Exploiting geographic dependencies for real estate appraisal: a mutual perspective of ranking and clustering. It is traditionally a challenge for home buyers to understand, compare and contrast the investment values of real estates. While a number of estate appraisal methods have been developed to value real property, the performances of these methods have been limited by the traditional data sources for estate appraisal. However, with the development of new ways of collecting estate-related mobile data, there is a potential to leverage geographic dependencies of estates for enhancing estate appraisal. Indeed, the geographic dependencies of the value of an estate can be from the characteristics of its own neighborhood (individual), the values of its nearby estates (peer), and the prosperity of the affiliated latent business area (zone). To this end, in this paper, we propose a geographic method, named ClusRanking, for estate appraisal by leveraging the mutual enforcement of ranking and clustering power. ClusRanking is able to exploit geographic individual, peer, and zone dependencies in a probabilistic ranking model. Specifically, we first extract the geographic utility of estates from geography data, estimate the neighborhood popularity of estates by mining taxicab trajectory data, and model the influence of latent business areas via ClusRanking. Also, we use a linear model to fuse these three influential factors and predict estate investment values. Moreover, we simultaneously consider individual, peer and zone dependencies, and derive an estate-specific ranking likelihood as the objective function. Finally, we conduct a comprehensive evaluation with real-world estate related data, and the experimental results demonstrate the effectiveness of our method.

Question: What is the difference between a probabilistic ranking model and a probabilistic clustering model?


Turn-Taking Estimation Model Based on Joint Embedding of Lexical and Prosodic Contents. A natural conversation involves rapid exchanges of turns while talking. Taking turns at appropriate timing or intervals is a requisite feature for a dialog system as a conversation partner. This paper proposes a model that estimates the timing of turn-taking during verbal interactions. Unlike previous studies, our proposed model does not rely on a silence region between sentences since a dialog system must respond without large gaps or overlaps. We propose a Recurrent Neural Network (RNN) based model that takes the joint embedding of lexical and prosodic contents as its input to classify utterances into turn-taking related classes and estimates the turn-taking timing. To this end, we trained a neural network to embed the lexical contents, the fundamental frequencies, and the speech power into a joint embedding space. To learn meaningful embedding spaces, the prosodic features from each single utterance are pretrained using RNN and combined with utterance lexical embedding as the input of our proposed model. We tested this model on a spontaneous conversation dataset and confirmed that it outperformed the use of word embedding-based features.

Question: How to estimate the turn-taking timing?


Exploiting Tri-Relationship for Fake News Detection. Social media for news consumption is becoming popular nowadays. The low cost, easy access and rapid information dissemination of social media bring benefits for people to seek out news timely. However, it also causes the widespread of fake news, i.e., low-quality news pieces that are intentionally fabricated. The fake news brings about several negative effects on individual consumers, news ecosystem, and even society trust. Previous fake news detection methods mainly focus on news contents for deception classification or claim fact-checking. Recent Social and Psychology studies show potential importance to utilize social media data: 1) Confirmation bias effect reveals that consumers prefer to believe information that confirms their existing stances; 2) Echo chamber effect suggests that people tend to follow likeminded users and form segregated communities on social media. Even though users social engagements towards news on social media provide abundant auxiliary information for better detecting fake news, but existing work exploiting social engagements is rather limited. In this paper, we explore the correlations of publisher bias, news stance, and relevant user engagements simultaneously, and propose a Tri-Relationship Fake News detection framework (TriFN). We also provide two comprehensive real-world fake news datasets to facilitate fake news research. Experiments on these datasets demonstrate the effectiveness of the proposed approach. Introduction People nowadays tend to seek out and consume news from social media rather than traditional news organizations. For example, 62% of U.S. adults get news on social media in 2016, while in 2012, only 49 percent reported seeing news on social media. However, social media for news consumption is a double-edged sword. The quality of news on social media is much lower than traditional news organizations. Large volumes of "fake news", i.e., those news articles with intenti

Question: What is the difference between fake news and low-quality news?


Learning to Order Things. There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order, given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a two-stage approach in which one first learns by conventional means a preference function, of the form PREF , which indicates whether it is advisable to rank before . New instances are then ordered so as to maximize agreements with the learned preference function. We show that the problem of finding the ordering that agrees best with a preference function is NP-complete, even under very restrictive assumptions. Nevertheless, we describe a simple greedy algorithm that is guaranteed to find a good approximation. We then discuss an on-line learning algorithm, based on the "Hedge" algorithm, for finding a good linear combination of ranking "experts." We use the ordering algorithm combined with the on-line learning algorithm to find a combination of "search experts," each of which is a domain-specific query expansion strategy for a WWW search engine, and present experimental results that demonstrate the merits of our approach.

Question: How to learn to order things


Reranking and Self-Training for Parser Adaptation. Statistical parsers trained and tested on the Penn Wall Street Journal (WSJ) treebank have shown vast improvements over the last 10 years. Much of this improvement, however, is based upon an ever-increasing number of features to be trained on (typically) the WSJ treebank data. This has led to concern that such parsers may be too finely tuned to this corpus at the expense of portability to other genres. Such worries have merit. The standard "Charniak parser" checks in at a labeled precisionrecall f -measure of 89.7% on the Penn WSJ test set, but only 82.9% on the test set from the Brown treebank corpus. This paper should allay these fears. In particular, we show that the reranking parser described in Charniak and Johnson (2005) improves performance of the parser on Brown to 85.2%. Furthermore, use of the self-training techniques described in (McClosky et al., 2006) raise this to 87.8% (an error reduction of 28%) again without any use of labeled Brown data. This is remarkable since training the parser and reranker on labeled Brown data achieves only 88.4%.

Question: What is the difference between a labeled precisionrecall f -measure and a labeled precision?


Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy. We describe a new iterative method for parameter estimation of Gaussian mixtures. The new method is based on a framework developed by Kivinen and Warmuth for supervised on-line learning. In contrast to gradient descent and EM, which estimate the mixture's covariance matrices, the proposed method estimates the inverses of the covariance matrices. Furthennore, the new parameter estimation procedure can be applied in both on-line and batch settings. We show experimentally that it is typically faster than EM, and usually requires about half as many iterations as EM.

Question: How to estimate the parameters of a Gaussian mixture?


Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection. During expressive speech, the voice is enriched to convey not only the intended semantic message but also the emotional state of the speaker. The pitch contour is one of the important properties of speech that is affected by this emotional modulation. Although pitch features have been commonly used to recognize emotions, it is not clear what aspects of the pitch contour are the most emotionally salient. This paper presents an analysis of the statistics derived from the pitch contour. First, pitch features derived from emotional speech samples are compared with the ones derived from neutral speech, by using symmetric Kullback-Leibler distance. Then, the emotionally discriminative power of the pitch features is quantified by comparing nested logistic regression models. The results indicate that gross pitch contour statistics such as mean, maximum, minimum, and range are more emotionally prominent than features describing the pitch shape. Also, analyzing the pitch statistics at the utterance level is found to be more accurate and robust than analyzing the pitch statistics for shorter speech regions (e.g., voiced segments). Finally, the best features are selected to build a binary emotion detection system for distinguishing between emotional versus neutral speech. A new two-step approach is proposed. In the first step, reference models for the pitch features are trained with neutral speech, and the input features are contrasted with the neutral model. In the second step, a fitness measure is used to assess whether the input speech is similar to, in the case of neutral speech, or different from, in the case of emotional speech, the reference models. The proposed approach is tested with four acted emotional databases spanning different emotional categories, recording settings, speakers and languages. The results show that the recognition accuracy of the system is over 77% just with the

Question: What is the best way to analyze the pitch contour?


An Iterative Image Registration Technique with an Application to Stereo Vision. Image registration finds a variety of applications in computer vision. Unfortunately, traditional image registration techniques tend to be costly. We present a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration. Our technique is faster because it examines far fewer potential matches between the images than existing techniques. Furthermore, this registration technique can be generalized to handle rotation, scaling and shearing. We show show our technique can be adapted for use in a stereo vision system.

Question: What is a type of Newton-Raphson iteration?


From Facial Expression Recognition to Interpersonal Relation Prediction. Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. We investigate if such fine-grained and high-level relation traits can be characterized and quantified from face images in the wild. We address this challenging problem by first studying a deep network architecture for robust recognition of facial expressions. Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data. While conventional supervised training requires datasets with complete labels (e.g., all samples must be labeled with gender, age, and expression), we show that this requirement can be relaxed via a novel attribute propagation method. The approach further allows us to leverage the inherent correspondences between heterogeneous attribute sources despite the disparate distributions of different datasets. With the network we demonstrate state-of-the-art results on existing facial expression recognition benchmarks. To predict inter-personal relation, we use the expression recognition network as branches for a Siamese model. Extensive experiments show that our model is capable of mining mutual context of faces for accurate fine-grained interpersonal prediction.

Question: How to predict inter-personal relation?


Automatic Classification of Change Requests for Improved IT Service Quality. Faulty changes to the IT infrastructure can lead to critical system and application outages, and therefore cause serious economical losses. In this paper, we describe a change planning support tool that aims at assisting the change requesters in leveraging aggregated information associated with the change, like past failure reasons or best implementation practices. The thus gained knowledge can be used in the subsequent planning and implementation steps of the change. Optimal matching of change requests with the aggregated information is achieved through the classification of the change request into about 200 fine-grained activities. We propose to automatically classify the incoming change requests using various information retrieval and machine learning techniques. The cost of building the classifiers is reduced by employing active learning techniques or by leveraging labeled features. Historical tickets from two customers were used to empirically assess and compare the accuracy of the different classification approaches (Lucene index, multinomial logistic regression, and generalized expectation criteria).

Question: What is the cost of building a classifier?


Accurate and Efficient On-Chip Spectral Analysis for Built-In Testing and Calibration Approaches. The fast Fourier transform (FFT) algorithm is widely used as a standard tool to carry out spectral analysis because of its computational efficiency. However, the presence of multiple tones frequently requires a fine frequency resolution to achieve sufficient accuracy, which imposes the use of a large number of FFT points that results in large area and power overheads. In this paper, an FFT method is proposed for on-chip spectral analysis of multi-tone signals with particular harmonic and intermodulation components. This accurate FFT analysis approach is based on coherent sampling, but it requires a significantly smaller number of points to make the FFT realization more suitable for on-chip built-in testing and calibration applications that require area and power efficiency. The technique was assessed by comparing the simulation results from the proposed method of single and multiple tones with the simulation results obtained from the FFT of coherently sampled tones. The results indicate that the proper selection of test tone frequencies can avoid spectral leakage even with multiple narrowly spaced tones. When low-frequency signals are captured with an analog-to-digital converter (ADC) for on-chip analysis, the overall accuracy is limited by the ADC's resolution, linearity, noise, and bandwidth limitations. Post-layout simulations of a 16-point FFT showed that third-order intermodulation (IM3) testing with two tones can be performed with 1.5-dB accuracy for IM3 levels of up to 50 dB below the fundamental tones that are quantized with a 10-bit resolution. In a 45-nm CMOS technology, the layout area of the 16-point FFT for on-chip built-in testing is 0.073 mm2, and its estimated power consumption is 6.47 mW.

Question: What is the area of a 16-point FFT?


Geolocation of RF Emitters by Many UAVs. This paper presents an approach to using a large team of UAVs to find radio frequency (RF) emitting targets in a large area. Small, inexpensive UAVs that can collectively and rapidly determine the approximate location of intermittently broadcasting and mobile RF emitters have a range of applications in both military, e.g., for finding SAM batteries, and civilian, e.g., for finding lost hikers, domains. Received Signal Strength Indicator (RSSI) sensors on board the UAVs measure the strength of RF signals across a range of frequencies. The signals, although noisy and ambiguous due to structural noise, e.g., multipath effects, overlapping signals and sensor noise, allow estimates to be made of emitter locations. Generating a probability distribution over emitter locations requires integrating multiple signals from different UAVs into a Bayesian filter, hence requiring cooperation between the UAVs. Once likely target locations are identified, EO-camera equipped UAVs must be tasked to provide a video stream of the area to allow a user to identify the emitter.

Question: How do you find a lost hiker?


Real-Time Ray Tracing with CUDA. The graphics processors (GPUs) have recently emerged as a low-cost alternative for parallel programming. Since modern GPUs have great computational power as well as high memory bandwidth, running ray tracing on them has been an active field of research in computer graphics in recent years. Furthermore, the introduction of CUDA, a novel GPGPU architecture, has removed several limitations that the traditional GPU-based ray tracing suffered. In this paper, an implementation of high performance CUDA ray tracing is demonstrated. We focus on the performance and show how our design choices in various optimization lead to an implementation that outperforms the previous works. For reasonably complex scenes with simple shading, our implementation achieves the performance of 30 to 43 million traced rays per second. Our implementation also includes the effects of recursive specular reflection and refraction, which were less discussed in previous GPU-based ray tracing works.

Question: What is the performance of ray tracing on a GPU?


Studying Bluetooth Malware Propagation: The BlueBag Project. Bluetooth worms currently pose relatively little danger compared to Internet scanning worms. The BlueBag project shows targeted attacks through Bluetooth malware using proof-of-concept codes and mobile devices

Question: What is the BlueBag project?


Use of platelet-rich fibrin membrane following treatment of gingival recession: a randomized clinical trial.. This 6-month randomized controlled clinical study primarily aimed to compare the results achieved by the use of a platelet-rich fibrin (PRF) membrane or connective tissue graft (CTG) in the treatment of gingival recession and to evaluate the clinical impact of PRF on early wound healing and subjective patient discomfort. Use of a PRF membrane in gingival recession treatment provided acceptable clinical results, followed by enhanced wound healing and decreased subjective patient discomfort compared to CTG-treated gingival recessions. No difference could be found between PRF and CTG procedures in gingival recession therapy, except for a greater gain in keratinized tissue width obtained in the CTG group and enhanced wound healing associated with the PRF group.

Question: What is the difference between a PRF membrane and a CTG?


Residual Useful Life Prediction for Rolling Element Bearings Based on Multi-feature Fusion Regression. Residual useful life (RUL) prediction is critical in efficient implementation of condition-based maintenances for rolling element bearings (REBs). A multi-feature fusion regression method is reported for predicting the RUL of REBs in this work. In the proposed approach, locally linear embedding technique is employed to fuse original features for erecting a condition indicator of the REBs. The fused condition indicator is then modeled by an adaptive network-based fuzzy inference system for predicting the RUL. The present approach is applied to experimental data collected from an REB. Experimental results show that the proposed approach exhibits better performance than peer approaches. It is capable of accurately predicting the RUL of the REB.

Question: What is the residual useful life prediction for rolling element bearings?


An 87 fJ/conversion-step 12 b 10 MS/s SAR ADC using a minimum number of unit capacitors. This work proposes a 12 b 10 MS/s 0.11 lm CMOS successive-approximation register ADC based on a C-R hybrid DAC for low-power sensor applications. The proposed C-R DAC employs a 2-step split-capacitor array of upper seven bits and lower five bits to optimize power consumption and chip area at the target speed and resolution. A VCM-based switching method for the most significant bit and reference voltage segments from an insensitive R-string for the last two least significant bits minimize the number of unit capacitors required in the C-R hybrid DAC. The comparator accuracy is improved by an open-loop offset cancellation technique in the first-stage pre-amp. The prototype ADC in a 0.11 lm CMOS process demonstrates the measured differential nonlinearity and integral nonlinearity within 1.18 LSB and 1.42 LSB, respectively. The ADC shows a maximum signal-to-noise-and-distortion ratio of 63.9 dB and a maximum spurious-free dynamic range of 77.6 dB at 10 MS/s. The ADC with an active die area of 0.34 mm consumes 1.1 mW at 1.0 V and 10 MS/s, corresponding to a figure-of-merit of 87 fJ/conversion-step.

Question: What is the figure-of-merit of the ADC?


Goal Setting and Self-Efficacy During Self-Regulated Learning. This article focuses on the self-regulated learning processes of goal setting and perceived self-efficacy. Students enter learning activities with goals and self-efficacy for goal attainment. As learners work on tasks, they observe their own performances and evaluate their own goal progress. Self-efficacy and goal setting are affected by selfobservation, self-judgment, and self-reaction. When students perceive satisfactory goal progress, they feel capable of improving their skills; goal attainment, coupled with high self-efficacy, leads students to set new challenging goals. Research is reviewed on goal properties (specificity, proximity, difficulty), self-set goals, progress feedback, contracts and conferences, and conceptions of ability. Ways of teaching students to set realistic goals and evaluate progress include establishing upper and lower goal limits and employing games, contracts, and conferences. Future research might clarify the relation of goal setting and self-efficacy to transfer, goal orientations, and affective reactions. Article: Self-regulated learning occurs when students activate and sustain cognitions and behaviors systematically oriented toward attainment of learning goals. Self-regulated learning processes involve goal-directed activities that students instigate, modify, and sustain (Zimmerman, 1989). These activities include attending to instruction, processing and integrating knowledge, rehearsing information to be remembered, and developing and maintaining positive beliefs about learning capabilities and anticipated outcomes of actions (Schunk, 1989). This article focuses on two self-regulated learning processes: goal setting and perceived self-efficacy. As used in this article, a goal is what an individual is consciously trying to accomplish, goal setting involves establishing a goal and modifying it as necessary, and perceived self-efficacy refers to beliefs concerning one's ca

Question: What is self-regulated learning?


RFID Coverage Extension Using Microstrip-Patch Antenna Array [Wireless Corner]. In this paper, a UHF-band 2 times 2 microstrip phased-array antenna is designed and implemented to extend the coverage of an RFID reader system. The phased-array antenna has four microstrip-patch antennas, three Wilkinson power dividers, and a transmission-line phase shifter. These are printed on a dielectric substrate with a dielectric constant of 4.5. The array has dimensions of 34 cm times 45 cm, operating at a frequency of 867 MHz, as specified in RFID Gen2 protocol European standards. The phased-array antenna has a measured directivity of 12.1 dB, and the main-beam direction can be steered to angles of plusmn 40deg, with a HPBW of 90deg. The phased-array antenna is used as the receiving antenna in a commercial reader system. Experimental results indicate that the coverage of the RFID system with the phased-array antenna is superior to the coverage with a conventional broader-beamwidth microstrip-patch antenna. The proposed system can also be used for a wireless positioning system.

Question: What is the difference between a microstrip-patch antenna and a patch antenna?


SPANISH IMPERFECTO AND PRETÉRITO : TRUTH CONDITIONS AND AKTIONSART EFFECTS IN A SITUATION SEMANTICS*. offer an account of the semantics of these forms within a situation semantics, addressing a number of theoretically interesting questions about how to realize a semantics for tense and events in that type of framework. We argue that each of these forms is unambiguous, and that the apparent variety of readings attested for them derives from interaction with other factors in the course of interpretation. The meaning of the imperfecto is constrained to always reflect atelic aktionsart. In addition, it contains a modal element, and a contextually-given accessibility relation over situations constrains the interpretation of the modal in ways that give rise to all the attested readings. The pretérito is indeterminate with respect to aktionsart, neither telic nor atelic. One or the other aktionsart may be forced by other factors in the clause in which the pretérito occurs, as well as by pragmatic contrast with the possibility of using the imperfecto.

Question: What is the meaning of the imperfecto and the pretérito?


The long tail of recommender systems and how to leverage it. The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.

Question: How to leverage the long tail of recommender systems?


Deep Learning as Feature Encoding for Emotion Recognition. Deep learning is popular as an end-to-end framework extracting the prominent features and performing the classification also. In this paper, we extensively investigate deep networks as an alternate to feature encoding technique of lowlevel descriptors for emotion recognition on the benchmark EmoDB dataset. Fusion performance with such obtained encoded features with other available features is also investigated. Highest performance to date in the literature is observed.

Question: What is the benchmark EmoDB dataset?


Induction motor fault diagnosis using labview. Now-a-days, manufacturing companies of electrical machines are in need of easy and early fault detection techniques as they are attire by the customers. Though many researchers have developed many fault diagnosing techniques, user friendly software with automated long measurement process is needed so that the non-technical persons also can operate to identify the faults occurred. In our work the healthy induction motor is modeled in stationary reference frame. Faulty conditions are included in the model to analyze the effect of fault in the motor using the user friendly software, LabVIEW.

Question: How to model a induction motor?


Rfam 12.0: updates to the RNA families database. The Rfam database (available at http://rfam.xfam.org) is a collection of non-coding RNA families represented by manually curated sequence alignments, consensus secondary structures and annotation gathered from corresponding Wikipedia, taxonomy and ontology resources. In this article, we detail updates and improvements to the Rfam data and website for the Rfam 12.0 release. We describe the upgrade of our search pipeline to use Infernal 1.1 and demonstrate its improved homology detection ability by comparison with the previous version. The new pipeline is easier for users to apply to their own data sets, and we illustrate its ability to annotate RNAs in genomic and metagenomic data sets of various sizes. Rfam has been expanded to include 260 new families, including the well-studied large subunit ribosomal RNA family, and for the first time includes information on short sequence- and structure-based RNA motifs present within families.

Question: What is the rfam database?


Chapter 18 Building Intelligent Tutoring Systems : An Overview. This chapter addresses the challenge of building or authoring an Intelligent Tutoring System (ITS), along with the problems that have arisen and been dealt with, and the solutions that have been tested. We begin by clarifying what building an ITS entails, and then position today's systems in the overall historical context of ITS research. The chapter concludes with a series of open questions and an introduction to the other chapters in this part of the book.

Question: What is an ITS?


Predictors of Relationship Satisfaction in Online Romantic Relationships. Based on traditional theories of interpersonal relationship development and on the hyperpersonal communication theory, this study examined predictors of relationship satisfaction for individuals involved in online romantic relationships. One hundredfourteen individuals (N1⁄4114) involved in online romantic relationships, and who had only engaged in computer-mediated communication (CMC) with their partners, completed an online questionnaire about their relationships. Intimacy, trust, and communication satisfaction were found to be the strongest predictors of relationship satisfaction for individuals involved in online romances. Additionally, perceptions of relationship variables differed depending on relationship length and time spent communicating. Implications for interpersonal and hyperpersonal communication theories, and future investigation of online relationships, are discussed.

Question: What are the predictors of relationship satisfaction in online romances?


Development of knee power assist using backdrivable electro-hydrostatic actuator. Backdrivability is a keyword with rising importance, not only in humanoid robots, but also to wearable robots. Power assist robots, namely exoskeletons are expected to provide mobility and independence to elderly and people with disability. In such robots, without backdrivability, error between human and robot motion can be painful; sometimes dangerous. In this research, we apply a type of hydraulic actuator, specially designed to realize backdrivability, to exoskeletal robot to improve backdrivability. We present the basic methodology to apply such hydraulic actuator to wearable robots. We developed prototype of knee joint power assist exoskeleton and performed fundamental tests to verify the design method validity.

Question: What is backdrivability?


Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation. Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for this task based on the assumption that information from the same semantic context tends to belong to the same topic. This paper presents a novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs (KGs) to measure entity semantic relatedness for topical coherence modeling. The DSRM is directly trained on large-scale KGs and it maps heterogeneous types of knowledge of an entity from KGs to numerical feature vectors in a latent space such that the distance between two semantically-related entities is minimized. Compared with the state-ofthe-art relatedness approach proposed by (Milne and Witten, 2008a), the DSRM obtains 19.4% and 24.5% reductions in entity disambiguation errors on two publicly available datasets respectively.

Question: What is the state-ofthe-art relatedness approach?


Hybrid excited claw pole electric machine. The paper presents the concept and results of simulation and experimental research of a claw pole generator with hybrid excitation. Hybrid excitation is performed with a conventional coil located between two parts of the claw-shape rotor and additional permanent magnets which are placed on claw poles. Within the research first a simulation and next constructed experimental model has been developed on the basis of the mass-produced vehicle alternator. Experimental researches have shown that - at a suitable rotational speed - it is possible to self-excite of the generator without any additional source of electrical power.

Question: What is a claw pole generator?


Human wayfinding in information networks. Navigating information spaces is an essential part of our everyday lives, and in order to design efficient and user-friendly information systems, it is important to understand how humans navigate and find the information they are looking for. We perform a large-scale study of human wayfinding, in which, given a network of links between the concepts of Wikipedia, people play a game of finding a short path from a given start to a given target concept by following hyperlinks. What distinguishes our setup from other studies of human Web-browsing behavior is that in our case people navigate a graph of connections between concepts, and that the exact goal of the navigation is known ahead of time. We study more than 30,000 goal-directed human search paths and identify strategies people use when navigating information spaces. We find that human wayfinding, while mostly very efficient, differs from shortest paths in characteristic ways. Most subjects navigate through high-degree hubs in the early phase, while their search is guided by content features thereafter. We also observe a trade-off between simplicity and efficiency: conceptually simple solutions are more common but tend to be less efficient than more complex ones. Finally, we consider the task of predicting the target a user is trying to reach. We design a model and an efficient learning algorithm. Such predictive models of human wayfinding can be applied in intelligent browsing interfaces.

Question: What is the difference between a shortest path and a human wayfinding?


FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture. In this paper we address the problem of semantic labeling of indoor scenes on RGB-D data. With the availability of RGB-D cameras, it is expected that additional depth measurement will improve the accuracy. Here we investigate a solution how to incorporate complementary depth information into a semantic segmentation framework by making use of convolutional neural networks (CNNs). Recently encoder-decoder type fully convolutional CNN architectures have achieved a great success in the field of semantic segmentation. Motivated by this observation we propose an encoder-decoder type network, where the encoder part is composed of two branches of networks that simultaneously extract features from RGB and depth images and fuse depth features into the RGB feature maps as the network goes deeper. Comprehensive experimental evaluations demonstrate that the proposed fusion-based architecture achieves competitive results with the state-of-the-art methods on the challenging SUN RGB-D benchmark obtaining 76.27% global accuracy, 48.30% average class accuracy and 37.29% average intersectionover-union score.

Question: How to fuse depth information into RGB feature maps?


A Neural Network Color Classifier in HSV Color Space. In this paper, a neural network approach for color classification in HSV color space based on color JND (just noticeable difference) is presented. The HSV color samples are generated using an interactive tool based on JND concept and these samples are used for supervised training. A four layer feed forward neural network is trained for classifying a given color pair as similar or dissimilar pair. An interactive tool for color pixel comparison is developed for testing the color classifier on real life images. Research shows that neural network classifier in HSV color space works better than RGB classifier in terms of efficiency for segmentation purposes. Thus the experimentation results can be used for segmenting real life images.

Question: What is a neural network?


A Hierarchical Security Framework for Defending Against Sophisticated Attacks on Wireless Sensor Networks in Smart Cities. In smart cities, wireless sensor networks (WSNs) act as a type of core infrastructure that collects data from the city to implement smart services. The security of WSNs is one of the key issues of smart cities. In resource-restrained WSNs, dynamic ongoing or unknown attacks usually steer clear of isolated defense components. Therefore, to resolve this problem, we propose a hierarchical framework based on chance discovery and usage control (UCON) technologies to improve the security of WSNs while still taking the low-complexity and high security requirements of WSNs into account. The features of continuous decision and dynamic attributes in UCON can address ongoing attacks using advanced persistent threat detection. In addition, we use a dynamic adaptive chance discovery mechanism to detect unknown attacks. To design and implement a system using the mechanism described above, a unified framework is proposed in which low-level attack detection with simple rules is performed in sensors, and high-level attack detection with complex rules is performed in sinks and at the base station. Moreover, software-defined networking and network function virtualization technologies are used to perform attack mitigation when either low-level or high-level attacks are detected. An experiment was performed to acquire an attack data set for evaluation. Then, a simulation was created to evaluate the resource consumption and attack detection rate. The results demonstrate the feasibility and efficiency of the proposed scheme.

Question: How to defend against sophisticated attacks on wireless sensor networks in smart cities?


Joint detection and decoding for MIMO systems with polar codes. As well known, the near-optimal K-best detection is popular in multiple-input and multiple-output (MIMO) systems. In this paper, we first propose the joint approaches of K-best detection and polar decoding. For joint detection and decoding (JDD) approach, both hard and soft decisions are considered. The simplified successive cancellation (SSC) decoding is exploited for hard decision, and the successive cancellation list (SCL) decoding is used as soft decision. The system setup for JDD is als o introduced, in which the modulation points across several channels are considered together. Simulation results have demonstrated the performance advantage of the JDD algorithms over the separated ones. For 1/2-rate polar codes, JDD schemes show 50% complexity reduction compared to the separated ones. Furthermore, by employing SSC hard decoding, the JDD algorithm is promising for high-throughput and low-complexity application s.

Question: What is the complexity of the polar codes?


Driver behavior recognition based on deep convolutional neural networks. Traffic safety is a severe problem around the world. Many road accidents are normally related with the driver's unsafe driving behavior, e.g. eating while driving. In this work, we propose a vision-based solution to recognize the driver's behavior based on convolutional neural networks. Specifically, given an image, skin-like regions are extracted by Gaussian Mixture Model, which are passed to a deep convolutional neural networks model, namely R*CNN, to generate action labels. The skin-like regions are able to provide abundant semantic information with sufficient discriminative capability. Also, R*CNN is able to select the most informative regions from candidates to facilitate the final action recognition. We tested the proposed methods on Southeast University Driving-posture Dataset and achieve mean Average Precision(mAP) of 97.76% on the dataset which prove the proposed method is effective in drivers's action recognition.

Question: Driver behavior recognition based on deep convolutional neural networks


An Empirical Analysis of Deep Network Loss Surfaces. The training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model. Unfortunately, these functions are of high dimension and non-convex and hence difficult to characterize. In this paper, we empirically investigate the geometry of the loss functions for state-of-the-art networks with multiple stochastic optimization methods. We do this through several experiments that are visualized on polygons to understand how and when these stochastic optimization methods find local minima.

Question: What is the geometry of the loss surfaces of deep neural networks?


Compacting Neural Network Classifiers via Dropout Training. We introduce dropout compaction, a novel method for training feed-forward neural networks which realizes the performance gains of training a large model with dropout regularization, yet extracts a compact neural network for run-time efficiency. In the proposed method, we introduce a sparsity-inducing prior on the per unit dropout retention probability so that the optimizer can effectively prune hidden units during training. By changing the prior hyperparameters, we can control the size of the resulting network. We performed a systematic comparison of dropout compaction and competing methods on several realworld speech recognition tasks and found that dropout compaction achieved comparable accuracy with fewer than 50% of the hidden units, translating to a 2.5x speedup in run-time.

Question: How to train a neural network?


Towards Extracting Faithful and Descriptive Representations of Latent Variable Models. Methods that use latent representations of data, such as matrix and tensor factorization or deep neural methods, are becoming increasingly popular for applications such as knowledge base population and recommendation systems. These approaches have been shown to be very robust and scalable but, in contrast to more symbolic approaches, lack interpretability. This makes debugging such models difficult, and might result in users not trusting the predictions of such systems. To overcome this issue we propose to extract an interpretable proxy model from a predictive latent variable model. We use a socalled pedagogical method, where we query our predictive model to obtain observations needed for learning a descriptive model. We describe two families of (presumably more) descriptive models, simple logic rules and Bayesian networks, and show how members of these families provide descriptive representations of matrix factorization models. Preliminary experiments on knowledge extraction from text indicate that even though Bayesian networks may be more faithful to a matrix factorization model than the logic rules, the latter are possibly more useful for interpretation and debugging.

Question: What is a descriptive model?


Diode-clamped multilevel converters: a practicable way to balance DC-link voltages. The converter topologies identified as diode-clamped multilevel (DCM) or, equivalently, as multipoint clamped (MPC), are rarely used in industrial applications, owing to some serious drawbacks involving mainly the stacked bank of capacitors that constitutes their multilevel dc link. The balance of the capacitor voltages is not possible in all operating conditions when the MPC converter possesses a passive front end. On the other hand, in ac/dc/ac power conversion, the back-to-back connection of a multilevel rectifier with a multilevel inverter allows the balance of the dc-link capacitor voltages and, at the same time, it offers the power-factor-correction capability at the mains ac input. An effective balancing strategy suitable for MPC conversion systems with any number of dc-link capacitors is presented here. The strategy has been carefully studied to optimize the converter efficiency. The simulation results related to a high-power conversion system (up to 10 MW) characterized by four intermediate dc-link capacitors are shown.

Question: How to balance the capacitor voltages in a multilevel converter?


Ensemble Pruning Using Reinforcement Learning. Multiple Classifier systems have been developed in order to improve classification accuracy using methodologies for effective classifier combination. Classical approaches use heuristics, statistical tests, or a meta-learning level in order to find out the optimal combination function. We study this problem from a Reinforcement Learning perspective. In our modeling, an agent tries to learn the best policy for selecting classifiers by exploring a state space and considering a future cumulative reward from the environment. We evaluate our approach by comparing with state-of-the-art combination methods and obtain very promising results.

Question: What is reinforcement learning?


An efficient sparse metric learning in high-dimensional space via l1-penalized log-determinant regularization. This paper proposes an efficient sparse metric learning algorithm in high dimensional space via an l1-penalized log-determinant regularization. Compare to the most existing distance metric learning algorithms, the proposed algorithm exploits the sparsity nature underlying the intrinsic high dimensional feature space. This sparsity prior of learning distance metric serves to regularize the complexity of the distance model especially in the "less example number p and high dimension d" setting. Theoretically, by analogy to the covariance estimation problem, we find the proposed distance learning algorithm has a consistent result at rate O (√m2 log d)/n) to the target distance matrix with at most m nonzeros per row. Moreover, from the implementation perspective, this l1-penalized log-determinant formulation can be efficiently optimized in a block coordinate descent fashion which is much faster than the standard semi-definite programming which has been widely adopted in many other advanced distance learning algorithms. We compare this algorithm with other state-of-the-art ones on various datasets and competitive results are obtained.

Question: What is the difference between the l1-penalized log-determinant regularization and the l1-regularization?


Leadership style and patient safety: implications for nurse managers.. OBJECTIVE The purpose of this study was to explore the relationship between nurse manager (NM) leadership style and safety climate.   BACKGROUND Nursing leaders are needed who will change the environment and increase patient safety. Hospital NMs are positioned to impact day-to-day operations. Therefore, it is essential to inform nurse executives regarding the impact of leadership style on patient safety.   METHODS A descriptive correlational study was conducted in 41 nursing departments across 9 hospitals. The hospital unit safety climate survey and multifactorial leadership questionnaire were completed by 466 staff nurses. Bivariate and regression analyses were conducted to determine how well leadership style predicted safety climate.   RESULTS Transformational leadership style was demonstrated as a positive contributor to safety climate, whereas laissez-faire leadership style was shown to negatively contribute to unit socialization and a culture of blame.   CONCLUSIONS Nursing leaders must concentrate on developing transformational leadership skills while also diminishing negative leadership styles.

Question: How does leadership style impact patient safety?


Boosting Neural Machine Translation. Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art performance. This results in very high computation cost, slowing down research and industrialisation. In this paper, we propose to alleviate this problem with several training methods based on data boosting and bootstrap with no modifications to the neural network. It imitates the learning process of humans, which typically spend more time when learning "difficult" concepts than easier ones. We experiment on an English-French translation task showing accuracy improvements of up to 1.63 BLEU while saving 20% of training time.

Question: How to boost neural machine translation?


A multichannel CSMA MAC protocol for multihop wireless networks. We describe a new carrier–sense multiple access (CSMA) protocol for multihop wireless networks, sometimes also called ad hoc networks. The CSMA protocol divides the available bandwidth into several channels and selects an id le channel randomly for packet transmission. It also employs a notion of "soft" channel reservation as it gives preference to the channel that was used for the last successful transmission. We show via simulations that this multichannel CSMA protocol provides a higher throughput compared to its single channel counterpart by reducing the packet loss due to collisions. We also show that the use of channel reservation provides better performance than multichannel CSMA with purely random idle channel selection.

Question: What is a multichannel CSMA protocol?


Quality Management in Systems Development: An Organizational System Perspective. We identify top management leadership, a sophisticated management infrastructure, process management efficacy, and stakeholder participation as important elements of a quality-oriented organizational system for software development. A model interrelating these constructs and quality performance is proposed. Data collected through a national survey of IS executives in Fortune 1000 companies and government agencies was used to Robert Zmud was the accepting senior editor for this paper. test the model using a Partial Least Squares analysis methodology. Our results suggest that software quality goals are best attained when top management creates a management infrastructure that promotes improvements in process design and encourages stakeholders to evolve the design of the development processes. Our results also suggest that all elements of the organizational system need to be developed in order to attain quality goals and that piecemeal adoption of select quality management practices are unlikely to be effective. Implications of this research for IS theory and practice are discussed.

Question: What is the difference between a management infrastructure and a management system?


Building Better Open-Source Tools to Support Fairness in Automated Scoring. Automated scoring of written and spoken responses is an NLP application that can significantly impact lives especially when deployed as part of high-stakes tests such as the GRE® and the TOEFL®. Ethical considerations require that automated scoring algorithms treat all testtakers fairly. The educational measurement community has done significant research on fairness in assessments and automated scoring systems must incorporate their recommendations. The best way to do that is by making available automated, non-proprietary tools to NLP researchers that directly incorporate these recommendations and generate the analyses needed to help identify and resolve biases in their scoring systems. In this paper, we attempt to provide such a solution.

Question: What is the best way to build better open-source tools to support fairness in automated scoring?


An Optimal Auction Mechanism for Mobile Edge Caching. With the explosive growth of wireless data, mobile edge caching has emerged as a promising paradigm to support mobile traffic recently, in which the service providers (SPs) prefetch some popular contents in advance and cache them locally at the network edge. When requested, those locally cached contents can be directly delivered to users with low latency, thus alleviating the traffic load over backhaul channels during peak hours and enhancing the quality-of-experience (QoE) of users simultaneously. Due to the limited available cache space, it makes sense for the SP to cache the most profitable contents. Nevertheless, users' true valuations of contents are their private knowledge, which is unknown to the SP in general. This information asymmetry poses a significant challenge for effective caching at the SP side. Further, the cached contents can be delivered with different quality, which needs to be chosen judiciously to balance delivery costs and user satisfaction. To tackle these difficulties, in this paper, we propose an optimal auction mechanism from the perspective of the SP. In the auction, the SP determines the cache space allocation over contents and user payments based on the users' (possibly untruthful) reports of their valuations so that the SP's expected revenue is maximized. The advocated mechanism is designed to elicit true valuations from the users (incentive compatibility) and to incentivize user participation (individual rationality). In addition, we devise a computationally efficient method for calculating the optimal cache space allocation and user payments. We further examine the optimal choice of the content delivery quality for the case with a large number of users and derive a closed-form solution to compute the optimal delivery quality. Finally, extensive simulations are implemented to evaluate the performance of the proposed optimal auction mechanism, and the impact of various model paramet

Question: What is the optimal auction mechanism for mobile edge caching?


An Improved Speech Enhancement Method based on Teager Energy Operator and Perceptual Wavelet Packet Decomposition. According to the distribution characteristic of noise and clean speech signal in the frequency domain, a new speech enhancement method based on teager energy operator (TEO) and perceptual wavelet packet decomposition (PWPD) is proposed. Firstly, a modified Mask construction method is made to protect the acoustic cues at the low frequencies. Then a level-dependent parameter is introduced to further adjust the thresholds in light of the noise distribution feature. At last the sub-bands which have very little influence are set directly 0 to improve the signal-to-noise ratio (SNR) and reduce the computation load. Simulation results show that, under different kinds of noise environments, this new method not only enhances the signal-to-noise ratio (SNR) and perceptual evaluation of speech quality (PESQ), but also reduces the computation load, which is very advantageous for real-time realizing.

Question: What is the distribution characteristic of noise and clean speech signal in the frequency domain?


Static detection of malicious JavaScript-bearing PDF documents. Despite the recent security improvements in Adobe's PDF viewer, its underlying code base remains vulnerable to novel exploits. A steady flow of rapidly evolving PDF malware observed in the wild substantiates the need for novel protection instruments beyond the classical signature-based scanners. In this contribution we present a technique for detection of JavaScript-bearing malicious PDF documents based on static analysis of extracted JavaScript code. Compared to previous work, mostly based on dynamic analysis, our method incurs an order of magnitude lower run-time overhead and does not require special instrumentation. Due to its efficiency we were able to evaluate it on an extremely large real-life dataset obtained from the VirusTotal malware upload portal. Our method has proved to be effective against both known and unknown malware and suitable for large-scale batch processing.

Question: How to detect malicious javascript-bearing pdf documents?


X-Ku Wide-Bandwidth GaN HEMT MMIC Amplifier with Small Deviation of Output Power and PAE. A new design methodology was proposed to obtain wide-bandwidth and flat-group-delay reactive-matching GaN HEMT MMIC amplifiers. Frequency dependence of the optimal source and load impedance of a GaN HEMT are derived as polynomial equations and matching circuits are designed by small signal simulation without the use of large-signal transistor model and large-signal simulation. Fabricated GaN HEMT MMIC amplifiers, which show a small deviation of Pout and PAE in the range of 8-18 GHz, prove that our methodology is suitable for the design of a wide-bandwidth MMIC amplifier.

Question: What is the difference between a wide-bandwidth and a narrow-bandwidth amplifier?


Client-side rate adaptation scheme for HTTP adaptive streaming based on playout buffer model. HTTP Adaptive Streaming (HAS) is an adaptive bitrate streaming technique which is able to adapt to the network conditions using conventional HTTP web servers. An HAS player periodically requests pre-encoded video chunks by sending an HTTP GET message. When the downloading a video chunk is finished, the player estimates the network bandwidth by calculating the goodput and adjusts the video quality based on its estimates. However, the bandwidth estimation in application layer is pretty inaccurate due to its architectural limitation. We show that inaccurate bandwidth estimation in rate adaptation may incur serious rate oscillations which is poor quality-of-experience for users. In this paper, we propose a buffer-based rate adaptation scheme which eliminates the bandwidth estimation step in rate adaptation to provide a smooth playback of HTTP-based streaming. We evaluate the performance of the HAS player implemented in the ns-3 network simulator. Our simulation results show that the proposed scheme significantly improves the stability by replacing bandwidth estimation with buffer occupancy estimation.

Question: How to improve the stability of HAS?


Object Category Understanding via Eye Fixations on Freehand Sketches. The study of eye gaze fixations on photographic images is an active research area. In contrast, the image sub-category of freehand sketches has not received as much attention for such studies. In this paper, we analyze the results of a free-viewing gaze fixation study conducted on 3904 freehand sketches distributed across 160 object categories. Our analysis shows that fixation sequences exhibit marked consistency within a sketch, across sketches of a category and even across suitably grouped sets of categories. This multi-level consistency is remarkable given the variability in depiction and extreme image content sparsity that characterizes hand-drawn object sketches. In this paper, we show that the multi-level consistency in the fixation data can be exploited to 1) predict a test sketch's category given only its fixation sequence and 2) build a computational model which predicts part-labels underlying fixations on objects. We hope that our findings motivate the community to deem sketch-like representations worthy of gaze-based studies vis-a-vis photographic images.

Question: What is the difference between a freehand sketch and a photograph?


A comparative study between honey and povidone iodine as dressing solution for Wagner type II diabetic foot ulcers.. Honey dressing has been used to promote wound healing for years but scanty scientific studies did not provide enough evidences to justify it benefits in the treatment of diabetic foot ulcers. We conducted a prospective study to compare the effect of honey dressing for Wagner's grade-II diabetic foot ulcers with controlled dressing group (povidone iodine followed by normal saline). Surgical debridement and appropriate antibiotics were prescribed in all patients. There were 30 patients age between 31 to 65-years-old (mean of 52.1 years). The mean healing time in the standard dressing group was 15.4 days (range 9-36 days) compared to 14.4 days (range 7-26 days) in the honey group (p < 0.005). In conclusion, ulcer healing was not significantly different in both study groups. Honey dressing is a safe alternative dressing for Wagner grade-II diabetic foot ulcers.

Question: What is the difference between honey and povidone iodine as dressing solution for Wagner type II diabetic foot ulcers?


A multi-objective artificial bee colony algorithm. This work presents a multi-objective optimization method based on the artificial bee colony, called the MOABC, for optimizing problems with multiple objectives. The MOABC uses a grid-based approach to adaptively assess the Pareto frontmaintained in an external archive. The external archive is used to control the flying behaviours of the individuals and structuring the bee colony. The employed bees adjust their trajectories based on the non-dominated solutionsmaintained in the external archive. On the other hand, the onlooker bees select the food sources advertised by the employed bees to update their positions. The qualities of these food sources are computed based on the Pareto dominance notion. The scout bees are used by the MOABC to get rid of food sources with poor qualities. The proposed algorithm was evaluated on a set of standard test problems in comparison with other state-of-the-art algorithms. Experimental results indicate that the proposed approach is competitive compared to other algorithms considered in this work. © 2011 Elsevier B.V. All rights reserved.

Question: What is the difference between a multi-objective optimization algorithm and a single-objective optimization algorithm?


Learning recursion: Multiple nested and crossed dependencies. Language acquisition in both natural and artificial language learning settings crucially depends on extracting information from ordered sequences. A shared sequence learning mechanism is thus assumed to underlie both natural and artificial language learning. A growing body of empirical evidence is consistent with this hypothesis. By means of artificial language learning experiments, we may therefore gain more insight in this shared mechanism. In this paper, we review empirical evidence from artificial language learning and computational modeling studies, as well as natural language data, and suggest that there are two key factors that help determine processing complexity in sequence learning, and thus in natural language processing. We propose that the specific ordering of non-adjacent dependencies (i.e. nested or crossed), as well as the number of non-adjacent dependencies to be resolved simultaneously (i.e. two or three) are important factors in gaining more insight into the boundaries of human sequence learning; and thus, also in natural language processing. The implications for theories of linguistic competence are discussed.

Question: What is the difference between nested and crossed dependencies?


Motion Capture and Retargeting of Fish by Monocular Camera. Accurate motion capture and flexible retargeting of underwater creatures such as fish remain to be difficult due to the long-lasting challenges of marker attachment and feature description for soft bodies in the underwater environment. Despite limited new research progresses appeared in recent years, the fish motion retargeting with a desirable motion pattern in real-time remains elusive. Strongly motivated by our ambitious goal of achieving high-quality data-driven fish animation with a light-weight, mobile device, this paper develops a novel framework of motion capturing and retargeting for a fish. We capture the motion of actual fish by a monocular camera without the utility of any marker. The elliptical Fourier coefficients are then integrated into the contour-based feature extraction process to analyze the fish swimming patterns. This novel approach can obtain the motion information in a robust way, with smooth medial axis as the descriptor for a soft fish body. For motion retargeting, we propose a two-level scheme to properly transfer the captured motion into new models, such as 2D meshes (with texture) generated from pictures or 3D models designed by artists, regardless of different body geometry and fin proportions among various species. Both motion capture and retargeting processes are functioning in real time. Hence, the system can simultaneously create fish animation with variation, while obtaining video sequences of real fish by a monocular camera.

Question: How to retarget fish?


Probabilistic Semantics and Pragmatics: Uncertainty in Language and Thought. Language is used to communicate ideas. Ideas are mental tools for coping with a complex and uncertain world. Thus human conceptual structures should be key to language meaning, and probability—the mathematics of uncertainty— should be indispensable for describing both language and thought. Indeed, probabilistic models are enormously useful in modeling human cognition (Tenenbaum et al., 2011) and aspects of natural language (Bod et al., 2003; Chater et al., 2006). With a few early exceptions (e.g. Adams, 1975; Cohen, 1999b), probabilistic tools have only recently been used in natural language semantics and pragmatics. In this chapter we synthesize several of these modeling advances, exploring a formal model of interpretation grounded, via lexical semantics and pragmatic inference, in conceptual structure. Flexible human cognition is derived in large part from our ability to imagine possibilities (or possible worlds). A rich set of concepts, intuitive theories, and other mental representations support imagining and reasoning about possible worlds—together we will call these the conceptual lexicon. We posit that this collection of concepts also forms the set of primitive elements available for lexical semantics: word meanings can be built from the pieces of conceptual structure. Larger semantic structures are then built from word meanings by composition, ultimately resulting in a sentence meaning which is a phrase in the "language of thought" provided by the conceptual lexicon. This expression is truth-functional in that it takes on a Boolean value for each imagined world, and it can thus be used as the basis for belief updating. However, the connection between cognition, semantics, and belief is not direct: because language must flexibly adapt to the context of communication, the connection between lexical representation and interpreted meaning is mediated by pragmatic inference.

Question: What is the language of thought?


Semantic portrait color transfer with internet images. We present a novel color transfer method for portraits by exploring their high-level semantic information. First, a database is set up which consists of a collection of portrait images download from the Internet, and each of them is manually segmented using image matting as a preprocessing step. Second, we search the database using Face++ to find the images with similar poses to a given source portrait image, and choose one satisfactory image from the results as the target. Third, we extract portrait foregrounds from both source and target images. Then, the system extracts the semantic information, such as faces, eyes, eyebrows, lips, teeth, etc., from the extracted foreground of the source using image matting algorithms. After that, we perform color transfer between corresponding parts with the same semantic information. We get the final transferred result by seamlessly compositing different parts together using alpha blending. Experimental results show that our semantics-driven approach can generate better color transfer results for portraits than previous methods and provide users a new means to retouch their portraits.

Question: How to transfer color from one image to another?


New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications. Al~traet--In this paper, we propose a new method to obtain the Euclidean distance transformation and the Voronoi diagram based on the exact Euclidean metric for an n-dimensional picture. We present four algorithms to perform the transformation which are constructed by the serial composition of n-dimensional filters. When performed by a general purpose computer, they are faster than the method by H. Yamada for a two-dimensional picture. Those algorithms require only one n-dimensional array for storing input/output pictures and a single one-dimensional array for a work area, if an input picture needs not be preserved.

Question: What is the euclidean distance transformation?


Structured Sparse Method for Hyperspectral Unmixing. Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method from the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly. Index Terms Hyperspectral Unmixing (HU), Hyperspectral Image Analysis, Structured Sparse NMF (SS-NMF), Mixed Pixel, Nonnegative Matrix Factorization (NMF).

Question: How to unmix hyperspectral data?


Broadband and High-Gain SIW-Fed Antenna Array for 5G Applications. A broadband and high-gain substrate integrated waveguide (SIW)-fed antenna array is demonstrated for 5G applications. Aperture coupling is adopted as the interconnecting method in the two-layer SIW feeding network. Two pairs of metallic posts act as dipoles and are surrounded by an air-filled cavity. The height of the cavity is designed to be higher than that of the posts to broaden the bandwidth. The measured impedance bandwidth of the proposed <inline-formula> <tex-math notation="LaTeX">$8{\times} 8$ </tex-math></inline-formula> antenna array is 16.3% from 35.4 to 41.7 GHz for <inline-formula> <tex-math notation="LaTeX">$|S_{11}|\leqslant -10$ </tex-math></inline-formula> dB, and the measured peak gain is 26.7 dBi at 40 GHz. The measured radiation efficiency of the antenna array is 83.2% at 40 GHz. The proposed antenna array which is suitable for 5G applications at 37 and 39 GHz bands shows stable radiation patterns, high gain, and broad bandwidth.

Question: What is the difference between a broadband and high-gain substrate integrated waveguide (SIW)-fed antenna array and a broadband and high-gain substrate integrated waveguide (SIW)-fed antenna array?


A survey of dynamic replication and replica selection strategies based on data mining techniques in data grids. Mining grid data is an interesting research field which aims at analyzing grid systems with data mining techniques in order to efficiently discover new meaningful knowledge to enhance grid management. In this paper, we focus particularly on how extracted knowledge enables enhancing data replication and replica selection strategies which are important data management techniques commonly used in data grids. Indeed, relevant knowledge such as file access patterns, file correlations, user or job access behavior, prediction of future behavior or network performance, and so on, can be efficiently discovered. These findings are then used to enhance both data replication and replica selection strategies. Various works in this respect are then discussed along with their merits and demerits. In addition, we propose a new guideline to data mining application in the context of data replication and replica selection strategies. & 2015 Elsevier Ltd. All rights reserved.

Question: What is the difference between data mining and data replication?


Sizing equation and Finite Element Analysis optimum design of axial-flux permanent-magnet motor for electric vehicle direct drive. This paper presents the design process of a slotted TORUS axial-flux permanent-magnet motor suitable for direct drive of an electric vehicle through sizing equation and Finite Element Analysis. AFPM motor is a high torque density motor which can be easily and compactly mounted into the automobile's wheel fitting the rim perfectly. A double-sided slotted AFPM motor with 6 rotor poles for high torque density and stable rotation is the preliminary design. In order to determine the design requirements, a simple vehicle dynamic model evaluating the vehicle performance considering an automobile's typical-trip cursing scenario is considered. Axial-flux permanent-magnet machine's fundamental theory and sizing equation are applied to obtain the initial design parameters of the motor with the highest possible torque. The FEA of designed motor was conducted via commercial Vector Field Opera-3D 14.0 software for evaluation and accuracy enhancement of the design parameters. FEA simulation results are compared with those results obtained from sizing equation showing a good agreement of flux density values in various parts of the designed motor at no-load condition. The motor meets all the requirements and limitations of the electric vehicle fitting the shape and the size of a classical rim of the vehicle wheel.

Question: What is the size of the canadian military?


Appropriating and assessing heuristics for mobile computing. Mobile computing presents formidable challenges not only to the design of applications but also to each and every phase of the systems lifecycle. In particular, the HCI community is still struggling with the challenges that mobile computing poses to evaluation. Expert-based evaluation techniques are well known and they do enable a relatively quick and easy evaluation. Heuristic evaluation, in particular, has been widely applied and investigated, most likely due to its efficiency in detecting most of usability flaws at front of a rather limited investment of time and human resources in the evaluation. However, the capacity of expert-based techniques to capture contextual factors in mobile computing is a major concern. In this paper, we report an effort for realizing usability heuristics appropriate for mobile computing. The effort intends to capture contextual requirements while still drawing from the inexpensive and flexible nature of heuristic-based techniques. This work has been carried out in the context of a research project task geared toward developing a heuristic-based evaluation methodology for mobile computing. This paper describes the methodology that we adopted toward realizing mobile heuristics. It also reports a study that we carried out in order to assess the relevance of the realized mobile heuristics by comparing their performance with that of the standard/traditional usability heuristics. The study yielded positive results in terms of the number of usability flaws identified and the severity ranking assigned.

Question: What is the heuristic evaluation?


The weight consistency matrix framework for general non-binary LDPC code optimization: Applications in flash memories. Transmission channels underlying modern memory systems, e.g., Flash memories, possess a significant amount of asymmetry. While existing LDPC codes optimized for symmetric, AWGN-like channels are being actively considered for Flash applications, we demonstrate that, due to channel asymmetry, such approaches are fairly inadequate. We propose a new, general, combinatorial framework for the analysis and design of non-binary LDPC (NB-LDPC) codes for asymmetric channels. We introduce a refined definition of absorbing sets, which we call general absorbing sets (GASs), and an important subclass of GASs, which we refer to as general absorbing sets of type two (GASTs). Additionally, we study the combinatorial properties of GASTs. We then present the weight consistency matrix (WCM), which succinctly captures key properties in a GAST. Based on these new concepts, we then develop a general code optimization framework, and demonstrate its effectiveness on the realistic highly-asymmetric normal-Laplace mixture (NLM) Flash channel. Our optimized codes enjoy over one order (resp., half of an order) of magnitude performance gain in the uncorrectable BER (UBER) relative to the unoptimized codes (resp. the codes optimized for symmetric channels).

Question: What is the weight consistency matrix?


Seahawk: Stack Overflow in the IDE. Services, such as Stack Overflow, offer a web platform to programmers for discussing technical issues, in form of Question and Answers (Q&A). Since Q&A services store the discussions, the generated crowd knowledge can be accessed and consumed by a large audience for a long time. Nevertheless, Q&A services are detached from the development environments used by programmers: Developers have to tap into this crowd knowledge through web browsers and cannot smoothly integrate it into their workflow. This situation hinders part of the benefits of Q&A services. To better leverage the crowd knowledge of Q&A services, we created Seahawk, an Eclipse plugin that supports an integrated and largely automated approach to assist programmers using Stack Overflow. Seahawk formulates queries automatically from the active context in the IDE, presents a ranked and interactive list of results, lets users import code samples in discussions through drag & drop and link Stack Overflow discussions and source code persistently as a support for team work. Video Demo URL: http://youtu.be/DkqhiU9FYPI

Question: What is the seahawk plugin?


Fingerprint Template Protection: From Theory to Practice. One of the potential vulnerabilities in a biometric system is the leakage of biometric template information, which may lead to serious security and privacy threats. Most of the available template protection techniques fail to meet all the desired requirements of a practical biometric system like revocability, security, privacy, and high matching accuracy. In particular, protecting the fingerprint templates has been a difficult problem due to large intra-user variations (e.g., rotation, translation, nonlinear deformation, and partial prints). There are two fundamental challenges in any fingerprint template protection scheme. First, we need to select an appropriate representation scheme that captures most of the discriminatory information, but is sufficiently invariant to changes in finger placement and can be secured using available template protection algorithms. Secondly, we need to automatically align or register the fingerprints obtained during enrollment and matching without using any information that could reveal the features, which uniquely characterize a fingerprint. This chapter analyzes how these two challenges are being addressed in practice and how the design choices affect the trade-off between the security and matching accuracy. Though much progress has been made over the last decade, we believe that fingerprint template protection algorithms are still not sufficiently robust to be incorporated into practical fingerprint recognition systems. Anil K. Jain Department of Computer Science & Engineering, Michigan State University, East Lansing, MI 48824, USA and Department of Brain & Cognitive Engineering, Korea University, Seoul. e-mail: jain@cse.msu.edu, Karthik Nandakumar Institute for Infocomm Research, A*STAR, Fusionopolis, Singapore. e-mail: knandakumar@i2r.astar.edu.sg, Abhishek Nagar Department of Computer Science & Engineering, Michigan State University, East Lansing, MI 48824, USA. e-mail: n

Question: Fingerprint template protection


VSP: A Virtual Smartphone Platform to Enhance the Capability of Physical Smartphone. Smart mobile devices have become ubiquitous, and people prefer to choose smartphone in daily life rather than use traditional personal computer. However, due to the hardware capability, performance of smartphones varies greatly and sometimes it cannot meet the demand of users. Furthermore, if smartphone is attacked by malicious application, the local private sensitive information will be leaked which in turn will cause huge losses. This paper proposes VSP, a Virtual Smartphone Platform. It offers a new way to enhance the capability of physical smartphone by providing virtual smartphone (VS) deployed in cloud. Users operate VS remotely through the thin-client of VSP on physical smartphones, and ignore the limits of physical mobile devices. The isolation of VS in cloud platform guarantees the security between VSes, and also prevents access to private sensitive information on physical devices. The evaluation indicates that average bandwidth cost with zlib is about 80 kBps and it is suitable for use in practice. Index Terms—Virtualization, Android, Remote Display, Cloud Computing, Ant Colony Optimization.

Question: What is the average bandwidth cost with zlib?


An MEG-based brain–computer interface (BCI). Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

Question: How does an MEG-based BCI work?


Fabrication of extended-release patient-tailored prednisolone tablets via fused deposition modelling (FDM) 3D printing.. Rapid and reliable tailoring of the dose of controlled release tablets to suit an individual patient is a major challenge for personalized medicine. The aim of this work was to investigate the feasibility of using a fused deposition modelling (FDM) based 3D printer to fabricate extended release tablet using prednisolone loaded poly(vinyl alcohol) (PVA) filaments and to control its dose. Prednisolone was loaded into a PVA-based (1.75 mm) filament at approximately 1.9% w/w via incubation in a saturated methanolic solution of prednisolone. The physical form of the drug was assessed using differential scanning calorimetry (DSC) and X-ray powder diffraction (XRPD). Dose accuracy and in vitro drug release patterns were assessed using HPLC and pH change flow-through dissolution test. Prednisolone loaded PVA filament demonstrated an ability to be fabricated into regular ellipse-shaped solid tablets using the FDM-based 3D printer. It was possible to control the mass of printed tablet through manipulating the volume of the design (R(2) = 0.9983). On printing tablets with target drug contents of 2, 3, 4, 5, 7.5 and 10mg, a good correlation between target and achieved dose was obtained (R(2) = 0.9904) with a dose accuracy range of 88.7-107%. Thermal analysis and XRPD indicated that the majority of prednisolone existed in amorphous form within the tablets. In vitro drug release from 3D printed tablets was extended up to 24h. FDM based 3D printing is a promising method to produce and control the dose of extended release tablets, providing a highly adjustable, affordable, minimally sized, digitally controlled platform for producing patient-tailored medicines.

Question: How does 3D printing work?


Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models. Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. *image of a blue car* "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison.

Question: How does the encoder-decoder pipeline work?


A Scalable Multiphysics Network Simulation Using PETSc DMNetwork. A scientific framework for simulations of large-scale networks, such as is required for the analysis of critical infrastructure interaction and interdependencies, is needed for applications on exascale computers. Such a framework must be able to manage heterogeneous physics and unstructured topology, and must be reusable. To this end we have developed DMNetwork, a class in PETSc that provides data and topology management and migration for network problems, along with multiphysics solvers to exploit the problem structure. It eases the application development cycle by providing the necessary infrastructure through simple abstractions to define and query the network. This paper presents the design of the DMNetwork, illustrates its user interface, and demonstrates its ability to solve large network problems through the numerical simulation of a water pipe network with more than 2 billion variables on extreme-scale computers using up to 30,000 processor cores.

Question: What is a scalable multiphysics network simulation using PETSc DMNetwork?


Neural networks in building QSAR models.. This chapter critically reviews some of the important methods being used for building quantitative structure-activity relationship (QSAR) models using the artificial neural networks (ANNs). It attends predominantly to the use of multilayer ANNs in the regression analysis of structure-activity data. The highlighted topics cover the approximating ability of ANNs, the interpretability of the resulting models, the issues of generalization and memorization, the problems of overfitting and overtraining, the learning dynamics, regularization, and the use of neural network ensembles. The next part of the chapter focuses attention on the use of descriptors. It reviews different descriptor selection and preprocessing techniques; considers the use of the substituent, substructural, and superstructural descriptors in building common QSAR models; the use of molecular field descriptors in three-dimensional QSAR studies; along with the prospects of "direct" graph-based QSAR analysis. The chapter starts with a short historical survey of the main milestones in this area.

Question: What is a QSAR model?


Low cost high performance uncertainty quantification. Uncertainty quantification in risk analysis has become a key application. In this context, computing the diagonal of inverse covariance matrices is of paramount importance. Standard techniques, that employ matrix factorizations, incur a cubic cost which quickly becomes intractable with the current explosion of data sizes. In this work we reduce this complexity to quadratic with the synergy of two algorithms that gracefully complement each other and lead to a radically different approach. First, we turned to stochastic estimation of the diagonal. This allowed us to cast the problem as a linear system with a relatively small number of multiple right hand sides. Second, for this linear system we developed a novel, mixed precision, iterative refinement scheme, which uses iterative solvers instead of matrix factorizations. We demonstrate that the new framework not only achieves the much needed quadratic cost but in addition offers excellent opportunities for scaling at massively parallel environments. We based our implementation on BLAS 3 kernels that ensure very high processor performance. We achieved a peak performance of 730 TFlops on 72 BG/P racks, with a sustained performance 73% of theoretical peak. We stress that the techniques presented in this work are quite general and applicable to several other important applications.

Question: What is the cost of computing the diagonal of an inverse covariance matrix?


Video Captioning by Adversarial LSTM. In this paper, we propose a novel approach to video captioning based on adversarial learning and long short-term memory (LSTM). With this solution concept, we aim at compensating for the deficiencies of LSTM-based video captioning methods that generally show potential to effectively handle temporal nature of video data when generating captions but also typically suffer from exponential error accumulation. Specifically, we adopt a standard generative adversarial network (GAN) architecture, characterized by an interplay of two competing processes: a "generator" that generates textual sentences given the visual content of a video and a "discriminator" that controls the accuracy of the generated sentences. The discriminator acts as an "adversary" toward the generator, and with its controlling mechanism, it helps the generator to become more accurate. For the generator module, we take an existing video captioning concept using LSTM network. For the discriminator, we propose a novel realization specifically tuned for the video captioning problem and taking both the sentences and video features as input. This leads to our proposed LSTM–GAN system architecture, for which we show experimentally to significantly outperform the existing methods on standard public datasets.

Question: What is adversarial learning?


The Nature, Importance, and Difficulty of Machine Ethics. The question of whether machine ethics exists or might exist in the future is difficult to answer if we can't agree on what counts as machine ethics. Some might argue that machine ethics obviously exists because humans are machines and humans have ethics. Others could argue that machine ethics obviously doesn't exist because ethics is simply emotional expression and machines can't have emotions. A wide range of positions on machine ethics are possible, and a discussion of the issue could rapidly propel us into deep and unsettled philosophical issues. Perhaps, understandably, few in the scientific arena pursue the issue of machine ethics. As we expand computers' decision-making roles in practical matters, such as computers driving cars, ethical considerations are inevitable. Computer scientists and engineers must examine the possibilities for machine ethics because, knowingly or not, they've already engaged in some form of it. Before we can discuss possible implementations of machine ethics, however, we need to be clear about what we're asserting or denying

Question: What is machine ethics?


Summarizing Search Results with Community-Based Question Answering. Previous work on snippet generation focused mainly on how to produce one snippet for an individual search result. This paper aims to generate snippets as a comprehensive overview for an entity query (e.g., flu) in a search-result page. Our approach first extracts the attributes (e.g., Symptom and diagnose) of the categories (e.g., Disease) from a community-based question-answering (CQA) website, and then generates the snippets based on how central a sentence is to the meaning of the query, its category, and how well it diversifies the attributes. Integer Linear Programming (ILP) is adopted to find the optimal sentence set. The experiments are conducted on Wikipedia and Yahoo! Answers. Experimental results demonstrate the effectiveness of our approach, compared to an existing commercial search engine and several summarization baselines.

Question: How to summarize search results with community-based question answering?


Analytic Performance Model of a Main-Memory Index Structure. Efficient evaluation of multi-dimensional range queries in a main-memory database is an important, but difficult task. State-of-the-art techniques rely on optimised sequential scans or tree-based structures. For range queries with small result sets, sequential scans exhibit poor asymptotic performance. Also, as the dimensionality of the data set increases, the performance of tree-based structures degenerates due to the curse of dimensionality. Recent literature proposed the Elf, a main-memory structure that is optimised for the case of such multi-dimensional low-selectivity queries. The Elf outperforms other state-ofthe-art methods in manually tuned scenarios. However, choosing an optimal parameter configuration for the Elf is vital, since for poor configurations, the search performance degrades rapidly. Consequently, further knowledge about the behaviour of the Elf in different configurations is required to achieve robust performance. In this thesis, we therefore propose a numerical cost model for the Elf. Like all main-memory index structures, the Elf response time is not dominated by disk accesses, refusing a straightforward analysis. Our model predicts the size and shape of the Elf region that is examined during search. We propose that the response time of a search is linear to the size of this region. Furthermore, we study the impact of skewed data distributions and correlations on the shape of the Elf. We find that they lead to behaviour that is accurately describable through simple reductions in attribute cardinality. Our experimental results indicate that for data sets of up to 15 dimensions, our cost model predicts the size of the examined Elf region with relative errors below 5%. Furthermore, we find that the size of the Elf region examined during search predicts the response time with an accuracy of 80 %.

Question: What is the cost model of the elf?


Statistical learning by 8-month-old infants.. Learners rely on a combination of experience-independent and experience-dependent mechanisms to extract information from the environment. Language acquisition involves both types of mechanisms, but most theorists emphasize the relative importance of experience-independent mechanisms. The present study shows that a fundamental task of language acquisition, segmentation of words from fluent speech, can be accomplished by 8-month-old infants based solely on the statistical relationships between neighboring speech sounds. Moreover, this word segmentation was based on statistical learning from only 2 minutes of exposure, suggesting that infants have access to a powerful mechanism for the computation of statistical properties of the language input.

Question: How do infants learn language?


The Cricket location-support system. This paper presents the design, implementation, and evaluation of Cricket, a location-support system for in-building, mobile, location-dependent applications. It allows applications running on mobile and static nodes to learn their physical location by using listeners that hear and analyze information from beacons spread throughout the building. Cricket is the result of several design goals, including user privacy, decentralized administration, network heterogeneity, and low cost. Rather than explicitly tracking user location, Cricket helps devices learn where they are and lets them decide whom to advertise this information to; it does not rely on any centralized management or control and there is no explicit coordination between beacons; it provides information to devices regardless of their type of network connectivity; and each Cricket device is made from off-the-shelf components and costs less than U.S. $10. We describe the randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy. Our experience with Cricket shows that several location-dependent applications such as in-building active maps and device control can be developed with little effort or manual configuration.

Question: What is the location-support system for in-building, mobile, location-dependent applications?


Contextual Dueling Bandits. We consider the problem of learning to choose actions using contextual information when provided with limited feedback in the form of relative pairwise comparisons. We study this problem in the dueling-bandits framework of Yue et al. (2009), which we extend to incorporate context. Roughly, the learner's goal is to find the best policy, or way of behaving, in some space of policies, although "best" is not always so clearly defined. Here, we propose a new and natural solution concept, rooted in game theory, called a von Neumann winner, a randomized policy that beats or ties every other policy. We show that this notion overcomes important limitations of existing solutions, particularly the Condorcet winner which has typically been used in the past, but which requires strong and often unrealistic assumptions. We then present three efficient algorithms for online learning in our setting, and for approximating a von Neumann winner from batch-like data. The first of these algorithms achieves particularly low regret, even when data is adversarial, although its time and space requirements are linear in the size of the policy space. The other two algorithms require time and space only logarithmic in the size of the policy space when provided access to an oracle for solving classification problems on the space.

Question: What is the best policy?