An investigation into language model data augmentation for low-resourced STT and KWS. This paper reports on investigations using two techniques for language model text data augmentation for low-resourced automatic speech recognition and keyword search. Lowresourced languages are characterized by limited training materials, which typically results in high out-of-vocabulary (OOV) rates and poor language model estimates. One technique makes use of recurrent neural networks (RNNs) using word or subword units. Word-based RNNs keep the same system vocabulary, so they cannot reduce the OOV, whereas subword units can reduce the OOV but generate many false combinations. A complementary technique is based on automatic machine translation, which requires parallel texts and is able to add words to the vocabulary. These methods were assessed on 10 languages in the context of the Babel program and NIST OpenKWS evaluation. Although improvements vary across languages with both methods, small gains were generally observed in terms of word error rate reduction and improved keyword search performance.

Question: What is the Babel program?


Integrating movement in academic classrooms: understanding, applying and advancing the knowledge base.. In the context of comprehensive and coordinated approaches to school health, academic classrooms have gained attention as a promising setting for increasing physical activity and reducing sedentary time among children. The aims of this paper are to review the rationale and knowledge base related to movement integration in academic classrooms, consider the practical applications of current knowledge to interventions and teacher education, and suggest directions for future research. Specifically, this paper (i) situates movement integration amid policy and research related to children's health and the school as a health-promoting environment; (ii) highlights the benefits of movement integration; (iii) summarizes movement integration programs and interventions; (iv) examines factors associated with classroom teachers' movement integration; (v) offers strategies for translating research to practice and (vi) forwards recommendations for future inquiry related to the effectiveness and sustainability of efforts to integrate movement into classroom routines. This paper provides a comprehensive resource for developing state-of-the-art initiatives to maximize children's movement in academic classrooms as a key strategy for important goals in both education and public health.

Question: What is the rationale for integrating movement into academic classrooms?


Lightly supervised recognition for automatic alignment of large coherent speech recordings. Large quantities of audio data with associated text such as audiobooks are nowadays available. These data are attractive for a range of research areas as they include features that go beyond the level of single sentences. The proposed approach allows high quality transcriptions and associated alignments of this form of data to be automatically generated. It combines information from lightly supervised recognition and the original text to yield the final transcription. The scheme is fully automatic and has been successfully applied to a number of audiobooks. Performance measurements show low word/sentence error rates as well as high sentence boundary accuracy.

Question: How to align large quantities of audio data?


Indoor Localization Using Camera Phones. Indoor localization has long been a goal of pervasive computing research. In this paper, we explore the possibility of determining user's location based on the camera images received from a smart phone. In our system, the smart phone is worn by the user as a pendant and images are periodically captured and transmitted over GPRS to a web server. The web server returns the location of the user by comparing the received images with images stored in a database. We tested our system inside the Computer Science department building. Preliminary results show that user's location can be determined correctly with more than 80% probability of success. As opposed to earlier solutions for indoor localization, this approach does not have any infrastructure requirements. The only cost is that of building an image database.

Question: How to determine user's location based on camera images?


Place navigation impaired in rats with hippocampal lesions. Electrophysiological studies have shown that single cells in the hippocampus respond during spatial learning and exploration1–4, some firing only when animals enter specific and restricted areas of a familiar environment. Deficits in spatial learning and memory are found after lesions of the hippocampus and its extrinsic fibre connections5,6 following damage to the medial septal nucleus which successfully disrupts the hippocampal theta rhythm7, and in senescent rats which also show a correlated reduction in synaptic enhancement on the perforant path input to the hippocampus8. We now report, using a novel behavioural procedure requiring search for a hidden goal, that, in addition to a spatial discrimination impairment, total hippocampal lesions also cause a profound and lasting placenavigational impairment that can be dissociated from correlated motor, motivational and reinforcement aspects of the procedure.

Question: What is the function of the hippocampus?


Deep Sentiment Representation Based on CNN and LSTM. Traditional machine learning techniques, including support vector machine (SVM), random walk, and so on, have been applied in various tasks of text sentiment analysis, which makes poor generalization ability in terms of complex classification problem. In recent years, deep learning has made a breakthrough in the research of Natural Language Processing. Convolutional neural network (CNN) and recurrent neural networks (RNNs) are two mainstream methods of deep learning in document and sentence modeling. In this paper, a model of capturing deep sentiment representation based on CNN and long short-term memory recurrent neural network (LSTM) is proposed. The model uses the pre-trained word vectors as input and employs CNN to gain significant local features of the text, then features are fed to two-layer LSTMs, which can extract context-dependent features and generate sentence representation for sentiment classification. We evaluate the proposed model by conducting a series of experiments on dataset. The experimental results show that the model we designed outperforms existing CNN, LSTM, CNN-LSTM (our implement of one-layer LSTM directly stacked on one-layer CNN) and SVM (support vector machine).

Question: What is the difference between deep learning and traditional machine learning?


Processing Analytical Queries over Encrypted Data. MONOMI is a system for securely executing analytical workloads over sensitive data on an untrusted database server. MONOMI works by encrypting the entire database and running queries over the encrypted data. MONOMI introduces split client/server query execution, which can execute arbitrarily complex queries over encrypted data, as well as several techniques that improve performance for such workloads, including per-row precomputation, space-efficient encryption, grouped homomorphic addition, and pre-filtering. Since these optimizations are good for some queries but not others, MONOMI introduces a designer for choosing an efficient physical design at the server for a given workload, and a planner to choose an efficient execution plan for a given query at runtime. A prototype of MONOMI running on top of Postgres can execute most of the queries from the TPC-H benchmark with a median overhead of only 1.24× (ranging from 1.03× to 2.33×) compared to an un-encrypted Postgres database where a compromised server would reveal all data.

Question: What is the overhead of MONOMI?


MACA: a modified author co-citation analysis method combined with general descriptive metadata of citations. Author co-citation analysis (ACA) is a well-known and frequently-used method to exhibit the academic researchers and the professional field sketch according to co-citation relationships between authors in an article set. However, visualizing subtle examination is limited because only author co-citation information is required in ACA. The proposed method, called modified author co-citation analysis (MACA), exploits author co-citation relationship, citations published time, citations published carriers, and citations keywords, to construct MACA-based co-citation matrices. According to the results of our experiments: (1) MACA shows a good clustering result with more delicacy and more clearness; (2) more information involved in co-citation analysis performs good visual acuity; (3) in visualization of co-citation network produced by MACA, the points in different categories have far more distance, and the points indicating authors in the same category are closer together. As a result, the proposed MACA is found that more detailed and subtle information of a knowledge domain analyzed can be obtained, compared to ACA.

Question: What is the difference between MACA and ACA?


Correcting the Triplet Selection Bias for Triplet Loss. Triplet loss, popular for metric learning, has made a great success in many computer vision tasks, such as fine-grained image classification, image retrieval, and face recognition. Considering that the number of triplets grows cubically with the size of training data, triplet selection is thus indispensable for efficiently training with triplet loss. However, in practice, the training is usually very sensitive to the selection of triplets, e.g., it almost does not converge with randomly selected triplets and selecting the hardest triplets also leads to bad local minima. We argue that the bias in the selection of triplets degrades the performance of learning with triplet loss. In this paper, we propose a new variant of triplet loss, which tries to reduce the bias in triplet selection by adaptively correcting the distribution shift on the selected triplets. We refer to this new triplet loss as adapted triplet loss. We conduct a number of experiments on MNIST and Fashion-MNIST for image classification, and on CARS196, CUB200-2011, and Stanford Online Products for image retrieval. The experimental results demonstrate the effectiveness of the proposed method.

Question: How to correct the triplet selection bias for triplet loss?


Human activity recognition using inertial sensors with invariance to sensor orientation. This work deals with the task of human daily activity recognition using miniature inertial sensors. The proposed method reduces sensitivity to the position and orientation of the sensor on the body, which is inherent in traditional methods, by transforming the observed signals to a "virtual" sensor orientation. By means of this computationally low-cost transform, the inputs to the classification algorithm are made invariant to sensor orientation, despite the signals being recorded from arbitrary sensor placements. Classification results show that improved performance, in terms of both precision and recall, is achieved with the transformed signals, relative to classification using raw sensor signals, and the algorithm performs competitively compared to the state-of-the-art. Activity recognition using data from a sensor with completely unknown orientation is shown to perform very well over a long term recording in a real-life setting.

Question: How to recognize human activity using inertial sensors?


Design principles for sensemaking support systems in environmental sustainability transformations. Correspondence: Stefan Seidel, University of Liechtenstein, Fuerst-Franz-Josef-Strasse 23, 9490 Vaduz, Liechtenstein. Tel: +423 265 1303; E-mail: stefan.seidel@uni.li Abstract This paper reports on the results of a design science research (DSR) study that develops design principles for information systems (IS) that support organisational sensemaking in environmental sustainability transformations. We identify initial design principles based on salient affordances required in organisational sensemaking and revise them through three rounds of developing, demonstrating and evaluating a prototypical implementation. Through our analysis, we learn how IS can support essential sensemaking practices in environmental sustainability transformations, including experiencing disruptive ambiguity through the provision of environmental data, noticing and bracketing, engaging in an open and inclusive communication and presuming potential alternative environmentally responsible actions. We make two key contributions: First, we provide a set of theory-inspired design principles for IS that support sensemaking in sustainability transformations, and revise them empirically using a DSR method. Second, we show how the concept of affordances can be used in DSR to investigate how IS can support organisational practices. While our findings are based on the investigation of the substantive context of environmental sustainability transformation, we suggest that they might be applicable in a broader set of contexts of organisational sensemaking and thus for a broader class of sensemaking support systems. European Journal of Information Systems (2017). doi:10.1057/s41303-017-0039-0

Question: How can information systems support sensemaking in environmental sustainability transformations?


Survey on Computer Vision for UAVs: Current Developments and Trends. During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges C. Kanellakis ( ) · G. Nikolakopoulos Luleå University of Technology, Luleå, Sweden e-mail: christoforos.kanellakis@ltu.se G. Nikolakopoulos e-mail: geonik@ltu.se addressed and future trends in autonomous agent development will be also provided.

Question: What is the size of the canadian military?


Popularity prediction of images and videos on Instagram. We live in a world surrounded by numerous social media platforms, applications and websites which produce various texts, images and videos (posts) daily. People share their moments with their friends and families via these tools to keep in touch. This extensiveness of social media has led to an expansion of information in various forms. It is difficult to imagine someone totally unfamiliar with these concepts and not having posted any content on a platform. All users, ranging from individuals to large companies, want to get the most of their audiences' attention. Nevertheless, the problem is that not all these posts are admired and noticed by their audience. Therefore, it would be important to know what characteristics a post should have to become the most popular. Studying this enormous data will develop a knowledge from which we can understand the best way to publish our posts. To this end, we gathered images and videos from Instagram accounts and we used some image/video context features to predict the number of likes a post obtains as a meaning of popularity through some regression and classification methods. By the experiments with 10-fold cross-validation, we get the results of Popularity Score prediction with 0.002 in RMSE and Popularity Class prediction with 90.77% accuracy. As we know, this study is the first exploring of Iranian Instagram users for popularity prediction.

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


Implementing an Effective Test Automation Framework. Testing automation tools enable developers and/or testers to easily automate the entire process of testing in software development. Nevertheless, adopting automated testing is not easy and unsuccessful due to a lack of key information and skills. In order to help solve such problems, we have designed a new framework to support a clear overview of the test design and/or plan for automating tests in distributed environments. Those that are new to testing do not need to delve into complex automation tools or test scripts. This framework allows a programmer or tester to graphically specify the granularity of execution control and aids communication between various stakeholders and software developers. It also enables them to perform the automated Continuous Integration environment. In this paper, we describe details on experiences and usage of the proposed framework.

Question: What is the difference between a framework and a tool?


Correlation analysis of MQTT loss and delay according to QoS level. MQTT is an open protocol developed and released by IBM. To ensure the reliability of message transmission, MQTT supports three levels of QoS. In this paper, we analyze MQTT message transmission process which consists of real wired/wireless publish client, broker server and Subscribe client. By transmitting messages through 3 levels of QoS with various sizes of payloads, we have captured packets to analyze end-to-end delays and message loss.

Question: What is the correlation between MQTT loss and delay?


Pattern electrical stimulation of the human retina. Experiments were conducted to study if electrical stimulation of the retinal surface can elicit visual sensation in individuals blind from end-stage retinitis pigmentosa (RP) or age-related macular degeneration (AMD). Under local anesthesia, different stimulating electrodes were inserted through the eyewall and positioned over the surface of the retina. Subjects' psychophysical responses to electrical stimulation were recorded. Subjects perceived simple forms in response to pattern electrical stimulation of the retina. A non-flickering perception was created with stimulating frequencies between 40 and 50 Hz. The stimulation threshold was dependent on the targeted retinal area (macular versus extramacular).

Question: What is the threshold for electrical stimulation of the retina?


Pattern frequency representation for time series classification. The paper presents a new method for data transformation. The obtained data format enhances the efficiency of the time series classification. The transformation is realized in four steps, namely: time series segmentation, segment's feature representation, segments' binning and pattern frequency representation. The method is independent of the nature of the data, and it works well with temporal data from diverse areas. Its ability is proved by classification of different real datasets and is also compared with the results of other classification methods.

Question: What is the time series classification?


Foofah: A Programming-By-Example System for Synthesizing Data Transformation Programs. Advancements in new data analysis and visualization technologies have resulted in wide applicability of data-driven decision making. However, raw data from various sources must be wrangled into a suitable form before they are processed by the downstream data tools. People traditionally write data transformation programs to automate this process, and such work is cumbersome and tedious.  We built a system called FOOFAH for helping the user easily synthesize a desired data transformation program. Our system minimizes the user's effort by only asking for a small illustrative example comprised of the raw input data and the target transformed output; FOOFAH then synthesizes a program that can perform the desired data transformation. This demonstration showcases how the user can apply FOOFAH to real-world data transformation tasks.

Question: What is a data transformation program?


Incorporating Loose-Structured Knowledge into LSTM with Recall Gate for Conversation Modeling. Modeling human conversations is the essence for building satisfying chat-bots with multi-turn dialog ability. Conversation modeling will notably benefit from domain knowledge since the relationships between sentences can be clarified due to semantic hints introduced by knowledge. In this paper, a deep neural network is proposed to incorporate background knowledge for conversation modeling. Through a specially designed Recall gate, domain knowledge can be transformed into the extra global memory of Long Short-Term Memory (LSTM), so as to enhance LSTM by cooperating with its local memory to capture the implicit semantic relevance between sentences within conversations. In addition, this paper introduces the loose structured domain knowledge base, which can be built with slight amount of manual work and easily adopted by the Recall gate. Our model is evaluated on the context-oriented response selecting task, and experimental results on both two datasets have shown that our approach is promising for modeling human conversations and building key components of automatic chatting systems.

Question: How to incorporate domain knowledge into LSTM?


ElasticSwitch: practical work-conserving bandwidth guarantees for cloud computing. While cloud computing providers offer guaranteed allocations for resources such as CPU and memory, they do not offer any guarantees for network resources. The lack of network guarantees prevents tenants from predicting lower bounds on the performance of their applications. The research community has recognized this limitation but, unfortunately, prior solutions have significant limitations: either they are inefficient, because they are not work-conserving, or they are impractical, because they require expensive switch support or congestion-free network cores.  In this paper, we propose ElasticSwitch, an efficient and practical approach for providing bandwidth guarantees. ElasticSwitch is efficient because it utilizes the spare bandwidth from unreserved capacity or underutilized reservations. ElasticSwitch is practical because it can be fully implemented in hypervisors, without requiring a specific topology or any support from switches. Because hypervisors operate mostly independently, there is no need for complex coordination between them or with a central controller. Our experiments, with a prototype implementation on a 100-server testbed, demonstrate that ElasticSwitch provides bandwidth guarantees and is work-conserving, even in challenging situations.

Question: What is elastic switch?


A unified API gateway for high availability clusters. High-availability (HA) clusters are widely used to provide high availability services. Recently, many HA cluster solutions and products have been proposed or developed by different organizations. However, each HA cluster system has its own administrative tool and application programming interface (API). Besides, vendor lock-in makes customers dependent on the specific vendors' own high availability clusters. Therefore, it is very complicated to simultaneously manage various HA clusters. To solve this problem, a novel SOA-based Unified API Gateway for high-availability Clusters (UAGC) is proposed in this paper. Under UAGC, cluster services are conveniently managed in a unified way, which is independent of platforms or programming languages. Six web service interfaces are implemented in UAGC to cover most cluster functions. A UAGC-based web service (UAGCService) is implemented with WCF. The experimental results show that UAGCService has good performances.

Question: What is a unified API gateway for high availability clusters?


ADMM for harmonic retrieval from one-bit sampling with time-varying thresholds. Parameter estimation of sinusoids is a problem of significance in many practical applications. This problem is revisited through a new alternating direction method of multipliers (ADMM) based approach, where the unknown parameters are estimated from one-bit quantized noisy measurements with time varying thresholds. The proposed method is computationally efficient and easy to implement, since each ADMM update has simple closed-form formula. Moreover, it provides accurate estimates by exploiting group sparsity hidden in the signal model. Simulations demonstrate the effectiveness of our algorithm.

Question: What is the difference between the ADMM and the alternating direction method of multipliers?


Delay-Constrained Hybrid Computation Offloading With Cloud and Fog Computing. To satisfy the delay constraint, the computation tasks can be offloaded to some computing servers, referred to as offloading destinations. Different to most of existing works which usually consider only a single type of offloading destinations, in this paper, we study the hybrid computation offloading problem considering diverse computation and communication capabilities of two types of offloading destinations, i.e., cloud computing servers and fog computing servers. The aim is to minimize the total energy consumption for both communication and computation while completing the computation tasks within a given delay constraint. It is quite challenging because the delay cannot be easily formulated as an explicit expression but depends on the embedded communication-computation scheduling problem for the computation offloading to different destinations. To solve the computation offloading problem, we first define a new concept named computation energy efficiency and divide the problem into four subproblems according to the computation energy efficiency of different types of computation offloading and the maximum tolerable delay. For each subproblem, we give a closed-form computation offloading solution with the analysis of communication-computation scheduling under the delay constraint. The numerical results show that the proposed hybrid computation offloading solution achieves lower energy consumption than the conventional single-type computation offloading under the delay constraint.

Question: What is the delay constraint?


Interactive surface design with interlocking elements. We present an interactive tool for designing physical surfaces made from flexible interlocking quadrilateral elements of a single size and shape. With the element shape fixed, the design task becomes one of finding a discrete structure---i.e., element connectivity and binary orientations---that leads to a desired geometry. In order to address this challenging problem of combinatorial geometry, we propose a forward modeling tool that allows the user to interactively explore the space of feasible designs. Paralleling principles from conventional modeling software, our approach leverages a library of base shapes that can be instantiated, combined, and extended using two fundamental operations: merging and extrusion. In order to assist the user in building the designs, we furthermore propose a method to automatically generate assembly instructions. We demonstrate the versatility of our method by creating a diverse set of digital and physical examples that can serve as personalized lamps or decorative items.

Question: What is the size of the library of base shapes?


Parsing sewing patterns into 3D garments. We present techniques for automatically parsing existing sewing patterns and converting them into 3D garment models. Our parser takes a sewing pattern in PDF format as input and starts by extracting the set of panels and styling elements (e.g. darts, pleats and hemlines) contained in the pattern. It then applies a combination of machine learning and integer programming to infer how the panels must be stitched together to form the garment. Our system includes an interactive garment simulator that takes the parsed result and generates the corresponding 3D model. Our fully automatic approach correctly parses 68% of the sewing patterns in our collection. Most of the remaining patterns contain only a few errors that can be quickly corrected within the garment simulator. Finally we present two applications that take advantage of our collection of parsed sewing patterns. Our garment hybrids application lets users smoothly interpolate multiple garments in the 2D space of patterns. Our sketch-based search application allows users to navigate the pattern collection by drawing the shape of panels.

Question: How to parse sewing patterns into 3D garments?


Diagnosing Error in Temporal Action Detectors. Despite the recent progress in video understanding and the continuous rate of improvement in temporal action localization throughout the years, it is still unclear how far (or close?) we are to solving the problem. To this end, we introduce a new diagnostic tool to analyze the performance of temporal action detectors in videos and compare different methods beyond a single scalar metric. We exemplify the use of our tool by analyzing the performance of the top rewarded entries in the latest ActivityNet action localization challenge. Our analysis shows that the most impactful areas to work on are: strategies to better handle temporal context around the instances, improving the robustness w.r.t. the instance absolute and relative size, and strategies to reduce the localization errors. Moreover, our experimental analysis finds the lack of agreement among annotator is not a major roadblock to attain progress in the field. Our diagnostic tool is publicly available to keep fueling the minds of other researchers with additional insights about their algorithms.

Question: What is the best way to diagnose temporal action detectors?


Visual analytics of spatial interaction patterns for pandemic decision support. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Population mobility, i.e. the movement and contact of individuals across geographic space, is one of the essential factors that determine the course of a pandemic disease spread. This research views both individual-based daily activities and a pandemic spread as spatial interaction problems, where locations interact with each other via the visitors that they share or the virus that is transmitted from one place to another. The research proposes a general visual analytic approach to synthesize very large spatial interaction data and discover interesting (and unknown) patterns. The proposed approach involves a suite of visual and computational techniques, including (1) a new graph partitioning method to segment a very large interaction graph into a moderate number of spatially contiguous subgraphs (regions); (2) a reorderable matrix, with regions 'optimally' ordered on the diagonal, to effectively present a holistic view of major spatial interaction patterns; and (3) a modified flow map, interactively linked to the reorderable matrix, to enable pattern interpretation in a geographical context. The implemented system is able to visualize both people's daily mov

Question: What is the population mobility of a pandemic disease?


Computers and Creativity. The Painting Fool is software that we hope will one day be taken seriously as a creative artist in its own right. This aim is being pursued as an Artificial Intelligence (AI) project, with the hope that the technical difficulties overcome along the way will lead to new and improved generic AI techniques. It is also being pursued as a sociological project, where the effect of software which might be deemed as creative is tested in the art world and the wider public. In this chapter, we summarise our progress so far in The Painting Fool project. To do this, we first compare and contrast The Painting Fool with software of a similar nature arising from AI and graphics projects. We follow this with a discussion of the guiding principles from Computational Creativity research that we adhere to in building the software. We then describe five projects with The Painting Fool where our aim has been to produce increasingly interesting and culturally valuable pieces of art. We end by discussing the issues raised in building an automated painter, and describe further work and future prospects for the project. By studying both the technical difficulties and sociological issues involved in engineering software for creative purposes, we hope to help usher in a new era where computers routinely act as our creative collaborators, as well as independent and creative artists, musicians, writers, designers, engineers and scientists, and contribute in meaningful and interesting ways to human culture.

Question: What is the difference between AI and graphics?


Freiburger Fragebogen zur körperlichen Aktivität-Entwicklung, Prüfung und Anwendung. Ziel vorliegender Untersuchung war die Entwicklung und Validierung eines Fragebogens zur Erfassung gesundheitswirksamer körperlicher Aktivität, sowie die Andwendung des Instruments in einer Stichprobe der erwachsenen Freiburger Wohnbevölkerung. Die Reliabilität des Fragebogens wurde anhand von Test-Retest-Untersuchungen, die im Abstand von 2 Wochen und 6 Monaten durchgeführt wurden, überprüft. Die hohen Korrelationen zwischen den Untersuchungen weisen auf eine hohe Reliabilität unseres Instruments hin. Trends im Sinne von saisonal bedingten Schwankungen fanden wir nur beim Rad fahren und der Gartenarbeit, sowie bei der abgeleiteten Basis-und Gesamtaktivität. Die Validierung erfolgte indirekt über die maximale Sauerstoffaufnahme. Es bestand eine enge Korrelation zwischen der maximalen Sauerstoffaufnahme und dem Umfang der Sportaktivitäten (partieller Korrelationskoeffizient: r=0.422, p<0.01). Die mit dem Fragebogen erhobenen Daten waren in sich konsistent. Personen, die sich selbst als "aktiver als ihre Altersgenossen" eingeschätzt hatten, waren, bezogen auf die Sport-(r=0.334, p<0.01) und Gesamtaktivität (r=0.282, p<0.05), tatsächlich aktiver als ihre Altersgenossen. Die Befragung einer (systematischen) Stichprobe der Freiburger Wohnbevölkerung (n-612, 20–98 Jahre) ergab mittlere Gesamtaktivitätsumfänge von 9,2 h/Woche. Aktivitäten mit leichten bis moderaten Belastungsintensitäten machten den grössten Teil der Gesamtaktivität aus. Die Aktivitätsmuster waren abhängig von Alter und Geschlecht. In Anlehnung an die Empfehlungen zum Bewegungssoll waren, nach Paffenbarger (2000 kcal/Woche Gesamtaktivität), 40% der Befragten zu wenig aktiv, nach den Empfehlungen des American College of Sports Medicine (1000 kcal/Woche durch Training) waren dies 63% der Befragten. Aim of the present study was to design a questionnaire to assess health related physical activity, to validate the instrument an

Question: What is the difference between the Canadian Armed Forces and the Canadian Forces?


Evidence-Based Psychological Assessment.. In recent years there has been increasing emphasis on evidence-based practice in psychology (EBPP), and as is true in most health care professions, the primary focus of EBPP has been on treatment. Comparatively little attention has been devoted to applying the principles of EBPP to psychological assessment, despite the fact that assessment plays a central role in myriad domains of empirical and applied psychology (e.g., research, forensics, behavioral health, risk management, diagnosis and classification in mental health settings, documentation of neuropsychological impairment and recovery, personnel selection and placement in organizational contexts). This article outlines the central elements of evidence-based psychological assessment (EBPA), using the American Psychological Association's tripartite definition of EBPP as integration of the best available research with clinical expertise in the context of patient characteristics, culture, and preferences. After discussing strategies for conceptualizing and operationalizing evidence-based testing and evidence-based assessment, 6 core skills and 3 meta-skills that underlie proficiency in psychological assessment are described. The integration of patient characteristics, culture, and preferences is discussed in terms of the complex interaction of patient and assessor identities and values throughout the assessment process. A preliminary framework for implementing EBPA is offered, and avenues for continued refinement and growth are described.

Question: What is evidence-based psychological assessment?


Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods.

Question: What is a support vector machine?


Combining CPU and GPU architectures for fast similarity search. The Signature Quadratic Form Distance on feature signatures represents a flexible distance-based similarity model for effective content-based multimedia retrieval. Although metric indexing approaches are able to speed up query processing by two orders of magnitude, their applicability to large-scale multimedia databases containing billions of images is still a challenging issue. In this paper, we propose a parallel approach that balances the utilization of CPU and many-core GPUs for efficient similarity search with the Signature Quadratic Form Distance. In particular, we show how to process multiple distance computations and other parts of the search procedure in parallel, achieving maximal performance of the combined CPU/GPU system. The experimental evaluation demonstrates that our approach implemented on a common workstation with 2 GPU cards outperforms traditional parallel implementation on a high-end 48-core NUMA server in terms of efficiency almost by an order of magnitude. If we consider also the price of the high-end server that is ten times higher than that of the GPU workstation then, based on price/performance ratio, the GPU-based similarity search beats the CPU-based solution by almost two orders of magnitude. Although proposed for the SQFD, our approach of fast GPU-based similarity search is applicable for any distance function that is efficiently parallelizable in the SIMT execution model.

Question: How to combine CPU and GPU architectures for fast similarity search?


Evaluating Sentiment Analysis in the Context of Securities Trading. There are numerous studies suggesting that published news stories have an important effect on the direction of the stock market, its volatility, the volume of trades, and the value of individual stocks mentioned in the news. There is even some published research suggesting that automated sentiment analysis of news documents, quarterly reports, blogs and/or twitter data can be productively used as part of a trading strategy. This paper presents just such a family of trading strategies, and then uses this application to re-examine some of the tacit assumptions behind how sentiment analyzers are generally evaluated, in spite of the contexts of their application. This discrepancy comes at a cost.

Question: What is the cost of using sentiment analysis in the context of securities trading?


Attentional and affective concomitants of meditation: a cross-sectional study..

Question: What is the affective and attentional concomitants of meditation?


The nature of feelings: evolutionary and neurobiological origins. Feelings are mental experiences of body states. They signify physiological need (for example, hunger), tissue injury (for example, pain), optimal function (for example, well-being), threats to the organism (for example, fear or anger) or specific social interactions (for example, compassion, gratitude or love). Feelings constitute a crucial component of the mechanisms of life regulation, from simple to complex. Their neural substrates can be found at all levels of the nervous system, from individual neurons to subcortical nuclei and cortical regions.

Question: What is the nature of feelings?


A Laplacian Framework for Option Discovery in Reinforcement Learning. Representation learning and option discovery are two of the biggest challenges in reinforcement learning (RL). Proto-RL is a well known approach for representation learning in MDPs. The representations learned with this framework are called proto-value functions (PVFs). In this paper we address the option discovery problem by showing how PVFs implicitly define options. We do it by introducing eigenpurposes, intrinsic reward functions derived from the learned representations. The options discovered from eigenpurposes traverse the principal directions of the state space. They are useful for multiple tasks because they are independent of the agents' intentions. Moreover, by capturing the diffusion process of a random walk, different options act at different time scales, making them helpful for exploration strategies. We demonstrate features of eigenpurposes in traditional tabular domains as well as in Atari 2600 games.

Question: What is a Laplacian framework for option discovery in reinforcement learning?


Trends in Obesity Among Adults in the United States, 2005 to 2014.. IMPORTANCE Between 1980 and 2000, the prevalence of obesity increased significantly among adult men and women in the United States; further significant increases were observed through 2003-2004 for men but not women. Subsequent comparisons of data from 2003-2004 with data through 2011-2012 showed no significant increases for men or women.   OBJECTIVE To examine obesity prevalence for 2013-2014 and trends over the decade from 2005 through 2014 adjusting for sex, age, race/Hispanic origin, smoking status, and education.   DESIGN, SETTING, AND PARTICIPANTS Analysis of data obtained from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional, nationally representative health examination survey of the US civilian noninstitutionalized population that includes measured weight and height.   EXPOSURES Survey period.   MAIN OUTCOMES AND MEASURES Prevalence of obesity (body mass index ≥30) and class 3 obesity (body mass index ≥40).   RESULTS This report is based on data from 2638 adult men (mean age, 46.8 years) and 2817 women (mean age, 48.4 years) from the most recent 2 years (2013-2014) of NHANES and data from 21,013 participants in previous NHANES surveys from 2005 through 2012. For the years 2013-2014, the overall age-adjusted prevalence of obesity was 37.7% (95% CI, 35.8%-39.7%); among men, it was 35.0% (95% CI, 32.8%-37.3%); and among women, it was 40.4% (95% CI, 37.6%-43.3%). The corresponding prevalence of class 3 obesity overall was 7.7% (95% CI, 6.2%-9.3%); among men, it was 5.5% (95% CI, 4.0%-7.2%); and among women, it was 9.9% (95% CI, 7.5%-12.3%). Analyses of changes over the decade from 2005 through 2014, adjusted for age, race/Hispanic origin, smoking status, and education, showed significant increasing linear trends among women for overall obesity (P = .004) and for class 3 obesity (P = .01) but not among men (P = .30 for overall obesity; P = .14 for class 3 obesity).

Question: What is the prevalence of obesity in the united states?


Rationale for the RBAC96 family of access control models. A family of role-based access control (RBAC) models, referred to here as the RBAC96 models, was recently published by the author and his colleagues. This paper gives our rationale for the major decisions in developing these models and discusses alternatives that were considered.

Question: What is the rationale for the RBAC96 family of access control models?


Internet of things capability and alliance: Entrepreneurial orientation, market orientation and product and process innovation. Internet of things capability and alliance: entrepreneurial orientation, market orientation and product and process innovation Xiaoyu Yu Bang Nguyen Yi Chen Article information: To cite this document: Xiaoyu Yu Bang Nguyen Yi Chen , (2016),"Internet of things capability and alliance: entrepreneurial orientation, market orientation and product and process innovation", Internet Research, Vol. 26 Iss 2 pp. Permanent link to this document: http://dx.doi.org/10.1108/IntR-10-2014-0265

Question: What is the Internet of things?


Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images. Semantic segmentation has emerged as a mainstream method in very-high-resolution remote sensing land-use/land-cover applications. In this paper, we first review the state-of-the-art semantic segmentation models in both computer vision and remote sensing fields. Subsequently, we introduce two semantic segmentation frameworks: SNFCN and SDFCN, both of which contain deep fully convolutional networks with shortcut blocks. We adopt an overlay strategy as the postprocessing method. Based on our frameworks, we conducted experiments on two online ISPRS datasets: Vaihingen and Potsdam. The results indicate that our frameworks achieve higher overall accuracy than the classic FCN-8s and SegNet models. In addition, our postprocessing method can increase the overall accuracy by about 1%–2% and help to eliminate "salt and pepper" phenomena and block effects.

Question: What is the state of the art in semantic segmentation?


Learning a Probabilistic Topology Discovering Model for Scene Categorization. A recent advance in scene categorization prefers a topological based modeling to capture the existence and relationships among different scene components. To that effect, local features are typically used to handle photographing variances such as occlusions and clutters. However, in many cases, the local features alone cannot well capture the scene semantics since they are extracted from tiny regions (e.g., 4 × 4 patches) within an image. In this paper, we mine a discriminative topology and a low-redundant topology from the local descriptors under a probabilistic perspective, which are further integrated into a boosting framework for scene categorization. In particular, by decomposing a scene image into basic components, a graphlet model is used to describe their spatial interactions. Accordingly, scene categorization is formulated as an intergraphlet matching problem. The above procedure is further accelerated by introducing a probabilistic based representative topology selection scheme that makes the pairwise graphlet comparison trackable despite their exponentially increasing volumes. The selected graphlets are highly discriminative and independent, characterizing the topological characteristics of scene images. A weak learner is subsequently trained for each topology, which are boosted together to jointly describe the scene image. In our experiment, the visualized graphlets demonstrate that the mined topological patterns are representative to scene categories, and our proposed method beats state-of-the-art models on five popular scene data sets.

Question: What is a weak learner?


Multi-criteria ABC analysis using artificial-intelligence-based classification techniques. ABC analysis is a popular and effective method used to classify inventory items into specific categories that can be managed and controlled separately. Conventional ABC analysis classifies inventory items three categories: A, B, or C based on annual dollar usage of an inventory item. Multi-criteria inventory classification has been proposed by a number of researchers in order to take other important criteria into consideration. These researchers have compared artificial-intelligence (AI)-based classification techniques with traditional multiple discriminant analysis (MDA). Examples of these AI-based techniques include support vector machines (SVMs), backpropagation networks (BPNs), and the k-nearest neighbor (k-NN) algorithm. To test the effectiveness of these techniques, classification results based on four benchmark techniques are compared. The results show that AI-based techniques demonstrate superior accuracy to MDA. Statistical analysis reveals that SVM enables more accurate classification than other AI-based techniques. This finding suggests the possibility of implementing AI-based techniques for multi-criteria ABC analysis in enterprise resource planning (ERP) systems. 2010 Elsevier Ltd. All rights reserved.

Question: What is the difference between artificial intelligence and artificial neural networks?


Evidence in Management and Organizational Science : Assembling the Field ' s Full Weight of Scientific Knowledge Through Syntheses. This chapter advocates the good scientific practice of systematic research syntheses in Management and Organizational Science (MOS). A research synthesis is the systematic accumulation, analysis and reflective interpretation of the full body of relevant empirical evidence related to a question. It is the * Corresponding author. Email: rousseau@andrew.cmu.edu 476 • The Academy of Management Annals critical first step in effective use of scientific evidence. Synthesis is not a conventional literature review. Literature reviews are often position papers, cherrypicking studies to advocate a point of view. Instead, syntheses systematically identify where research findings are clear (and where they aren't), a key first step to establishing the conclusions science supports. Syntheses are also important for identifying contested findings and productive lines for future research. Uses of MOS evidence, that is, the motives for undertaking a research synthesis include scientific discovery and explanation, improved management practice guidelines, and formulating public policy. We identify six criteria for establishing the evidentiary value of a body of primary studies in MOS. We then pinpoint the stumbling blocks currently keeping the field from making effective use of its ever-expanding base of empirical studies. Finally, this chapter outlines (a) an approach to research synthesis suitable to the domain of MOS; and (b) supporting practices to make synthesis a collective MOS project. Evidence in Management and Organizational Science It is the nature of the object that determines the form of its possible science. (Bhaskar, 1998, p. 3) Uncertain knowledge is better than ignorance. (Mitchell, 2000, p. 9) This chapter is motivated by the failure of Management and Organizational Science (MOS) to date to make full effective use of its available research

Question: What is the nature of the object that determines the form of its possible science?


Shape-aware Instance Segmentation. We address the problem of instance-level semantic segmentation, which aims at jointly detecting, segmenting and classifying every individual object in an image. In this context, existing methods typically propose candidate objects, usually as bounding boxes, and directly predict a binary mask within each such proposal. As a consequence, they cannot recover from errors in the object candidate generation process, such as too small or shifted boxes. In this paper, we introduce a novel object segment representation based on the distance transform of the object masks. We then design an object mask network (OMN) with a new residual-deconvolution architecture that infers such a representation and decodes it into the final binary object mask. This allows us to predict masks that go beyond the scope of the bounding boxes and are thus robust to inaccurate object candidates. We integrate our OMN into a Multitask Network Cascade framework, and learn the resulting shape-aware instance segmentation (SAIS) network in an end-to-end manner. Our experiments on the PASCAL VOC 2012 and the CityScapes datasets demonstrate the benefits of our approach, which outperforms the state-of-the-art in both object proposal generation and instance segmentation.

Question: What is the difference between instance segmentation and semantic segmentation?


Autonomous indoor object tracking with the Parrot AR.Drone. This article presents an image-based visual servoing system for indoor visual tracking of 3D moving objects by an Unmanned Aerial Vehicle. This system autonomously follows a 3D moving target object, maintaining it with a fixed distance and centered on its image plane. The initial setup is tested in a detailed simulation environment. The system is then validated on flights in indoor scenarios using the Parrot AR.Drone and the CMT tracker, demonstrating the robustness of the system to differences in object features, environmental clutter, and target trajectory. The obtained results indicate that the proposed system is suitable for complex controls task, such object surveillance and pursuit.

Question: How to track a moving object in indoor environment?


Development of Inchworm In-Pipe Robot Based on Self-Locking Mechanism. In-pipe robots are used to carry sensors and some other repairing instruments to perform inspection and maintenance jobs inside pipelines. In this paper, a self-locking mechanism is presented to improve the traction ability of in-pipe robots and avoid their traditional limitations. The structure of this type of in-pipe robot is presented and some critical design issues on the principle of self-locking mechanism are discussed. Prototypes of 19 mm diameter were produced, and related experiments were performed on specially designed test platform. The traction ability of the proposed in-pipe robot was measured experimentally to be 15.2 N, far beyond its maximum static friction of 0.35 N with the inner surface of pipeline. It means that this development has broken the traditional limitation of in-pipe robot whose traction ability was smaller than its maximum static friction with the pipeline.

Question: How to improve the traction ability of in-pipe robot?


Centrality in Social Networks Conceptual Clarification. The intuitive background for measures of structural centrality in social networks is reviewed aPzd existing measures are evaluated in terms of their consistency with intuitions and their interpretability. Three distinct intuitive conceptions of centrality are uncovered and existing measures are refined to embody these conceptions. Three measures are developed for each concept, one absolute and one relative measure of the ~entra~~t~~ of ~os~tio~ls in a network, and one relenting the degree of centralization of the entire network. The implications of these measures for the experimental study of small groups is examined.

Question: What is the difference between centrality and centralization?


App Miscategorization Detection: A Case Study on Google Play. An ongoing challenge in the rapidly evolving app market ecosystem is to maintain the integrity of app categories. At the time of registration, app developers have to select, what they believe, is the most appropriate category for their apps. Besides the inherent ambiguity of selecting the right category, the approach leaves open the possibility of misuse and potential gaming by the registrant. Periodically, the app store will refine the list of categories available and potentially reassign the apps. However, it has been observed that the mismatch between the description of the app and the category it belongs to, continues to persist. Although some common mechanisms (e.g., a complaint-driven or manual checking) exist, they limit the response time to detect miscategorized apps and still open the challenge on categorization. We introduce FRAC+: (FR)amework for (A)pp (C)ategorization. FRAC+ has the following salient features: (i) it is based on a data-driven topic model and automatically suggests the categories appropriate for the app store, and (ii) it can detect miscategorizated apps. Extensive experiments attest to the performance of FRAC+. Experiments on Google Play shows that FRAC+'s topics are more aligned with Google's new categories and 0.35-1.10 percent game apps are detected to be miscategorized.

Question: What is the difference between a data-driven topic model and a manually curated topic model?


A direct approach to estimating surfaces in tomographic data. Under ideal circumstances, the inverse of the radon transform is computable, and sequences of measured projections are sufficient to obtain accurate estimates of volume densities. In situations where the sinogram data is incomplete, the radon transform is noninvertable, and attempts to reconstruct greyscale density values result in reconstruction artifacts that can undermine the effectiveness of subsequent processing. This paper presents a direct approach to the segmentation of incomplete tomographic data. The strategy is to impose a fairly simple model on the data, and treat segmentation as a problem of estimating the interface between two substances of somewhat homogeneous density. The segmentation is achieved by simultaneously deforming a surface model and updating density parameters in order to achieve a best fit between the projected model and the measured sinograms. The deformation is implemented with level-set surface models, calculated at the resolution of the input data. Relative to previous work, this paper makes several contributions. First is a proper derivation of the deformation of surface models that move according to a gradient descent on a likelihood measure. We also present a series of computational innovations that make this direct surface-fitting approach feasible with state-of-the-art computers. Another contribution is the demonstration of the effectiveness of this approach on under-constrained tomographic problems, using both simulated and real datasets.

Question: How to segment tomographic data?


Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping. Artificial neural networks (ANNs) have been widely applied in electricity price forecasts due to their nonlinear modeling capabilities. However, it is well known that in general, traditional training methods for ANNs such as back-propagation (BP) approach are normally slow and it could be trapped into local optima. In this paper, a fast electricity market price forecast method is proposed based on a recently emerged learning method for single hidden layer feed-forward neural networks, the extreme learning machine (ELM), to overcome these drawbacks. The new approach also has improved price intervals forecast accuracy by incorporating bootstrapping method for uncertainty estimations. Case studies based on chaos time series and Australian National Electricity Market price series show that the proposed method can effectively capture the nonlinearity from the highly volatile price data series with much less computation time compared with other methods. The results show the great potential of this proposed approach for online accurate price forecasting for the spot market prices analysis.

Question: What is the electricity price forecasting with extreme learning machine and bootstrapping?


Identifying the Higgs Boson. This paper discusses the identification of the Higgs boson subatomic particle from jet pull energy colorflow images of the particles' decay, as modeled by the ATLAS Experiment at the Large Hadron Collider in CERN [1]. The Higgs field is an hypothesized energy field thought to permeate the entire universe. Without it, the Standard Model of particle physics would break down, as atomic particles would not have the required mass to attract each other, leading them to simply float around in the universe at the speed of light [20]. Its proof would completely alter our understanding of mass as a physical property, making the discovery of the Higgs field the fundamental unanswered question in particle physics in the last halfcentury [13]. The Standard Model suggests that if the Higgs field were to exist, then its quantum excitation, a particle referred to as the Higgs boson, would also have to exist [20]. The Large Hadron Collider (LHC), tasked with finding this particle, consisting of multiple super-powered electromagnets, collides charged particles traveling at near lightspeed. These collisions deform space upon impact, breaking the charged particles into subatomic constituents. It has been hypothesized that with a proton collision at high enough energy, the Higgs boson would decay in observable ways. The ATLAS detector at the LHC records 40 million proton collisions a second, making human curation of these events unfeasible [14]. An accurate classification system that would label the most promising observations as Higgs boson particle decays is, therefore, required. In collaboration with SLAC and the ATLAS Experiment at CERN we were given access to energy images for both Higgs boson and gluon decay, referred to as signal and background respectively, seen in Figures 1 and 2. The purpose of this project was to build a binary classifier which, given the colorflow energy image of the decay of an unknown particle, would accurately distinguish whet

Question: What is the purpose of this project?


Online and Linear-Time Attention by Enforcing Monotonic Alignments. Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-tosequence problems. However, the fact that soft attention mechanisms perform a pass over the entire input sequence when producing each element in the output sequence precludes their use in online settings and results in a quadratic time complexity. Based on the insight that the alignment between input and output sequence elements is monotonic in many problems of interest, we propose an end-to-end differentiable method for learning monotonic alignments which, at test time, enables computing attention online and in linear time. We validate our approach on sentence summarization, machine translation, and online speech recognition problems and achieve results competitive with existing sequence-tosequence models.

Question: What is the time complexity of attention?


Efficient Mean-shift Clustering Using Gaussian KD-Tree. Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed on large data set containing tens of millions of points at interactive rate. The key in our method is a new scheme for approximating mean shift procedure using a greatly reduced feature space. This reduced feature space is adaptive clustering of the original data set, and is generated by applying adaptive KD-tree in a high-dimensional affinity space. The proposed method significantly reduces the computational cost while obtaining almost the same clustering results as the standard mean shift procedure. We present several kinds of data clustering applications to illustrate the efficiency of the proposed method, including image and video segmentation, static geometry model and time-varying sequences

Question: How to cluster large data set?


Overcoming the Bell-Shaped Dose-Response of Cannabidiol by Using Cannabis Extract Enriched in Cannabidiol. Cannabidiol (CBD), a major constituent of Cannabis, has been shown to be a powerful anti‐in‐ flammatory and anti‐anxiety drug, without exerting a psychotropic effect. However, when given either intraperitoneally or orally as a purified product, a bell‐shaped dose‐response was observed, which limits its clinical use. In the present study, we have studied in mice the anti‐inflammatory and anti‐nociceptive activities of standardized plant extracts derived from the Cannabis sativa L., clone 202, which is highly enriched in CBD and hardly contains any psychoactive ingredients. In stark contrast to purified CBD, the clone 202 extract, when given either intraperitoneally or orally, provided a clear correlation between the anti‐inflammatory and anti‐nociceptive responses and the dose, with increasing responses upon increasing doses, which makes this plant medicine ideal for clinical uses. The clone 202 extract reduced zymosan‐induced paw swelling and pain in mice, and prevented TNFα production in vivo. It is likely that other components in the extract synergize with CBD to achieve the desired anti‐inflammatory action that may contribute to overcoming the bell‐shaped dose‐response of purified CBD. We therefore propose that Cannabis clone 202 (Avi‐ dekel) extract is superior over CBD for the treatment of inflammatory conditions.

Question: What is the bell‐shaped dose‐response of purified CBD?


GeoClustering: A web service for geospatial clustering. Geospatial clustering is a very important research topic in the field of "Geospatial Knowledge Discovery." Its main purpose is to group similar objects, from large geospatial datasets, into clusters, based on the objects" spatial and non-spatial attributes. This capability is very useful for better understanding the patterns and distributions of geographical phenomena. With the emerging availability of large amounts of geospatial data on the Internet, the demand for powerful methods of extracting meaningful information from large amounts of web-based geospatial data is thus also rapidly increasing. In this chapter, we present a geospatial clustering web service called GeoClustering. With this service, users are able to cluster online data sources easily and to then conveniently visualize the clustering results through a friendly map-centric user interface. Since it is web-based, GeoClustering offers a platform that can be accessed by anyone from anywhere at any time. To achieve better interoperability, GeoClustering offers an API and follows the OGC standard data format for spatial data exchange.

Question: What is the purpose of geospatial clustering?


Fisher Discrimination Dictionary Learning for sparse representation. Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a novel dictionary learning (DL) method to improve the pattern classification performance. Based on the Fisher discrimination criterion, a structured dictionary, whose dictionary atoms have correspondence to the class labels, is learned so that the reconstruction error after sparse coding can be used for pattern classification. Meanwhile, the Fisher discrimination criterion is imposed on the coding coefficients so that they have small within-class scatter but big between-class scatter. A new classification scheme associated with the proposed Fisher discrimination DL (FDDL) method is then presented by using both the discriminative information in the reconstruction error and sparse coding coefficients. The proposed FDDL is extensively evaluated on benchmark image databases in comparison with existing sparse representation and DL based classification methods.

Question: What is the Fisher discrimination criterion?


HDTLR: A CNN based Hierarchical Detector for Traffic Lights. Reliable traffic light detection is one crucial key component for autonomous driving in urban areas. This includes the extraction of direction arrows contained within the traffic lights as an autonomous car will need this information for selecting the traffic light corresponding to its current lane. Current state of the art traffic light detection systems are not able to provide such information. Within this work we present a hierarchical traffic light detection algorithm, which is able to detect traffic lights and determine their state and contained direction information within one CNN forward pass. This Hierarchical DeepTLR (HDTLR) outperforms current state of the art traffic light detection algorithms in state aware detection and can detect traffic lights with direction information down to a size of 4 pixel in width at a frequency of 12 frames per second.

Question: What is a hierarchical deep learning algorithm?


Evaluating faces on trustworthiness: an extension of systems for recognition of emotions signaling approach/avoidance behaviors.. People routinely make various trait judgments from facial appearance, and such judgments affect important social outcomes. These judgments are highly correlated with each other, reflecting the fact that valence evaluation permeates trait judgments from faces. Trustworthiness judgments best approximate this evaluation, consistent with evidence about the involvement of the amygdala in the implicit evaluation of face trustworthiness. Based on computer modeling and behavioral experiments, I argue that face evaluation is an extension of functionally adaptive systems for understanding the communicative meaning of emotional expressions. Specifically, in the absence of diagnostic emotional cues, trustworthiness judgments are an attempt to infer behavioral intentions signaling approach/avoidance behaviors. Correspondingly, these judgments are derived from facial features that resemble emotional expressions signaling such behaviors: happiness and anger for the positive and negative ends of the trustworthiness continuum, respectively. The emotion overgeneralization hypothesis can explain highly efficient but not necessarily accurate trait judgments from faces, a pattern that appears puzzling from an evolutionary point of view and also generates novel predictions about brain responses to faces. Specifically, this hypothesis predicts a nonlinear response in the amygdala to face trustworthiness, confirmed in functional magnetic resonance imaging (fMRI) studies, and dissociations between processing of facial identity and face evaluation, confirmed in studies with developmental prosopagnosics. I conclude with some methodological implications for the study of face evaluation, focusing on the advantages of formally modeling representation of faces on social dimensions.

Question: How do we evaluate faces?


Comparative evaluation of text- and citation-based plagiarism detection approaches using guttenplag. Various approaches for plagiarism detection exist. All are based on more or less sophisticated text analysis methods such as string matching, fingerprinting or style comparison. In this paper a new approach called Citation-based Plagiarism Detection is evaluated using a doctoral thesis, in which a volunteer crowd-sourcing project called GuttenPlag identified substantial amounts of plagiarism through careful manual inspection. This new approach is able to identify similar and plagiarized documents based on the citations used in the text. It is shown that citation-based plagiarism detection performs significantly better than text-based procedures in identifying strong paraphrasing, translation and some idea plagiarism. Detection rates can be improved by combining citation-based with text-based plagiarism detection.

Question: What is the difference between text- and citation-based plagiarism detection?


Making Neural Programming Architectures Generalize via Recursion. Empirically, neural networks that attempt to learn programs from data have exhibited poor generalizability. Moreover, it has traditionally been difficult to reason about the behavior of these models beyond a certain level of input complexity. In order to address these issues, we propose augmenting neural architectures with a key abstraction: recursion. As an application, we implement recursion in the Neural Programmer-Interpreter framework on four tasks: grade-school addition, bubble sort, topological sort, and quicksort. We demonstrate superior generalizability and interpretability with small amounts of training data. Recursion divides the problem into smaller pieces and drastically reduces the domain of each neural network component, making it tractable to prove guarantees about the overall system's behavior. Our experience suggests that in order for neural architectures to robustly learn program semantics, it is necessary to incorporate a concept like recursion.

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


Scaling Agile Development in Mechatronic Organizations - A Comparative Case Study. Agile software development principles enable companies to successfully and quickly deliver software by meeting their customers' expectations while focusing on high quality. Many companies working with pure software systems have adopted these principles, but implementing them in companies dealing with non-pure software products is challenging. We identified a set of goals and practices to support large-scale agile development in companies that develop software-intense mechatronic systems. We used an inductive approach based on empirical data collected during a longitudinal study with six companies in the Nordic region. The data collection took place over two years through focus group workshops, individual on-site interviews, and complementary surveys. The primary benefit of large-scale agile development is improved quality, enabled by practices that support regular or continuous integration between teams delivering software, hardware, and mechanics. In this regard, the most beneficial integration cycle for deliveries is every four weeks, while continuous integration on a daily basis would favor software teams, other disciplines does not seem to benefit from faster integration cycles. We identified 108 goals and development practices supporting agile principles among the companies, most of them concerned with integration, therefrom, 26 agile practices are unique to the mechatronics domain to support adopting agile beyond pure software development teams. 16 of these practices are considered as key enablers, confirmed by our control cases.

Question: What are the key agile practices for mechatronic development?


Rollable Multisegment Dielectric Elastomer Minimum Energy Structures for a Deployable Microsatellite Gripper. Debris in space presents an ever-increasing problem for spacecraft in Earth orbit. As a step in the mitigation of this issue, the CleanSpace One (CSO) microsatellite has been proposed. Its mission is to perform active debris removal of a decommissioned nanosatellite (the CubeSat SwissCube). An important aspect of this project is the development of the gripper system that will entrap the capture target. We present the development of rollable dielectric elastomer minimum energy structures (DEMES) as the main component of CSO's deployable gripper. DEMES consist of a prestretched dielectric elastomer actuator membrane bonded to a flexible frame. The actuator finds equilibrium in bending when the prestretch is released and the bending angle can be changed by the application of a voltage bias. The inherent flexibility and lightweight nature of the DEMES enables the gripper to be stored in a rolled-up state prior to deployment. We fabricated proof-of-concept actuators of three different geometries using a robust and repeatable fabrication methodology. The resulting actuators were mechanically resilient to external deformation, and display conformability to objects of varying shapes and sizes. Actuator mass is less than 0.65 g and all the actuators presented survived the rolling-up and subsequent deployment process. Our devices demonstrate a maximum change of bending angle of more than 60° and a maximum gripping (reaction) force of 2.2 mN for a single actuator.

Question: What is the minimum energy structure?


Reconfigurable antennas in cognitive radio that can think for themselves?. This paper discusses the use of reconfigurable antennas in cognitive radio. Most of the emphasis on cognitive radio so far has been in the area of spectral estimation and signal classification. In this paper we show that once a cognitive device manages to learn the RF environment (cognition part), from past observations and decisions using machine learning techniques, we can use the collected data to train reconfigurable antennas to adapt to any change in the RF environment.

Question: What is cognitive radio?


Predicting risk of software changes. Reducing the number of software failures is one of the most challenging problems of software production. We assume that software development proceeds as a series of changes and model the probability that a change to software will cause a failure. We use predictors based on the properties of a change itself. Such predictors include size in lines of code added, deleted, and unmodified; diffusion of the change and its component subchanges, as reflected in the number of files, modules, and subsystems touched, or changed; several measures of developer experience; and the type of change and its subchanges (fault fixes or new code). The model is built on historic information and is used to predict the risk of new changes. In this paper we apply the model to 5ESS® software updates and find that change diffusion and developer experience are essential to predicting failures. The predictive model is implemented as a Web-based tool to allow timely prediction of change quality. The ability to predict the quality of change enables us to make appropriate decisions regarding inspection, testing, and delivery. Historic information on software changes is recorded in many commercial software projects, suggesting that our results can be easily and widely applied in practice.

Question: How can we predict the risk of software changes?


Extended Bandwidth Instantaneous Current Sharing Scheme for Parallel UPS Systems. This paper investigates the suitability of instantaneous average current sharing scheme (IACS) for the parallel operation of three-phase uninterruptible power supply systems. A discrete-time model is developed for the analysis and design of the control loops. Some key issues are discussed based on the model and it is found that there is a compromise between system stability and current sharing performance with the conventional IACS scheme when the lengths of interconnecting cables are not negligible. Subsequently, an improved IACS scheme is proposed that ensures proper current sharing by extending the close-loop bandwidth. Its performance is analytically predicted and subsequently validated by experimental results on a 15-kW laboratory prototype.

Question: What is the current sharing scheme?


Allocating indivisible goods. The problem of allocating divisible goods has enjoyed a lot of attention in both mathematics (e.g. the cake-cutting problem) and economics (e.g. market equilibria). On the other hand, the natural requirement of indivisible goods has been somewhat neglected, perhaps because of its more complicated nature. In this work we study a fairness criterion, called the Max-Min Fairness problem, for k players who want to allocate among themselves m indivisible goods. Each player has a specified valuation function on the subsets of the goods and the goal is to split the goods between the players so as to maximize the minimum valuation. Viewing the problem from a game theoretic perspective, we show that for two players and additive valuations the expected minimum of the (randomized) cut-and-choose mechanism is a 1/2-approximation of the optimum. To complement this result we show that no truthful mechanism can compute the exact optimum.We also consider the algorithmic perspective when the (true) additive valuation functions are part of the input. We present a simple 1/(m - k + 1) approximation algorithm which allocates to every player at least 1/k fraction of the value of all but the k - 1 heaviest items. We also give an algorithm with additive error against the fractional optimum bounded by the value of the largest item. The two approximation algorithms are incomparable in the sense that there exist instances when one outperforms the other.

Question: What is the max-min fairness problem?


A Top-Down Compiler for Sentential Decision Diagrams. The sentential decision diagram (SDD) has been recently proposed as a new tractable representation of Boolean functions that generalizes the influential ordered binary decision diagram (OBDD). Empirically, compiling CNFs into SDDs has yielded significant improvements in both time and space over compiling them into OBDDs, using a bottomup compilation approach. In this work, we present a top-down CNF to SDD compiler that is based on techniques from the SAT literature. We compare the presented compiler empirically to the state-ofthe-art, bottom-up SDD compiler, showing ordersof-magnitude improvements in compilation time.

Question: What is a top-down compiler?


SystemT: a system for declarative information extraction. As applications within and outside the enterprise encounter increasing volumes of unstructured data, there has been renewed interest in the area of information extraction (IE) -- the discipline concerned with extracting structured information from unstructured text. Classical IE techniques developed by the NLP community were based on cascading grammars and regular expressions. However, due to the inherent limitations of grammarbased extraction, these techniques are unable to: (i) scale to large data sets, and (ii) support the expressivity requirements of complex information tasks. At the IBM Almaden Research Center, we are developing SystemT, an IE system that addresses these limitations by adopting an algebraic approach. By leveraging well-understood database concepts such as declarative queries and costbased optimization, SystemT enables scalable execution of complex information extraction tasks. In this paper, we motivate the SystemT approach to information extraction. We describe our extraction algebra and demonstrate the effectiveness of our optimization techniques in providing orders of magnitude reduction in the running time of complex extraction tasks.

Question: What is the running time of a complex information extraction task?


3-Dimensional Localization via RFID Tag Array. In this paper, we propose 3DLoc, which performs 3-dimensional localization on the tagged objects by using the RFID tag arrays. 3DLoc deploys three arrays of RFID tags on three mutually orthogonal surfaces of each object. When performing 3D localization, 3DLoc continuously moves the RFID antenna and scans the tagged objects in a 2-dimensional space right in front of the tagged objects. It then estimates the object's 3D position according to the phases from the tag arrays. By referring to the fixed layout of the tag array, we use Angle of Arrival-based schemes to accurately estimate the tagged objects' orientation and 3D coordinates in the 3D space. To suppress the localization errors caused by the multipath effect, we use the linear relationship of the AoA parameters to remove the unexpected outliers from the estimated results. We have implemented a prototype system and evaluated the actual performance in the real complex environment. The experimental results show that 3DLoc achieves the mean accuracy of 10cm in free space and 15.3cm in the multipath environment for the tagged object.

Question: How to perform 3D localization on the tagged objects?


Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media. Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.

Question: What is the impact of DDIs on patients?


A Statistical Approach to Mechanized Encoding and Searching of Literary Information. Written communication of ideas is carried out on the basis of statistical probability in that a writer chooses that level of subject specificity and that combination of words which he feels will convey the most meaning. Since this process varies among individuals and since similar ideas are therefore relayed at different levels of specificity and by means of different words, the problem of literature searching by machines still presents major difficulties. A statistical approach to this problem will be outlined and the various steps of a system based on this approach will be described. Steps include the statistical analysis of a collection of documents in a field of interest, the establishment of a set of "notions" and the vocabulary by which they are expressed, the compilation of a thesaurus-type dictionary and index, the automatic encoding of documents by machine with the aid of such a dictionary, the encoding of topological notations (such as branched structures), the recording of the coded information, the establishment of a searching pattern for finding pertinent information, and the programming of appropriate machines to carry out a search.

Question: What is a statistical approach to literature searching?


80ºC, 50-Gb/s Directly Modulated InGaAlAs BH-DFB Lasers. Direct modulation at 50 Gb/s of 1.3-μm InGaAlAs DFB lasers operating at up to 80°C was experimentally demonstrated by using a ridge-shaped buried heterostructure.

Question: What is the temperature of the laser?


Sex for fun: a synthesis of human and animal neurobiology. Sex is a fundamental pleasure, and crucial to the survival of our species. Though not many people would disagree with the proposition that sexual behaviour depends on the brain, the neuroscientific study of human sex is still relatively taboo and much remains to be discovered. On the contrary, excellent experimental animal models (mostly rat) are available that have uncovered major behavioural, neurochemical, and neuroanatomical characteristics of sexual behaviour. Restructuring sexual behaviour into broader terms reflecting behavioural states (wanting, liking, and inhibition) facilitates species comparison, revealing many similarities between animal and human sexual pleasure cycles, some of which can serve as potential avenues of new human sex research. In particular, behavioural and brain evidence clearly shows that motivational and consummatory phases are fundamentally distinct, and that genitally-induced sexual reward is a major factor in sexual learning mechanisms.

Question: What is the difference between the human and animal sexual pleasure cycles?


3-Level high power converter with press pack IGBT. Converteam recently has introduced the MV7000 series of medium voltage drives, designed with Press Pack IGBT (PPI) in 3-level NPC topology in the power range from 4 MW to 32 MW [3]. PPI combines the benefits of disk type device technology with state-of-the-art semiconductor technology. This paper explains relevant technical features and design issues related to MV7000 including direct series connection of devices.

Question: What is the difference between a press pack IGBT and a disk type IGBT?


Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting. Latent space models are effective tools for statistical modeling and exploration of network data. These models can effectively model real world network characteristics such as degree heterogeneity, transitivity, homophily, etc. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing likelihood for a special class of inner-product models while working simultaneously for a wide range of different latent space models, such as distance models, which allow latent vectors to affect edge formation in flexible ways. These fitting methods, especially the one based on projected gradient descent, are fast and scalable to large networks. We obtain their rates of convergence for both inner-product models and beyond. The effectiveness of the modeling approach and fitting algorithms is demonstrated on five real world network datasets for different statistical tasks, including community detection with and without edge covariates, and network assisted learning.

Question: What is the difference between a nuclear norm penalization and a projected gradient descent?


The distance function effect on k-nearest neighbor classification for medical datasets. INTRODUCTION K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output.   CASE DESCRIPTION Since the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually.   DISCUSSION AND EVALUATION The experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets.   CONCLUSIONS In this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best.

Question: What is the distance function effect on k-nearest neighbor classification for medical datasets?


Checking Business Process Evolution. Business processes support the modeling and the implementation of software as workflows of local and inter-process activities. Taking over structuring and composition, evolution has become a central concern in software development. We advocate it should be taken into account as soon as the modeling of business processes, which can thereafter be made executable using process engines or model-to-code transformations. We show here that business process evolution needs formal analysis in order to compare different versions of processes, identify precisely the differences between them, and ensure the desired consistency. To reach this objective, we first present a model transformation from the BPMN standard notation to the LNT process algebra. We then propose a set of relations for comparing business processes at the formal model level. With reference to related work, we propose a richer set of comparison primitives supporting renaming, refinement, propertyand context-awareness. Thanks to an implementation of our approach that can be used through a Web application, we put the checking of evolution within the reach of business process designers.

Question: How to check business process evolution?


Controlling privacy in recommender systems. Recommender systems involve an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about their preferences. In this paper, we explore a two-tiered notion of privacy where there is a small set of "public" users who are willing to share their preferences openly, and a large set of "private" users who require privacy guarantees. We show theoretically and demonstrate empirically that a moderate number of public users with no access to private user information already suffices for reasonable accuracy. Moreover, we introduce a new privacy concept for gleaning relational information from private users while maintaining a first order deniability. We demonstrate gains from controlled access to private user preferences.

Question: How to control privacy in recommender systems?


Limitations of the approximation capabilities of neural networks with one hidden layer. Let s/> 1 be an integer and W be the class of all functions having integrable partial derivatives on [0, 1] s. We are interested in the minimum number of neurons in a neural network with a single hidden layer required in order to provide a mean approximation order of a preassigned e > 0 to each function in W. We prove that this number cannot be O(e -s log(I/e)) if a spline-like localization is required. This cannot be improved even if one allows different neurons to evaluate different activation functions, even depending upon the target function. Nevertheless, for any 6 > 0, a network with O(c -s-6) neurons can be constructed to provide this order of approximation, with localization. Analogous results are also valid for other L p n o r m s .

Question: What is the minimum number of neurons in a neural network with a single hidden layer required in order to provide a mean approximation order of a preassigned e > 0 to each function in W?


Applying the design structure matrix to system decomposition and integration problems: a review and new directions. Systems engineering of products, processes, and organizations requires tools and techniques for system decomposition and integration. A design structure matrix (DSM) provides a simple, compact, and visual representation of a complex system that supports innovative solutions to decomposition and integration problems. The advantages of DSMsvis-à-visalternative system representation and analysis techniques have led to their increasing use in a variety of contexts, including product development, project planning, project management, systems engineering, and organization design. This paper reviews two types of DSMs, static and time-based DSMs, and four DSM applications: 1) Component-Basedor Architecture DSM, useful for modeling system component relationships and facilitating appropriate architectural decomposition strategies; 2) Team-Basedor Organization DSM, beneficial for designing integrated organization structures that account for team interactions; 3)Activity-Basedor Schedule DSM, advantageous for modeling the information flow among process activities; and 4)Parameter-Based(or low-level schedule)DSM, effective for integrating low-level design processes based on physical design parameter relationships. A discussion of each application is accompanied by an industrial example. The review leads to conclusions regarding the benefits of DSMs in practice and barriers to their use. The paper also discusses research directions and new DSM applications, both of which may be approached with a perspective on the four types of DSMs and their relationships.

Question: What is a design structure matrix?


Emotional intelligence as a standard intelligence.. The authors have claimed that emotional intelligence (EI) meets traditional standards for an intelligence (J. D. Mayer, D. R. Caruso, & P. Salovey, 1999). R. D. Roberts, M. Zeidner, and G. Matthews (2001) questioned whether that claim was warranted. The central issue raised by Roberts et al. concerning Mayer et al. (1999) is whether there are correct answers to questions on tests purporting to measure EI as a set of abilities. To address this issue (and others), the present authors briefly restate their view of intelligence, emotion, and EI. They then present arguments for the reasonableness of measuring EI as an ability, indicate that correct answers exist, and summarize recent data suggesting that such measures are, indeed, reliable.

Question: What is emotional intelligence?


Asymmetrical duty cycle permits zero switching loss in PWM circuits with no conduction loss penalty. Operating a bridge-type PWM (pulse width modulation) switch mode power converter with asymmetrical duty ratios can eliminate switching losses with no increase in conduction loss. This circuit topology combines the best features of resonant (zero switching loss) and switch mode (low conclusion loss) circuits. Design equations are presented for just such a circuit.<<ETX>>

Question: How to design a PWM converter with zero switching loss?


A convolutional neural networks denoising approach for salt and pepper noise. The salt and pepper noise, especially the one with extremely high percentage of impulses, brings a significant challenge to image denoising. In this paper, we propose a non-local switching filter convolutional neural network denoising algorithm, named NLSF-CNN, for salt and pepper noise. As its name suggested, our NLSF-CNN consists of two steps, i.e., a NLSF processing step and a CNN training step. First, we develop a NLSF pre-processing step for noisy images using non-local information. Then, the pre-processed images are divided into patches and used for CNN training, leading to a CNN denoising model for future noisy images. We conduct a number of experiments to evaluate the effectiveness of NLSF-CNN. Experimental results show that NLSF-CNN outperforms the state-of-the-art denoising algorithms with a few training images.

Question: What is a convolutional neural network?