choice of whether to avoid paying taxes is considered a decision under uncertainty; cost is treated as a product of the probability of being caught and the cost of punishment, whereas benefit is simply the money saved by avoiding payment. However, there is no cost associated with the very act of lying (Michael Alingham and Agnar Sandmo, 1972). Another example is the game theoretic treatment of "cheap talk" (Crawford and Joel Sobel,

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


Achieving lightweight trustworthy traceability. Despite the fact that traceability is a required element of almost all safety-critical software development processes, the trace data is often incomplete, inaccurate, redundant, conflicting, and outdated. As a result, it is neither trusted nor trustworthy. In this vision paper we propose a philosophical change in the traceability landscape which transforms traceability from a heavy-weight process producing untrusted trace links, to a light-weight results-oriented trustworthy solution. Current traceability practices which retard agility are cast away and replaced with a disciplined, just-in-time approach. The novelty of our solution lies in a clear separation of trusted trace links from untrusted ones, the change in perspective from `living-with' inacurate traces toward rigorous and ongoing debridement of stale links from the trusted pool, and the notion of synthesizing available `project exhaust' as evidence to systematically construct or reconstruct purposed, highly-focused trace links.

Question: What is a philosophical change in the traceability landscape?


REVISED NOTE ON LEARNING QUADRATIC ASSIGNMENT WITH GRAPH NEURAL NETWORKS. Inverse problems correspond to a certain type of optimization problems formulated over appropriate input distributions. Recently, there has been a growing interest in understanding the computational hardness of these optimization problems, not only in the worst case, but in an average-complexity sense under this same input distribution.In this revised note, we are interested in studying another aspect of hardness, related to the ability to learn how to solve a problem by simply observing a collection of previously solved instances. These 'planted solutions' are used to supervise the training of an appropriate predictive model that parametrizes a broad class of algorithms, with the hope that the resulting model will provide good accuracy-complexity tradeoffs in the average sense.We illustrate this setup on the Quadratic Assignment Problem, a fundamental problem in Network Science. We observe that data-driven models based on Graph Neural Networks offer intriguingly good performance, even in regimes where standard relaxation based techniques appear to suffer.

Question: What is a graph neural network?


Dual-Band and Polarization-Flexible Cavity Antenna Based on Substrate Integrated Waveguide. In this letter, a novel dual-band and polarization-flexible substrate integrated waveguide (SIW) cavity antenna is proposed. The SIW cavity used for the antenna is excited by a conventional TE120 mode for its first resonance. With the intervention of the slot, a second resonance excited by a modified- TE120 mode is also generated, thereby providing a broadside radiation pattern at two resonant frequencies. In addition, the proposed antenna has two orthogonal feeding lines. Therefore, it is possible to provide any six major polarization states. In this letter, three major polarization cases are simulated and compared to measured results. Since modern communication systems require multifunctional antennas, the proposed antenna concept is a promising candidate.

Question: What is a dual-band and polarization-flexible cavity antenna?


SLAM-based Pseudo-GNSS/INS localization system for indoor LiDAR mobile mapping systems. The emergence of Mobile Mapping Systems (MMS) has set a marked paradigm in the photogrammetric and mapping community that has not only facilitated comprehensive 3D mapping of different environments but has also paved way for new aspects of applied research in this direction. Out of the many essential blocks that make these MMS a viable tool for mapping, the positioning and orientation module is considered to be a crucial yet an expensive component. The integration of such a module with mapping sensors has allowed for the extensive implementation of such systems to provide high-quality maps. However, while such systems do not lack in system robustness and performance in general, the deployment of these systems is restricted to applications and environments where a consistent availability of GNSS signals is assured. Extending these MMS to GNSS-denied areas, such as indoor environments, is therefore quite challenging and necessitates the development of an alternative module that can act as a viable substitute to GNSS/INS for system operation without having to resort to an exhaustive modification of the same to function in GNSS-denied locations. In this research, such a case has been considered for the implementation of an indoor MMS using an Unmanned Ground Vehicle (UGV) and a 3D laser scanner for the task of generating high density maps of GNSS-denied indoor areas. To mitigate the absence of GNSS data, this paper proposes a Pseudo-GNSS/INS module integrated framework which utilizes probabilistic Simultaneous Localization and Mapping (SLAM) techniques to estimate the platform pose and heading from 3D laser scanner data. This proposed framework has been implemented based on three major notions: (i) using geometric methods for sparse point cloud extraction to carry out real-time SLAM, (ii) generating position data and geo-referencing signals from these real-time SLAM pose estimates, a

Question: What is the difference between a pseudo-GNSS and a GNSS?


CONTEXTUAL CLASSIFICATION OF POINT CLOUD DATA BY EXPLOITING INDIVIDUAL 3D NEIGBOURHOODS. The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.

Question: How to classify point cloud data?


LexIt: A Computational Resource on Italian Argument Structure. The aim of this paper is to introduce LexIt, a computational framework for the automatic acquisition and exploration of distributional information about Italian verbs, nouns and adjectives, freely available through a web interface at the address http://sesia.humnet.unipi.it/lexit. LexIt is the first large-scale resource for Italian in which subcategorization and semantic selection properties are characterized fully on distributional ground: in the paper we describe both the process of data extraction and the evaluation of the subcategorization frames extracted with LexIt.

Question: What is the aim of this paper?


Dynamic XML documents with distribution and replication. The advent of XML as a universal exchange format, and of Web services as a basis for distributed computing, has fostered the apparition of a new class of documents: dynamic XML documents. These are XML documents where some data is given explicitly while other parts are given only intensionally by means of embedded calls to web services that can be called to generate the required information. By the sole presence of Web services, dynamic documents already include inherently some form of distributed computation. A higher level of distribution that also allows (fragments of) dynamic documents to be distributed and/or replicated over several sites is highly desirable in today's Web architecture, and in fact is also relevant for regular (non dynamic) documents.The goal of this paper is to study new issues raised by the distribution and replication of dynamic XML data. Our study has originated in the context of the Active XML system [1, 3, 22] but the results are applicable to many other systems supporting dynamic XML data. Starting from a data model and a query language, we describe a complete framework for distributed and replicated dynamic XML documents. We provide a comprehensive cost model for query evaluation and show how it applies to user queries and service calls. Finally, we describe an algorithm that, for a given peer, chooses data and services that the peer should replicate to improve the efficiency of maintaining and querying its dynamic data.

Question: What is the cost of evaluating a query on a dynamic XML document?


ProfileDroid: multi-layer profiling of android applications. The Android platform lacks tools for assessing and monitoring apps in a systematic way. This lack of tools is particularly problematic when combined with the open nature of Google Play, the main app distribution channel. As our key contribution, we design and implement ProfileDroid, a comprehensive, multi-layer system for monitoring and profiling apps. Our approach is arguably the first to profile apps at four layers: (a) static, or app specification, (b) user interaction, (c) operating system, and (d) network. We evaluate 27 free and paid Android apps and make several observations: (a) we identify discrepancies between the app specification and app execution, (b) free versions of apps could end up costing more than their paid counterparts, due to an order of magnitude increase in traffic, (c) most network traffic is not encrypted, (d) apps communicate with many more sources than users might expect---as many as 13, and (e) we find that 22 out of 27 apps communicate with Google during execution. ProfileDroid is the first step towards a systematic approach for (a) generating cost-effective but comprehensive app profiles, and (b) identifying inconsistencies and surprising behaviors.

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


PuppetGAN: Transferring Disentangled Properties from Synthetic to Real Images. In this work we propose a model that enables controlled manipulation of visual attributes of real "target" images (e.g. lighting, expression or pose) using only implicit supervision with synthetic "source" exemplars. Specifically, our model learns a shared low-dimensional representation of input images from both domains in which a property of interest is isolated from other content features of the input. By using triplets of synthetic images that demonstrate modification of the visual property that we would like to control (for example mouth opening) we are able to perform disentanglement of image representations with respect to this property without using explicit attribute labels in either domain. Since our technique relies on triplets instead of explicit labels, it can be applied to shape, texture, lighting, or other properties that are difficult to measure or represent as explicit conditioners. We quantitatively analyze the degree to which trained models learn to isolate the property of interest from other content features with a proof-of-concept digit dataset and demonstrate results in a far more difficult setting, learning to manipulate real faces using a synthetic 3D faces dataset. We also explore limitations of our model with respect to differences in distributions of properties observed in two domains.

Question: How does the model work?


Image-based Localization with Spatial LSTMs. In this work we propose a new CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes. CNNs allow us to learn suitable feature representations for localization that are robust against motion blur and illumination changes. We make use of LSTM units on the CNN output in spatial coordinates in order to capture contextual information. This substantially enlarges the receptive field of each pixel leading to drastic improvements in localization performance. We provide extensive quantitative comparison of CNN-based vs SIFT-based localization methods, showing the weaknesses and strengths of each. Furthermore, we present a new large-scale indoor dataset with accurate ground truth from a laser scanner. Experimental results on both indoor and outdoor public datasets show our method outperforms existing deep architectures, and can localize images in hard conditions, e.g., in the presence of mostly textureless surfaces.

Question: What is the difference between a CNN and a LSTM?


Selective Intervention and Internal Hybrids: Interpreting and Learning from the Rise and Decline of the Oticon Spaghetti Organization. Infusing hierarchies with elements of market control has become a much-used way of simultaneously increasing entrepreneurialism and motivation in firms. However, this paper argues that such "internal hybrids," particularly in their radical forms, are inherently hard to successfully design and implement, because of fundamental credibility problems related to managerial promises to not intervene in delegated decision-making  an incentive problem that is often referred to as the "problem of selective intervention." This theoretical theme is developed and illustrated, using the case of the world-leading Danish hearing aids producer, Oticon. In the beginning of the 1990s, Oticon became famous for its radical internal hybrid, the "spaghetti organization." Recent work has interpreted the spaghetti organization as a radical attempt to foster dynamic capabilities by imposing loose coupling on the organization, neglecting, however, that about a decade later, the spaghetti organization has given way to a more traditional matrix organization. This paper presents an organizational economics interpretation of organizational changes in Oticon, and argues that a strong liability of the spaghetti organization was the above incentive problem. Motivation in Oticon was strongly harmed by selective intervention on the part of top-management Changing the organizational structure was one means of repairing these motivational problems. Refutable implications are developed, both for the understanding of efficient design of internal hybrids, and for the more general issue of the distinction between firms and markets, as well as the choice between internal and external hybrids.

Question: What is the difference between a firm and a market?


A New Clustering Method Based On Type-2 Fuzzy Similarity and Inclusion Measures. Similarity and inclusion measures between type-2 fuzzy sets have a wide range of applications. New similarity and inclusion measures between type-2 fuzzy sets are respectively defined in this paper. The properties of the measures are discussed. Some examples are used to compare the proposed measures with the existing results. Numerical results show that the proposed measures are more reasonable. Similarity measures and Yang and Shih's algorithm are combined as a clustering method for type-2 fuzzy data. Clustering results demonstrate that the proposed similarity measures are better than the existing measures.

Question: What is a clustering method based on type-2 fuzzy similarity and inclusion measures?


Design and experimental evaluation of the hydraulically actuated prototype leg of the HyQ robot. This paper focuses on the design and experimental evaluation of a hydraulically actuated robot leg. The evaluation of the leg prototype is an important milestone in the development of HyQ, a Hydraulically actuated Quadruped robot. The prototype features two rotary joints actuated by hydraulic cylinders and has a mass of 4.5kg. We performed several experiments with the leg prototype attached to a vertical slider to tests the robustness of the mechanical design and the hydraulic actuation system. Besides the experimental evaluation of the hydraulic components, we also extensively studied the sensor data of the leg during periodic hopping. The results show that hydraulic actuation is suitable for legged robots because of its high power-to-weight ratio, fast response and ability to cope with high impact force peaks. Furthermore, we compare the cylinder force data obtained by the load cell with the calculated value based on the cylinder pressures to analyze if it is possible to eliminate this sensory system redundancy in the future. Through these studies, weaknesses of the design were identified and suggestions on how to improve them are presented.

Question: What is the mass of the leg?


Neural NILM: Deep Neural Networks Applied to Energy Disaggregation. Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.

Question: What is the difference between a factorial hidden Markov model and a neural network?


Why and How Investors Use ESG Information : Evidence from a Global Survey. Using survey data from a sample of senior investmen professionals from mainstream (i.e. not SRI funds ) investment organizations we provide insights into w hy and how investors use reported environmental, social and governance (ESG) information. The primar y reason survey respondents consider ESG information in investment decisions is because they consider it financially material to investment performance. ESG information is perceived to provid e information primarily about risk rather than a company's competitive positioning. There is no one siz fits all, with the financial materiality of di fferent ESG issues varying across sectors. Lack of comparab ility due to the lack of reporting standards is the primary impediment to the use of ESG information. M ost frequently, the information is used to screen companies with the most often used method being neg ative screening. However, negative screening is perceived as the least investment beneficial while ful integration into stock valuation and positive screening considered more beneficial. Respondents expect nega tiv screening to be used less in the future, while positive screening and active ownership to be used more.

Question: How do investors use ESG information?


High-level visual features for underwater place recognition. This paper reports on a method to perform robust visual relocalization between temporally separated sets of underwater images gathered by a robot. The place recognition and relocalization problem is more challenging in the underwater environment mainly due to three factors: 1) changes in illumination; 2) long-term changes in the visual appearance of features because of phenomena like biofouling on man-made structures and growth or movement in natural features; and 3) low density of visually salient features for image matching. To address these challenges, a patch-based feature matching approach is proposed, which uses image segmentation and local intensity contrast to locate salient patches and HOG description to make correspondences between patches. Compared to traditional point-based features that are sensitive to dramatic appearance changes underwater, patch-based features are able to encode higher level information such as shape or structure which tends to persist across years in underwater environments. The algorithm is evaluated on real data, from multiple years, collected by a Hovering Autonomous Underwater Vehicle for ship hull inspection. Results in relocalization performance across missions from different years are compared to other traditional methods.

Question: How do you perform relocalization underwater?


Sensory integration dysfunction affects efficacy of speech therapy on children with functional articulation disorders. BACKGROUND Articulation disorders in young children are due to defects occurring at a certain stage in sensory and motor development. Some children with functional articulation disorders may also have sensory integration dysfunction (SID). We hypothesized that speech therapy would be less efficacious in children with SID than in those without SID. Hence, the purpose of this study was to compare the efficacy of speech therapy in two groups of children with functional articulation disorders: those without and those with SID.   METHOD A total of 30 young children with functional articulation disorders were divided into two groups, the no-SID group (15 children) and the SID group (15 children). The number of pronunciation mistakes was evaluated before and after speech therapy.   RESULTS There were no statistically significant differences in age, sex, sibling order, education of parents, and pretest number of mistakes in pronunciation between the two groups (P > 0.05). The mean and standard deviation in the pre- and post-test number of mistakes in pronunciation were 10.5 ± 3.2 and 3.3 ± 3.3 in the no-SID group, and 10.1 ± 2.9 and 6.9 ± 3.5 in the SID group, respectively. Results showed great changes after speech therapy treatment (F = 70.393; P < 0.001) and interaction between the pre/post speech therapy treatment and groups (F = 11.119; P = 0.002).   CONCLUSIONS Speech therapy can improve the articulation performance of children who have functional articulation disorders whether or not they have SID, but it results in significantly greater improvement in children without SID. SID may affect the treatment efficiency of speech therapy in young children with articulation disorders.

Question: What is the difference between functional articulation disorders and articulation disorders?


Electrically Large Zero-Phase-Shift Line Grid-Array UHF Near-Field RFID Reader Antenna. A grid-array antenna using a zero-phase-shift (ZPS) line is proposed to enlarge the interrogation zone of a reader antenna for near-field ultra-high-frequency (UHF) radio frequency identification (RFID) system. The proposed grid-array antenna is composed of a number of grid cells and a double-sided parallel-strip line feeding network. Each grid cell, namely segmented loop constructed by the ZPS line, has uniform and single-direction flowing current along the line. By configuration of the cells sharing a common side with its adjacent grid cells which carry reverse-direction flowing current, a grid-array antenna is formed to generate a strong and uniform magnetic-field distribution over a large interrogation zone even when the perimeter of the interrogation zone reaches up to 3λ (where λ is the operating wavelength in free space) or larger. As an example, a grid-array antenna with 1 × 2 segmented ZPS line loop cells implemented onto a piece of FR4 printed board (PCB) is designed and prototyped. The results show that the grid-array antenna achieves the impedance matching over the frequency range from 790 to 1040 MHz and produces strong and uniform magnetic-field distribution over an interrogation zone of 308 mm × 150 mm.

Question: What is a grid-array antenna?


The Central Nervous System and the Gut Microbiome. Neurodevelopment is a complex process governed by both intrinsic and extrinsic signals. While historically studied by researching the brain, inputs from the periphery impact many neurological conditions. Indeed, emerging data suggest communication between the gut and the brain in anxiety, depression, cognition, and autism spectrum disorder (ASD). The development of a healthy, functional brain depends on key pre- and post-natal events that integrate environmental cues, such as molecular signals from the gut. These cues largely originate from the microbiome, the consortium of symbiotic bacteria that reside within all animals. Research over the past few years reveals that the gut microbiome plays a role in basic neurogenerative processes such as the formation of the blood-brain barrier, myelination, neurogenesis, and microglia maturation and also modulates many aspects of animal behavior. Herein, we discuss the biological intersection of neurodevelopment and the microbiome and explore the hypothesis that gut bacteria are integral contributors to development and function of the nervous system and to the balance between mental health and disease.

Question: What is the gut microbiome?


An architecture for the aggregation and analysis of scholarly usage data. Although recording of usage data is common in scholarly information services, its exploitation for the creation of value-added services remains limited due to concerns regarding, among others, user privacy, data validity, and the lack of accepted standards for the representation, sharing and aggregation of usage data. This paper presents a technical, standards-based architecture for sharing usage information, which we have designed and implemented. In this architecture, OpenURL-compliant linking servers aggregate usage information of a specific user community as it navigates the distributed information environment that it has access to. This usage information is made OAI-PMH harvestable so that usage information exposed by many linking servers can be aggregated to facilitate the creation of value-added services with a reach beyond that of a single community or a single information service. This paper also discusses issues that were encountered when implementing the proposed approach, and it presents preliminary results obtained from analyzing a usage data set containing about 3,500,000 requests aggregated by a federation of linking servers at the California State University system over a 20 month period.

Question: What is a linking server?


Learning From Semi-Supervised Weak-Label Data. Multi-label learning deals with data objects associated with multiple labels simultaneously. Previous studies typically assume that for each instance, the full set of relevant labels associated with each training instance is given. In many applications such as image annotation, however, it's usually difficult to get the full label set for each instance and only a partial or even empty set of relevant labels is available. We call this kind of problem as 'semi-supervised weak-label learning' problem. In this work we propose the SSWL (Semi-Supervised Weak-Label) method to address this problem. Both instance similarity and label similarity are considered for the complement of missing labels. Ensemble of multiple models are utilized to improve the robustness when label information is insufficient. We formulate the objective as a bi-convex optimization problem with an efficient block coordinate descent algorithm. Experiments validate the effectiveness of SSWL.

Question: What is the objective of the algorithm?


Revealing Event Saliency in Unconstrained Video Collection. Recent progresses in multimedia event detection have enabled us to find videos about a predefined event from a large-scale video collection. Research towards more intrinsic unsupervised video understanding is an interesting but understudied field. Specifically, given a collection of videos sharing a common event of interest, the goal is to discover the salient fragments, i.e., the curt video fragments that can concisely portray the underlying event of interest, from each video. To explore this novel direction, this paper proposes an unsupervised event saliency revealing framework. It first extracts features from multiple modalities to represent each shot in the given video collection. Then, these shots are clustered to build the cluster-level event saliency revealing framework, which explores useful information cues (i.e., the intra-cluster prior, inter-cluster discriminability, and inter-cluster smoothness) by a concise optimization model. Compared with the existing methods, our approach could highlight the intrinsic stimulus of the unseen event within a video in an unsupervised fashion. Thus, it could potentially benefit to a wide range of multimedia tasks like video browsing, understanding, and search. To quantitatively verify the proposed method, we systematically compare the method to a number of baseline methods on the TRECVID benchmarks. Experimental results have demonstrated its effectiveness and efficiency.

Question: What is the unsupervised event saliency revealing framework?


Telecommunication Fraud Detection Using Data Mining techniques. This document presents the final report of the thesis "Telecommunication Fraud Detection Using Data Mining Techniques", were a study is made over the effect of the unbalanced data, generated by the Telecommunications Industry, in the construction and performance of classifiers that allows the detection and prevention of frauds. In this subject, an unbalanced data set is characterized by an uneven class distribution where the amount of fraudulent instances (positive) is substantially smaller than the amount normal instances (negative). This will result in a classifier which is most likely to classify data has belonging to the normal class then to the fraud class. At first, an overall inspection is made over the data characteristics and the Naive Bayes model, which is the classifier selected to do the anomaly detection on these experiments. After the characteristics are presented, a feature engineering stage is done with the intent to extend the information contained in the data creating a depper relation with the data itself and model characteristics. A previously proposed solution that consists on undersampling the most abundant class (normal) before building the model, is presented and tested. In the end, the new proposals are presented. The first proposal is to study the effects of changing the intrinsic class distribution parameter in the Naive Bayes model and evaluate its performance. The second proposal consists in estimating margin values that when applied to the model output, attempt to bring more positive instances from previous negative classification. All of these suggested models are validated over a monte-carlo experiment, using data with and without the engineered features.

Question: What is the Naive Bayes model?


CRMA: collision-resistant multiple access. Efficiently sharing spectrum among multiple users is critical to wireless network performance. In this paper, we propose a novel spectrum sharing protocol called Collision-Resistant Multiple Access (CRMA) to achieve high efficiency. In CRMA, each transmitter views the OFDM physical layer as multiple orthogonal but sharable channels, and independently selects a few channels for transmission. The transmissions that share the same channel naturally add up in the air. The receiver extracts the received signals from all the channels and efficiently decodes the transmissions by solving a simple linear system. We implement our approach in the Qualnet simulator and show that it yields significant improvement over existing spectrum sharing schemes. We also demonstrate the feasibility of our approach using implementation and experiments on GNU Radios.

Question: What is the difference between CRMA and OFDM?


Born Digital: Understanding the First Generation of Digital Natives. Perhaps you have seen them on the train in the morning, adjusting their iPods and texting with their preternaturally agile thumbs. Or perhaps one sits in the cubicle next to yours, cycling through a seemingly endless loop of news feeds, YouTube videos, cell phone calls, and work. Perhaps you have one living in your home, capable of reprogramming the wireless router and leaping insurmountable DVR instructions in a single, tech-savvy bound.

Question: What is the first generation of digital natives?


Force Jacket: Pneumatically-Actuated Jacket for Embodied Haptic Experiences. Immersive experiences seek to engage the full sensory system in ways that words, pictures, or touch alone cannot. With respect to the haptic system, however, physical feedback has been provided primarily with handheld tactile experiences or vibration-based designs, largely ignoring both pressure receptors and the full upper-body area as conduits for expressing meaning that is consistent with sight and sound. We extend the potential for immersion along these dimensions with the Force Jacket, a novel array of pneumatically-actuated airbags and force sensors that provide precisely directed force and high frequency vibrations to the upper body. We describe the pneumatic hardware and force control algorithms, user studies to verify perception of airbag location and pressure magnitude, and subsequent studies to define full-torso, pressure and vibration-based feel effects such as punch, hug, and snake moving across the body. We also discuss the use of those effects in prototype virtual reality applications.

Question: What is the force jacket?


Generating Descriptions with Grounded and Co-referenced People. Learning how to generate descriptions of images or videos received major interest both in the Computer Vision and Natural Language Processing communities. While a few works have proposed to learn a grounding during the generation process in an unsupervised way (via an attention mechanism), it remains unclear how good the quality of the grounding is and whether it benefits the description quality. In this work we propose a movie description model which learns to generate description and jointly ground (localize) the mentioned characters as well as do visual co-reference resolution between pairs of consecutive sentences/clips. We also propose to use weak localization supervision through character mentions provided in movie descriptions to learn the character grounding. At training time, we first learn how to localize characters by relating their visual appearance to mentions in the descriptions via a semi-supervised approach. We then provide this (noisy) supervision into our description model which greatly improves its performance. Our proposed description model improves over prior work w.r.t. generated description quality and additionally provides grounding and local co-reference resolution. We evaluate it on the MPII Movie Description dataset using automatic and human evaluation measures and using our newly collected grounding and co-reference data for characters.

Question: How to generate descriptions of images or videos?


Clustering cancer gene expression data: a comparative study. The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using "classic" clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context. We present the first large-scale analysis of seven different clustering methods and four proximity measures for the analysis of 35 cancer gene expression data sets. Our results reveal that the finite mixture of Gaussians, followed closely by k-means, exhibited the best performance in terms of recovering the true structure of the data sets. These methods also exhibited, on average, the smallest difference between the actual number of classes in the data sets and the best number of clusters as indicated by our validation criteria. Furthermore, hierarchical methods, which have been widely used by the medical community, exhibited a poorer recovery performance than that of the other methods evaluated. Moreover, as a stable basis for the assessment and comparison of different clustering methods for cancer gene expression data, this study provides a common group of data sets (benchmark data sets) to be shared among researchers and used for comparisons with new methods. The data sets analyzed in this study are available at http://algorithmics.molgen.mpg.de/Supplements/CompCancer/ .

Question: What is the best clustering method for cancer gene expression data?


A Single-Stage Single-Switch LED Driver Based on Class-E Converter. This paper proposes a single-stage single-switch light-emitting diode (LED) driver that integrates a buck-boost circuit with a Class-E resonant converter by sharing single-switch device. The buck-boost circuit, working as a power-factor correction (PFC) stage, operates in the discontinuous conduction mode (DCM) to shape the input current. The Class-E converter steps down the voltage to drive the LED. For specific component parameters, the Class-E converter can achieve soft-switching on the power switch and two rectifier diodes, and reduce switch losses at high frequencies. Electrolytic capacitors are used in the proposed converter to achieve a lower cost, but the system reliability decreases. To overcome this disadvantage, film capacitors can be used, but the current ripple increases. Neglecting the cost, multilayered film capacitors are the best option, if higher reliability and lower current ripple are required. The proposed driver features high efficiency (90.8%), and a high power factor (PF) (0.995). In this paper, analytical results and design considerations at 100 kHz are presented, and a 100-W prototype with 110 VAC input was built to validate the theoretical analysis.

Question: How to design a single-stage single-switch LED driver?


Distributional Semantics Resources for Biomedical Text Processing. The openly available biomedical literature contains over 5 billion words in publication abstracts and full texts. Recent advances in unsupervised language processing methods have made it possible to make use of such large unannotated corpora for building statistical language models and inducing high quality vector space representations, which are, in turn, of utility in many tasks such as text classification, named entity recognition and query expansion. In this study, we introduce the first set of such language resources created from analysis of the entire available biomedical literature, including a dataset of all 1to 5-grams and their probabilities in these texts and new models of word semantics. We discuss the opportunities created by these resources and demonstrate their application. All resources introduced in this study are available under open licenses at http://bio.nlplab.org.

Question: What is the first set of language resources created from analysis of the entire available biomedical literature?


A Vector Space for Distributional Semantics for Entailment. Distributional semantics creates vectorspace representations that capture many forms of semantic similarity, but their relation to semantic entailment has been less clear. We propose a vector-space model which provides a formal foundation for a distributional semantics of entailment. Using a mean-field approximation, we develop approximate inference procedures and entailment operators over vectors of probabilities of features being known (versus unknown). We use this framework to reinterpret an existing distributionalsemantic model (Word2Vec) as approximating an entailment-based model of the distributions of words in contexts, thereby predicting lexical entailment relations. In both unsupervised and semi-supervised experiments on hyponymy detection, we get substantial improvements over previous results.

Question: What is a vector space for distributional semantics?


Methods for Pretreatment of Lignocellulosic Biomass for Efficient Hydrolysis and Biofuel Production. Industrial & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Review Methods for Pretreatment of Lignocellulosic Biomass for Efficient Hydrolysis and Biofuel Production Parveen Kumar, Diane M. Barrett, Michael J. Delwiche, and Pieter Stroeve Ind. Eng. Chem. Res., Article ASAP • DOI: 10.1021/ie801542g • Publication Date (Web): 20 March 2009 Downloaded from http://pubs.acs.org on March 26, 2009

Question: What is the chemical formula of lignocellulosic biomass?


Towards 3D object maps for autonomous household robots. This paper describes a mapping system that acquires 3D object models of man-made indoor environments such as kitchens. The system segments and geometrically reconstructs cabinets with doors, tables, drawers, and shelves, objects that are important for robots retrieving and manipulating objects in these environments. The system also acquires models of objects of daily use such glasses, plates, and ingredients. The models enable the recognition of the objects in cluttered scenes and the classification of newly encountered objects. Key technical contributions include (1) a robust, accurate, and efficient algorithm for constructing complete object models from 3D point clouds constituting partial object views, (2) feature-based recognition procedures for cabinets, tables, and other task-relevant furniture objects, and (3) automatic inference of object instance and class signatures for objects of daily use that enable robots to reliably recognize the objects in cluttered and real task contexts. We present results from the sensor-based mapping of a real kitchen.

Question: What is a kitchen?


Loosecut: Interactive image segmentation with loosely bounded boxes. One popular approach to interactively segment an object of interest from an image is to annotate a bounding box that covers the object, followed by a binary labeling. However, the existing algorithms for such interactive image segmentation prefer a bounding box that tightly encloses the object. This increases the annotation burden, and prevents these algorithms from utilizing automatically detected bounding boxes. In this paper, we develop a new LooseCut algorithm that can handle cases where the bounding box only loosely covers the object. We propose a new Markov Random Fields (MRF) model for segmentation with loosely bounded boxes, including an additional energy term to encourage consistent labeling of similar-appearance pixels and a global similarity constraint to better distinguish the foreground and background. This MRF model is then solved by an iterated max-flow algorithm. We evaluate LooseCut in three public image datasets, and show its better performance against several state-of-the-art methods when increasing the bounding-box size.

Question: What is the difference between a loosely and tightly bounding box?


Minimum Text Corpus Selection for Limited Domain Speech Synthesis. This paper concerns limited domain TTS system based on the concatenative method, and presents an algorithm capable to extract the minimal domainoriented text corpus from the real data of the given domain, while still reaching the maximum coverage of the domain. The proposed approach ensures that the least amount of texts are extracted, containing the most common phrases and (possibly) all the words from the domain. At the same time, it ensures that appropriate phrase overlapping is kept, allowing to find smooth concatenation in the overlapped regions to reach high quality synthesized speech. In addition, several recommendations allowing a speaker to record the corpus more fluently and comfortably are presented and discussed. The corpus building is tested and evaluated on several domains differing in size and nature, and the authors present the results of the algorithm and demonstrate the advantages of using the domain oriented corpus for speech synthesis.

Question: What is the minimum text corpus selection for limited domain speech synthesis?


A Timing-Based Scheme for Rogue AP Detection. This paper considers a category of rogue access points (APs) that pretend to be legitimate APs to lure users to connect to them. We propose a practical timing-based technique that allows the user to avoid connecting to rogue APs. Our detection scheme is a client-centric approach that employs the round trip time between the user and the DNS server to independently determine whether an AP is a rogue AP without assistance from the WLAN operator. We implemented our detection technique on commercially available wireless cards to evaluate their performance. Extensive experiments have demonstrated the accuracy, effectiveness, and robustness of our approach. The algorithm achieves close to 100 percent accuracy in distinguishing rogue APs from legitimate APs in lightly loaded traffic conditions, and larger than 60 percent accuracy in heavy traffic conditions. At the same time, the detection only requires less than 1 second for lightly-loaded traffic conditions and tens of seconds for heavy traffic conditions.

Question: What is a rogue access point?


Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays. Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images. Harvesting such localizations from existing datasets allows for the creation of improved data sources for computer-aided diagnosis and retrospective analyses. We train a convolutional neural network (CNN) for image classification and propose an attention mining (AM) strategy to improve the model's sensitivity or saliency to disease patterns. The intuition of AM is that once the most salient disease area is blocked or hidden from the CNN model, it will pay attention to alternative image regions, while still attempting to make correct predictions. However, the model requires to be properly constrained during AM, otherwise, it may overfit to uncorrelated image parts and forget the valuable knowledge that it has learned from the original image classification task. To alleviate such side effects, we then design a knowledge preservation (KP) loss, which minimizes the discrepancy between responses for X-ray images from the original and the updated networks. Furthermore, we modify the CNN model to include multi-scale aggregation (MSA), improving its localization ability on small-scale disease findings, e.g., lung nodules. We experimentally validate our method on the publicly-available ChestXray14 dataset, outperforming a class activation map (CAM)-based approach, and demonstrating the value of our novel framework for mining disease locations.

Question: What is the canadian military?


Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images. This paper addresses the problem of amodal perception of 3D object detection. The task is to not only find object localizations in the 3D world, but also estimate their physical sizes and poses, even if only parts of them are visible in the RGB-D image. Recent approaches have attempted to harness point cloud from depth channel to exploit 3D features directly in the 3D space and demonstrated the superiority over traditional 2.5D representation approaches. We revisit the amodal 3D detection problem by sticking to the 2.5D representation framework, and directly relate 2.5D visual appearance to 3D objects. We propose a novel 3D object detection system that simultaneously predicts objects 3D locations, physical sizes, and orientations in indoor scenes. Experiments on the NYUV2 dataset show our algorithm significantly outperforms the state-of-the-art and indicates 2.5D representation is capable of encoding features for 3D amodal object detection. All source code and data is on https://github.com/phoenixnn/Amodal3Det.

Question: How to detect 3D objects in indoor scenes?


Unsupervised Salience Learning for Person Re-identification. Human eyes can recognize person identities based on some small salient regions. However, such valuable salient information is often hidden when computing similarities of images with existing approaches. Moreover, many existing approaches learn discriminative features and handle drastic viewpoint change in a supervised way and require labeling new training data for a different pair of camera views. In this paper, we propose a novel perspective for person re-identification based on unsupervised salience learning. Distinctive features are extracted without requiring identity labels in the training procedure. First, we apply adjacency constrained patch matching to build dense correspondence between image pairs, which shows effectiveness in handling misalignment caused by large viewpoint and pose variations. Second, we learn human salience in an unsupervised manner. To improve the performance of person re-identification, human salience is incorporated in patch matching to find reliable and discriminative matched patches. The effectiveness of our approach is validated on the widely used VIPeR dataset and ETHZ dataset.

Question: How does unsupervised salience learning improve person re-identification?


Development trends for human monoclonal antibody therapeutics. Fully human monoclonal antibodies (mAbs) are a promising and rapidly growing category of targeted therapeutic agents. The first such agents were developed during the 1980s, but none achieved clinical or commercial success. Advances in technology to generate the molecules for study — in particular, transgenic mice and yeast or phage display — renewed interest in the development of human mAbs during the 1990s. In 2002, adalimumab became the first human mAb to be approved by the US Food and Drug Administration (FDA). Since then, an additional six human mAbs have received FDA approval: panitumumab, golimumab, canakinumab, ustekinumab, ofatumumab and denosumab. In addition, 3 candidates (raxibacumab, belimumab and ipilimumab) are currently under review by the FDA, 7 are in Phase III studies and 81 are in either Phase I or II studies. Here, we analyse data on 147 human mAbs that have entered clinical study to highlight trends in their development and approval, which may help inform future studies of this class of therapeutic agents.

Question: What is the largest number of human monoclonal antibodies that have been approved?


Simulation Model Verification and Validation: Increasing the Users' Confidence. This paper sets simulation model verification and validation (V&V) in the context of the process of performing a simulation study. Various different forms of V&V need to take place depending on the stage that has been reached. Since the phases of a study are performed in an iterative manner, so too are the various forms of V&V. A number of difficulties with verifying and validating models are discussed, after which a series of V&V methods are described. V&V is seen as a process of increasing confidence in a model, and not one of demonstrating absolute accuracy.

Question: What is the difference between simulation model verification and validation?


Does Inclusion of CIO in Top Management Team Impact Firm Performance? Evidence from a Long-Term Event Analysis. Inclusion of chief information officers (CIOs) in top management teams (TMTs) increases the functional and knowledge diversity of the teams, and elevates the role of IT in strategic business decisions. Considering that firms are increasingly relying on IT for operational efficiency and competitive advantage, TMTs with IT diversity can be expected to have superior performance compared to those without. While many studies have argued conceptually the importance of CIOs membership in TMTs, very few studies examined the performance effects of this membership. The goal of this study is to investigate this relationship. Moreover, we consider contingency factors that may have an effect on the focal relationship. Using firm-level secondary data and long-term event study methodology, we show that inclusion of CIO in TMT has a significant positive effect on firm performance, and this positive effect is larger for firms in dynamic environments and during the more recent years.

Question: Does inclusion of CIO in Top Management Team Impact Firm Performance? Evidence from a Long-Term Event Analysis.


Projection micro-stereolithography using digital micro-mirror dynamic mask. We present in this paper the development of a high-resolution projection micro-stereolithography (P SL) process by using the Digital Micromirror Device (DMDTM, Texas Instruments) as a dynamic mask. This unique technology provides a parallel fabrication of complex three-dimensional (3D) microstructures used for micro electro-mechanical systems (MEMS). Based on the understanding of underlying mechanisms, a process model has been developed with all critical parameters obtained from the experimental measurement. By coupling the experimental measurement and the process model, the photon-induced curing behavior of the resin has been quantitatively studied. The role o erty of the r n d ©

Question: What is the difference between a micro-stereolithography and a micro-mirror dynamic mask?


Partially Observable Reinforcement Learning for Intelligent Transportation Systems. Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as 'Partially Detected Intelligent Transportation Systems'. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.

Question: What is a partially observable ITS?


Document clustering algorithms, representations and evaluation for information retrieval. Digital collections of data continue to grow exponentially as the information age continues to infiltrate every aspect of society. These sources of data take many different forms such as unstructured text on the world wide web, sensor data, images, video, sound, results of scientific experiments and customer profiles for marketing. Clustering is an unsupervised learning approach that groups similar examples together without any human labeling of the data. Due to the very broad nature of this definition, there have been many different approaches to clustering explored in the scientific literature. This thesis addresses the computational efficiency of document clustering in an information retrieval setting. This includes compressed representations, efficient and scalable algorithms, and evaluation with a specific use case for increasing efficiency of a distributed and parallel search engine. Furthermore, it addresses the evaluation of document cluster quality for large scale collections containing millions to billions of documents where complete labeling of a collection is impractical. The cluster hypothesis from information retrieval is also tested using several different approaches throughout the thesis. This research introduces novel approaches to clustering algorithms, document representations, and clustering evaluation. The combination of document clustering algorithms and document representations outlined in this thesis are able to process large scale web search data sets. This has not previously been reported in the literature without resorting to sampling and producing a small number of clusters. Furthermore, the algorithms are able to do this on a single machine. However, they can also function in a distributed and parallel setting to further increase their scalability. This thesis introduces new clustering algorithms that can also be applied to problems outside the info

Question: What is the difference between clustering and classification?


Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm. Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.

Question: What is the least square support vector machine?


Hierarchical Disentangled Representations. Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner. A number of recent efforts have focused on learning representations that disentangle statistically independent axes of variation, often by introducing suitable modifications of the objective function. We synthesize this growing body of literature by formulating a generalization of the evidence lower bound that explicitly represents the trade-offs between sparsity of the latent code, bijectivity of representations, and coverage of the support of the empirical data distribution. Our objective is also suitable to learning hierarchical representations that disentangle blocks of variables whilst allowing for some degree of correlations within blocks. Experiments on a range of datasets demonstrate that learned representations contain interpretable features, are able to learn discrete attributes, and generalize to unseen combinations of factors.

Question: What is a hierarchical representation?


MarrNet: 3D Shape Reconstruction via 2.5D Sketches. 3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible 3D shapes. This introduces challenges for learning-based approaches, as 3D object annotations are scarce in real images. Previous work chose to train on synthetic data with ground truth 3D information, but suffered from domain adaptation when tested on real data. In this work, we propose MarrNet, an end-to-end trainable model that sequentially estimates 2.5D sketches and 3D object shape. Our disentangled, two-step formulation has three advantages. First, compared to full 3D shape, 2.5D sketches are much easier to be recovered from a 2D image; models that recover 2.5D sketches are also more likely to transfer from synthetic to real data. Second, for 3D reconstruction from 2.5D sketches, systems can learn purely from synthetic data. This is because we can easily render realistic 2.5D sketches without modeling object appearance variations in real images, including lighting, texture, etc. This further relieves the domain adaptation problem. Third, we derive differentiable projective functions from 3D shape to 2.5D sketches; the framework is therefore end-to-end trainable on real images, requiring no human annotations. Our model achieves state-of-the-art performance on 3D shape reconstruction.

Question: How to reconstruct 3D shape from 2.5D sketches?


Assessing psychopathic attributes in a noninstitutionalized population.. The present study examined antisocial dispositions in 487 university students. Primary and secondary psychopathy scales were developed to assess a protopsychopathic interpersonal philosophy. An antisocial action scale also was developed for purposes of validation. The primary, secondary, and antisocial action scales were correlated with each other and with boredom susceptibility and disinhibition but not with experience seeking and thrill and adventure seeking. Secondary psychopathy was associated with trait anxiety. Multiple regression analysis revealed that the strongest predictors of antisocial action were disinhibition, primary psychopathy, secondary psychopathy, and sex, whereas thrill and adventure seeking was a negative predictor. This argues against a singular behavioral inhibition system mediating both antisocial and risk-taking behavior. These findings are also consistent with the view that psychopathy is a continuous dimension.

Question: What is the difference between primary and secondary psychopathy?


Sorry, I only speak natural language: a pattern-based, data-driven and guided approach to mapping natural language queries to SPARQL. We present a new interface based on natural language to support users in specifying their queries with respect to RDF datasets. The approach relies on a number of predefined patterns that uniquely determine a type of SPARQL query. The approach is incremental and assisted in that it guides a user step by step in specifying a query by incrementally parsing the input and providing suggestions for completion at every stage. The methodology to specify the patterns is informed by empirical distributions of SPARQL query types as found in standard query logs. So far, we have implemented the approach using two patterns only as proof-of-concept. The coverage of the pattern library will be extended in the future. We will also provide an evaluation of the approach on the well-known QALD dataset.

Question: What is the name of the approach?


A Mixed Fragmentation Methodology For Initial Distributed Database Design. We deene mixed fragmentation as a process of simultaneously applying the horizontal and vertical fragmentation on a relation. It can be achieved in one of two ways: by performing horizontal fragmentation followed by vertical fragmentation or by performing vertical fragmentation followed by horizontal fragmentation. The need for mixed fragmentation arises in distributed databases because database users usually access subsets of data which are vertical and horizontal fragments of global relations and there is a need to process queries or transactions that would access these fragments optimally. We present algorithms for generating candidate vertical and horizontal fragmentation schemes and propose a methodology for distributed database design using these fragmentation schemes. When applied together these schemes form a grid. This grid consisting of cells is then merged to form mixed fragments so as to minimize the number of disk accesses required to process the distributed transactions. We have implemented the vertical and horizontal fragmentation algorithms and are developing a Distributed Database Design Tool to support the proposed methodology.

Question: What is a mixed fragmentation?


Across the Digital Divide: A Cross-Country Multi-Technology Analysis of the Determinants of IT Penetration. This paper studies the country-level digital divide across successive generations of IT, providing detailed insights into the magnitude and changing nature of the divide. We examine a panel of 40 countries from 1985-2001, based on data from three distinct generations of IT: mainframes, personal computers, and the Internet. Using two measures of IT penetration, we conduct an empirical investigation of socio-economic factors driving the digital divide. We find that IT penetration is positively associated with national income for all three technology generations, and the association between penetration and income is stronger for countries with higher levels of IT penetration. We also examine other demographic and economic factors, going beyond income, and find significant differences in the nature of their effects across countries at different stages of IT adoption. Importantly, factors that previously may have been expanding the divide with earlier technologies are narrowing the gap as the Internet becomes the defining technology of the Information Age.

Question: What is the digital divide?


Metasurface holograms reaching 80% efficiency.. Surfaces covered by ultrathin plasmonic structures--so-called metasurfaces--have recently been shown to be capable of completely controlling the phase of light, representing a new paradigm for the design of innovative optical elements such as ultrathin flat lenses, directional couplers for surface plasmon polaritons and wave plate vortex beam generation. Among the various types of metasurfaces, geometric metasurfaces, which consist of an array of plasmonic nanorods with spatially varying orientations, have shown superior phase control due to the geometric nature of their phase profile. Metasurfaces have recently been used to make computer-generated holograms, but the hologram efficiency remained too low at visible wavelengths for practical purposes. Here, we report the design and realization of a geometric metasurface hologram reaching diffraction efficiencies of 80% at 825 nm and a broad bandwidth between 630 nm and 1,050 nm. The 16-level-phase computer-generated hologram demonstrated here combines the advantages of a geometric metasurface for the superior control of the phase profile and of reflectarrays for achieving high polarization conversion efficiency. Specifically, the design of the hologram integrates a ground metal plane with a geometric metasurface that enhances the conversion efficiency between the two circular polarization states, leading to high diffraction efficiency without complicating the fabrication process. Because of these advantages, our strategy could be viable for various practical holographic applications.

Question: What is a geometric metasurface?


Reliable detection and skew correction method of license plate for PTZ camera-based license plate recognition system. The skewness of license plates affects negatively all processing steps of license plate recognition including character segmentation and character classification. The skewness of the license plate in the captured image may appear severely under PTZ camera environments since license plate appears to have various postures in the captured image due to panning and tilting positions of PTZ cameras. Thus far, most of previous works on this subject have mainly resolved skewness problem due to rotation in plane, but not rotation in depth. In this paper, by utilizing planar projective transformation (planar homography), we propose a reliable skew correction method of license plates which handles also the skewness due to rotation in depth. For more reliable deskewing process, the license plate needs to be detected more reliably so that this paper also proposes a reliable license plate detection method. The effectiveness of the proposed skew correction method as well as the proposed license plate detection method is verified though experiments.

Question: How to detect license plate?


Corporate Governance and Corporate Social Responsibility Synergies and Interrelationships. Manuscript Type: Empirical Research Questions/Issue: This paper seeks to explore the interrelationships between corporate governance (CG) and corporate social responsibility (CSR): first, theoretically, by reviewing the literature and surveying various postulations on offer; second, empirically, by investigating the conception and interpretation of this relationship in the context of a sample of firms operating in Lebanon. Accordingly, the paper seeks to highlight the increasing cross-connects or interfaces between CG and CSR, capitalizing on fresh insights from a developing country perspective. Research Findings/Results: A qualitative interpretive research methodology was adopted, drawing on in-depth interviews with the top managers of eight corporations operating in Lebanon, with the findings suggesting that the majority of managers conceive of CG as a necessary pillar for sustainable CSR. These findings are significant and interesting, implying that recent preoccupation with CG in developing countries is starting to be counterbalanced by some interest/attention to CSR, with growing appreciation of their interdependencies and the need to move beyond CG conformance toward voluntary CSR performance. Theoretical Implications: This study makes two important contributions. First, it suggests that there is a salient two-way relationship and increasing overlap between CG and CSR. While much previous literature has researched CG and CSR independently, this paper makes the case for considering them jointly and systematically. Second, the paper outlines a number of theoretical propositions that can serve as the basis for future research on the topic, particularly in developing countries, given that the data and theoretical propositions are both derived from and tailored to developing country contexts. Practical Implications: This study can potentially alert managers to the increasing

Question: What is the difference between corporate governance and corporate social responsibility?


MARSSx 86 : A Full System Simulator for x 86 CPUs. We present MARSS, an open source, fast, full system simulation tool built on QEMU to support cycle-accurate simulation of superscalar homogeneous and heterogeneous multicore x86 processors. MARSS includes detailed models of coherent caches, interconnections, chipsets, memory and IO devices. MARSS simulates the execution of all software components in the system, including unmodified binaries of applications, OS and libraries.

Question: What is a full system simulator?


Feeling Love and Doing More for Distant Others : Specific Positive Emotions Differentially Affect Prosocial Consumption. Marketers often employ a variety of positive emotions to encourage consumption or promote a particular behavior (e.g., buying, donating, recycling) to benefit an organization or cause. The authors show that specific positive emotions do not universally increase prosocial behavior but, rather, encourage different types of prosocial behavior. Four studies show that whereas positive emotions (i.e., love, hope, pride, and compassion) all induce prosocial behavior toward close entities (relative to a neutral emotional state), only love induces prosocial behavior toward distant others and international organizations. Love's effect is driven by a distinct form of broadening, characterized by extending feelings of social connection and the boundary of caring to be more inclusive of others regardless of relatedness. Love—as a trait and a momentary emotion— is unique among positive emotions in fostering connectedness that other positive emotions (hope and pride) do not and broadening behavior in a way that other connected emotions (compassion) do not. This research contributes to the broaden-and-build theory of positive emotion by demonstrating a distinct type of broadening for love and adds an important qualification to the general finding that positive emotions uniformly encourage prosocial behavior.

Question: What is the difference between love and compassion?


MOTHERS WHO KILL THEIR CHILDREN A literature review. Maternal filicide, the murder of a child by its mother, is a complex phenomenon with various causes and characteristics. Research, by means of the development of several classification systems and in identifying particular risk factors, has been conducted with the aim of better prevention of this emotionally evocative crime. Various disciplines have offered a wide range of perspectives on why women kill their biological children. These are intended to yield a better understanding of the aetiology of this crime. This literature review delineates three dominant perspectives: psychiatric, psychological, and sociological. The main findings of each perspective are discussed. However, these three perspectives frequently operate in conjunction with each other in that both intrapsychic and interpersonal dynamics play a role in acts of maternal filicide. The most vulnerable women appear to be those who have had a severely deficient developmental history (trauma and/or grossly inadequate parenting), those who experience current difficult psychosocial circumstances, and those who have been diagnosed with a psychiatric illness. However, not all women who experience such problems kill their children. In this regard, individual differences have an important role to play and more carefully delineated future research is suggested. One of the most significant findings of this literature review is that there appears to be a paucity of systematic research on the South African phenomenon of parental child homicide.

Question: What is the most vulnerable group of women who kill their children?


Mop: an efficient and generic runtime verification framework. Monitoring-Oriented Programming (MOP1) [21, 18, 22, 19] is a formal framework for software development and analysis, in which the developer specifies desired properties using definable specification formalisms, along with code to execute when properties are violated or validated. The MOP framework automatically generates monitors from the specified properties and then integrates them together with the user-defined code into the original system.  The previous design of MOP only allowed specifications without parameters, so it could not be used to state and monitor safety properties referring to two or more related objects. In this paper we propose a parametric specification formalism-independent extension of MOP, together with an implementation of JavaMOP that supports parameters. In our current implementation, parametric specifications are translated into AspectJ code and then weaved into the application using off-the-shelf AspectJ compilers; hence, MOP specifications can be seen as formal or logical aspects.  Our JavaMOP implementation was extensively evaluated on two benchmarks, Dacapo [14] and Tracematches [8], showing that runtime verification in general and MOP in particular are feasible. In some of the examples, millions of monitor instances are generated, each observing a set of related objects. To keep the runtime overhead of monitoring and event observation low, we devised and implemented a decentralized indexing optimization. Less than 8% of the experiments showed more than 10% runtime overhead; in most cases our tool generates monitoring code as efficient as the hand-optimized code. Despite its genericity, JavaMOP is empirically shown to be more efficient than runtime verification systems specialized and optimized for particular specification formalisms. Many property violations were detected during our experiments; some of them are benign, others indicate defects in programs. Many of these are

Question: What is the difference between a generic and a parametric specification formalism?


A survey on IoT communication and computation frameworks: An industrial perspective. This paper surveys fog computing and embedded systems platforms as the building blocks of Internet of Things (IoT). Many concepts around IoT architectures, with various examples, are also discussed. This paper reviews a high-level conceptual layered architecture for IoT from a computational perspective. The architecture incorporates fog computing to address several issues associated with cloud computing; however, it is never a binary decision between fog and cloud. Many of the world's physical objects are being embedded with sensors and actuators, tied by communication infrastructures, and managed by computational algorithms. IoT sensor networks and embedded systems connecting smart objects are revolutionizing how we approach our daily lives, health care, energy, and transportation. Such computational needs are addressed with an array of various models and frameworks. In an attempt to consolidate the use of these models, this paper reviews the state-of-the-art research in IoT, cloud computing, and fog computing.

Question: What is the state of the art in IoT?


Question: What is industrial robotics?


Oral-motor function and feeding intervention.. This article presents the elements of the Oral Motor Intervention section of the Infant Care Path for Physical Therapy in the Neonatal Intensive Care Unit (NICU). The types of physical therapy interventions presented in this path are evidence based as well as infant driven and family focused. In the context of anticipated maturation of suck-swallow-breathe coordination, the timing and methods for initiation of oral feedings and transition from gavage to full breast or bottle-feedings are presented with supporting evidence.

Question: What is the evidence based intervention for oral-motor function and feeding?


Combining document representations for known-item search. This paper investigates the pre-conditions for successful combination of document representations formed from structural markup for the task of known-item search. As this task is very similar to work in meta-search and data fusion, we adapt several hypotheses from those research areas and investigate them in this context. To investigate these hypotheses, we present a mixture-based language model and also examine many of the current meta-search algorithms. We find that compatible output from systems is important for successful combination of document representations. We also demonstrate that combining low performing document representations can improve performance, but not consistently. We find that the techniques best suited for this task are robust to the inclusion of poorly performing document representations. We also explore the role of variance of results across systems and its impact on the performance of fusion, with the surprising result that the correct documents have higher variance across document representations than highly ranking incorrect documents.

Question: What is the role of variance of results across systems in the performance of fusion?


Role of IL-33 in inflammation and disease. Interleukin (IL)-33 is a new member of the IL-1 superfamily of cytokines that is expressed by mainly stromal cells, such as epithelial and endothelial cells, and its expression is upregulated following pro-inflammatory stimulation. IL-33 can function both as a traditional cytokine and as a nuclear factor regulating gene transcription. It is thought to function as an 'alarmin' released following cell necrosis to alerting the immune system to tissue damage or stress. It mediates its biological effects via interaction with the receptors ST2 (IL-1RL1) and IL-1 receptor accessory protein (IL-1RAcP), both of which are widely expressed, particularly by innate immune cells and T helper 2 (Th2) cells. IL-33 strongly induces Th2 cytokine production from these cells and can promote the pathogenesis of Th2-related disease such as asthma, atopic dermatitis and anaphylaxis. However, IL-33 has shown various protective effects in cardiovascular diseases such as atherosclerosis, obesity, type 2 diabetes and cardiac remodeling. Thus, the effects of IL-33 are either pro- or anti-inflammatory depending on the disease and the model. In this review the role of IL-33 in the inflammation of several disease pathologies will be discussed, with particular emphasis on recent advances.

Question: What is the role of IL-33 in inflammation?


The Airport Ground Movement Problem : Past and Current Research and Future Directions. Determining efficient airport operations is an important and critical problem for airports, airlines, passengers and other stakeholders. Moreover, it is likely to become even more so given the traffic increases which are expected over the next few years. The ground movement problem forms the link between other airside problems, such as arrival sequencing, departure sequencing and gate/stand allocation. This paper provides an overview, categorisation and critical examination of the previous research for ground movement and highlights various important open areas of research. Of particular importance is the question of the integration of various airport operations and their relationships which are considered in this paper.

Question: What is the airport ground movement problem?


Low complexity photo sensor dead pixel detection algorithm. Although the number of pixels in image sensors is increasing exponentially, production techniques have only been able to linearly reduce the probability that a pixel will be defective. The result is a rapidly increasing probability that a sensor will contain one or more defective pixels. The defect pixel detection and defect pixel correction are operated separately but the former must employ before the latter is in use. Traditional detection scheme, which finds the defect pixels during manufacturing, is not able to discover the spread defect pixels years late. Consequently, the lifetime and robust defect pixel detection technique, which identifies the fault pixels when camera is in use, is more practical and developed. The paper presents a two stages dead pixel detection technique without complicated mathematic computations so that the embedded devices can easily implement it. Using six dead pixel types are tested and the experimental result indicates that it can be accelerated more than four times the detection time.

Question: What is a dead pixel?


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

Question: How to use JSTOR?


Learning Actionable Representations from Visual Observations. In this work we explore a new approach for robots to teach themselves about the world simply by observing it. In particular we investigate the effectiveness of learning task-agnostic representations for continuous control tasks. We extend Time-Contrastive Networks (TCN) that learn from visual observations by embedding multiple frames jointly in the embedding space as opposed to a single frame. We show that by doing so, we are now able to encode both position and velocity attributes significantly more accurately. We test the usefulness of this self-supervised approach in a reinforcement learning setting. We show that the representations learned by agents observing themselves take random actions, or other agents perform tasks successfully, can enable the learning of continuous control policies using algorithms like Proximal Policy Optimization (PPO) using only the learned embeddings as input. We also demonstrate significant improvements on the real-world Pouring dataset with a relative error reduction of 39.4% for motion attributes and 11.1% for static attributes compared to the single-frame baseline. Video results are available at https://sites.google.com/view/actionablerepresentations

Question: What is the pouring dataset?


Fast Optimal Transport Averaging of Neuroimaging Data. Knowing how the Human brain is anatomically and functionally organized at the level of a group of healthy individuals or patients is the primary goal of neuroimaging research. Yet computing an average of brain imaging data defined over a voxel grid or a triangulation remains a challenge. Data are large, the geometry of the brain is complex and the between subjects variability leads to spatially or temporally non-overlapping effects of interest. To address the problem of variability, data are commonly smoothed before performing a linear group averaging. In this work we build on ideas originally introduced by Kantorovich to propose a new algorithm that can average efficiently non-normalized data defined over arbitrary discrete domains using transportation metrics. We show how Kantorovich means can be linked to Wasserstein barycenters in order to take advantage of the entropic smoothing approach used by. It leads to a smooth convex optimization problem and an algorithm with strong convergence guarantees. We illustrate the versatility of this tool and its empirical behavior on functional neuroimaging data, functional MRI and magnetoencephalography (MEG) source estimates, defined on voxel grids and triangulations of the folded cortical surface.

Question: How to compute an average of brain imaging data defined over a voxel grid or a triangulation?


Crafting Integrated Multichannel Retailing Strategies. Multichannel retailing is the set of activities involved in selling merchandise or services to consumers through more than one channel. Multichannel retailers dominate today's retail landscape. While there are many benefits of operating multiple channels, these retailers also face many challenges. In this article, we discuss the key issues concerning multichannel retailing, including the motivations and constraints of going multichannel, the challenges of crafting multichannel retailing strategies and opportunities for creating synergies across channels, key retail mix decisions facing multichannel retailers, and the dynamics of multichannel retailing. We synthesize current knowledge drawn from the academic literature and industry practice, and discuss potential directions for future research. © 2010 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Question: What is the difference between multichannel retailing and multichannel retailing?


Graph-based mining of multiple object usage patterns. The interplay of multiple objects in object-oriented programming often follows specific protocols, for example certain orders of method calls and/or control structure constraints among them that are parts of the intended object usages. Unfortunately, the information is not always documented. That creates long learning curve, and importantly, leads to subtle problems due to the misuse of objects.  In this paper, we propose GrouMiner, a novel graph-based approach for mining the usage patterns of one or multiple objects. GrouMiner approach includes a graph-based representation for multiple object usages, a pattern mining algorithm, and an anomaly detection technique that are efficient, accurate, and resilient to software changes. Our experiments on several real-world programs show that our prototype is able to find useful usage patterns with multiple objects and control structures, and to translate them into user-friendly code skeletons to assist developers in programming. It could also detect the usage anomalies that caused yet undiscovered defects and code smells in those programs.

Question: What is a graph-based mining of multiple object usages?