A probabilistic theory of clustering. Data clustering is typically considered a subjective process, which makes it problematic. For instance, how does one make statistical inferences based on clustering? The matter is di0erent with pattern classi1cation, for which two fundamental characteristics can be stated: (1) the error of a classi1er can be estimated using "test data," and (2) a classi1er can be learned using "training data." This paper presents a probabilistic theory of clustering, including both learning (training) and error estimation (testing). The theory is based on operators on random labeled point processes. It includes an error criterion in the context of random point sets and representation of the Bayes (optimal) cluster operator for a given random labeled point process. Training is illustrated using a nearest-neighbor approach, and trained cluster operators are compared to several classical clustering algorithms. ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Question: What is a probabilistic theory of clustering?


A wearable system to assist walking of Parkinson s disease patients.. BACKGROUND About 50% of the patients with advanced Parkinson's disease (PD) suffer from freezing of gait (FOG), which is a sudden and transient inability to walk. It often causes falls, interferes with daily activities and significantly impairs quality of life. Because gait deficits in PD patients are often resistant to pharmacologic treatment, effective non-pharmacologic treatments are of special interest.   OBJECTIVES The goal of our study is to evaluate the concept of a wearable device that can obtain real-time gait data, processes them and provides assistance based on pre-determined specifications.   METHODS We developed a real-time wearable FOG detection system that automatically provides a cueing sound when FOG is detected and which stays until the subject resumes walking. We evaluated our wearable assistive technology in a study with 10 PD patients. Over eight hours of data was recorded and a questionnaire was filled out by each patient.   RESULTS Two hundred and thirty-seven FOG events have been identified by professional physiotherapists in a post-hoc video analysis. The device detected the FOG events online with a sensitivity of 73.1% and a specificity of 81.6% on a 0.5 sec frame-based evaluation.   CONCLUSIONS With this study we show that online assistive feedback for PD patients is possible. We present and discuss the patients' and physiotherapists' perspectives on wearability and performance of the wearable assistant as well as their gait performance when using the assistant and point out the next research steps. Our results demonstrate the benefit of such a context-aware system and motivate further studies.

Question: What is the difference between a wearable system and a wearable assistant?


Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications. The availability of location information has become a key factor in today's communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the global positioning system (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.

Question: What is the difference between indoor and outdoor localization?


Listening to polyphonic music recruits domain-general attention and working memory circuits.. Polyphonic music combines multiple auditory streams to create complex auditory scenes, thus providing a tool for investigating the neural mechanisms that orient attention in natural auditory contexts. Across two fMRI experiments, we varied stimuli and task demands in order to identify the cortical areas that are activated during attentive listening to real music. In individual experiments and in a conjunction analysis of the two experiments, we found bilateral blood oxygen level dependent (BOLD) signal increases in temporal (the superior temporal gyrus), parietal (the intraparietal sulcus), and frontal (the precentral sulcus, the inferior frontal sulcus and gyrus, and the frontal operculum) areas during selective and global listening, as compared with passive rest without musical stimulation. Direct comparisons of the listening conditions showed significant differences between attending to single timbres (instruments) and attending across multiple instruments, although the patterns that were observed depended on the relative demands of the tasks being compared. The overall pattern of BOLD signal increases indicated that attentive listening to music recruits neural circuits underlying multiple forms of working memory, attention, semantic processing, target detection, and motor imagery. Thus, attentive listening to music appears to be enabled by areas that serve general functions, rather than by music-specific cortical modules.

Question: What is the difference between listening to music and listening to music?


Global asymptotic and robust stability of recurrent neural networks with time delays. In this paper, two related problems, global asymptotic stability (GAS) and global robust stability (GRS) of neural networks with time delays, are studied. First, GAS of delayed neural networks is discussed based on Lyapunov method and linear matrix inequality. New criteria are given to ascertain the GAS of delayed neural networks. In the designs and applications of neural networks, it is necessary to consider the deviation effects of bounded perturbations of network parameters. In this case, a delayed neural network must be formulated as a interval neural network model. Several sufficient conditions are derived for the existence, uniqueness, and GRS of equilibria for interval neural networks with time delays by use of a new Lyapunov function and matrix inequality. These results are less restrictive than those given in the earlier references.

Question: What is the difference between global asymptotic stability and global robust stability?


All-hex meshing using closed-form induced polycube. The polycube-based hexahedralization methods are robust to generate all-hex meshes without internal singularities. They avoid the difficulty to control the global singularity structure for a valid hexahedralization in frame-field based methods. To thoroughly utilize this advantage, we propose to use a frame field without internal singularities to guide the polycube construction. Theoretically, our method extends the vector fields associated with the polycube from exact forms to closed forms, which are curl free everywhere but may be not globally integrable. The closed forms give additional degrees of freedom to deal with the topological structure of high-genus models, and also provide better initial axis alignment for subsequent polycube generation. We demonstrate the advantages of our method on various models, ranging from genus-zero models to high-genus ones, and from single-boundary models to multiple-boundary ones.

Question: How to generate all-hex meshes without internal singularities?


INTEGRATION OF BIM AND GIS : THE DEVELOPMENT OF THE CITYGML GEOBIM EXTENSION. There is a growing interest in the integration of BIM and GIS. However, most of the research is focused on importing BIM data in GIS applications and vice versa. Real integration of BIM and GIS is using the strong parts of the GIS technology in BIM, and of course the strong parts from BIM technology in GIS. In this paper a mix of strong parts from both worlds is integrated in a single project. The paper describes the development of a CityGML extension called GeoBIM to get semantic IFC data into a GIS context. The conversion of IFC to CityGML (including the GeoBIM extension) is implemented in the open source Building Information Modelserver. This contribution was selected in a double blind review process to be published within the Lecture Notes in Geoinformation and Cartography series (Springer-Verlag, Heidelberg). Advances in 3D Geo-Information Sciences Kolbe, Thomas H.; König, Gerhard; Nagel, Claus (Eds.) 2011, X ISBN 978-3-642-12669-7, Hardcover Date of Publication: January 5, 2011 Series Editors: Cartwright, W., Gartner, G., Meng, L., Peterson, M.P. ISSN: 1863-2246 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXVIII-4/W15 5th International 3D GeoInfo Conference, November 3-4, 2010, Berlin, Germany 193

Question: What is the development of the citygml geo-information extension?


Instantaneous lateral velocity estimation of a vehicle using Doppler radar. High-resolution image radars open new opportunities for estimating velocity and direction of movement of extended objects from a single observation. Since radar sensors only measure the radial velocity, a tracking system is normally used to determine the velocity vector of the object. A stable velocity is estimated after several frames at the earliest, resulting in a significant loss of time for reacting to certain situations such as cross-traffic. The following paper presents a robust and model-free approach to determine the velocity vector of an extended target. In contrast to the Kalman filter, it does not require data association in time and space. An instant (~ 50 ms) and bias free estimation of its velocity vector is possible. Our approach can handle noise and systematic variations (e.g., micro-Doppler of wheels) in the signal. It is optimized to deal with measurement errors of the radar sensor not only in the radial velocity, but in the azimuth position as well. The accuracy of this method is increased by the fusion of multiple radar sensors.

Question: How to estimate the velocity of a vehicle using radar?


About relationship between business text patterns and financial performance in corporate data. This study uses text and data mining to investigate the relationship between the text patterns of annual reports published by US listed companies and sales performance. Taking previous research a step further, although annual reports show only past and present financial information, analyzing text content can identify sentences or patterns that indicate the future business performance of a company. First, we examine the relation pattern between business risk factors and current business performance. For this purpose, we select companies belonging to two categories of US SIC (Standard Industry Classification) in the IT sector, 7370 and 7373, which include Twitter, Facebook, Google, Yahoo, etc. We manually collect sales and business risk information for a total of 54 companies that submitted an annual report (Form 10-K) for the last three years in these two categories. To establish a correlation between patterns of text and sales performance, four hypotheses were set and tested. To verify the hypotheses, statistical analysis of sales, statistical analysis of text sentences, sentiment analysis of sentences, clustering, dendrogram visualization, keyword extraction, and word-cloud visualization techniques are used. The results show that text length has some correlation with sales performance, and that patterns of frequently appearing words are correlated with the sales performance. However, a sentiment analysis indicates that the positive or negative tone of a report is not related to sales performance.

Question: What is the relation between text patterns and sales performance?


Brain-computer interfacing in disorders of consciousness.. BACKGROUND Recent neuroimaging research has strikingly demonstrated the existence of covert awareness in some patients with disorders of consciousness (DoC). These findings have highlighted the potential for the development of simple brain-computer interfaces (BCI) as a diagnosis in behaviourally unresponsive patients.   OBJECTIVES This study here reviews current EEG-based BCIs that hold potential for assessing and eventually assisting patients with DoC. It highlights key areas for further development that might eventually make their application feasible in this challenging patient group.   METHODS The major types of BCIs proposed in the literature are considered, namely those based on the P3 potential, sensorimotor rhythms, steady state oscillations and slow cortical potentials. In each case, a brief overview of the relevant literature is provided and then their relative merits for BCI applications in DoC are considered.   RESULTS A range of BCI designs have been proposed and tested for enabling communication in fully conscious, paralysed patients. Although many of these have potential applicability for patients with DoC, they share some key challenges that need to be overcome, including limitations of stimulation modality, feedback, user training and consistency.   CONCLUSION Future work will need to address the technical and practical challenges facing reliable implementation at the patient's bedside.

Question: What is the difference between a brain-computer interface and a brain-machine interface?


Research on nursing handoffs for medical and surgical settings: an integrative review.. AIMS To synthesize outcomes from research on handoffs to guide future computerization of the process on medical and surgical units.   BACKGROUND Handoffs can create important information gaps, omissions and errors in patient care. Authors call for the computerization of handoffs; however, a synthesis of the literature is not yet available that might guide computerization.   DATA SOURCES PubMed, CINAHL, Cochrane, PsycINFO, Scopus and a handoff database from Cohen and Hilligoss.   DESIGN Integrative literature review.   REVIEW METHODS This integrative review included studies from 1980-March 2011 in peer-reviewed journals. Exclusions were studies outside medical and surgical units, handoff education and nurses' perceptions.   RESULTS The search strategy yielded a total of 247 references; 81 were retrieved, read and rated for relevance and research quality. A set of 30 articles met relevance criteria.   CONCLUSION Studies about handoff functions and rituals are saturated topics. Verbal handoffs serve important functions beyond information transfer and should be retained. Greater consideration is needed on analysing handoffs from a patient-centred perspective. Handoff methods should be highly tailored to nurses and their contextual needs. The current preference for bedside handoffs is not supported by available evidence. The specific handoff structure for all units may be less important than having a structure for contextually based handoffs. Research on pertinent information content for contextually based handoffs is an urgent need. Without it, handoff computerization is not likely to be successful. Researchers need to use more sophisticated experimental research designs, control for individual and unit differences and improve sampling frames.

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


Segmentation of Medical Ultrasound Images Using Convolutional Neural Networks with Noisy Activating Functions. The attempts to segment medical ultrasound images have had limited success than the attempts to segment images from other medical imaging modalities. In this project, we attempt to segment medical ultrasound images using convolutional neural networks (CNNs) with a group of noisy activation functions which have recently been demonstrated to improve the performance of neural networks. We report on the segmentation results using a U-Net-like CNN with noisy rectified linear unit (NReLU) functions, noisy hard sigmoid (NHSigmoid) functions, and noisy hard tanh (NHTanh) function on a small data set.

Question: What is a U-Net-like CNN?


Brief screening instrument for post-traumatic stress disorder.. BACKGROUND Brief screening instruments appear to be a viable way of detecting post-traumatic stress disorder (PTSD) but none has yet been adequately validated.   AIMS To test and cross-validate a brief instrument that is simple to administer and score.   METHOD Forty-one survivors of a rail crash were administered a questionnaire, followed by a structured clinical interview 1 week later.   RESULTS Excellent prediction of a PTSD diagnosis was provided by respondents endorsing at least six re-experiencing or arousal symptoms, in any combination. The findings were replicated on data from a previous study of 157 crime victims.   CONCLUSIONS Performance of the new measure was equivalent to agreement achieved between two full clinical interviews.

Question: What is a brief screening instrument for post-traumatic stress disorder?


Dynamic queries for visual information seeking. Considers how dynamic queries allow users to "fly through" databases by adjusting widgets and viewing the animated results. In studies, users reacted to this approach with an enthusiasm more commonly associated with video games. Adoption requires research into retrieval and display algorithms and user-interface design. The author discusses how experts may benefit from visual interfaces because they will be able to formulate more complex queries and interpret intricate results.<<ETX>>

Question: What is the difference between visual information seeking and visual analytics?


Text and Object Detection on Billboards. To captivate people's attention in a blink of an eye, a billboard as a commercial advertisement must be attractive and informative. When space means money like on the billboard, much important information must be dropped out. Unfortunately, insufficient details and the difficulty of accessing those details might easily kill the customer's attention in a flash as well. In this work, we propose a system that automatically connects a billboard with the product website instantly. More specifically, given the billboard image as an input, the system will lead users immediately to the product website for more details, or contact information. The results demonstrate that our system is significantly outperform the Google image search baseline where the product website is always included in the top 10 websites. Finally, We posit that the system can save the customers' time and complexity to access their product of interest, resulting the increasing of product sale.

Question: How to detect text and objects on a billboard?


Near-lighting Photometric Stereo for unknown scene distance and medium attenuation. Photometric Stereo in murky water is subject to light attenuation and near-field illumination, and the resulting image formation model is complex. Apart from the scene normals and albedo, the incident illumination varies per-pixel and it depends on the scene depth and the attenuation coefficient of the medium. When these are unknown, e.g. in a realistic scenario where a robotic platform explores an underwater scene (unknown shape and distance) within the dynamic subsea environment (unknown scattering level), Photometric Stereo becomes ambiguous. Previous approaches have tackled the problem by assuming distant-lighting and resorting to external hardware for estimating the unknown model variables. In our work, we show that the Photometric Stereo problem can be determined as soon as some additional constraints regarding the scene albedo and the presence of pixels with local intensity maxima within the image are incorporated into the optimization framework. Our proposed solution leads to effective Photometric Stereo and yields detailed 3D reconstruction of objects in murky water when the scene distance and the medium attenuation are unknown. We evaluate our work using both numerical simulations and real experiments in the controlled environment of a water tank and real port water using a remotely operated vehicle. © 2016 Elsevier B.V. All rights reserved.

Question: How to determine the scene distance and medium attenuation?


#Proana: Pro-Eating Disorder Socialization on Twitter.. PURPOSE Pro-eating disorder (ED) online movements support engagement with ED lifestyles and are associated with negative health consequences for adolescents with EDs. Twitter is a popular social media site among adolescents that provides a unique setting for Pro-ED content to be publicly exchanged. The purpose of this study was to investigate Pro-ED Twitter profiles' references to EDs and how their social connections (followers) reference EDs.   METHODS A purposeful sample of 45 Pro-ED profiles was selected from Twitter. Profile information, all tweets, and a random sample of 100 of their followers' profile information were collected for content analysis using the Twitter Application Programming Interface. A codebook based on ED screening guidelines was applied to evaluate ED references. For each Pro-ED profile, proportion of tweets with ED references and proportion of followers with ED references in their own profile were evaluated.   RESULTS In total, our 45 Pro-ED profiles generated 4,245 tweets for analysis. A median of 36.4% of profiles' tweets contained ED references. Pro-ED profiles had a median of 173 followers, and a median of 44.5% of followers had ED references. Pro-ED profiles with more tweets with ED references also tended to have more followers with ED references (β = .37, p < .01).   CONCLUSIONS Findings suggest that profiles which self-identify as Pro-ED express disordered eating patterns through tweets and have an audience of followers, many of whom also reference ED in their own profiles. ED socialization on Twitter might provide social support, but in the Pro-ED context this activity might also reinforce an ED identity.

Question: How many followers does a pro-eating disorder twitter account have?


Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model. Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees. In this paper, we first propose to use the syntactic graph to represent three types of syntactic information, i.e., word order, dependency and constituency features. We further employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-ofthe-art on Jobs640, ATIS and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.

Question: What is the state-ofthe-art on Jobs640, ATIS and Geo880?


A Smart Cloud Robotic System Based on Cloud Computing Services. In this paper, we present a smart service robotic system based on cloud computing services. The design and implementation of infrastructure, computation components and communication components are introduced. The proposed system can alleviate the complex computation and storage load of robots to cloud and provide various services to the robots. The computation components can dynamically allocate resources to the robots. The communication components allow easy access of the robots and provide flexible resource management. Furthermore, we modeled the task-scheduling problem and proposed a max-heaps algorithm. The simulation results demonstrate that the proposed algorithm minimized the overall task costs.

Question: What is a smart service robotic system based on cloud computing services?


Machine Learning for Performance Prediction in Mobile Cellular Networks. In this paper, we discuss the application of machine learning techniques for performance prediction problems in wireless networks. These problems often involve using existing measurement data to predict network performance where direct measurements are not available. We explore the performance of existing machine learning algorithms for these problems and propose a simple taxonomy of main problem categories. As an example, we use an extensive real-world drive test data set to show that classical machine learning methods such as Gaussian process regression, exponential smoothing of time series, and random forests can yield excellent prediction results. Applying these methods to the management of wireless mobile networks has the potential to significantly reduce operational costs while simultaneously improving user experience. We also discuss key challenges for future work, especially with the focus on practical deployment of machine learning techniques for performance prediction in mobile wireless networks.

Question: What is the difference between machine learning and artificial intelligence?


Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review. Stress is a major problem of our society, as it is the cause of many health problems and huge economic losses in companies. Continuous high mental workloads and non-stop technological development, which leads to constant change and need for adaptation, makes the problem increasingly serious for office workers. To prevent stress from becoming chronic and provoking irreversible damages, it is necessary to detect it in its early stages. Unfortunately, an automatic, continuous and unobtrusive early stress detection method does not exist yet. The multimodal nature of stress and the research conducted in this area suggest that the developed method will depend on several modalities. Thus, this work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behavioural modalities, along with contextual measurements, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system.

Question: What is the difference between a stress and a stressor?


Blind PSF estimation and methods of deconvolution optimization. We have shown that the left side null space of the autoregression (AR) matrix operator is the lexicographical presentation of the point spread function (PSF) on condition the AR parameters are common for original and blurred images. The method of inverse PSF evaluation with regularization functional as the function of surface area is offered. The inverse PSF was used for primary image estimation. Two methods of original image estimate optimization were designed basing on maximum entropy generalization of sought and blurred images conditional probability density and regularization. The first method uses balanced variations of convolution and deconvolution transforms to obtaining iterative schema of image optimization. The variations balance was defined by dynamic regularization basing on condition of iteration process convergence. The regularization has dynamic character because depends on current and previous image estimate variations. The second method implements the regularization of deconvolution optimization in curved space with metric defined on image estimate surface. It is basing on target functional invariance to fluctuations of optimal argument value. The given iterative schemas have faster convergence in comparison with known ones, so they can be used for reconstruction of high resolution images series in real time.

Question: How to estimate the PSF of a blurred image?


Toolkit-Based High-Performance Data Mining of Large Data on MapReduce Clusters. The enormous growth of data in a variety of applications has increased the need for high performance data mining based on distributed environments. However, standard data mining toolkits per se do not allow the usage of computing clusters. The success of MapReduce for analyzing large data has raised a general interest in applying this model to other, data intensive applications. Unfortunately current research has not lead to an integration of GUI based data mining toolkits with distributed file system based MapReduce systems. This paper defines novel principles for modeling and design of the user interface, the storage model and the computational model necessary for the integration of such systems. Additionally, it introduces a novel system architecture for interactive GUI based data mining of large data on clusters based on MapReduce that overcomes the limitations of data mining toolkits. As an empirical demonstration we show an implementation based on Weka and Hadoop.

Question: What is the difference between a data mining toolkit and a data mining tool?


Asymptotic Confidence Regions for High-Dimensional Structured Sparsity. In the setting of high-dimensional linear regression models, we propose two frameworks for constructing pointwise and group confidence sets for penalized estimators, which incorporate prior knowledge about the organization of the nonzero coefficients. This is done by desparsifying the estimator by S. van de Geer and B. Stucky and S. van de Geer <italic>et al.</italic>, then using an appropriate estimator for the precision matrix <inline-formula> <tex-math notation="LaTeX">$\Theta$</tex-math></inline-formula>. In order to estimate the precision matrix a corresponding structured matrix norm penalty has to be introduced. After normalization the result is an asymptotic pivot. The asymptotic behavior is studied and simulations are added to study the differences between the two schemes.

Question: How to construct confidence sets for high-dimensional structured sparse regression models?


Innovative Cost Effective Approach for Cell Phone Based Remote Controlled Embedded System for Irrigation. This work describes the development of innovative low cost cell phone based remote control application for induction motor-pump based irrigation in agriculture. Rural areas in many states of India are plagued by frequent power cuts and abnormal voltage conditions. The developed system ensures that water is distributed to field whenever normal conditions exist based on task specified. A novel concept of miscall for specified duration has been used to reduce the operational cost of the system and for the convenience of farmers facing difficulty in typing messages. Information is exchanged in form of miscalls / message between the system and the user cell phones. The system is based on AVR ATMega32 micro-controller and includes protection against single phasing, over-current, dry running and other desirable features. DS1307 and DS18S20 are used for time and temperature measurement respectively. It is expected that system will relieve hardships of farmers relating water distribution to a great extent.

Question: What is the innovative cost effective approach for cell phone based remote controlled embedded system for irrigation?


Doc 2 Cube : Automated Document Allocation to Text Cube via Dimension-Aware Joint Embedding. Data cube is a cornerstone architecture in multidimensional analysis of structured datasets. It is highly desirable to conduct multidimensional analysis on text corpora with cube structures for various text-intensive applications in healthcare, business intelligence, and social media analysis. However, one bottleneck to constructing text cube is to automatically put millions of documents into the right cells in such a text cube so that quality multidimensional analysis can be conducted afterwards—it is too expensive to allocate documents manually or rely on massively labeled data. We propose Doc2Cube, a method that constructs a text cube from a given text corpus in an unsupervised way. Initially, only the label names (e.g., USA, China) of each dimension (e.g., location) are provided instead of any labeled data. Doc2Cube leverages label names as weak supervision signals and iteratively performs joint embedding of labels, terms, and documents to uncover their semantic similarities. To generate joint embeddings that are discriminative for cube construction, Doc2Cube learns dimension-tailored document representations by selectively focusing on terms that are highly label-indicative in each dimension. Furthermore, Doc2Cube alleviates label sparsity by propagating the information from label names to other terms and enriching the labeled term set. Our experiments on a real news corpus demonstrate that Doc2Cube outperforms existing methods significantly. Doc2Cube is a technology transferred to U.S. Army Research Lab and is a core component of the EventCube system that is being deployed for multidimensional news and social media data analysis.

Question: How to construct a text cube from a given text corpus?


Are improvements in shame and self-compassion early in eating disorders treatment associated with better patient outcomes?. Compassion-focused therapy (CFT; Gilbert, 2005, 2009) is a transdiagnostic treatment approach focused on building self-compassion and reducing shame. It is based on the theory that feelings of shame contribute to the maintenance of psychopathology, whereas self-compassion contributes to the alleviation of shame and psychopathology. We sought to test this theory in a transdiagnostic sample of eating disorder patients by examining whether larger improvements in shame and self-compassion early in treatment would facilitate faster eating disorder symptom remission over 12 weeks. Participants were 97 patients with an eating disorder admitted to specialized day hospital or inpatient treatment. They completed the Eating Disorder Examination-Questionnaire, Experiences of Shame Scale, and Self-Compassion Scale at intake, and again after weeks 3, 6, 9, and 12. Multilevel modeling revealed that patients who experienced greater decreases in their level of shame in the first 4 weeks of treatment had faster decreases in their eating disorder symptoms over 12 weeks of treatment. In addition, patients who had greater increases in their level of self-compassion early in treatment had faster decreases in their feelings of shame over 12 weeks, even when controlling for their early change in eating disorder symptoms. These results suggest that CFT theory may help to explain the maintenance of eating disorders. Clinically, findings suggest that intervening with shame early in treatment, perhaps by building patients' self-compassion, may promote better eating disorders treatment response.

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


A novel three-phase DC/DC converter for high-power applications. This paper proposes a new three-phase series resonant converter. The principle working of the converter is described analytically in detail for switching frequencies equal to the resonant frequency and for switching frequencies greater than the resonant frequency. Additionally, detailed simulations are performed to analyse the converter. Based on the analysis, design criteria for a 5 kW breadboard are derived. The 5 kW breadboard version of the converter has been built to validate the analytical investigations and simulations experimentally. Moreover, the breadboard is used to investigate the ZVS and ZCS possibilities of the topology and the influence of the deadtime and the switching frequency on the overall efficiency.

Question: What is a novel three-phase DC/DC converter for high-power applications?


Minimising semantic drift with Mutual Exclusion Bootstrapping. Iterative bootstrapping techniques are commonly used to extract lexical semantic resources from raw text. Their major weakness is that, without costly human intervention, the extracted terms (often rapidly) drift from the meaning of the original seed terms. In this paper we propose Mutual Exclusion bootstrapping (MEB) in which multiple semantic classes compete for each extracted term. This significantly reduces the problem of semantic drift by providing boundaries for the semantic classes. We demonstrate the superiority of MEB to standard bootstrapping in extracting named entities from the Google Web 1T 5-grams. Finally, we demonstrate that MEB is a multi-way cut problem over semantic classes, terms and contexts.

Question: How do you extract named entities from the web?


Circular Pulley Versus Variable Radius Pulley: Optimal Design Methodologies and Dynamic Characteristics Analysis. Human-centered robotics has received growing interest in low-impedance actuations. In particular, pneumatic artificial muscles (PAMs) provide compliance and high force-to-weight ratio, which allow for safe actuation. However, several performance drawbacks prevent PAMs from being more pervasive. Although many approaches have been proposed to overcome the low control bandwidth of PAMs, some limitations of PAMs, such as restricted workspace and torque capacity, remain to be addressed. This paper analyzes the characteristics and limitations of PAMs-driven joints and subsequently provides an optimization strategy for circular pulleys (CPs) in order to improve joint torque capacity over a large workspace. In addition to CPs, this paper proposes a design methodology to synthesize a pair of variable radius pulleys (VRPs) for further improvement. Simulation and experimental results show that newly synthesized VRPs significantly improve torque capacity in the enlarged workspace without loss of dynamic performance. Finally, the characteristics of CPs and VRPs are discussed in terms of physical human-robot interaction.

Question: What is the difference between a circular pulley and a variable radius pulley?


Electronic Payments of Small Amounts. This note considers the application of electronic cash to transactions in which many small amounts must be paid to the same payee and in which it is not possible to just pay the total amount afterwards. The most notable example of such a transaction is payment for phone calls. If currently published electronic cash systems are used and a full payment protocol is executed for each of the small amounts, the overall complexity of the system will be prohibitively large (time, storage and communication). This note describes how such payments can be handled in a wide class of payment systems. The solution is very easy to adapt as it only in uences the payment and deposit transactions involving such payments. Furthermore, making and verifying each small payment requires very little computation and communication, and the total complexity of both transactions is comparable to that of a payment of a xed amount.

Question: How to handle small payments in electronic payments?


A comparative study of data mining techniques for credit scoring in banking. Credit is becoming one of the most important incomes of banking. Past studies indicate that the credit risk scoring model has been better for Logistic Regression and Neural Network. The purpose of this paper is to conduct a comparative study on the accuracy of classification models and reduce the credit risk. In this paper, we use data mining of enterprise software to construct four classification models, namely, decision tree, logistic regression, neural network and support vector machine, for credit scoring in banking. We conduct a systematic comparison and analysis on the accuracy of 17 classification models for credit scoring in banking. The contribution of this paper is that we use different classification methods to construct classification models and compare classification models accuracy, and the evidence demonstrates that the support vector machine models have higher accuracy rates and therefore outperform past classification methods in the context of credit scoring in banking.

Question: What is the purpose of this paper?


Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts. We present a Bayesian tensor factorization model for inferring latent group structures from dynamic pairwise interaction patterns. For decades, political scientists have collected and analyzed records of the form "country i took action a toward country j at time t" - known as dyadic events - in order to form and test theories of international relations. We represent these event data as a tensor of counts and develop Bayesian Poisson tensor factorization to infer a low-dimensional, interpretable representation of their salient patterns. We demonstrate that our model's predictive performance is better than that of standard non-negative tensor factorization methods. We also provide a comparison of our variational updates to their maximum likelihood counterparts. In doing so, we identify a better way to form point estimates of the latent factors than that typically used in Bayesian Poisson matrix factorization. Finally, we showcase our model as an exploratory analysis tool for political scientists. We show that the inferred latent factor matrices capture interpretable multilateral relations that both conform to and inform our knowledge of international a airs.

Question: How do you infer a low-dimensional, interpretable representation of the salient patterns of dyadic events?


Deep spatial autoencoders for visuomotor learning. Reinforcement learning provides a powerful and flexible framework for automated acquisition of robotic motion skills. However, applying reinforcement learning requires a sufficiently detailed representation of the state, including the configuration of task-relevant objects. We present an approach that automates state-space construction by learning a state representation directly from camera images. Our method uses a deep spatial autoencoder to acquire a set of feature points that describe the environment for the current task, such as the positions of objects, and then learns a motion skill with these feature points using an efficient reinforcement learning method based on local linear models. The resulting controller reacts continuously to the learned feature points, allowing the robot to dynamically manipulate objects in the world with closed-loop control. We demonstrate our method with a PR2 robot on tasks that include pushing a free-standing toy block, picking up a bag of rice using a spatula, and hanging a loop of rope on a hook at various positions. In each task, our method automatically learns to track task-relevant objects and manipulate their configuration with the robot's arm.

Question: How does reinforcement learning work?


An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed. Integration of goal-driven, top-down attention and image-driven, bottom-up attention is crucial for visual search. Yet, previous research has mostly focused on models that are purely top-down or bottom-up. Here, we propose a new model that combines both. The bottom-up component computes the visual salience of scene locations in different feature maps extracted at multiple spatial scales. The topdown component uses accumulated statistical knowledge of the visual features of the desired search target and background clutter, to optimally tune the bottom-up maps such that target detection speed is maximized. Testing on 750 artificial and natural scenes shows that the model's predictions are consistent with a large body of available literature on human psychophysics of visual search. These results suggest that our model may provide good approximation of how humans combine bottom-up and top-down cues such as to optimize target detection speed.

Question: What is the model's name?


Budgeted Optimization with Constrained Experiments. Motivated by a real-world problem, we study a novel budgeted optimization problem where the goal is to optimize an unknown function f(·) given a budget by requesting a sequence of samples from the function. In our setting, however, evaluating the function at precisely specified points is not practically possible due to prohibitive costs. Instead, we can only request constrained experiments. A constrained experiment, denoted by Q, specifies a subset of the input space for the experimenter to sample the function from. The outcome of Q includes a sampled experiment x, and its function output f(x). Importantly, as the constraints of Q become looser, the cost of fulfilling the request decreases, but the uncertainty about the location x increases. Our goal is to manage this trade-off by selecting a set of constrained experiments that best optimize f(·) within the budget. We study this problem in two different settings, the non-sequential (or batch) setting where a set of constrained experiments is selected at once, and the sequential setting where experiments are selected one at a time. We evaluate our proposed methods for both settings using synthetic and real functions. The experimental results demonstrate the efficacy of the proposed methods.

Question: What is the budgeted optimization with constrained experiments?


Central Lip Lift as Aesthetic and Physiognomic Plastic Surgery: The Effect on Lower Facial Profile.. BACKGROUND A central lip lift was introduced to Westerners in 1980s. However, no studies have been conducted on the facial aesthetic and physiognomic perspectives of a central lip lift in the Asian population.   OBJECTIVES The authors presented the central lip lift as aesthetic and physiognomic treatment in Asians and explained its effect on lower facial profile.   METHODS A retrospective chart review was performed in 202 cases of asians. The authors analyzed patient age, cause of long philtrum, purpose of the treatment, and postoperative satisfaction. The authors then performed an anthropometric assessment and a photographic analysis.   RESULTS The vertical disproportion of the lower face was improved after the treatment, and there was significant shortening of the philtrum length (P < .001) and an increase in a visible upper vermilion (P < .001). In Westerners, a long philtrum was mainly caused by the aging process. Aging patients (range, 40-59 years) underwent the central lip lift for upper lip rejuvenation. In contrast, in Asia, a long philtrum was primarily caused by bone retraction after an orthognathic surgery or orthodontic procedure. Young patients (range, 20-39 years old) underwent the central lip lift to correct a relatively lengthened philtrum after 2-jaw surgery. Furthermore, about half of the patients (52.0%) underwent the central lip lift for facial physiognomic improvement.   CONCLUSIONS In today's multiracial society, plastic surgeons planning a central lip lift in Asian patients should consider both aesthetic and physiognomic perspectives. Regardless of the aesthetic outcome, the surgeon should strive to maximize patient satisfaction.   LEVEL OF EVIDENCE 4 Therapeutic.

Question: What is the effect of a central lip lift on lower facial profile?


Firmalice - Automatic Detection of Authentication Bypass Vulnerabilities in Binary Firmware. Embedded devices have become ubiquitous, and they are used in a range of privacy-sensitive and security-critical applications. Most of these devices run proprietary software, and little documentation is available about the software's inner workings. In some cases, the cost of the hardware and protection mechanisms might make access to the devices themselves infeasible. Analyzing the software that is present in such environments is challenging, but necessary, if the risks associated with software bugs and vulnerabilities must be avoided. As a matter of fact, recent studies revealed the presence of backdoors in a number of embedded devices available on the market. In this paper, we present Firmalice, a binary analysis framework to support the analysis of firmware running on embedded devices. Firmalice builds on top of a symbolic execution engine, and techniques, such as program slicing, to increase its scalability. Furthermore, Firmalice utilizes a novel model of authentication bypass flaws, based on the attacker's ability to determine the required inputs to perform privileged operations. We evaluated Firmalice on the firmware of three commercially-available devices, and were able to detect authentication bypass backdoors in two of them. Additionally, Firmalice was able to determine that the backdoor in the third firmware sample was not exploitable by an attacker without knowledge of a set of unprivileged credentials.

Question: Firmalice - Automatic Detection of Authentication Bypass Vulnerabilities in Binary Firmware


Performance analysis of supervised machine learning algorithms for text classification. The demand of text classification is growing significantly in web searching, data mining, web ranking, recommendation systems and so many other fields of information and technology. This paper illustrates the text classification process on different dataset using some standard supervised machine learning techniques. Text documents can be classified through various kinds of classifiers. Labeled text documents are used to classify the text in supervised classifications. This paper applied these classifiers on different kinds of labeled documents and measures the accuracy of the classifiers. An Artificial Neural Network (ANN) model using Back Propagation Network (BPN) is used with several other models to create an independent platform for labeled and supervised text classification process. An existing benchmark approach is used to analysis the performance of classification using labeled documents. Experimental analysis on real data reveals which model works well in terms of classification accuracy.

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


G2RHA:Group-to-Route Handover Authentication Scheme for Mobile Relays in LTE-A High-Speed Rail Networks. The introduction of mobile relay node (MRN) in long-term evolution advanced high-speed rail networks is an attractive method to provide uninterrupted connectivity for the group of user equipments on board. However, MRNs still suffer from frequent handovers and several security threats due to several rounds of message exchange and the insecure air interface between MRNs and donor eNBs (DeNBs). In this paper, we propose a group-to-route handover authentication scheme based on trajectory prediction for mobile relays. By our scheme, all of the DeNBs deployed along the trajectory can be formed a route-DeNB group and all of the MRNs deployed in the same train can construct a MRN group. Compared with the current 3GPP standards and other related schemes, our scheme can accomplish the mutual authentication and key agreement between the MRN group and the target DeNB with ideal efficiency in terms of authentication signaling overhead, bandwidth consumption, and computational cost. Security analysis by using the BAN logic and formal verification tool Scyther show that our scheme can work correctly and withstand several protocol attacks.

Question: What is the difference between LTE-A and LTE?


Handover decision using fuzzy MADM in heterogeneous networks. In the next generation heterogeneous wireless networks, a user with a multi-interface terminal may have a network access from different service providers using various technologies. It is believed that handover decision is based on multiple criteria as well as user preference. Various approaches have been proposed to solve the handover decision problem, but the choice of decision method appears to be arbitrary and some of the methods even give disputable results. In this paper, a new handover criteria is introduced along with a new handover decision strategy. In addition, handover decision is identified us a fuzzy multiple attribute decision making (MADM) problem, and fuzzy logic is applied to deal with the imprecise information of some criteria and user preference. After a systematic analysis of various fuzzy MADM methods, a feasible approach is presented. In the end, examples are provided illustrating the proposed methods and the sensitivity of the methods is also analysed.

Question: What is a heterogeneous wireless network?


Fundamentals of Parameterized Complexity. It's not surprisingly when entering this site to get the book. One of the popular books now is the fundamentals of parameterized complexity. You may be confused because you can't find the book in the book store around your city. Commonly, the popular book will be sold quickly. And when you have found the store to buy the book, it will be so hurt when you run out of it. This is why, searching for this popular book in this website will give you benefit. You will not run out of this book.

Question: What is the book about?


Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting. Data streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to non-stationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems.

Question: What is the difference between a data stream and a data set?


Temporal Sequence Learning, Prediction, and Control: A Review of Different Models and Their Relation to Biological Mechanisms. In this review, we compare methods for temporal sequence learning (TSL) across the disciplines machine-control, classical conditioning, neuronal models for TSL as well as spike-timing-dependent plasticity (STDP). This review introduces the most influential models and focuses on two questions: To what degree are reward-based (e.g., TD learning) and correlation-based (Hebbian) learning related? and How do the different models correspond to possibly underlying biological mechanisms of synaptic plasticity? We first compare the different models in an open-loop condition, where behavioral feedback does not alter the learning. Here we observe that reward-based and correlation-based learning are indeed very similar. Machine control is then used to introduce the problem of closed-loop control (e.g., actor-critic architectures). Here the problem of evaluative (rewards) versus nonevaluative (correlations) feedback from the environment will be discussed, showing that both learning approaches are fundamentally different in the closed-loop condition. In trying to answer the second question, we compare neuronal versions of the different learning architectures to the anatomy of the involved brain structures (basal-ganglia, thalamus, and cortex) and the molecular biophysics of glutamatergic and dopaminergic synapses. Finally, we discuss the different algorithms used to model STDP and compare them to reward-based learning rules. Certain similarities are found in spite of the strongly different timescales. Here we focus on the biophysics of the different calcium-release mechanisms known to be involved in STDP.

Question: What is the difference between reward-based and correlation-based learning?


Reversible Alopecia with Localized Scalp Necrosis After Accidental Embolization of the Parietal Artery with Hyaluronic Acid. Hyaluronic acid (HA) filler injection is widely used for soft-tissue augmentation. Complications associated with HA filling are not uncommon; however, HA-induced alopecia is a rarely reported complication that could result in severe secondary psychological trauma. The etiology, clinical traits, treatment strategies, outcomes, and possible reversibility of HA-induced alopecia have not been characterized. Here, we report a case in which bilateral temple injections of 6.5 mL of HA led to persistent pain over the left scalp for several days. Although the pain was relieved at day 9 after 600 U of hyaluronidase were injected in the left temple, the patient developed localized alopecia at the left temporoparietal region with central skin necrosis at day 15. After topical applications of recombinant bovine basic fibroblast growth factor gel and 2% minoxidil spay, the necrotic skin wound was healed at day 42. Hair regrowth and normal hair density were restored at day 74. Analyses of Doppler ultrasound examinations and histopathology of the skin biopsy suggested that mild ischemia of the left temporoparietal region led to reversible alopecia, while the permanent hair loss in the left parietal area was associated with severe skin ischemia. Therefore, the key to treatment would be to focus on the effective correction of severe ischemia-induced skin necrosis to prevent permanent hair loss. Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Question: What is the etiology of HA-induced alopecia?


Cycling Down Under : A Comparative Analysis of Bicycling Trends and Policies in Sydney and Melbourne. The purpose of this paper is to document and explain differences in cycling between Australia's two largest cities. Our comparative case study analysis is based on a wide range of statistical datasets, secondary reports, and interviews with a panel of 22 bicycling policy and planning experts. The main finding is that cycling levels in Melbourne are roughly twice as high as in Sydney and have been growing three times as fast in recent years. The difference is due to Melbourne's more favorable topography, climate, and road network as well as more supportive public policies. In particular, Melbourne has more and better integrated cycling infrastructure as well as more extensive cycling programs, advocacy, and promotional events. Melbourne also benefits from safer cycling than Sydney, which suffers from a lack of traffic-protected cycling facilities and aggressive motorist behavior toward cyclists on the road. While cycling has been increasing in Australia, it remains at very low levels relative to northern Europe, where both land use and transport policies are far more supportive of bicycling while discouraging car use through numerous restrictions and financial disincentives.

Question: What is the difference between the two largest cities in Australia?


A Comparative Analysis of Crowdsourced Natural Language Corpora for Spoken Dialog Systems. Recent spoken dialog systems have been able to recognize freely spoken user input in restricted domains thanks to statistical methods in the automatic speech recognition. These methods require a high number of natural language utterances to train the speech recognition engine and to assess the quality of the system. Since human speech offers many variants associated with a single intent, a high number of user utterances have to be elicited. Developers are therefore turning to crowdsourcing to collect this data. This paper compares three different methods to elicit multiple utterances for given semantics via crowd sourcing, namely with pictures, with text and with semantic entities. Specifically, we compare the methods with regard to the number of valid data and linguistic variance, whereby a quantitative and qualitative approach is proposed. In our study, the method with text led to a high variance in the utterances and a relatively low rate of invalid data.

Question: What is the difference between a quantitative and qualitative approach?


A New Approach of Digital Forensic Model for Digital Forensic Investigation. The research introduces a structured and consistent approach for digital forensic investigation. Digital forensic science provides tools, techniques and scientifically proven methods that can be used to acquire and analyze digital evidence. The digital forensic investigation must be retrieved to obtain the evidence that will be accepted in the court. This research focuses on a structured and consistent approach to digital forensic investigation. This research aims at identifying activities that facilitate and improves digital forensic investigation process. Existing digital forensic framework will be reviewed and then the analysis will be compiled. The result from the evaluation will produce a new model to improve the whole investigation process.

Question: What is a digital forensic investigation?


Automatic Detection and Classification of Brain Hemorrhages. Computer-aided diagnosis systems have been the focus of many research endeavors. They are based on the idea of processing and analyzing images of different parts of the human body for a quick and accurate diagnosis. In this paper, the aforementioned approach is followed to detect whether a brain hemorrhage exists or not in a Computed Topography (CT) scans of the brain. Moreover, the type of the hemorrhage is identified. The implemented system consists of several stages that include image preprocessing, image segmentation, feature extraction, and classification. The results of the conducted experiments are very promising. A recognition rate of 100% is attained for detecting whether a brain hemorrhage exists or not. For the hemorrhage type classification, more than 92% accuracy is achieved. Key–Words:brain hemorrhage, brain ct scans, machine learning, image processing, image segmentation

Question: How to detect brain hemorrhages in CT scans?


Real-time tracking on adaptive critic design with uniformly ultimately bounded condition. In this paper, we proposed a new nonlinear tracking controller based on heuristic dynamic programming (HDP) with the tracking filter. Specifically, we integrate a goal network into the regular HDP design and provide the critic network with detailed internal reward signal to help the value function approximation. The architecture is explicitly explained with the tracking filter, goal network, critic network and action network, respectively. We provide the stability analysis of our proposed controller with Lyapunov approach. It is shown that the filtered tracking errors and the weights estimation errors in neural networks are all uniformly ultimately bounded (UUB) under certain conditions. Finally, we compare our proposed approach with regular HDP approach in virtual reality (VR)/Simulink environment to justify the improved control performance.

Question: What is the difference between adaptive critic design and regular HDP design?


Analysis of blood samples for counting leukemia cells using Support vector machine and nearest neighbour. analysis of blood samples for counting leukemia cells is discussed in this paper. support vector machine and nearest neighbour concept is presented in this paper. The identification of blood disorders, it can lead to classification of certain diseases related to blood. Several classification techniques are investigated till today. One of the best methods for classification techniques nearest neighbour and SVM (Support Vector Machine).By identifying and counting blood cell within the blood smear using classification techniques it's quite possible to detect so many diseases. If one of the new classifier is used i.e. nearest neighbour and SVM it is quiet possible to detect the cancer cell from the blood cell counting. Keyword: Blood, Classification technique, Nearest Neighbour Network, SVM

Question: What is the nearest neighbour network?


Developing multiple hypotheses in behavioral ecology. Researchers in behavioral ecology are increasingly turning to research methods that allow the simultaneous evaluation of hypotheses. This approach has great potential to increase our scientific understanding, but researchers interested in the approach should be aware of its long and somewhat contentious history. Also, prior to implementing multiple hypothesis evaluation, researchers should be aware of the importance of clearly specifying a priori hypotheses. This is one of the more difficult aspects of research based on multiple hypothesis evaluation, and we outline and provide examples of three approaches for doing so. Finally, multiple hypothesis evaluation has some limitations important to behavioral ecologists; we discuss two practical issues behavioral ecologists are likely to face.

Question: What is behavioral ecology?


High quality & low thermal resistance eutectic flip chip LED bonding. Die attach process plays a key role in the thermal management of high power LED packages by governing the thermal resistance of the solder interfaces between the LED chips and the substrate materials. In this paper, comparison was made between the GaN based flip chip LED packages fabricated by (1) heated collet and (2) flux reflow eutectic die attach process. Thermal transient characteristics of the samples had been investigated based on the evaluation of the differential structure function and chip temperature measurement by infrared microscopy. It was shown that LEDs with higher void content would result in higher thermal resistance and chip temperature. Under high power operation at 6W, it was estimated that the junction temperature would exceed 80°C with void content 40%, which was at least 10°C higher comparing to that with void content 20%. Based on the result, LEDs samples bonded by heated collet die attach process had an average void content of 8.8% with 0.9% standard deviation, which were smaller comparing to the samples bonded by flux reflow die attach process (40% void content with standard deviation 20.5%). Due to the advantage of small void content control, it is possible to fabricate high power LED package with low thermal resistance, consistent and reliable die attach quality by using heated collet die attach process.

Question: What is the void content of the sample?


A design process for embedding knowledge management in everyday work. Knowledge Management Software must be embedded in processes of knowledge workers' everyday practice. In order to attain a seamless design, regarding the special qualities and requirements of knowledge work, detailed studies of the existing work processes and analysis of the used knowledge are necessary. Participation of the knowledge owners and future users us an important factor for success of knowledge management systems. In this paper we describe characteristics of knowledge work motivating the usage of participatory design techniques. We suggest a design process for developing or improving knowledge management, which includes ethnographic surveys, user participation in cyclic improvement, scenario based design, and the use of multiple design artifacts and documents. Finally we explain the benefits of our approach. The paper is based on a case study we carried out to design and introduce a knowledge management system in a training company.

Question: What is a design process for embedding knowledge management in everyday work?


INTRINSIC AND EXTRINSIC MOTIVATION 1. A central tenet of economics is that individuals respond to incentives. For psychologists and sociologists, by contrast, rewards and punishments are often counterproductive, because they undermine "intrinsic motivation". We reconcile these two views, showing how performance incentives offered by an informed principal (manager, teacher, parent) can adversely impact an agent's (worker, child) perception of the task, or of his own abilities. Incentives are then only weak reinforcers in the short run, and negative reinforcers in the long run. We also study the effects of empowerment, help and excuses on motivation, as well as situations of ego-bashing reflecting a battle for dominance within a relationship.

Question: What is the difference between intrinsic and extrinsic motivation?


Text-based adventures of the golovin AI agent. The domain of text-based adventure games has been recently established as a new challenge of creating the agent that is both able to understand natural language, and acts intelligently in text-described environments. In this paper, we present our approach to tackle the problem. Our agent, named Golovin, takes advantage of the limited game domain. We use genre-related corpora (including fantasy books and decompiled games) to create language models suitable to this domain. Moreover, we embed mechanisms that allow us to specify, and separately handle, important tasks as fighting opponents, managing inventory, and navigating on the game map. We validated usefulness of these mechanisms, measuring agent's performance on the set of 50 interactive fiction games. Finally, we show that our agent plays on a level comparable to the winner of the last year Text-Based Adventure AI Competition.

Question: What is the domain of text-based adventure games?


Adaptive Background Mixture Models for Real-Time Tracking. A common method for real-time segmentation of moving regions in image sequences involves "background subtraction," or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow.

Question: What is the adaptive background mixture model?


Gut microbiota functions: metabolism of nutrients and other food components. The diverse microbial community that inhabits the human gut has an extensive metabolic repertoire that is distinct from, but complements the activity of mammalian enzymes in the liver and gut mucosa and includes functions essential for host digestion. As such, the gut microbiota is a key factor in shaping the biochemical profile of the diet and, therefore, its impact on host health and disease. The important role that the gut microbiota appears to play in human metabolism and health has stimulated research into the identification of specific microorganisms involved in different processes, and the elucidation of metabolic pathways, particularly those associated with metabolism of dietary components and some host-generated substances. In the first part of the review, we discuss the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates (to short chain fatty acids and gases), proteins, plant polyphenols, bile acids, and vitamins. The second part of the review focuses on the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism. These include mathematical models, omics techniques, isolated microbes, and enzyme assays.

Question: What is the gut microbiota?


Mastering the game of Go with deep neural networks and tree search. The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'value networks' to evaluate board positions and 'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

Question: How to play go?


Turing Computability with Neural Nets. This paper shows the existence of a finite neural network, made up of sigmoidal nenrons, which simulates a nniversal Turing machine. It is composed of less then lo5 synchronously evolving processors, interconnected linearly. High-order connections are not required.

Question: What is a neural network?


HMDB: A large video database for human motion recognition. With nearly one billion online videos viewed everyday, an emerging new frontier in computer vision research is recognition and search in video. While much effort has been devoted to the collection and annotation of large scalable static image datasets containing thousands of image categories, human action datasets lag far behind. Current action recognition databases contain on the order of ten different action categories collected under fairly controlled conditions. State-of-the-art performance on these datasets is now near ceiling and thus there is a need for the design and creation of new benchmarks. To address this issue we collected the largest action video database to-date with 51 action categories, which in total contain around 7,000 manually annotated clips extracted from a variety of sources ranging from digitized movies to YouTube. We use this database to evaluate the performance of two representative computer vision systems for action recognition and explore the robustness of these methods under various conditions such as camera motion, viewpoint, video quality and occlusion.

Question: What is the largest video database for human motion recognition?


Random Projection Based Representations for Learning Policies in Deterministic Atari Games. Recent advances in sample efficient reinforcement learning algorithms in quasi-deterministic environments highlight the requirement for computationally inexpensive visual representations. Here we investigate non-parametric dimensionality reduction techniques based on random linear transformations and we provide empirical evidence on the importance of high-variance projections using sparse random matrices in the context of episodic controllers learning deterministic policies. We also propose a novel Maximum-Variance Random Projection and improve on the performance of the original Model-Free Episodic Control results with respect to both sample efficiency and final average score.

Question: What is the importance of high-variance projections?


Robotic versus Open Partial Nephrectomy: A Systematic Review and Meta-Analysis. OBJECTIVES To critically review the currently available evidence of studies comparing robotic partial nephrectomy (RPN) and open partial nephrectomy (OPN).   MATERIALS AND METHODS A comprehensive review of the literature from Pubmed, Web of Science and Scopus was performed in October 2013. All relevant studies comparing RPN with OPN were included for further screening. A cumulative meta-analysis of all comparative studies was performed and publication bias was assessed by a funnel plot.   RESULTS Eight studies were included for the analysis, including a total of 3418 patients (757 patients in the robotic group and 2661 patients in the open group). Although RPN procedures had a longer operative time (weighted mean difference [WMD]: 40.89; 95% confidence interval [CI], 14.39-67.40; p = 0.002), patients in this group benefited from a lower perioperative complication rate (19.3% for RPN and 29.5% for OPN; odds ratio [OR]: 0.53; 95%CI, 0.42-0.67; p<0.00001), shorter hospital stay (WMD: -2.78; 95%CI, -3.36 to -1.92; p<0.00001), less estimated blood loss(WMD: -106.83; 95%CI, -176.4 to -37.27; p = 0.003). Transfusions, conversion to radical nephrectomy, ischemia time and estimated GFR change, margin status, and overall cost were comparable between the two techniques. The main limitation of the present meta-analysis is the non-randomization of all included studies.   CONCLUSIONS RPN appears to be an efficient alternative to OPN with the advantages of a lower rate of perioperative complications, shorter length of hospital stay and less blood loss. Nevertheless, high quality prospective randomized studies with longer follow-up period are needed to confirm these findings.

Question: What is the difference between robotic and open partial nephrectomy?


A comparison of approaches to large-scale data analysis. There is currently considerable enthusiasm around the MapReduce (MR) paradigm for large-scale data analysis [17]. Although the basic control flow of this framework has existed in parallel SQL database management systems (DBMS) for over 20 years, some have called MR a dramatically new computing model [8, 17]. In this paper, we describe and compare both paradigms. Furthermore, we evaluate both kinds of systems in terms of performance and development complexity. To this end, we define a benchmark consisting of a collection of tasks that we have run on an open source version of MR as well as on two parallel DBMSs. For each task, we measure each system's performance for various degrees of parallelism on a cluster of 100 nodes. Our results reveal some interesting trade-offs. Although the process to load data into and tune the execution of parallel DBMSs took much longer than the MR system, the observed performance of these DBMSs was strikingly better. We speculate about the causes of the dramatic performance difference and consider implementation concepts that future systems should take from both kinds of architectures.

Question: What is the difference between the MapReduce and the parallel DBMS?


A Reduced Reference Image Quality assessment for Multiply Distorted Images. In this paper, we propose a new Reduced Reference Image Quality Metric for multiply degraded images based on a features extraction step and its combination. The selected features are extracted from the original image and its degraded version. Some of them aim to quantify the level of the considered degradation types, while the others quantify its sharpness. These features are then combined to obtain a single value, which corresponds to the predicted subjective score. Our method has been evaluated and compared in terms of correlation with subjective judgments to some recent methods by using the LIVE Multiply Distorted Image Quality Database.

Question: What is a reduced reference image quality metric?


COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints. Constraint-based clustering algorithms exploit background knowledge to construct clusterings that are aligned with the interests of a particular user. This background knowledge is often obtained by allowing the clustering system to pose pairwise queries to the user: should these two elements be in the same cluster or not? Active clustering methods aim to minimize the number of queries needed to obtain a good clustering by querying the most informative pairs first. Ideally, a user should be able to answer a couple of these queries, inspect the resulting clustering, and repeat these two steps until a satisfactory result is obtained. We present COBRAS, an approach to active clustering with pairwise constraints that is suited for such an interactive clustering process. A core concept in COBRAS is that of a super-instance: a local region in the data in which all instances are assumed to belong to the same cluster. COBRAS constructs such superinstances in a top-down manner to produce highquality results early on in the clustering process, and keeps refining these super-instances as more pairwise queries are given to get more detailed clusterings later on. We experimentally demonstrate that COBRAS produces good clusterings at fast run times, making it an excellent candidate for the iterative clustering scenario outlined above.

Question: What is a super-instance?


A Handwritten Character Recognition System Using Directional Element Feature and Asymmetric Mahalanobis Distance. This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distance (AMD) are proposed for rough classification and fine classification. With this recognition system, the experimental result of the database ETL9B reaches to 99.42%.

Question: What is the database ETL9B?


Variable Rate Image Compression with Recurrent Neural Networks. Although image compression has been actively studied for decades, there has been relatively little research on learning to compress images with modern neural networks. Standard approaches, such as those employing patch-based autoencoders, have shown a great deal of promise but cannot compete with popular image codecs because they fail to address three questions: 1) how to effectively binarize activations: in the absence of binarization, a bottleneck layer alone tends not to lead to efficient compression; 2) how to achieve variable-rate encoding: a standard autoencoder generates a fixed-length code for each fixed-resolution input patch, resulting in the same cost for lowand high-entropy patches, and requiring the network to be completely retrained to achieve different compression rates; and 3) how to avoid block artifacts: patch-based approaches are prone to block discontinuities. We propose a general framework for variable-rate image compression and a novel architecture based on convolutional and deconvolutional recurrent networks, including LSTMs, that address these issues and report promising results compared to existing baseline codecs. We evaluate the proposed methods on a large-scale benchmark consisting of tiny images (32× 32), which proves to be very challenging for all the methods.

Question: How to compress images with neural networks?


Understanding consumers' continuance intention towards mobile purchase: A theoretical framework and empirical study - A case of China. Although mobile purchase is convenient in the age of mobile commerce, many consumers still do not utilize mobile purchase to its full potential. From the mobile vendor's perspective, retaining current customers and facilitating their continued purchase are crucial to create profitability and achieve a sustainable development. An understanding of the continuance intention towards mobile purchase can provide insights into mobile vendors' marketing campaigns. Therefore, it is important to examine the determinants that impact continuance intentions of consumers for mobile purchase. Drawing upon information success model, flow theory and trust, this study proposed and empirically tested an integrated model to better understand the determinants of consumers' continued intention to purchase on mobile sites. Empirical data from 462 users who had experience with mobile purchase were tested against the proposed research model by using structural equation modelling (SEM). The results indicated that information quality, and privacy and security concerns are the main factors affecting trust, whereas service quality is the main factor affecting flow. System quality, and privacy and security concerns affect satisfaction. Trust affects flow, which in turn affects satisfaction. These three factors together affect continued intention towards mobile purchase. The findings of this study provide several important implications for mobile commerce research and practice. 2015 Elsevier Ltd. All rights reserved.

Question: What is the difference between a theoretical framework and an empirical study?


Root Gap Correction with a Deep Inpainting Model. Imaging roots of growing plants in a non-invasive and affordable fashion has been a long-standing problem in image-assisted plant breeding and phenotyping. One of the most affordable and diffuse approaches is the use of mesocosms, where plants are grown in soil against a glass surface that permits the roots visualization and imaging. However, due to soil and the fact that the plant root is a 2D projection of a 3D object, parts of the root are occluded. As a result, even under perfect root segmentation, the resulting images contain several gaps that may hinder the extraction of finely grained root system architecture traits. We propose an effective deep neural network to recover gaps from disconnected root segments. We train a fully supervised encoder-decoder deep CNN that, given an image containing gaps as input, generates an inpainted version, recovering the missing parts. Since in real data ground-truth is lacking, we use synthetic root images [10] that we artificially perturb by introducing gaps to train and evaluate our approach. We show that our network can work both in dicot and monocot cases in reducing root gaps. We also show promising exemplary results in real data from chickpea root architectures.

Question: How to correct gaps in root images?


Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. We investigate how reinforcement learning agents can learn to cooperate. Drawing inspiration from human societies, in which successful coordination of many individuals is often facilitated by hierarchical organisation, we introduce Feudal Multiagent Hierarchies (FMH). In this framework, a 'manager' agent, which is tasked with maximising the environmentally-determined reward function, learns to communicate subgoals to multiple, simultaneously-operating, 'worker' agents. Workers, which are rewarded for achieving managerial subgoals, take concurrent actions in the world. We outline the structure of FMH and demonstrate its potential for decentralised learning and control. We find that, given an adequate set of subgoals from which to choose, FMH performs, and particularly scales, substantially better than cooperative approaches that use a shared reward function.

Question: How does feudal multi-agent hierarchies work?


Internet of Robotic Things-Converging Sensing / Actuating , Hypoconnectivity , Artificial Intelligence and IoT Platforms. The Internet of Things (IoT) concept is evolving rapidly and influencing new developments in various application domains, such as the Internet of Mobile Things (IoMT), Autonomous Internet of Things (A-IoT), Autonomous System of Things (ASoT), Internet of Autonomous Things (IoAT), Internet of Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc. that are progressing/advancing by using IoT technology. The IoT influence represents new development and deployment challenges in different areas such as seamless platform integration, context based cognitive network integration, new mobile sensor/actuator network paradigms, things identification (addressing, naming in IoT) and dynamic things discoverability and many others. The IoRT represents new convergence challenges and their need

Question: What is the Internet of Robotic Things?


Automated leukemia detection in blood microscopic images using statistical texture analysis. Pathological image analysis plays a significant role in effective disease diagnostics. Quantitative microscopy has supplemented clinicians with accurate results for diagnosis of dreaded diseases such as leukemia, hepatitis, AIDS, psoriasis. In this paper we present a texture based approach for automated leukemia detection. Acute lymphocytic leukemia (ALL) is a malignant disease characterized by the accumulation of lymphoblast in the bone marrow. Texture features of the blood nucleus are investigated for diagnostic prediction of ALL. Other shape features are also extracted to classify a lymphocytic cell in the blood image into normal lymphocyte or lymphoblast (blasts). Initial segmentation is done using K-means clustering which segregates leukocytes or white blood cells (WBC) from other blood components i.e. erythrocytes and platelets. The results of K-means are used for evaluating individual cell shape, texture and other features for final detection of leukemia. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.

Question: What is the difference between a hematologist and a pathologist?


Event-based Sensing for Space Situational Awareness. A revolutionary type of imaging device, known as a silicon retina or event-based sensor, has recently been developed and is gaining in popularity in the field of artificial vision systems. These devices are inspired by a biological retina and operate in a significantly different way to traditional CCD-based imaging sensors. While a CCD produces frames of pixel intensities, an event-based sensor produces a continuous stream of events, each of which is generated when a pixel detects a change in log light intensity. These pixels operate asynchronously and independently, producing an event-based output with high temporal resolution. There are also no fixed exposure times, allowing these devices to offer a very high dynamic range independently for each pixel. Additionally, these devices offer high-speed, low power operation and a sparse spatiotemporal output. As a consequence, the data from these sensors must be interpreted in a significantly different way to traditional imaging sensors and this paper explores the advantages this technology provides for space imaging. The applicability and capabilities of event-based sensors for SSA applications are demonstrated through telescope field trials. Trial results have confirmed that the devices are capable of observing resident space objects from LEO through to GEO orbital regimes. Significantly, observations of RSOs were made during both day-time and nighttime (terminator) conditions without modification to the camera or optics. The event based sensor's ability to image stars and satellites during day-time hours offers a dramatic capability increase for terrestrial optical sensors. This paper shows the field testing and validation of two different architectures of event-based imaging sensors. An eventbased sensor's asynchronous output has an intrinsically low data-rate. In addition to low-bandwidth communications requirements, the low weight, low-power and high-speed make t

Question: What is the difference between a CCD and an event-based sensor?


Learning Robust Visual-Semantic Embeddings. Many of the existing methods for learning joint embedding of images and text use only supervised information from paired images and its textual attributes. Taking advantage of the recent success of unsupervised learning in deep neural networks, we propose an end-to-end learning framework that is able to extract more robust multi-modal representations across domains. The proposed method combines representation learning models (i.e., auto-encoders) together with cross-domain learning criteria (i.e., Maximum Mean Discrepancy loss) to learn joint embeddings for semantic and visual features. A novel technique of unsupervised-data adaptation inference is introduced to construct more comprehensive embeddings for both labeled and unlabeled data. We evaluate our method on Animals with Attributes and Caltech-UCSD Birds 200-2011 dataset with a wide range of applications, including zero and few-shot image recognition and retrieval, from inductive to transductive settings. Empirically, we show that our frame-work improves over the current state of the art on many of the considered tasks.

Question: What is the maximum mean discrepancy loss?


Prevalence of chronic low back pain: systematic review. OBJECTIVE To estimate worldwide prevalence of chronic low back pain according to age and sex.   METHODS We consulted Medline (PubMed), LILACS and EMBASE electronic databases. The search strategy used the following descriptors and combinations: back pain, prevalence, musculoskeletal diseases, chronic musculoskeletal pain, rheumatic, low back pain, musculoskeletal disorders and chronic low back pain. We selected cross-sectional population-based or cohort studies that assessed chronic low back pain as an outcome. We also assessed the quality of the selected studies as well as the chronic low back pain prevalence according to age and sex.   RESULTS The review included 28 studies. Based on our qualitative evaluation, around one third of the studies had low scores, mainly due to high non-response rates. Chronic low back pain prevalence was 4.2% in individuals aged between 24 and 39 years old and 19.6% in those aged between 20 and 59. Of nine studies with individuals aged 18 and above, six reported chronic low back pain between 3.9% and 10.2% and three, prevalence between 13.1% and 20.3%. In the Brazilian older population, chronic low back pain prevalence was 25.4%.   CONCLUSIONS Chronic low back pain prevalence increases linearly from the third decade of life on, until the 60 years of age, being more prevalent in women. Methodological approaches aiming to reduce high heterogeneity in case definitions of chronic low back pain are essential to consistency and comparative analysis between studies. A standard chronic low back pain definition should include the precise description of the anatomical area, pain duration and limitation level.

Question: What is the prevalence of chronic low back pain in the canadian military?


A unified view of gradient-based attribution methods for Deep Neural Networks. Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years. While several methods have been proposed to explain network predictions, only few attempts to analyze them from a theoretical perspective have been made in the past. In this work we analyze various state-of-the-art attribution methods and prove unexplored connections between them. We also show how some methods can be reformulated and more conveniently implemented. Finally, we perform an empirical evaluation with six attribution methods on a variety of tasks and architectures and discuss their strengths and limitations.

Question: What is the unified view of gradient-based attribution methods for Deep Neural Networks?


Sanity Check: A Strong Alignment and Information Retrieval Baseline for Question Answering. While increasingly complex approaches to question answering (QA) have been proposed, the true gain of these systems, particularly with respect to their expensive training requirements, can be in- flated when they are not compared to adequate baselines. Here we propose an unsupervised, simple, and fast alignment and informa- tion retrieval baseline that incorporates two novel contributions: a one-to-many alignment between query and document terms and negative alignment as a proxy for discriminative information. Our approach not only outperforms all conventional baselines as well as many supervised recurrent neural networks, but also approaches the state of the art for supervised systems on three QA datasets. With only three hyperparameters, we achieve 47% P@1 on an 8th grade Science QA dataset, 32.9% P@1 on a Yahoo! answers QA dataset and 64% MAP on WikiQA.

Question: What is the state of the art for question answering?


Effect of written emotional expression on immune function in patients with human immunodeficiency virus infection: a randomized trial.. OBJECTIVES To determine whether writing about emotional topics compared with writing about neutral topics could affect CD4+ lymphocyte count and human immunodeficiency virus (HIV) viral load among HIV-infected patients.   METHODS Thirty-seven HIV-infected patients were randomly allocated to 2 writing conditions focusing on emotional or control topics. Participants wrote for 4 days, 30 minutes per day. The CD4+ lymphocyte count and HIV viral load were measured at baseline and at 2 weeks, 3 months, and 6 months after writing.   RESULTS The emotional writing participants rated their essays as more personal, valuable, and emotional than those in the control condition. Relative to the drop in HIV viral load, CD4+ lymphocyte counts increased after the intervention for participants in the emotional writing condition compared with control writing participants.   CONCLUSIONS The results are consistent with those of previous studies using emotional writing in other patient groups. Based on the self-reports of the value of writing and the preliminary laboratory findings, the results suggest that emotional writing may provide benefit for patients with HIV infection.

Question: What is the effect of writing about emotional topics on immune function in patients with HIV infection?


Automatic Model Generation from Documentation for Java API Functions. Modern software systems are becoming increasingly complex, relying on a lot of third-party library support. Library behaviors are hence an integral part of software behaviors. Analyzing them is as important as analyzing the software itself. However, analyzing libraries is highly challenging due to the lack of source code, implementation in different languages, and complex optimizations. We observe that many Java library functions provide excellent documentation, which concisely describes the functionalities of the functions. We develop a novel technique that can construct models for Java API functions by analyzing the documentation. These models are simpler implementations in Java compared to the original ones and hence easier to analyze. More importantly, they provide the same functionalities as the original functions. Our technique successfully models 326 functions from 14 widely used Java classes. We also use these models in static taint analysis on Android apps and dynamic slicing for Java programs, demonstrating the effectiveness and efficiency of our models.

Question: How to model java library functions?