Smart RDF Data Storage in Graph Databases. Graph Database Management Systems (GDBMs) provide an effective and efficient solution to data storage in current scenarios where data are more and more connected, graph models are widely used, and systems need to scale to large data sets. In particular, the conversion of the persistent layer of an application from a RDF to a graph data store can be convenient but it is usually an hard task for database administrators. In this paper we propose a methodology to convert a RDF data store to a graph database by exploiting the ontology and the constraints of the source. We provide experimental results that show the feasibility of our solution and the efficiency of query answering over the target database.

Question: How to convert a rdf database to a graph database?


Series WP-1207 A Source of Bias in Public Opinion Stability. A long acknowledged but seldom addressed problem with political communication experiments concerns the use of captive participants. Study participants rarely have the opportunity to choose information themselves, instead receiving whatever information the experimenter provides. Druckman and his co-authors relax this assumption in the context of an over-time framing experiment focused on opinions about health care policy. Their results dramatically deviate from extant understandings of over-time communication effects. Allowing individuals to choose information for themselves—a common situation on many political issues—leads to the preeminence of early frames and the rejection of later frames. Instead of opinion decay, they find dogmatic adherence to opinions formed in response to the first frame to which participants were exposed (i.e., staunch opinion stability). The effects match those that occur when early frames are repeated multiple times. The results suggest that opinion stability may often reflect biased information seeking. Moreover, the findings have implications for a range of topics including the micro-macro disconnect in studies of public opinion, political polarization, normative evaluations of public opinion, the role of inequality considerations in the debate about health care, and perhaps most importantly, the design of experimental studies of public opinion. *The authors thank Cengiz Erisen, Larry Jacobs, Jon Krosnick, Heather Madonia, Leslie McCall, Spencer Piston, David Sterrett, and seminar participants in Northwestern's Applied Quantitative Methods Workshop for helpful comments and advice.

Question: What is the difference between opinion stability and opinion decay?


SHEEP, GOATS, LAMBS and WOLVES: a statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation. Performance variabilit y in speech and speaker recognition systems can be attributed to many factors. One major factor, which is often acknowledged but seldom analyzed, is inherent differences in the recognizabilit y of different speakers. In speaker recognition systems such differences are characterized by the use of animal names for different types of speakers, including sheep, goats, lambs and wolves, depending on their behavior with respect to automatic recognition systems. In this paper we propose statistical tests for the existence of these animals and apply these tests to hunt for such animals using results from the 1998 NIST speaker recognition evaluation.

Question: What is the difference between sheep, goats, lambs and wolves?


Design criteria for the RF section of UHF and microwave passive RFID transponders. A set of design criteria for the radio-frequency (RF) section of long-range passive RF identification (RFID) transponders operating in the 2.45-GHz or 868-MHz industrial, scientific, and medical (ISM) frequency ranges is derived in this paper, focusing in particular on the voltage multiplier, the power-matching network, and the backscatter modulation. The paper discusses the design tradeoffs between the error probability at the reader receiver and the converted RF-dc power at the transponder, determining the regions of the design space that allow optimization of the operating range and the data rate of the RFID system.

Question: What is the design criteria for the RF section of UHF and microwave passive RFID transponders?


Adaptive Online Hyper-Parameters Tuning for Ad Event-Prediction Models. Yahoo's native advertising (also known as Gemini native) is one of its fastest growing businesses, reaching a run-rate of several hundred Millions USD in the past year. Driving the Gemini native models that are used to predict both, click probability (pCTR) and conversion probability (pCONV), is Offset a feature enhanced collaborative-filtering (CF) based event prediction algorithm. Offset is a one-pass algorithm that updates its model for every new batch of logged data using a stochastic gradient descent (SGD) based approach. As most learning algorithms, Offset includes several hyper-parameters that can be tuned to provide best performance for a given system conditions. Since the marketplace environment is very dynamic and influenced by seasonality and other temporal factors, having a fixed single set of hyper-parameters (or configuration) for the learning algorithm is sub-optimal. In this work we present an online hyper-parameters tuning algorithm, which takes advantage of the system parallel map-reduce based architecture, and strives to adapt the hyper-parameters set to provide the best performance at a specific time interval. Online evaluation via bucket testing of the tuning algorithm showed a significant 4.3% revenue lift overall traffic, and a staggering 8.3% lift over Yahoo HomePage section traffic. Since then, the tuning algorithm was pushed into production, tuning both clickand conversionprediction models, and is generating a hefty estimated revenue lift of 5% yearly for Yahoo Gemini native. The proposed tuning mechanism can be easily generalized to fit any learning algorithm that continuously learns on incoming streaming data, in order to adapt its hyper-parameters to temporal changes.

Question: How does the tuning algorithm work?


Quantum Inspired Genetic Algorithms. |A novel evolutionary computing method | quantum inspired genetic algorithms | is introduced, where concepts and principles of quantum mechanics are used to inform and inspire more eecient evolutionary computing methods. The basic terminology of quantum mechanics is introduced before a comparison is made between a classical genetic algorithm and a quantum inspired method for the travelling salesperson problem. It is informally shown that the quantum inspired genetic algorithm performs better than the classical counterpart for a small domain. The paper concludes with some speculative comments concerning the relationship between quantum inspired genetic algorithms and various complexity classes.

Question: What is a quantum inspired genetic algorithm?


Circle grid fractal pattern for calibration at different camera zoom levels. Camera calibration patterns and fiducial markers are basic technique for 3D measurement to create Computer Graphics (CG) and Augmented Reality (AR). A checkerboard is widely used as a calibration pattern and a circle or ring grid provides higher precision [Datta et al. 2009], and matrix codes are common as AR markers.

Question: What is a circle grid fractal pattern?


JHU-ISI Gesture and Skill Assessment Working Set ( JIGSAWS ) : A Surgical Activity Dataset for Human Motion Modeling. Dexterous surgical activity is of interest to many researchers in human motion modeling. In this paper, we describe a dataset of surgical activities and release it for public use. The dataset was captured using the da Vinci Surgical System and consists of kinematic and video from eight surgeons with different levels of skill performing five repetitions of three elementary surgical tasks on a bench-top model. The tasks, which include suturing, knot-tying and needle-passing, are standard components of most surgical skills training curricula. In addition to kinematic and video data captured from the da Vinci Surgical System, we are also releasing manual annotations of surgical gestures (atomic activity segments), surgical skill using global rating scores, a standardized cross-validation experimental setup, and a C++/Matlab toolkits for analyzing surgical gestures using hidden Markov models and using linear dynamical systems. We refer to the dataset as the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS) to indicate the collaboration between Johns Hopkins University (JHU) and Intuitive Surgical Inc. (ISI), Sunnyvale, CA, on collecting these data.

Question: What is a surgical activity dataset?


Bonding-wire triangular spiral inductor for on-chip switching power converters. This work presents the first design and modelling of bonding-wire-based triangular spiral inductors (Fig. 1), targeting their application to on-chip switching power converters. It is demonstrated that the equilateral triangular shape compared to other polygonal shapes best balances the inductive density as well as the total Equivalent Series Resistance (ESR). Afterwards, a design procedure is presented in order to optimize the inductor design, in terms of ESR and occupied area reduction. Finally, finite-elements simulation results of an optimized design (27nH, 1 Ω) are presented to validate the proposed expressions.

Question: What is the best shape for an inductor?


AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION.

Question: What is the name of the algorithm?


Residual Memory Networks in Language Modeling: Improving the Reputation of Feed-Forward Networks. We introduce the Residual Memory Network (RMN) architecture to language modeling. RMN is an architecture of feedforward neural networks that incorporates residual connections and time-delay connections that allow us to naturally incorporate information from a substantial time context. As this is the first time RMNs are applied for language modeling, we thoroughly investigate their behaviour on the well studied Penn Treebank corpus. We change the model slightly for the needs of language modeling, reducing both its time and memory consumption. Our results show that RMN is a suitable choice for small-sized neural language models: With test perplexity 112.7 and as few as 2.3M parameters, they out-perform both a much larger vanilla RNN (PPL 124, 8M parameters) and a similarly sized LSTM (PPL 115, 2.08M parameters), while being only by less than 3 perplexity points worse than twice as big LSTM.

Question: What is the first language model?


How to handle ARP in a software-defined network. The Address Resolution Protocol (ARP) enables communication between IP-speaking nodes in a local network by reconstructing the hardware (MAC) address associated with the IP address of an interface. This is not needed in a Software-Defined Network (SDN), because each device can forward packets without the need to learn this association. We tackle the interoperability problem arising between standard network devices (end systems, routers), that rely on ARP, and SDN datapaths, that do not handle ARP packets natively. In particular, we propose a general approach to handle ARP in a SDN, that is applicable in several network scenarios, is transparent for existing devices, and can coexist with any packet forwarding logic implemented in the controller. Our approach reduces ARP traffic by confining it to the edge of SDNs and requires a minimal set of flow entries in the datapaths. We argument about its applicability and confirm it with experiments performed on SDN datapaths from a range of different vendors.

Question: How to handle ARP in a software-defined network.


Avatar-independent scripting for real-time gesture animation. When animation of a humanoid figure is to be generated at run-time, instead of by replaying precomposed motion clips, some method is required of specifying the avatar's movements in a form from which the required motion data can be automatically generated. This form must be of a more abstract nature than raw motion data: ideally, it should be independent of the particular avatar's proportions, and both writable by hand and suitable for automatic generation from higher-level descriptions of the required actions. We describe here the development and implementation of such a scripting language for the particular area of sign languages of the deaf, called SiGML (Signing Gesture Markup Language), based on the existing HamNoSys notation for sign languages. We conclude by suggesting how this work may be extended to more general animation for interactive virtual reality applications.

Question: What is the sign language of the deaf?


CIDEr: Consensus-based image description evaluation. Automatically describing an image with a sentence is a long-standing challenge in computer vision and natural language processing. Due to recent progress in object detection, attribute classification, action recognition, etc., there is renewed interest in this area. However, evaluating the quality of descriptions has proven to be challenging. We propose a novel paradigm for evaluating image descriptions that uses human consensus. This paradigm consists of three main parts: a new triplet-based method of collecting human annotations to measure consensus, a new automated metric that captures consensus, and two new datasets: PASCAL-50S and ABSTRACT-50S that contain 50 sentences describing each image. Our simple metric captures human judgment of consensus better than existing metrics across sentences generated by various sources. We also evaluate five state-of-the-art image description approaches using this new protocol and provide a benchmark for future comparisons. A version of CIDEr named CIDEr-D is available as a part of MS COCO evaluation server to enable systematic evaluation and benchmarking.

Question: What is the consensus-based image description evaluation?


Methods for Evaluating Text Extraction Toolkits: An Exploratory Investigation. Text extraction tools are vital for obtaining the textual content of computer files and for using the electronic text in a wide variety of applications, including search and natural language processing. However, when extraction tools fail, they convert once reliable electronic text into garbled text, or no text at all. The techniques and tools for validating the accuracy of these text extraction tools are conspicuously absent from academia and industry. This paper contributes to closing this gap. We discuss an exploratory investigation into a method and a set of tools for evaluating a text extraction toolkit. Although this effort focuses on the popular open source Apache Tika toolkit and the govdocs1 corpus, the method generally applies to other text extraction toolkits and corpora.

Question: What is a text extraction toolkit?


An approach to fuzzy control of nonlinear systems: stability and design issues.

Question: What is a nonlinear system?


Subcutaneous penile vein thrombosis (Penile Mondor's Disease): pathogenesis, diagnosis, and therapy.. OBJECTIVES In international studies, only a few data are available on subcutaneous penile vein thrombosis. The pathogenesis is unknown, and no general recommendation exists regarding therapy.   METHODS A total of 25 patients with the clinical picture of a "superficial penile vein thrombosis" were treated at our policlinic. All patients had noted sudden and almost painless indurations on the penile dorsal surface. The extent of the thrombosis varied. Detailed anamnesis, ultrasonography, and routine laboratory tests were performed for all patients, knowing that primary therapy was conservative.   RESULTS No patient indicated any pain. Some reported a feeling of tension in the area of the thrombosis. In all patients, the thrombosis occurred in the dorsal penis shaft. It was close to the sulcus coronarius in 21 patients, near the penis root in 3, and in the entire penis shaft in 1 patient. The length of the thrombotic vein was between 2 and 4 cm. The ultrasound results were similar for all patients. The primary treatment was conservative for all patients. Recovery was achieved in more than 92% of cases (23 of 25 patients) using conservative therapy, which consisted of local dressing with heparin ointment (10,000 IU) and oral application of an antiphlogistic for 14 days. In 2 cases, thrombectomy was necessary.   CONCLUSIONS Extended imaging diagnosis does not improve the evaluation of the extent of a superficial penile vein thrombosis. Conservative primary therapy consisting of heparin ointment and oral application of antiphlogistics is sufficient. If the thrombosis persists after conservative therapy, surgery is indicated.

Question: What is the pathogenesis of penile Mondor's disease?


Sentiment Analysis on Twitter Data. Abstract – Now-a-days social networking sites are at the boom, so large amount of data is generated. Millions of people are sharing their views daily on micro blogging sites, since it contains short and simple expressions. In this paper, we will discuss about a paradigm to extract the sentiment from a famous micro blogging service, Twitter, where users post their opinions for everything. In this paper, we will discuss the existing analysis of twitter dataset with data mining approach such as use of Sentiment analysis algorithm using machine learning algorithms. An approach is introduced that automatically classifies the sentiments of Tweets taken from Twitter dataset as in [1]. These messages or tweets are classified as positive, negative or neutral with respect to a query term. This is very useful for the companies who want to know the feedback about their product brands or the customers who want to search the opinion from others about product before purchase. We will use machine learning algorithms for classifying the sentiment of Twitter messages using distant supervision which is discussed in [8]. The training data consists of Twitter messages with emoticons, acronyms which are used as noisy labels discussed in [4]. We examine sentiment analysis on Twitter data. The contributions of this survey paper are: (1) we use Parts Of Speech (POS)-specific prior polarity features. (2) We also use a tree kernel to prevent the need for monotonous feature engineering.

Question: What is the difference between sentiment analysis and opinion mining?


Application of Requirement-oriented Data Quality Evaluation Method. The data quality directly determines the data further analysis and other applications. With the important of data quality, data is a major asset in software applications and information systems. Getting and ensuring high quality data is critical to the software and related business operations. At present, there are many methods and techniques of data quality assessment or evaluation. Based on the evaluation criteria of data quality, the paper mainly starts from the needs of application software and uses the requirements as a guide in the evaluation process. Define evaluation criteria based on user-defined requirements. In this paper, the method proposed in a practical case has been applied, and achieved good practical results.

Question: What is the method of data quality evaluation?


Fast bayesian matching pursuit. A low-complexity recursive procedure is presented for minimum mean squared error (MMSE) estimation in linear regression models. A Gaussian mixture is chosen as the prior on the unknown parameter vector. The algorithm returns both an approximate MMSE estimate of the parameter vector and a set of high posterior probability mixing parameters. Emphasis is given to the case of a sparse parameter vector. Numerical simulations demonstrate estimation performance and illustrate the distinctions between MMSE estimation and MAP model selection. The set of high probability mixing parameters not only provides MAP basis selection, but also yields relative probabilities that reveal potential ambiguity in the sparse model.

Question: What is the difference between MAP and MMSE estimation?


Towards a psychographic user model from mobile phone usage. Knowing the users' personality can be a strategic advantage for the design of adaptive and personalized user interfaces. In this paper, we present the results of a first trial conducted with the aim of inferring people's personality traits based on their mobile phone call behavior. Initial findings corroborate the efficacy of using call detail records (CDR) and Social Network Analysis (SNA) of the call graph to infer the Big Five personality factors. On-going work includes a large-scale study that shall refine the accuracy of the models with a reduced margin of error.

Question: How can we infer people's personality based on their mobile phone usage?


Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks. Designing optimal treatment plans for patients with comorbidities requires accurate cause-specific mortality prognosis. Motivated by the recent availability of linked electronic health records, we develop a nonparametric Bayesian model for survival analysis with competing risks, which can be used for jointly assessing a patient's risk of multiple (competing) adverse outcomes. The model views a patient's survival times with respect to the competing risks as the outputs of a deep multi-task Gaussian process (DMGP), the inputs to which are the patients' covariates. Unlike parametric survival analysis methods based on Cox and Weibull models, our model uses DMGPs to capture complex non-linear interactions between the patients' covariates and cause-specific survival times, thereby learning flexible patient-specific and cause-specific survival curves, all in a data-driven fashion without explicit parametric assumptions on the hazard rates. We propose a variational inference algorithm that is capable of learning the model parameters from time-to-event data while handling right censoring. Experiments on synthetic and real data show that our model outperforms the state-of-the-art survival models.

Question: What is a deep multi-task Gaussian process?


Question: What is the difference between a passion fruit and a kiwi fruit?


An Inoperability Input-Output Model (IIM) for disruption propagation analysis. Today's Supply Chains (SCs) are global and getting more complex, due to the exigencies of the world economy, making them hard to manage. This is exacerbated by the increasingly frequent occurrence of natural disasters and other high impact disruptions. To stay competitive, companies are therefore seeking ways to better understand the impact of such disruptions to their SCs. In addressing this need, this paper proposes an approach for disruption propagation analysis. We develop a method based on a variation of an Inoperability Input-Output Model (adapted from a Leontief I-O model) to quantify the impact of disruptions across the entire SC. We then analyse the factors that have the most influential impacts on the SCs during disruptions. Initial results show that trading volume handled by a company/ node is an important factor in determining the disruption impact to SCs, besides the number of SC partners (connections) as implied from previous work.

Question: What is the impact of disruptions on supply chains?


The CES-D Scale : A Self-Report Depression Scale for Research in the General Population.

Question: What is the CES-D scale?


Non-parametric Model for Background Subtraction. Background subtraction is a method typically used to segment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a novel non-parametric background model and a background subtraction approach. The model can handle situations where the background of the scene is cluttered and not completely static but contains small motions such as tree branches and bushes. The model estimates the probability of observing pixel intensity values based on a sample of intensity values for each pixel. The model adapts quickly to changes in the scene which enables very sensitive detection of moving targets. We also show how the model can use color information to suppress detection of shadows. The implementation of the model runs in real-time for both gray level and color imagery. Evaluation shows that this approach achieves very sensitive detection with very low false alarm rates.

Question: What is a non-parametric model?


Walk detection and step counting on unconstrained smartphones. Smartphone pedometry offers the possibility of ubiquitous health monitoring, context awareness and indoor location tracking through Pedestrian Dead Reckoning (PDR) systems. However, there is currently no detailed understanding of how well pedometry works when applied to smartphones in typical, unconstrained use.  This paper evaluates common walk detection (WD) and step counting (SC) algorithms applied to smartphone sensor data. Using a large dataset (27 people, 130 walks, 6 smartphone placements) optimal algorithm parameters are provided and applied to the data. The results favour the use of standard deviation thresholding (WD) and windowed peak detection (SC) with error rates of less than 3%. Of the six different placements, only the back trouser pocket is found to degrade the step counting performance significantly, resulting in undercounting for many algorithms.

Question: How well does pedometry work on smartphones?


kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels. Many kinds of memory safety vulnerabilities have been endangering software systems for decades. Amongst other approaches, fuzzing is a promising technique to unveil various software faults. Recently, feedback-guided fuzzing demonstrated its power, producing a steady stream of security-critical software bugs. Most fuzzing efforts—especially feedback fuzzing—are limited to user space components of an operating system (OS), although bugs in kernel components are more severe, because they allow an attacker to gain access to a system with full privileges. Unfortunately, kernel components are difficult to fuzz as feedback mechanisms (i.e., guided code coverage) cannot be easily applied. Additionally, non-determinism due to interrupts, kernel threads, statefulness, and similar mechanisms poses problems. Furthermore, if a process fuzzes its own kernel, a kernel crash highly impacts the performance of the fuzzer as the OS needs to reboot. In this paper, we approach the problem of coverageguided kernel fuzzing in an OS-independent and hardware-assisted way: We utilize a hypervisor and Intel's Processor Trace (PT) technology. This allows us to remain independent of the target OS as we just require a small user space component that interacts with the targeted OS. As a result, our approach introduces almost no performance overhead, even in cases where the OS crashes, and performs up to 17,000 executions per second on an off-the-shelf laptop. We developed a framework called kernel-AFL (kAFL) to assess the security of Linux, macOS, and Windows kernel components. Among many crashes, we uncovered several flaws in the ext4 driver for Linux, the HFS and APFS file system of macOS, and the NTFS driver of Windows.

Question: How to fuzz a kernel?


Expert Systems and Probabilistic Network Models. Follow up what we will offer in this article about expert systems and probabilistic network models. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.

Question: What is the best seller book today?


MobiMine: Monitoring the Stock Market from a PDA. This paper describes an experimental mobile data mining system that allows intelligent monitoring of time-critical financial data from a hand-held PDA. It presents the overall system architecture and the philosophy behind the design. It explores one particular aspect of the system---automated construction of personalized focus area that calls for user's attention. This module works using data mining techniques. The paper describes the data mining component of the system that employs a novel Fourier analysis-based approach to efficiently represent, visualize, and communicate decision trees over limited bandwidth wireless networks. The paper also discusses a quadratic programming-based personalization module that runs on the PDAs and the multi-media based user-interfaces. It reports experimental results using an ad hoc peer-to-peer IEEE 802.11 wireless network.

Question: What is a PDA?


A New Paradigm for Collision-free Hashing: Incrementality at Reduced Cost. We present a simple, new paradigm for the design of collision-free hash functions. Any function emanating from this paradigm is incremental. (This means that if a message x which I have previously hashed is modi ed to x0 then rather than having to re-compute the hash of x 0 from scratch, I can quickly \update" the old hash value to the new one, in time proportional to the amount of modi cation made in x to get x.) Also any function emanating from this paradigm is parallelizable, useful for hardware implementation. We derive several speci c functions from our paradigm. All use a standard hash function, assumed ideal, and some algebraic operations. The rst function, MuHASH, uses one modular multiplication per block of the message, making it reasonably e cient, and signi cantly faster than previous incremental hash functions. Its security is proven, based on the hardness of the discrete logarithm problem. A second function, AdHASH, is even faster, using additions instead of multiplications, with security proven given either that approximation of the length of shortest lattice vectors is hard or that the weighted subset sum problem is hard. A third function, LtHASH, is a practical variant of recent lattice based functions, with security proven based, again on the hardness of shortest lattice vector approximation. Dept. of Computer Science & Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. E-Mail: mihir@cs.ucsd.edu. URL: http://www-cse.ucsd.edu/users/mihir. Supported in part by NSF CAREER Award CCR-9624439 and a Packard Foundation Fellowship in Science and Engineering. yMIT Laboratory for Computer Science, 545 Technology Square, Cambridge, MA 02139, USA. E-Mail: miccianc@theory.lcs.mit.edu. Supported in part by DARPA contract DABT63-96-C-0018.

Question: What is the difference between a hash function and a hash table?


Comprehensive and Efficient Protection of Kernel Control Data. Protecting kernel control data (e.g., function pointers and return addresses) has been a serious issue plaguing rootkit defenders. In particular, rootkit authors only need to compromise one piece of control data to launch their attacks, while defenders need to protect thousands of such values widely scattered across kernel memory space. Worse, some of this data (e.g., return addresses) is volatile and can be dynamically generated at run time. Existing solutions, however, offer either incomplete protection or excessive performance overhead. To overcome these limitations, we present indexed hooks, a scheme that greatly facilitates kernel control-flow enforcement by thoroughly transforming and restricting kernel control data to take only legal jump targets (allowed by the kernel's control-flow graph). By doing so, we can severely limit the attackers' possibility of exploiting them as an infection vector to launch rootkit attacks. To validate our approach, we have developed a compiler-based prototype that implements this technique in the FreeBSD 8.0 kernel, transforming 49 025 control transfer instructions (~7.25% of the code base) to use indexed hooks instead of direct pointers. Our evaluation results indicate that our approach is generic, effective, and can be implemented on commodity hardware with a low performance overhead (<;5% based on benchmarks).

Question: How to protect kernel control data?


Recognising Complex Mental States from Naturalistic Human-Computer Interactions.

Question: What is the difference between a naturalistic and a non-naturalistic human-computer interaction?


Pose Estimation from Line Correspondences using Direct Linear Transformation. Ansar, the method by Ansar and Daniilidis (2003), implementation from Xu et al. (2016). Mirzaei, the method by Mirzaei and Roumeliotis (2011). RPnL, the method by Zhang et al. (2013). ASPnL, the method by Xu et al. (2016). LPnL Bar LS, the method by Xu et al. (2016). LPnL Bar ENull, the method by Xu et al. (2016). DLT-Lines, the method by Hartley and Zisserman (2004, p. 180), our implementation. DLT-Plücker-Lines, the method by Přibyl et al. (2015), our implementation. DLT-Combined-Lines, the proposed method.

Question: What is the difference between the methods?


Asymmetric division of labor in human skilled bimanual action: the kinematic chain as a model.. This article presents a tentative theoretical framework for the study of asymmetry in the context of human bimanual action. It is emphasized that in man most skilled manual activities involve two hands playing different roles, a fact that has been often overlooked in the experimental study of human manual lateralization. As an alternative to the current concepts of manual preference and manual superiority-whose relevance is limited to the particular case of unimanual actions-the more general concept of lateral preference is proposed to denote preference for one of the two possible ways of assigning two roles to two hands. A simple model describing man's favored intermanual division of labor in the model are the following. 1) The two hands represent two motors, that is, decomplexity is ignored in the suggested approach. 2) In man, the two manual motors cooperate with one another as if they were assembled in series, thereby forming a kinematic chain: In a right-hander allowed to follow his or her lateral preferences, motion produced by the right hand tends to articulate with motion produced by the left. It is suggested that the kinematic chain model may help in understanding the adaptive advantage of human manual specialization.

Question: What is the kinematic chain?


The Google effect in doctoral theses. It is often said that successive generations of researchers face an increasing educational burden due to knowledge accumulation. On the other hand, technological advancement over time can improve the productivity of researchers and even change their cognitive processes. This paper presents a longitudinal study (2004–2011) of citation behavior in doctoral theses at the Massachusetts Institute of Technology's Department of Electrical Engineering and Computer Science. It is found that the number of references cited has increased over the years. At the same time, there has been a decrease in the length of time in the doctoral program and a relative constancy in the culture of the department. This suggests that students are more productive in facing an increased knowledge burden, and indeed seem to encode prior literature as transactive memory to a greater extent, as evidenced by the greater use of older literature.

Question: How does the google effect work in doctoral theses?


Outliers , Level Shifts , and Variance Changes in Time Series. Outliers, level shifts, and variance changes are commonplace in applied time series analysis. However, their existence is often ignored and their impact is overlooked, for the lack of simple and useful methods to detect and handle those extraordinary events. The problem of detecting outliers, level shifts, and variance changes in a univariate time series is considered. The methods employed are extremely simple yet useful. Only the least squares techniques and residual variance ratios are used. The effectiveness of these simple methods is demonstrated by analysing three real data sets.

Question: What is the least squares technique?


Deep neural networks for recognizing online handwritten mathematical symbols. This paper presents application of deep learning to recognize online handwritten mathematical symbols. Recently various deep learning architectures such as Convolution neural network (CNN), Deep neural network (DNN) and Long short term memory (LSTM) RNN have been applied to fields such as computer vision, speech recognition and natural language processing where they have been shown to produce state-of-the-art results on various tasks. In this paper, we apply max-out-based CNN and BLSTM to image patterns created from online patterns and to the original online patterns, respectively and combine them. We also compare them with traditional recognition methods which are MRF and MQDF by carrying out some experiments on CROHME database.

Question: What is deep learning?


The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations. We present a new method for solving stochastic differential equations based on Galerkin projections and extensions of Wiener's polynomial chaos. Specifically, we represent the stochastic processes with an optimum trial basis from the Askey family of orthogonal polynomials that reduces the dimensionality of the system and leads to exponential convergence of the error. Several continuous and discrete processes are treated, and numerical examples show substantial speed-up compared to Monte Carlo simulations for low dimensional stochastic inputs.

Question: What is the Wiener-Askey Polynomial Chaos?


3D geo-database research: Retrospective and future directions. 3D geo-database research is a promising field to support challenging applications such as 3D urban planning, environmental monitoring, infrastructure management, and early warning or disaster management and response. In these fields, interdisciplinary research in GIScience and related fields is needed to support the modelling, analysis, management, and integration of large geo-referenced data sets, which describe human activities and geophysical phenomena. Geo-databases may serve as platforms to integrate 2D maps, 3D geo-scientific models, and other geo-referenced data. However, current geo-databases do not provide sufficient 3D data modelling and data handling techniques. New 3D geo-databases are needed to handle surface and volume models. This article first presents a 25-year retrospective of geo-database research. Data modelling, standards, and indexing of geo-data are discussed in detail. New directions for the development of 3D geo-databases to open new fields for interdisciplinary research are addressed. Two scenarios in the fields of early warning and emergency response demonstrate the combined management of human and geophysical phenomena. The article concludes with a critical outlook on open research problems. & 2011 Elsevier Ltd. All rights reserved.

Question: What is the size of the canadian military?


Data processing and calibration of a cross-pattern stripe projector. Dense surface acquisition is one of the most challenging tasks for optical 3-D measurement systems in applications such as inspection of industrial parts, reverse engineering, digitization of virtual reality models and robot guidance. In order to achieve high accuracy we need good hardware equipment, as well as sophisticated data processing methods. Since measurements should be feasible under real world conditions, accuracy, reliability and consistency are a major issue. Based on the experiences with a previous system, a detailed analysis of the performance was carried out, leading to a new hardware setup. On the software side, we improved our calibration procedure and replaced the phase shift technique previously used by a new processing scheme which we call line shift processing. This paper describes our new approach. Results are presented and compared to results derived from the previous system.

Question: What is the new approach?


MIMO radar: an idea whose time has come. It has recently been shown that multiple-input multiple-output (MIMO) antenna systems have the potential to improve dramatically the performance of communication systems over single antenna systems. Unlike beamforming, which presumes a high correlation between signals either transmitted or received by an array, the MIMO concept exploits the independence between signals at the array elements. In conventional radar, target scintillations are regarded as a nuisance parameter that degrades radar performance. The novelty of MIMO radar is that it takes the opposite view; namely, it capitalizes on target scintillations to improve the radar's performance. We introduce the MIMO concept for radar. The MIMO radar system under consideration consists of a transmit array with widely-spaced elements such that each views a different aspect of the target. The array at the receiver is a conventional array used for direction finding (DF). The system performance analysis is carried out in terms of the Cramer-Rao bound of the mean-square error in estimating the target direction. It is shown that MIMO radar leads to significant performance improvement in DF accuracy.

Question: What is the Cramer-Rao bound?


SDN-based distributed mobility management for 5G networks. Software-Defined Networking (SDN) is transforming the networking ecosystem. SDN allows network operators to easily and quickly introduce new services and flexibly adapt to their requirements, while simplifying the network management to reduce the cost of operation, maintenance and deployment. On the other hand, mobility is a key aspect for the future mobile networks. In this context, Distributed Mobility Management (DMM) has been recently introduced as a new trend to overcome the limitations of the today's mobility management protocols which are highly centralized and hierarchical. Driven from the fact that DMM and SDN share the same principle in which the data and control plane are decoupled, we propose a DMM solution based on SDN architecture called S-DMM. This solution offers a lot of advantages including no need to deploy any mobility-related component at the access router, independence of the underlying technologies, and per-flow mobility support. On one hand, the numerical results prove that S-DMM is more scalable than the legacy DMM. On the other hand, the experiment results from a real implementation show that S-DMM comes at no performance penalty (in terms of handover latency and end-to-end delay) compared to legacy DMM, yet at a slightly better management cost, which makes S-DMM a promising candidate for a mobility management solution in the context of 5G networks.

Question: What is the difference between SDN and DMM?


Automated video looping with progressive dynamism. Given a short video we create a representation that captures a spectrum of looping videos with varying levels of dynamism, ranging from a static image to a highly animated loop. In such a progressively dynamic video, scene liveliness can be adjusted interactively using a slider control. Applications include background images and slideshows, where the desired level of activity may depend on personal taste or mood. The representation also provides a segmentation of the scene into independently looping regions, enabling interactive local adjustment over dynamism. For a landscape scene, this control might correspond to selective animation and deanimation of grass motion, water ripples, and swaying trees. Converting arbitrary video to looping content is a challenging research problem. Unlike prior work, we explore an optimization in which each pixel automatically determines its own looping period. The resulting nested segmentation of static and dynamic scene regions forms an extremely compact representation.

Question: How to convert a video to a looping video?


Real time personalized search on social networks. Internet users are shifting from searching on traditional media to social network platforms (SNPs) to retrieve up-to-date and valuable information. SNPs have two unique characteristics: frequent content update and small world phenomenon. However, existing works are not able to support these two features simultaneously. To address this problem, we develop a general framework to enable real time personalized top-k query. Our framework is based on a general ranking function that incorporates time freshness, social relevance and textual similarity. To ensure efficient update and query processing, there are two key challenges. The first is to design an index structure that is update-friendly while supporting instant query processing. The second is to efficiently compute the social relevance in a complex graph. To address these challenges, we first design a novel 3D cube inverted index to support efficient pruning on the three dimensions simultaneously. Then we devise a cube based threshold algorithm to retrieve the top-k results, and propose several pruning techniques to optimize the social distance computation, whose cost dominates the query processing. Furthermore, we optimize the 3D index via a hierarchical partition method to enhance our pruning on the social dimension. Extensive experimental results on two real world large datasets demonstrate the efficiency and the robustness of our proposed solution.

Question: What is the size of the canadian military?


Online Learning for Time Series Prediction. In this paper we address the problem of predicting a time seri e using the ARMA (autoregressive moving average) model, under minimal assumptions on the noi se terms. Using regret minimization techniques, we develop effective online learning algorith ms for the prediction problem, withoutassuming that the noise terms are Gaussian, identically distribu ted or even independent. Furthermore, we show that our algorithm's performances asymptotically app roaches the performance of the best ARMA model in hindsight.

Question: What is the difference between the ARMA model and the ARIMA model?


Steering Behaviors For Autonomous Characters. This paper presents solutions for one requirement of autonomous characters in animation and games: the ability to navigate around their world in a life-like and improvisational manner. These "steering behaviors" are largely independent of the particulars of the character's means of locomotion. Combinations of steering behaviors can be used to achieve higher level goals (For example: get from here to there while avoiding obstacles, follow this corridor, join that group of characters...) This paper divides motion behavior into three levels. It will focus on the middle level of steering behaviors, briefly describe the lower level of locomotion, and touch lightly on the higher level of goal setting and strategy.

Question: What is the middle level of steering behaviors?


SmartStore: a new metadata organization paradigm with semantic-awareness for next-generation file systems. Existing storage systems using hierarchical directory tree do not meet scalability and functionality requirements for exponentially growing datasets and increasingly complex queries in Exabyte-level systems with billions of files. This paper proposes semantic-aware organization, called SmartStore, which exploits metadata semantics of files to judiciously aggregate correlated files into semantica-ware groups by using information retrieval tools. Decentralized design improves system scalability and reduces query latency for complex queries (range and top-k queries), which is conducive to constructing semantic-aware caching, and conventional filename-based query. SmartStore limits search scope of complex query to a single or a minimal number of semantically related groups and avoids or alleviates brute-force search in entire system. Extensive experiments using real-world traces show that SmartStore improves system scalability and reduces query latency over basic database approaches by one thousand times. To the best of our knowledge, this is the first study implementing complex queries in large-scale file systems.

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


Graph Embedding Problem Settings Graph Embedding Input Graph Embedding Output Homogeneous Graph Heterogeneous Graph Graph with Auxiliary Information Graph Constructed from Non-relational Data Node Embedding Edge Embedding Hybrid Embedding Whole-Graph Embedding Graph Embedding Techniques Matrix Facto. Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.

Question: What is graph embedding?


Dermoscopy in differentiating palmar syphiloderm from palmar papular psoriasis.. Palmar syphiloderm is one of the most common presentations of secondary syphilis and its recognition is of utmost importance in order to promptly identify such a disease and initiate appropriate workup/management. However, the differential diagnosis with palmar papular psoriasis often poses some difficulties, with consequent possible diagnostic errors/delays and prescription of improper therapies. In this report, we underline the role of dermoscopy as a supportive tool to facilitate the non-invasive recognition of palmar syphiloderm and its distinction from palmar papular psoriasis.

Question: What is the role of dermoscopy in the diagnosis of palmar syphiloderm?


Architecting for DevOps and Continuous Deployment. Development and Operations (DevOps) in the context of Continuous Deployment (CD) have emerged as an attractive software development movement, which tries to establish a strong connection between development and operations teams. CD is defined as the ability to quickly put new releases into production. We believe that DevOps/CD brings new challenges for architects, which considerably impacts both on their (architectural) design decisions and their organizational responsibilities. We assert that there is an important and urgent need of sufficient research work to gain a deep understanding of how DevOps/CD adoption can influence architecting, architectural decision-making processes and their outcomes in an organization. This PhD research is aimed at understanding and addressing new challenges for designing architectures for supporting DevOps in the context of CD.

Question: What is the difference between DevOps and Continuous Deployment?


Personality, Culture, and System Factors - Impact on Affective Response to Multimedia. Whilst affective responses to various forms and genres of multimedia content have been well researched, precious few studies have investigated the combined impact that multimedia system parameters and human factors have on affect. Consequently, in this paper we explore the role that two primordial dimensions of human factors personality and culture in conjunction with system factors frame rate, resolution, and bit rate have on user affect and enjoyment of multimedia presentations. To this end, a two-site, cross-cultural study was undertaken, the results of which produced three predictve models. Personality and Culture traits were shown statistically to represent 5.6% of the variance in positive affect, 13.6% in negative affect and 9.3% in enjoyment. The correlation between affect and enjoyment, was significant. Predictive modeling incorporating human factors showed about 8%, 7% and 9% improvement in predicting positive affect, negative affect and enjoyment respectively when compared to models trained only on system factors. Results and analysis indicate the significant role played by human factors in influencing affect that users experience while watching multimedia.

Question: What is the role of human factors in multimedia?


Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video. How can unlabeled video augment visual learning? Existing methods perform "slow" feature analysis, encouraging the representations of temporally close frames to exhibit only small differences. While this standard approach captures the fact that high-level visual signals change slowly over time, it fails to capture how the visual content changes. We propose to generalize slow feature analysis to "steady" feature analysis. The key idea is to impose a prior that higher order derivatives in the learned feature space must be small. To this end, we train a convolutional neural network with a regularizer on tuples of sequential frames from unlabeled video. It encourages feature changes over time to be smooth, i.e., similar to the most recent changes. Using five diverse datasets, including unlabeled YouTube and KITTI videos, we demonstrate our method's impact on object, scene, and action recognition tasks. We further show that our features learned from unlabeled video can even surpass a standard heavily supervised pretraining approach.

Question: How can unlabeled video augment visual learning?


Syntactically Guided Neural Machine Translation. We investigate the use of hierarchical phrase-based SMT lattices in end-to-end neural machine translation (NMT). Weight pushing transforms the Hiero scores for complete translation hypotheses, with the full translation grammar score and full ngram language model score, into posteriors compatible with NMT predictive probabilities. With a slightly modified NMT beam-search decoder we find gains over both Hiero and NMT decoding alone, with practical advantages in extending NMT to very large input and output vocabularies.

Question: What is the difference between a neural machine translation and a phrase-based machine translation?


Impact of Halo Doping on the Subthreshold Performance of Deep-Submicrometer CMOS Devices and Circuits for Ultralow Power Analog/Mixed-Signal Applications. In addition to its attractiveness for ultralow power applications, analog CMOS circuits based on the subthreshold operation of the devices are known to have significantly higher gain as compared to their superthreshold counterpart. The effects of halo [both double-halo (DH) and single-halo or lateral asymmetric channel (LAC)] doping on the subthreshold analog performance of 100-nm CMOS devices are systematically investigated for the first time with extensive process and device simulations. In the subthreshold region, although the halo doping is found to improve the device performance parameters for analog applications (such as gm/Id, output resistance and intrinsic gain) in general, the improvement is significant in the LAC devices. Low angle of tilt of the halo implant is found to give the best improvement in both the LAC and DH devices. Our results show that the CMOS amplifiers made with the halo implanted devices have higher voltage gain over their conventional counterpart, and a more than 100% improvement in the voltage gain is observed when LAC doping is made on both the p- and n-channel devices of the amplifier

Question: What is the voltage gain of a CMOS amplifier?


The Matrix Cookbook. Acknowledgements: We would like to thank the following for contributions and suggestions: Bill Baxter, Brian Templeton, Christian Rishøj, Christian Schröppel Douglas L. Theobald, Esben Hoegh-Rasmussen, Glynne Casteel, Jan Larsen, Jun Bin Gao, Jürgen Struckmeier, Kamil Dedecius, Korbinian Strimmer, Lars Christiansen, Lars Kai Hansen, Leland Wilkinson, Liguo He, Loic Thibaut, Miguel Barão, Ole Winther, Pavel Sakov, Stephan Hattinger, Vasile Sima, Vincent Rabaud, Zhaoshui He. We would also like thank The Oticon Foundation for funding our PhD studies.

Question: What is the matrix cookbook?


Procedural Generation of Roads. In this paper, we propose an automatic method for generating roads based on a weighted anisotropic shortest path algorithm. Given an input scene, we automatically create a path connecting an initial and a final point. The trajectory of the road minimizes a cost function that takes into account the different parameters of the scene including the slope of the terrain, natural obstacles such as rivers, lakes, mountains and forests. The road is generated by excavating the terrain along the path and instantiating generic parameterized models.

Question: What is a generic parameterized model?


CYUT Short Text Conversation System for NTCIR-12 STC. In this paper, we report how we build the system for Chinese subtask in NTCIR12 Short Text Conversation (STC) shared task. Our approach is to find the most related sentences for a given input sentence. The system is implemented based on the Lucene search engine. The result shows that our system can deal with the conversation that involves related sentences.

Question: How to build a short text conversation system?


Treatment of thoracolumbar burst fractures without neurologic deficit by indirect reduction and posterior instrumentation: bisegmental stabilization with monosegmental fusion. This study retrospectively reviews 20 sequential patients with thoracolumbar burst fractures without neurologic deficit. All patients were treated by indirect reduction, bisegmental posterior transpedicular instrumentation and monosegmental fusion. Clinical and radiological outcome was analyzed after an average follow-up of 6.4 years. Re-kyphosis of the entire segment including the cephaled disc was significant with loss of the entire postoperative correction over time. This did not influence the generally benign clinical outcome. Compared to its normal height the fused cephalad disc was reduced by 70% and the temporarily spanned caudal disc by 40%. Motion at the temporarily spanned segment could be detected in 11 patients at follow-up, with no relation to the clinical result. Posterior instrumentation of thoracolumbar burst fractures can initially reduce the segmental kyphosis completely. The loss of correction within the fractured vertebral body is small. However, disc space collapse leads to eventual complete loss of segmental reduction. Therefore, posterolateral fusion alone does not prevent disc space collapse. Nevertheless, clinical long-term results are favorable. However, if disc space collapse has to prevented, an interbody disc clearance and fusion is recommended.

Question: How does the loss of correction within the fractured vertebral body affect the clinical outcome?


Unsupervised Lesion Detection in Brain CT using Bayesian Convolutional Autoencoders. Normally, lesions are detected using supervised learning techniques that require labelled training data. We explore the use of Bayesian autoencoders to learn the variability of healthy tissue and detect lesions as unlikely events under the normative model. As a proof-of-concept, we test our method on registered 2D midaxial slices from CT imaging data. Our results indicate that our method achieves best performance in detecting lesions caused by bleeding compared to baselines.

Question: How do you detect lesions in brain CT?


Predictive Control of a Three-Level Boost Converter and an NPC Inverter for High-Power PMSG-Based Medium Voltage Wind Energy Conversion Systems. In this paper, a new medium voltage power converter topology using a diode rectifier, three-level boost (TLB) converter, and neutral-point-clamped (NPC) inverter is proposed for a high-power permanent magnet synchronous generator-based wind energy conversion system. The generator-side TLB converter performs the maximum power point tracking and balancing of dc-link capacitor voltages, while the grid-side NPC inverter regulates the net dc-bus voltage and reactive power to the grid. A significant improvement in the grid power quality is accomplished as the NPC inverter no longer controls the dc-link neutral point voltage. A model predictive strategy is proposed to control the complete system where the discrete-time models of the proposed power electronic converters are used to predict the future behavior of control variables. These predictions are evaluated using two independent cost functions, and the switching states which minimize these cost functions are selected and applied to the generator- and grid-side converters directly. In order to comply with the high-power application, the switching frequencies of the TLB converter and NPC inverter are minimized and maintained below 1.5 and 1 kHz, respectively. The proposed topology and control strategy are verified through MATLAB simulations on a 3-MW/3000-V/577-A system and dSPACE DS1103-based experiments on 3.6-kW/208-V/10-A prototype.

Question: How to control a three-level boost converter and an NPC inverter for high-power PMSG-based medium voltage wind energy conversion systems?


Dual Grid Array Antennas in a Thin-Profile Package for Flip-Chip Interconnection to Highly Integrated 60-GHz Radios. We examine the current development of highly integrated 60-GHz radios with an interest in antenna-circuit interfaces. We design and analyze grid array antennas with special attention to the differential feeding and the patterned ground plane. More importantly, we integrate two grid array antennas in a package; propose the way of assembling it to the system printed circuit board; and demonstrate a total solution of low cost and thin profile to highly integrated 60-GHz radios. We show that the package in low temperature cofired ceramic (LTCC) technology measures only 13×13×0.575 mm3 ; can carry a 60-GHz radio die of current and future sizes with flip-chip bonding; and achieves good antenna performance in the 60-GHz band with maximum gain of 13.5 and 14.5 dBi for the single-ended and differential antennas, respectively.

Question: What is the difference between a grid array and a patch array?


Pondering the Concept of Abstraction in (Illustrative) Visualization. We explore the concept of abstraction as it is used in visualization, with the ultimate goal of understanding and formally defining it. Researchers so far have used the concept of abstraction largely by intuition without a precise meaning. This lack of specificity left questions on the characteristics of abstraction, its variants, its control, or its ultimate potential for visualization and, in particular, illustrative visualization mostly unanswered. In this paper we thus provide a first formalization of the abstraction concept and discuss how this formalization affects the application of abstraction in a variety of visualization scenarios. Based on this discussion, we derive a number of open questions still waiting to be answered, thus formulating a research agenda for the use of abstraction for the visual representation and exploration of data. This paper, therefore, is intended to provide a contribution to the discussion of the theoretical foundations of our field, rather than attempting to provide a completed and final theory.

Question: What is the difference between abstraction and visualization?


Organophosphate Pesticide Exposure and Attention in Young Mexican-American Children: The CHAMACOS Study. BACKGROUND Exposure to organophosphate (OP) pesticides, well-known neurotoxicants, has been associated with neurobehavioral deficits in children.   OBJECTIVES We investigated whether OP exposure, as measured by urinary dialkyl phosphate (DAP) metabolites in pregnant women and their children, was associated with attention-related outcomes among Mexican-American children living in an agricultural region of California.   METHODS Children were assessed at ages 3.5 years (n = 331) and 5 years (n = 323). Mothers completed the Child Behavior Checklist (CBCL). We administered the NEPSY-II visual attention subtest to children at 3.5 years and Conners' Kiddie Continuous Performance Test (K-CPT) at 5 years. The K-CPT yielded a standardized attention deficit/hyperactivity disorder (ADHD) Confidence Index score. Psychometricians scored behavior of the 5-year-olds during testing using the Hillside Behavior Rating Scale.   RESULTS Prenatal DAPs (nanomoles per liter) were nonsignificantly associated with maternal report of attention problems and ADHD at age 3.5 years but were significantly related at age 5 years [CBCL attention problems: β = 0.7 points; 95% confidence interval (CI), 0.2-1.2; ADHD: β = 1.3; 95% CI, 0.4-2.1]. Prenatal DAPs were associated with scores on the K-CPT ADHD Confidence Index > 70th percentile [odds ratio (OR) = 5.1; 95% CI, 1.7-15.7] and with a composite ADHD indicator of the various measures (OR = 3.5; 95% CI, 1.1-10.7). Some outcomes exhibited evidence of effect modification by sex, with associations found only among boys. There was also limited evidence of associations between child DAPs and attention.   CONCLUSIONS In utero DAPs and, to a lesser extent, postnatal DAPs were associated adversely with attention as assessed by maternal report, psychometrician observation, and direct assessment. These associations were somewhat stronger at 5 years than at

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


Location recommendation for out-of-town users in location-based social networks. Most previous research on location recommendation services in location-based social networks (LBSNs) makes recommendations without considering where the targeted user is currently located. Such services may recommend a place near her hometown even if the user is traveling out of town. In this paper, we study the issues in making location recommendations for out-of-town users by taking into account user preference, social influence and geographical proximity. Accordingly, we propose a collaborative recommendation framework, called User Preference, Proximity and Social-Based Collaborative Filtering} (UPS-CF), to make location recommendation for mobile users in LBSNs. We validate our ideas by comprehensive experiments using real datasets collected from Foursquare and Gowalla. By comparing baseline algorithms and conventional collaborative filtering approach (and its variants), we show that UPS-CF exhibits the best performance. Additionally, we find that preference derived from similar users is important for in-town users while social influence becomes more important for out-of-town users.

Question: What is the location recommendation for out-of-town users in location-based social networks?


Topic Aware Neural Response Generation. We consider incorporating topic information into a sequenceto-sequence framework to generate informative and interesting responses for chatbots. To this end, we propose a topic aware sequence-to-sequence (TA-Seq2Seq) model. The model utilizes topics to simulate prior human knowledge that guides them to form informative and interesting responses in conversation, and leverages topic information in generation by a joint attention mechanism and a biased generation probability. The joint attention mechanism summarizes the hidden vectors of an input message as context vectors by message attention and synthesizes topic vectors by topic attention from the topic words of the message obtained from a pre-trained LDA model, with these vectors jointly affecting the generation of words in decoding. To increase the possibility of topic words appearing in responses, the model modifies the generation probability of topic words by adding an extra probability item to bias the overall distribution. Empirical studies on both automatic evaluation metrics and human annotations show that TA-Seq2Seq can generate more informative and interesting responses, significantly outperforming state-of-theart response generation models.

Question: How does topic aware neural response generation work?


Online Tourism Communities on the Path to Web 2.0: An Evaluation. In recent years a technological and sociological paradigm shift has taken place in the Internet that is often referred to as Web 2.0. Companies and individuals have started to adapt existing Web sites to the new standards and principles and created new types of Web services and communities. The tourism domain is no exception to this trend new tourism communities emerged and long-established ones integrated new features to keep up with this trend. In this paper we are evaluating eight tourism communities with respect to Web 2.0. Each community is evaluated based on a criteria catalogue that draws ideas from online community studies. The findings are discussed in the context of the tourist life cycle that is structured in a pre-trip, on-site and after-trip phase. The value for the traveller is highlighted for each phase and potential problems are discussed.

Question: What is the value of a tourism community?


Practical Option Pricing with Support Vector Regression and MART by. Support Vector Regression and MART by Ian I-En Choo Stanford University

Question: How to implement support vector regression?


Verb Semantics and Lexical Selection. This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentence as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.

Question: What is the difference between lexical selection and lexical selection?


Millimeter-Wave MIMO Prototype: Measurements and Experimental Results. Millimeter-wave MIMO systems are one of the candidate schemes for 5G wireless standardization efforts. In this context, the main contributions of this article are three-fold. First, we describe parallel sets of measurements at identical transmit-receive location pairs with 2.9, 29 and 61 GHz carrier frequencies in indoor office, shopping mall, and outdoor settings. These measurements provide insights on propagation, blockage and material penetration losses, and the key elements necessary in system design to make mm-Wave systems viable in practice. Second, one of these elements is hybrid beamforming necessary for better link margins by reaping the array gain with large antenna dimensions. From the class of fully-flexible hybrid beamformers, we describe a robust class of directional beamformers toward meeting the high data-rate requirements of mm-Wave systems. Third, leveraging these design insights, we then describe an experimental prototype system at 28 GHz that realizes high data rates on both the downlink and uplink and robustly maintains these rates in outdoor and indoor mobility scenarios. In addition to maintaining large signal constellation sizes in spite of radio frequency challenges, this prototype leverages the directional nature of the mm-Wave channel to perform seamless beam switching and handover across mm-Wave base stations, thereby overcoming the path losses in non-line-of-sight links and blockages encountered at mm-Wave frequencies.

Question: What is the difference between a hybrid beamformer and a directional beamformer?


Stochastic Gradient Hamiltonian Monte Carlo. Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a Metropolis-Hastings framework, enabling more efficient exploration of the state space than standard random-walk proposals. The popularity of such methods has grown significantly in recent years. However, a limitation of HMC methods is the required gradient computation for simulation of the Hamiltonian dynamical system—such computation is infeasible in problems involving a large sample size or streaming data. Instead, we must rely on a noisy gradient estimate computed from a subset of the data. In this paper, we explore the properties of such a stochastic gradient HMC approach. Surprisingly, the natural implementation of the stochastic approximation can be arbitrarily bad. To address this problem we introduce a variant that uses second-order Langevin dynamics with a friction term that counteracts the effects of the noisy gradient, maintaining the desired target distribution as the invariant distribution. Results on simulated data validate our theory. We also provide an application of our methods to a classification task using neural networks and to online Bayesian matrix factorization.

Question: What is the difference between HMC and MCMC?


Living in two homes-a Swedish national survey of wellbeing in 12 and 15 year olds with joint physical custody. BACKGROUND The practice of joint physical custody, where children spend equal time in each parent's home after they separate, is increasing in many countries. It is particularly common in Sweden, where this custody arrangement applies to 30 per cent of children with separated parents. The aim of this study was to examine children's health-related quality of life after parental separation, by comparing children living with both parents in nuclear families to those living in joint physical custody and other forms of domestic arrangements.   METHODS Data from a national Swedish classroom study of 164,580 children aged 12 and 15-years-old were analysed by two-level linear regression modelling. Z-scores were used to equalise scales for ten dimensions of wellbeing from the KIDSCREEN-52 and the KIDSCREEN-10 Index and analysed for children in joint physical custody in comparison with children living in nuclear families and mostly or only with one parent.   RESULTS Living in a nuclear family was positively associated with almost all aspects of wellbeing in comparison to children with separated parents. Children in joint physical custody experienced more positive outcomes, in terms of subjective wellbeing, family life and peer relations, than children living mostly or only with one parent. For the 12-year-olds, beta coefficients for moods and emotions ranged from -0.20 to -0.33 and peer relations from -0.11 to -0.20 for children in joint physical custody and living mostly or only with one parent. The corresponding estimates for the 15-year-olds varied from -0.08 to -0.28 and from -0.03 to -0.13 on these subscales. The 15-year-olds in joint physical custody were more likely than the 12-year-olds to report similar wellbeing levels on most outcomes to the children in nuclear families.   CONCLUSIONS Children who spent equal time living with both parents after a separatio

Question: What is the difference between joint physical custody and living with one parent?


"Strong beats skinny every time": Disordered eating and compulsive exercise in women who post fitspiration on Instagram.. OBJECTIVE Fitspiration is a recent Internet trend designed to motivate people to eat healthily and to exercise. The aim of the study was to investigate disordered eating and exercise in women who post fitspiration on Instagram.   METHOD Participants were 101 women who post fitspiration images on Instagram and a comparison group of 102 women who post travel images. Both groups completed measures of disordered eating and compulsive exercise.   RESULTS Women who post fitspiration images scored significantly higher on drive for thinness, bulimia, drive for muscularity, and compulsive exercise. Almost a fifth (17.5%) of these women were at risk for diagnosis of a clinical eating disorder, compared to 4.3% of the travel group. Compulsive exercise was related to disordered eating in both groups, but the relationship was significantly stronger for women who post fitspiration images.   DISCUSSION For some women, posting fitspiration images on Instagram may signify maladaptive eating and exercise behaviors. © 2016 Wiley Periodicals, Inc. (Int J Eat Disord 2017; 50:76-79).

Question: What is the difference between fitspiration and travel?


Designing Persian Floral Patterns using Circle Packing. In this paper, we present a novel approach toward generating floral patterns. We extract the essence of a pattern aside from its appearance and geometry into combinatorial elements. As a result, existing patterns can be reshaped while preserving their essence. Furthermore, we can create new patterns that adhere to high level concepts such as imperfect symmetry and visual balance. By decomposing floral patterns into a configuration of circles and angles, we can reconstruct this patterns on different surfaces given a conformal mapping.

Question: How to design a floral pattern?


Preliminary comparison of techniques for dealing with imbalance in software defect prediction. Imbalanced data is a common problem in data mining when dealing with classification problems, where samples of a class vastly outnumber other classes. In this situation, many data mining algorithms generate poor models as they try to optimize the overall accuracy and perform badly in classes with very few samples. Software Engineering data in general and defect prediction datasets are not an exception and in this paper, we compare different approaches, namely sampling, cost-sensitive, ensemble and hybrid approaches to the problem of defect prediction with different datasets preprocessed differently. We have used the well-known NASA datasets curated by Shepperd et al. There are differences in the results depending on the characteristics of the dataset and the evaluation metrics, especially if duplicates and inconsistencies are removed as a preprocessing step.  Further Results and replication package: http://www.cc.uah.es/drg/ease14

Question: What is the difference between sampling and cost-sensitive approaches?


CS principles goes to middle school: learning how to teach "Big Data". Spurred by evidence that students' future studies are highly influenced during middle school, recent efforts have seen a growing emphasis on introducing computer science to middle school learners. This paper reports on the in-progress development of a new middle school curricular module for Big Data, situated as part of a new CS Principles-based middle school curriculum. Big Data is of widespread societal importance and holds increasing implications for the computer science workforce. It also has appeal as a focus for middle school computer science because of its rich interplay with other important computer science principles. This paper examines three key aspects of a Big Data unit for middle school: its alignment with emerging curricular standards; the perspectives of middle school classroom teachers in mathematics, science, and language arts; and student feedback as explored during a middle school pilot study with a small subset of the planned curriculum. The results indicate that a Big Data unit holds great promise as part of a middle school computer science curriculum.

Question: What is the middle school curriculum?


Design and Field Test of a WSN Platform Prototype for Long-Term Environmental Monitoring. Long-term wildfire monitoring using distributed in situ temperature sensors is an accurate, yet demanding environmental monitoring application, which requires long-life, low-maintenance, low-cost sensors and a simple, fast, error-proof deployment procedure. We present in this paper the most important design considerations and optimizations of all elements of a low-cost WSN platform prototype for long-term, low-maintenance pervasive wildfire monitoring, its preparation for a nearly three-month field test, the analysis of the causes of failure during the test and the lessons learned for platform improvement. The main components of the total cost of the platform (nodes, deployment and maintenance) are carefully analyzed and optimized for this application. The gateways are designed to operate with resources that are generally used for sensor nodes, while the requirements and cost of the sensor nodes are significantly lower. We define and test in simulation and in the field experiment a simple, but effective communication protocol for this application. It helps to lower the cost of the nodes and field deployment procedure, while extending the theoretical lifetime of the sensor nodes to over 16 years on a single 1 Ah lithium battery.

Question: What is the cost of a node?


Robust Multi-pose Facial Expression Recognition. Previous research on facial expression recognition mainly focuses on near frontal face images, while in realistic interactive scenarios, the interested subjects may appear in arbitrary non-frontal poses. In this paper, we propose a framework to recognize six prototypical facial expressions, namely, anger, disgust, fear, joy, sadness and surprise, in an arbitrary head pose. We build a multi-pose training set by rendering 3D face scans from the BU-4DFE dynamic facial expression database [17] at 49 different viewpoints. We extract Local Binary Pattern (LBP) descriptors and further utilize multiple instance learning to mitigate the influence of inaccurate alignment in this challenging task. Experimental results demonstrate the power and validate the effectiveness of the proposed multi-pose facial expression recognition framework.

Question: How to recognize facial expressions in arbitrary poses?


Entity Retrieval via Entity Factoid Hierarchy. We propose that entity queries are generated via a two-step process: users first select entity facts that can distinguish target entities from the others; and then choose words to describe each selected fact. Based on this query generation paradigm, we propose a new entity representation model named as entity factoid hierarchy. An entity factoid hierarchy is a tree structure composed of factoid nodes. A factoid node describes one or more facts about the entity in different information granularities. The entity factoid hierarchy is constructed via a factor graph model, and the inference on the factor graph is achieved by a modified variant of Multiple-try Metropolis algorithm. Entity retrieval is performed by decomposing entity queries and computing the query likelihood on the entity factoid hierarchy. Using an array of benchmark datasets, we demonstrate that our proposed framework significantly improves the retrieval performance over existing models.

Question: How to retrieve entities from a database?


Process Mining Based on Regions of Languages. In this paper we give an overview, how to apply region based me thods for the synthesis of Petri nets from languages to process min ing. The research domain of process mining aims at constructing a process model from an event log, such that the process model can reproduce t he log, and does not allow for much more behaviour than shown in the log. We her e consider Petri nets to represent process models. Event logs can be int erpreted as finite languages. Region based synthesis methods can be used to constr u t a Petri net from a language generating the minimal net behaviour including t he given language. Therefore, it seems natural to apply such methods in the proc ess mining domain. There are several different region based methods in literat ure yielding different Petri nets. We adapt these methods to the process mining doma in and compare them concerning efficiency and usefulness of the resulting P etri net.

Question: What is the process mining domain?


On the Performance of Intel SGX. As cloud computing is widely used in various fields, more and more individuals and organizations are considering outsourcing data to the public cloud. However, the security of the cloud data has become the most prominent concern of many customers, especially those who possess a large volume of valuable and sensitive data. Although some technologies like Homomorphic Encryption were proposed to solve the problem of secure data, the result is still not satisfying. With the advent of Intel SGX processor, which aims to thoroughly eliminate the security concern of cloud environment in a hardware-assisted approach, it brings us a number of questions on its features and its practicability for the current cloud platform. To evaluate the potential impact of Intel SGX, we analyzed the current SGX programming mode and inferred some possible factors that may arise the overhead. To verify our performance hypothesis, we conducted a systematic study on SGX performance by a series of benchmark experiments. After analyzing the experiment result, we performed a workload characterization to help programmer better exploit the current availability of Intel SGX and identify feasible research directions.

Question: What is the overhead of Intel SGX?


Ultra Low Power Bioelectronics Fundamentals , Biomedical Applications , and Bio-inspired Systems. This book provides, for the first time, a broad and deep treatment of the fields of both ultra low power electronics and bioelectronics. It discusses fundamental principles and circuits for ultra low power electronic design and their applications in biomedical systems. It also discusses how ultra energy-efficient cellular and neural systems in biology can inspire revolutionary low power architectures in mixed-signal and RF electronics. The book presents a unique, unifying view of ultra low power analog and digital electronics and emphasizes the use of the ultra energy-efficient subthreshold regime of transistor operation in both. Chapters on batteries, energy harvesting, and the future of energy provide an understanding of fundamental relationships between energy use and energy generation at small scales and at large scales. A wealth of insights and examples from brain implants, cochlear implants, biomolecular sensing, cardiac devices, and bio-inspired systems make the book useful and engaging for students and practicing engineers.

Question: What is the difference between ultra low power electronics and bioelectronics?


FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge. The field of Automatic Facial Expression Analysis has grown rapidly in recent years. However, despite progress in new approaches as well as benchmarking efforts, most evaluations still focus on either posed expressions, near-frontal recordings, or both. This makes it hard to tell how existing expression recognition approaches perform under conditions where faces appear in a wide range of poses (or camera views), displaying ecologically valid expressions. The main obstacle for assessing this is the availability of suitable data, and the challenge proposed here addresses this limitation. The FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) extends FERA 2015 to the estimation of Action Units occurrence and intensity under different camera views. In this paper we present the third challenge in automatic recognition of facial expressions, to be held in conjunction with the 12th IEEE conference on Face and Gesture Recognition, May 2017, in Washington, United States. Two sub-challenges are defined: the detection of AU occurrence, and the estimation of AU intensity. In this work we outline the evaluation protocol, the data used, and the results of a baseline method for both sub-challenges.

Question: What is the difference between the first and second facial expression recognition challenge?


Computing persistent homology. We study the homology of a filtered d-dimensional simplicial complex K as a single algebraic entity and establish a correspondence that provides a simple description over fields. Our analysis enables us to derive a natural algorithm for computing persistent homology over an arbitrary field in any dimension. Our study also implies the lack of a simple classification over non-fields. Instead, we give an algorithm for computing individual persistent homology groups over an arbitrary PIDs in any dimension.

Question: What is the difference between a simplicial complex and a filtered complex?


Thing broker: a twitter for things. In the Web of Things, standard web technologies and protocols are used to represent and communicate with physical and virtual things. One challenge toward this vision is integrating things with different characteristics, protocols, interfaces and constraints while maintaining the simplicity and flexibility required for a variety of applications. In this paper we present the Thing Broker, a core platform for Web of Things that provides RESTFul interfaces to things using a Twitter-based set of abstractions and communication model. We present the key abstractions, a reference implementation and explain how a typical WoT application can be created using Thing Broker. We finish with a preliminary evaluation and draw some lessons from our experiences.

Question: What is a thing broker?


Comparison of Nanostring nCounter® Data on FFPE Colon Cancer Samples and Affymetrix Microarray Data on Matched Frozen Tissues. The prognosis of colorectal cancer (CRC) stage II and III patients remains a challenge due to the difficulties of finding robust biomarkers suitable for testing clinical samples. The majority of published gene signatures of CRC have been generated on fresh frozen colorectal tissues. Because collection of frozen tissue is not practical for routine surgical pathology practice, a clinical test that improves prognostic capabilities beyond standard pathological staging of colon cancer will need to be designed for formalin-fixed paraffin-embedded (FFPE) tissues. The NanoString nCounter® platform is a gene expression analysis tool developed for use with FFPE-derived samples. We designed a custom nCounter® codeset based on elements from multiple published fresh frozen tissue microarray-based prognostic gene signatures for colon cancer, and we used this platform to systematically compare gene expression data from FFPE with matched microarray array data from frozen tissues. Our results show moderate correlation of gene expression between two platforms and discovery of a small subset of genes as candidate biomarkers for colon cancer prognosis that are detectable and quantifiable in FFPE tissue sections.

Question: How do we compare gene expression data from FFPE with matched microarray data from frozen tissues?


Stock market prediction exploiting microblog sentiment analysis. This paper proposes a stock market prediction method exploiting sentiment analysis using financial microblogs (Sina Weibo). We analyze the microblog texts to find the financial sentiments, then combine the sentiments and the historical data of the Shanghai Composite Index (SH000001) to predict the stock market movements. Our framework includes three modules: Microblog Filter (MF), Sentiment Analysis (SA), and Stock Prediction (SP). The MF module is based on LDA to get the financial microblogs. The SA module first sets up a financial lexicon, then gets the sentiments of the microblogs obtained from the MF module. The SP module proposes a user-group model which adjusts the importance of different people, and combines it with stock historical data to predict the movement of the SH000001. We use about 6.1 million microblogs to test our method, and the result demonstrates our method is effective.

Question: How to predict the stock market?


Expression and export: recombinant protein production systems for Aspergillus. Several Aspergillus species, in particular Aspergillus niger and Aspergillus oryzae, are widely used as protein production hosts in various biotechnological applications. In order to improve the expression and secretion of recombinant proteins in these filamentous fungi, several novel genetic engineering strategies have been developed in recent years. This review describes state-of-the-art genetic manipulation technologies used for strain improvement, as well as recent advances in designing the most appropriate engineering strategy for a particular protein production process. Furthermore, current developments in identifying bottlenecks in the protein production and secretion pathways are described and novel approaches to overcome these limitations are introduced. An appropriate combination of expression vectors and optimized host strains will provide cell factories customized for each production process and expand the great potential of Aspergilli as biotechnology workhorses to more complex multi-step industrial applications.

Question: What is the difference between Aspergillus niger and Aspergillus oryzae?


Analyzing Hypersensitive AI: Instability in Corporate-Scale Machine Learning. Predictive geometric models deliver excellent results for many Machine Learning use cases. Despite their undoubted performance, neural predictive algorithms can show unexpected degrees of instability and variance, particularly when applied to large datasets. We present an approach to measure changes in geometric models with respect to both output consistency and topological stability. Considering the example of a recommender system using word2vec, we analyze the influence of single data points, approximation methods and parameter settings. Our findings can help to stabilize models where needed and to detect differences in informational value of data points on a large scale.

Question: How to measure changes in geometric models?


Cognitive control signals for neural prosthetics.. Recent development of neural prosthetics for assisting paralyzed patients has focused on decoding intended hand trajectories from motor cortical neurons and using this signal to control external devices. In this study, higher level signals related to the goals of movements were decoded from three monkeys and used to position cursors on a computer screen without the animals emitting any behavior. Their performance in this task improved over a period of weeks. Expected value signals related to fluid preference, the expected magnitude, or probability of reward were decoded simultaneously with the intended goal. For neural prosthetic applications, the goal signals can be used to operate computers, robots, and vehicles, whereas the expected value signals can be used to continuously monitor a paralyzed patient's preferences and motivation.

Question: How can we use neural prosthetics to control computers?


Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands.. Cortical neural prosthetics extract command signals from the brain with the goal to restore function in paralyzed or amputated patients. Continuous control signals can be extracted from the motor cortical areas, whereas neural activity from posterior parietal cortex (PPC) can be used to decode cognitive variables related to the goals of movement. Because typical activities of daily living comprise both continuous control tasks such as reaching, and tasks benefiting from discrete control such as typing on a keyboard, availability of both signals simultaneously would promise significant increases in performance and versatility. Here, we show that PPC can provide 3D hand trajectory information under natural conditions that would be encountered for prosthetic applications, thus allowing simultaneous extraction of continuous and discrete signals without requiring multisite surgical implants. We found that limb movements can be decoded robustly and with high accuracy from a small population of neural units under free gaze in a complex 3D point-to-point reaching task. Both animals' brain-control performance improved rapidly with practice, resulting in faster target acquisition and increasing accuracy. These findings disprove the notion that the motor cortical areas are the only candidate areas for continuous prosthetic command signals and, rather, suggests that PPC can provide equally useful trajectory signals in addition to discrete, cognitive variables. Hybrid use of continuous and discrete signals from PPC may enable a new generation of neural prostheses providing superior performance and additional flexibility in addressing individual patient needs.

Question: How can we decode brain signals to control a prosthetic limb?


Authenticity and Subjective Wellbeing within the Context of a Religious Organization. Although authenticity has a long history as a philosophical and psychological idea, this concept has received scarce attention in the business literature until very lately. Nevertheless, scholars belonging to a broad array of disciplines have pointed out the escalation in the individuals' search for authenticity within developed societies. Hence, the purpose of this paper is to assess the link between authenticity and subjective wellbeing within the rarely explored context of faith-driven organizations, where the management of emotions attains a particular significance. Specifically, this study links authenticity with subjective wellbeing among the distinct groups that shape a large international Catholic organization. This study uses Partial Least Squares (PLS) to test our research model and hypotheses. This paper covers two noteworthy research gaps. On the one hand, it provides evidence of the relationship between authenticity and subjective wellbeing within the context of religious organizations. On the other hand, our results suggest that this relationship is not homogeneous among the distinct groups that shape the organization. Implications of the research are finally discussed.

Question: What is the relationship between authenticity and subjective wellbeing within the context of a religious organization?