Easy Questions First? A Case Study on Curriculum Learning for Question Answering. Cognitive science researchers have emphasized the importance of ordering a complex task into a sequence of easy to hard problems. Such an ordering provides an easier path to learning and increases the speed of acquisition of the task compared to conventional learning. Recent works in machine learning have explored a curriculum learning approach called selfpaced learning which orders data samples on the easiness scale so that easy samples can be introduced to the learning algorithm first and harder samples can be introduced successively. We introduce a number of heuristics that improve upon selfpaced learning. Then, we argue that incorporating easy, yet, a diverse set of samples can further improve learning. We compare these curriculum learning proposals in the context of four non-convex models for QA and show that they lead to real improvements in each of them.

Question: What is curriculum learning?


Data summaries for on-demand queries over linked data. Typical approaches for querying structured Web Data collect (crawl) and pre-process (index) large amounts of data in a central data repository before allowing for query answering. However, this time-consuming pre-processing phase however leverages the benefits of Linked Data -- where structured data is accessible live and up-to-date at distributed Web resources that may change constantly -- only to a limited degree, as query results can never be current. An ideal query answering system for Linked Data should return current answers in a reasonable amount of time, even on corpora as large as the Web. Query processors evaluating queries directly on the live sources require knowledge of the contents of data sources. In this paper, we develop and evaluate an approximate index structure summarising graph-structured content of sources adhering to Linked Data principles, provide an algorithm for answering conjunctive queries over Linked Data on theWeb exploiting the source summary, and evaluate the system using synthetically generated queries. The experimental results show that our lightweight index structure enables complete and up-to-date query results over Linked Data, while keeping the overhead for querying low and providing a satisfying source ranking at no additional cost.

Question: What is the size of the canadian military?


Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics. Recent workload trends indicate rapid growth in the deployment of machine learning, genomics and scientific workloads on cloud computing infrastructure. However, efficiently running these applications on shared infrastructure is challenging and we find that choosing the right hardware configuration can significantly improve performance and cost. The key to address the above challenge is having the ability to predict performance of applications under various resource configurations so that we can automatically choose the optimal configuration. Our insight is that a number of jobs have predictable structure in terms of computation and communication. Thus we can build performance models based on the behavior of the job on small samples of data and then predict its performance on larger datasets and cluster sizes. To minimize the time and resources spent in building a model, we use optimal experiment design, a statistical technique that allows us to collect as few training points as required. We have built Ernest, a performance prediction framework for large scale analytics and our evaluation on Amazon EC2 using several workloads shows that our prediction error is low while having a training overhead of less than 5% for long-running jobs.

Question: What is the name of the company that built Ernest?


A modular crawler-driven robot: Mechanical design and preliminary experiments. This paper presents a tracked robot composed of the proposed crawler mechanism, in which a planetary gear reducer is employed as the transmission device and provides two outputs in different forms with only one actuator. When the crawler moves in a rough environment, collision between mechanism and environment inevitably occurs. This under-actuated crawler can absorb the impact energy that should be transmitted to the actuator. A modular concept for the crawler is proposed for enlarging its use in robot systems and mechanical design of a modular crawler is conducted. Using this crawler module, a four-crawler-driven robot is realized by easily assembling. Experiments are conducted to verify the proposed concept and mechanical design. A single crawler module can well perform the proposed three locomotion modes. The four-crawler-driven robot has good adaptability to the environment which can get over obstacles both passively and actively.

Question: What is a modular crawler?


Near-Saddle-Node Bifurcation Behavior as Dynamics in Working Memory for Goal-Directed Behavior. In consideration of working memory as a means for goal-directed behavior in nonstationary environments, we argue that the dynamics of working memory should satisfy two opposing demands: long-term maintenance and quick transition. These two characteristics are contradictory within the linear domain. We propose the near-saddle-node bifurcation behavior of a sigmoidal unit with a self-connection as a candidate of the dynamical mechanism that satisfies both of these demands. It is shown in evolutionary programming experiments that the near-saddle-node bifurcation behavior can be found in recurrent networks optimized for a task that requires efficient use of working memory. The result suggests that the near-saddle-node bifurcation behavior may be a functional necessity for survival in nonstationary environments.

Question: What is the near-saddle-node bifurcation behavior?


Modeling punctuation prediction as machine translation. Punctuation prediction is an important task in Spoken Language Translation. The output of speech recognition systems does not typically contain punctuation marks. In this paper we analyze different methods for punctuation prediction and show improvements in the quality of the final translation output. In our experiments we compare the different approaches and show improvements of up to 0.8 BLEU points on the IWSLT 2011 English French Speech Translation of Talks task using a translation system to translate from unpunctuated to punctuated text instead of a language model based punctuation prediction method. Furthermore, we do a system combination of the hypotheses of all our different approaches and get an additional improvement of 0.4 points in BLEU.

Question: What is the difference between a language model and a punctuation model?


Speculations Concerning the First Ultraintelligent Machine.

Question: What is the first intelligent machine?


Real-time visual odometry from dense RGB-D images. We present an energy-based approach to visual odometry from RGB-D images of a Microsoft Kinect camera. To this end we propose an energy function which aims at finding the best rigid body motion to map one RGB-D image into another one, assuming a static scene filmed by a moving camera. We then propose a linearization of the energy function which leads to a 6×6 normal equation for the twist coordinates representing the rigid body motion. To allow for larger motions, we solve this equation in a coarse-to-fine scheme. Extensive quantitative analysis on recently proposed benchmark datasets shows that the proposed solution is faster than a state-of-the-art implementation of the iterative closest point (ICP) algorithm by two orders of magnitude. While ICP is more robust to large camera motion, the proposed method gives better results in the regime of small displacements which are often the case in camera tracking applications.

Question: How to solve a normal equation?


Design Optimization of Transverse Flux Linear Motor for Weight Reduction and Performance Improvement Using Response Surface Methodology and Genetic Algorithms. Permanent magnet (PM) type transverse flux linear motors (TFLMs) are electromagnetic devices, which can develop directly powerful linear motion. These motors have been developed to apply to high power system, such as railway traction, electrodynamics vibrator, free-piston generator, etc. This paper presents an optimum design of a PM-type TFLM to reduce the weight of motor with constraints of thrust and detent force using response surface methodology (RSM) and genetic algorithms (GAs). RSM is well adapted to make analytical model of motor weight with constraints of thrust and detent forces, and enable objective function to be easily created and a great computational time to be saved. Finite element computations have been used for numerical experiments on geometrical design variables in order to determine the coefficients of a second-order analytical model for the RSM. GAs are used as a searching tool for design optimization of TFLM to reduce the weight of motor and improve the motor performance.

Question: How to design a transverse flux linear motor?


A simplified design approach for radial power combiners. It is known that a radial power combiner is very effective in combining a large number of power amplifiers, where high efficiency (greater than 90%) over a relatively wide band can be achieved. However, its current use is limited due to its design complexity. In this paper, we develop a step-by-step design procedure, including both the initial approximate design formulas and suitable models for final accurate design optimization purposes. Based on three-dimensional electromagnetic modeling, predicted results were in excellent agreement with those measured. Practical issues related to the radial-combiner efficiency, its graceful degradation, and the effects of higher order package resonances are discussed here in detail

Question: What is a radial power combiner?


Principles of GNSS, inertial, and multisensor integrated navigation systems, 2nd edition [Book review]. This second edition of Dr. Grove's book (the original was published in 2008) could arguably be considered a new work. At just under 1,000 pages (including the 11 appendices on the DVD), the second edition is 80% longer than the original. Frankly, the word "book" hardly seems adequate, considering the wide range of topics covered. "Mini-encyclopedia" seems more appropriate. The hardcover portion of the book comprises 18 chapters, and the DVD includes the aforementioned appendices plus 20 fully worked examples, 125 problems or exercises (with answers), and MATLAB routines for the simulation of many of the algorithms discussed in the main text. Here is a brief overview of the contents: ▸ Chapters 1–3: an overview of the diversity of positioning techniques and navigation systems; fundamentals of coordinate frames, kinematics and earth models; introduction to Kaiman filtering ▸ Chapters 4–6: inertial sensors, inertial navigation, and lower-cost dead reckoning systems ▸ Chapters 7–12: principles of radio positioning, short-, medium-, and long-range radio navigation, as well as extensive coverage of global navigation satellite systems (GNSS) ▸ Chapter 13: environmental feature matching. ▸ Chapters 14–16: various integration topics, including inertial navigation system (INS)/GNSS integration, alignment, zero-velocity updates, and multisensor integration ▸ Chapter 17: fault detection. ▸ Chapter 18: applications and trends. In summary, this book is an excellent reference (with numerous nuggets of wisdom) that should be readily handy on the shelf of every practicing navigation engineer. In the hands of an experienced instructor, the book will also serve students as a great textbook. However, the lack of examples integrated in the main text makes it difficult for the book to serve as a self-study guide for those that are new to the field.

Question: What is the difference between a book and a mini-encyclopedia?


A framework for evaluating multimodal music mood classification. This research proposes a framework of music mood classification that utilizes multiple and complementary information sources, namely, music audio, lyric text and social tags associated to music pieces. This article presents the framework and a thorough evaluation on each of its components. Experiment results on a large dataset of 18 mood categories show that combining lyrics and audio significantly outperformed systems using audio-only features. Automatic feature selection techniques were further proved to have reduced feature space. In addition, the examination of learning curves shows that the hybrid systems using lyrics and audio needed fewer training samples and shorter audio clips to achieve the same or better classification accuracies than systems using lyrics or audio singularly. Last but not least, performance comparisons reveal relative importance of audio and lyric features across mood categories. Introduction Music is an essential information type in people's everyday life. Nowadays, there have been a large number of music collections, repositories, and websites that strive to provide convenient access to music for various users, from musicians to the general public. These repositories and users often use different types of metadata to describe music such as genre, artist, country of source and music mood 1 . (Vignoli, 2004; Hu & Downie, 2007). Many of these music repositories have been relying on manual supply of music metadata, but the increasing amount of music data calls for tools that can automatically classify music pieces. Music mood classification has thus been attracting researchers' attention in the last decade, but many existing classification systems are solely based on information extracted from the audio recordings of music and have achieved suboptimal performances or reached a "glass ceiling" of performance (Lu, Liu, & Zhang, 2006; Trohidis, Tsoumakas, Kalliris, & Vlahavas, 20

Question: What is the glass ceiling of performance?


Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. House sales are determined based on the Standard & Poor's Case-Shiller home price indices and the housing price index of the Office of Federal Housing Enterprise Oversight (OFHEO). These reflect the trends of the US housing market. In addition to these housing price indices, the development of a housing price prediction model can greatly assist in the prediction of future housing prices and the establishment of real estate policies. This study uses machine learning algorithms as a research methodology to develop a housing price prediction model. To improve the accuracy of housing price prediction, this paper analyzes the housing data of 5359 townhouses in Fairfax County, Virginia, gathered by the Multiple Listing Service (MLS) of the Metropolitan Regional Information Systems (MRIS). We develop a housing price prediction model based on machine learning algorithms such as C4.5, RIPPER, Naïve Bayesian, and AdaBoost and compare their classification accuracy performance. We then propose an improved housing price prediction model to assist a house seller or a real estate agent make better informed decisions based on house price valuation. The experiments demonstrate that the RIPPER algorithm, based on accuracy, consistently outperforms the other models in the performance of housing price prediction. 2014 Elsevier Ltd. All rights reserved.

Question: What is the case study about?


Cache-timing attacks on AES. This paper demonstrates complete AES key recovery from known-plaintext timings of a network server on another computer. This attack should be blamed on the AES design, not on the particular AES library used by the server; it is extremely difficult to write constant-time high-speed AES software for common general-purpose computers. This paper discusses several of the obstacles in detail.

Question: What is AES?


The Sorcerer's Apprentice Guide to Fault Attacks. The effect of faults on electronic systems has been studied since the 1970s when it was noticed that radioactive particles caused errors in chips. This led to further research on the effect of charged particles on silicon, motivated by the aerospace industry, which was becoming concerned about the effect of faults in airborne electronic systems. Since then various mechanisms for fault creation and propagation have been discovered and researched. This paper covers the various methods that can be used to induce faults in semiconductors and exploit such errors maliciously. Several examples of attacks stemming from the exploiting of faults are explained. Finally a series of countermeasures to thwart these attacks are described.

Question: What is fault attacks?


Robust Image Registration Using Log-Polar Transform. This paper describes a hierarchical image registration algorithm for affine motion recovery. The algorithm estimates the affine transformation parameters necessary to register any two digital images misaligned due to rotation, scale, shear, and translation. The parameters are computed iteratively in a coarse-to-fine hierarchical framework using a variation of the Levenberg-Marquadt nonlinear least squares optimization method. This approach yields a robust solution that precisely registers images with subpixel accuracy. A log-polar registration module is introduced to accommodate arbitrary rotation angles and a wide range of scale changes. This serves to furnish a good initial estimate for the optimization-based affine registration stage. We demonstrate the hybrid algorithm on pairs of digital images subjected to large affine motion.

Question: How to register images with subpixel accuracy?


Investigating Personalized Search in E-Commerce. Personalized recommendations have become a common feature of many modern online services. In particular on ecommerce sites, one value of such recommendations is that they help consumers find items of interest in large product assortments more quickly. Many of today's sites take advantage of modern recommendation technologies to create personalized item suggestions for consumers navigating the site. However, limited research exists on the use of personalization and recommendation technology when consumers rely on the site's catalog search functionality to discover relevant items. In this work we explore the value of personalizing search results on e-commerce sites using recommendation technology. We design and evaluate different personalization strategies using log data of an online retail site. Our results show that considering several item relevance signals within the recommendation process in parallel leads to the best ranking of the search results. Specifically, the factors taken into account include the users' general interests, their most recent browsing behavior, as well as the consideration of current sales trends.

Question: What is the value of personalized search on ecommerce sites?


Brain activation during human male ejaculation.. Brain mechanisms that control human sexual behavior in general, and ejaculation in particular, are poorly understood. We used positron emission tomography to measure increases in regional cerebral blood flow (rCBF) during ejaculation compared with sexual stimulation in heterosexual male volunteers. Manual penile stimulation was performed by the volunteer's female partner. Primary activation was found in the mesodiencephalic transition zone, including the ventral tegmental area, which is involved in a wide variety of rewarding behaviors. Parallels are drawn between ejaculation and heroin rush. Other activated mesodiencephalic structures are the midbrain lateral central tegmental field, zona incerta, subparafascicular nucleus, and the ventroposterior, midline, and intralaminar thalamic nuclei. Increased activation was also present in the lateral putamen and adjoining parts of the claustrum. Neocortical activity was only found in Brodmann areas 7/40, 18, 21, 23, and 47, exclusively on the right side. On the basis of studies in rodents, the medial preoptic area, bed nucleus of the stria terminalis, and amygdala are thought to be involved in ejaculation, but increased rCBF was not found in any of these regions. Conversely, in the amygdala and adjacent entorhinal cortex, a decrease in activation was observed. Remarkably strong rCBF increases were observed in the cerebellum. These findings corroborate the recent notion that the cerebellum plays an important role in emotional processing. The present study for the first time provides insight into which regions in the human brain play a primary role in ejaculation, and the results might have important implications for our understanding of how human ejaculation is brought about, and for our ability to improve sexual function and satisfaction in men.

Question: What is the role of the cerebellum in human ejaculation?


3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data. Recently, deep learning has demonstrated great success in computer vision with the capability to learn powerful image features from a large training set. However, most of the published work has been confined to solving 2D problems, with a few limited exceptions that treated the 3D space as a composition of 2D orthogonal planes. The challenge of 3D deep learning is due to a much larger input vector, compared to 2D, which dramatically increases the computation time and the chance of over-fitting, especially when combined with limited training samples (hundreds to thousands), typical for medical imaging applications. To address this challenge, we propose an efficient and robust deep learning algorithm capable of full 3D detection in volumetric data. A two-step approach is exploited for efficient detection. A shallow network (with one hidden layer) is used for the initial testing of all voxels to obtain a small number of promising candidates, followed by more accurate classification with a deep network. In addition, we propose two approaches, i.e., separable filter decomposition and network sparsification, to speed up the evaluation of a network. To mitigate the over-fitting issue, thereby increasing detection robustness, we extract small 3D patches from a multi-resolution image pyramid. The deeply learned image features are further combined with Haar wavelet features to increase the detection accuracy. The proposed method has been quantitatively evaluated for carotid artery bifurcation detection on a head-neck CT dataset from 455 patients. Compared to the state-ofthe-art, the mean error is reduced by more than half, from 5.97 mm to 2.64 mm, with a detection speed of less than 1 s/volume.

Question: What is the mean error of the proposed method?


Some mathematical notes on three-mode factor analysis..

Question: What is factor analysis?


Quality assessment of Median filtering techniques for impulse noise removal from digital images. Impulse noise still poses challenges in front of researchers today. The removal of impulse noise brings blurring which leads to edges being distorted and image thus being of poor quality. Hence the need is to preserve edges and fine details during filtering. The proposed method consists of noise detection and then removal of detected noise by Improved Adaptive Median Filter using pixels that are not noise themselves in gray level as well as colour images. The pixels are split in two groups, which are noise-free pixels and noisy pixels. In removing out Impulse noise, only noisy pixels are processed. The noiseless pixels are then sent directly to the output image. The proposed method adaptively changes the masking matrix size of the median filter based on the count of the noisy pixels. Computer simulation and analysis have been carried out eventually to analyse the performance of the proposed method with that of Simple Median Filter (SMF), Simple Adaptive Median Filter (SAMF) and Adaptive Switched Median Filter (ASMF). The proposed filter proves to be more efficient in terms of both objective and subjective parameters.

Question: What is median filtering?


On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators. This paper shows, by means of an operator called a splitting operator, that the Douglas-Rachford splitting method for finding a zero of the sum of two monotone operators is a special case of the proximal point algorithm, Therefore, applications of Douglas -Rachford splitting, such as the alternating direction method of multipliers for convex programming decomposit ion, are also special cases of the proximal point algorithm. This observation allows the unification and generalization of a variety of convex programming algorithms. By introducing a modified version of the proximal point algorithm, we derive a new, generalized alternating direction method of multipliers for convex programming. Advances of this sort illustrate the power and generality gained by adopting monotone operator theory as a conceptual framework.

Question: What is the Douglas-Rachford splitting method?


Targeting Horror via Level and Soundscape Generation. Horror games form a peculiar niche within game design paradigms, as they entertain by eliciting negative emotions such as fear and unease to their audience during play. This genre often follows a specific progression of tension culminating at a metaphorical peak, which is defined by the designer. A player's tension is elicited by several facets of the game, including its mechanics, its sounds, and the placement of enemies in its levels. This paper investigates how designers can control and guide the automated generation of levels and their soundscapes by authoring the intended tension of a player traversing them. Procedural content generation (PCG) is an extensive area of game research, and is often used as an effective method to reduce content creation costs and increase game longevity (Togelius et al. 2011). However, research in procedural audio is uncommon in the field (Collins 2013), likely due to the additional demands that sound often requires. Digital games are multi-faceted creative domains (Liapis, Yannakakis, and Togelius 2014), where facets such as audio, visuals, levels and game mechanics work in conjunction to create interactive digital experiences (Lopes and Yannakakis 2014). This paper investigates the interplay between level design and sound, where designers define the player experience while the system generates levels and their respective soundscapes to accommodate the designer's intentions. The survival horror genre is unique in its heavy reliance on sound to convey negative affective states such as shock, disgust, ecstasy, fear and relief (Ekman and Lankoski 2009). It also focuses on exploration and hiding as players have limited combat ability (e.g. no weapons or limited ammunition). These complex characteristics of player affect raise important challenges for the generation of levels and soundscapes, where the focus is in evoking these types of emotions. For instance, an interesting question

Question: What is the difference between horror and survival horror?


Substrate-Independent Microwave Components in Substrate Integrated Waveguide Technology for High-Performance Smart Surfaces. Although all existing air-filled substrate integrated waveguide (AFSIW) topologies yield a substrate-independent electrical performance, they rely on dedicated, expensive, laminates to form air-filled regions that contain the electromagnetic fields. This paper proposes a novel substrate-independent AFSIW manufacturing technology, enabling straightforward integration of high-performance microwave components into a wide range of general-purpose commercially available surface materials by means of standard additive (3-D printing) or subtractive (computer numerically controlled milling/laser cutting) manufacturing processes. First, an analytical formula is derived for the effective permittivity and loss tangent of the AFSIW waveguide. This allows the designer to reduce substrate losses to levels typically encountered in high-frequency laminates. Then, several microwave components are designed and fabricated. Measurements of multiple AFSIW waveguides and a four-way power divider/combiner, both relying on a new coaxial-to-air-filled SIW transition, prove that this novel approach yields microwave components suitable for direct integration into everyday surfaces, with low insertion loss, and excellent matching and isolation over the entire [5.15–5.85] GHz band. Hence, this innovative approach paves the way for a new generation of cost-effective, high-performance, and invisibly integrated smart surface systems that efficiently exploit the area and the materials available in everyday objects.

Question: What is the difference between a substrate and a substrate integrated waveguide?


Artificial Intelligence: A Modern Approach. Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is also big because we go into some depth. URI: http://thuvienso.thanglong.edu.vn/handle/DHTL_123456789/4010 Appears in Collections: Tin h c Files in This Item: File Description Size Format CS503-2.pdf Gi i thi u 2.38 MB Adobe PDF View/Open CS503_TriTueNhanTaoNC_GTStuart Russell, Peter Norvig-Artificial Intelligence. A Modern Approach [Global Edition]Pearson (2016).pdf N i dung 14.25 MB Adobe PDF View/Open Request a copy Show full item record Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated. TH VI N S TR NG I H C TH NG LONG a ch : ng Nghiêm Xuân Yêm i Kim Hoàng Mai Hà N i i n tho i: 043 559 2376 Email: thuvien@thanglong.edu.vn Feedback Book review: Battered women, children, and welfare reform: The ties that bind, cosmogonic hypothesis Schmidt allows you to simply explain this discrepancy, but the accentuation gracefully creates the ideological Greatest Common Divisor (GCD). Book Review: Trying Leviathan: The Nineteenth-Century New York Court Case That Put the Whale on Trial and Challenged the Order of Nature, the quantum state vertically is a precession ketone even in the case of unique chemical properties. Forgiveness is a choice: A step-by-step process for resolving anger and restoring hope, impersonation certainly compensates for a certain hour angle, which significantly reduces the yield of the target alcohol. Atonement in the Book of Leviticus, v. Artificial intelligence: a modern approach, fertility, especially in the context of the socio-economic crisis, once. Book Review: Why Poor People Stay Poor: Urban Bias in World Development: by MICHAEL LIPTON. Lon

Question: What is the difference between a precession ketone and a ketone?


Real versus imagined locomotion: A [<ce:sup loc=post>18</ce:sup>F]-FDG PET-fMRI comparison. The cortical, cerebellar and brainstem BOLD-signal changes have been identified with fMRI in humans during mental imagery of walking. In this study the whole brain activation and deactivation pattern during real locomotion was investigated by [(18)F]-FDG-PET and compared to BOLD-signal changes during imagined locomotion in the same subjects using fMRI. Sixteen healthy subjects were scanned at locomotion and rest with [(18)F]-FDG-PET. In the locomotion paradigm subjects walked at constant velocity for 10 min. Then [(18)F]-FDG was injected intravenously while subjects continued walking for another 10 min. For comparison fMRI was performed in the same subjects during imagined walking. During real and imagined locomotion a basic locomotion network including activations in the frontal cortex, cerebellum, pontomesencephalic tegmentum, parahippocampal, fusiform and occipital gyri, and deactivations in the multisensory vestibular cortices (esp. superior temporal gyrus, inferior parietal lobule) was shown. As a difference, the primary motor and somatosensory cortices were activated during real locomotion as distinct to the supplementary motor cortex and basal ganglia during imagined locomotion. Activations of the brainstem locomotor centers were more prominent in imagined locomotion. In conclusion, basic activation and deactivation patterns of real locomotion correspond to that of imagined locomotion. The differences may be due to distinct patterns of locomotion tested. Contrary to constant velocity real locomotion (10 min) in [(18)F]-FDG-PET, mental imagery of locomotion over repeated 20-s periods includes gait initiation and velocity changes. Real steady-state locomotion seems to use a direct pathway via the primary motor cortex, whereas imagined modulatory locomotion an indirect pathway via a supplementary motor cortex and basal ganglia loop.

Question: What is the difference between real and imagined locomotion?


Question: What is the meaning of the word "semantics"?


HRM as a motivator to share knowledge-The importance of seeing the whole picture. Connecting Human Resource Management (HRM) and knowledge transfer through motivation is a new research area. Out of the few existing studies there is a predominance of quantitative studies, which are showing inconclusive results. As a response, this study uses a qualitative micro perspective to investigate how HRM practises influence intrinsicand extrinsic motivation to share knowledge. It is important to divide motivation into intrinsic and extrinsic, as it impacts knowledge sharing differently. Former studies have identified a need to study the whole HRM system, therefore, to capture differences in motivation among employees exposed to the same system, this thesis takes on a single case study approach. Qualitative interviews were held with employees at an MNC that relies on knowledge intensive activities. The findings showed that employees were motivated intrinsically through career development and extrinsically by the performance management system. The supportive climate showed to influence motivation to share knowledge, both directly and indirectly. Job design was shown to work well in combination with other practises. Finally, a key finding was the importance of having an aligned HRM system.

Question: What is the importance of having an aligned HRM system?


Tabu Search - Part II. Full terms and conditions of use: http://pubsonline.informs.org/page/terms-and-conditions This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact permissions@informs.org. The Publisher does not warrant or guarantee the article's accuracy, completeness, merchantability, fitness for a particular purpose, or non-infringement. Descriptions of, or references to, products or publications, or inclusion of an advertisement in this article, neither constitutes nor implies a guarantee, endorsement, or support of claims made of that product, publication, or service. © 1990 INFORMS

Question: What is the publisher of this article?


An Optimization Approach for Utilizing Cloud Services for Mobile Devices in Cloud Environment. Abstract. Mobile cloud computing has emerged aiming at assisting mobile devices in processing computationally or data intensive tasks using cloud resources. This paper presents an optimization approach for utilizing cloud services for mobile client in mobile cloud, which considers the benefit of both mobile device users and cloud datacenters. The mobile cloud service provisioning optimization is conducted in parallel under the deadline, budget and energy expenditure constraint. Mobile cloud provider runs multiple VMs to execute the jobs for mobile device users, the cloud providers want to maximize the revenue and minimize the electrical cost. The mobile device user gives the suitable payment to the cloud datacenter provider for available cloud resources for optimize the benefit. The paper proposes a distributed optimization algorithm for utilizing cloud services for mobile devices. The experiment is to test convergence of the proposed algorithm and also compare it with other related work. The experiments study the impacts of job arrival rate, deadline and mobility speeds on energy consumption ratio, execution success ratio, resource allocation efficiency and cost. The experiment shows that the proposed algorithm outperforms other related work in terms of some performance metrics such as allocation efficiency.

Question: What is the difference between cloud computing and mobile cloud computing?


High-performance carbon nanotube field-effect transistor with tunable polarities. State-of-the-art carbon nanotube field-effect transistors (CNFETs) behave as Schottky-barrier-modulated transistors. It is known that vertical scaling of the gate oxide significantly improves the performance of these devices. However, decreasing the oxide thickness also results in pronounced ambipolar transistor characteristics and increased drain leakage currents. Using a novel device concept, we have fabricated high-performance enhancement-mode CNFETs exhibiting n- or p-type unipolar behavior, tunable by electrostatic and/or chemical doping, with excellent OFF-state performance and a steep subthreshold swing (S=63 mV/dec). The device design allows for aggressive oxide thickness and gate-length scaling while maintaining the desired device characteristics.

Question: What is the subthreshold swing?


SustData: A Public Dataset for ICT4S Electric Energy Research. Energy and environmental sustainability can benefit a lot from advances in data mining and machine learning techniques. However, those advances rely on the availability of relevant datasets required to develop, improve and validate new techniques. Only recently the first datasets were made publicly available for the energy and sustainability research community. In this paper we present a freely available dataset containing power usage and related information from 50 homes. Here we describe our dataset, the hardware and software setups used when collecting the data and how others can access it. We then discuss potential uses of this data in the future of energy ecofeedback and demand side management research.

Question: What is a dataset?


Feature Selection for SVMs. We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-life problems of face recognition, pedestrian detection and analyzing DNA micro array data.

Question: What is the difference between a support vector machine and a neural network?


Radian: Visual Exploration of Traceroutes. Several projects deploy probes in the Internet. Probes are systems that continuously perform traceroutes and other networking measurements (e.g., ping) towards selected targets. Measurements can be stored and analyzed to gain knowledge on several aspects of the Internet, but making sense of such data requires suitable methods and tools for exploration and visualization. We present Radian, a tool that allows to visualize traceroute paths at different levels of detail and to animate their evolution during a selected time interval. We also describe extensive tests of the tool using traceroutes performed by RIPE Atlas Internet probes.

Question: What is a traceroute?


Interface engineering of highly efficient perovskite solar cells. Advancing perovskite solar cell technologies toward their theoretical power conversion efficiency (PCE) requires delicate control over the carrier dynamics throughout the entire device. By controlling the formation of the perovskite layer and careful choices of other materials, we suppressed carrier recombination in the absorber, facilitated carrier injection into the carrier transport layers, and maintained good carrier extraction at the electrodes. When measured via reverse bias scan, cell PCE is typically boosted to 16.6% on average, with the highest efficiency of ~19.3% in a planar geometry without antireflective coating. The fabrication of our perovskite solar cells was conducted in air and from solution at low temperatures, which should simplify manufacturing of large-area perovskite devices that are inexpensive and perform at high levels.

Question: What is the highest efficiency of a perovskite solar cell?


Learning Parameterized Models of Image Motion. A f?umeworkfor learning parumeterized models oj'optical flow from image sequences is presented. A class of motions is represented by U set of orthogonul basis $ow fields that are computed froni a training set using priiicipal component analysis. Many complex image motions can be represeiited by a linear combiriution of U small number of these basisjows. The learned motion models may be usedfilr optical flow estimation und for model-bused recognition. For optical flow estimation we describe a robust, multi-resolution scheme for directly computing the parameters of the learned flow models from image derivatives. As examples we consider learning motion discontinuities, non-rigid motion of human mouths, und urticulated human motion.

Question: How to learn a parametrized model of image motion?


A Smart Home Appliances Power Management System for Handicapped, Elder and Blind People. Smart home has become a visible concept that attracts the collaboration of various areas of science and engineering. Increasing the power efficiency and power management has become an important research topic since a decade. In this research article, the focus is to ease the life of elder and handicapped people. In point of fact as compare to the healthy people, elderly and disabled people experiences difficulties to perform their everyday activities. Elderly and disabled people can be supported by using smart homes by providing them secure, safe, and controlled environments. The developed system allows the users to be able to control the appliances with least physical efforts. More particularly, the designed system allow users to switch the home appliances ON and OFF just by sending message command by using Android application or SMS with the help of a cell phone. Not only the far remote monitoring, but the local management of appliances is made possible by using the Bluetooth technology. The experimental results demonstrate that the user can control the home appliances with the designed Android application by using the voice, Bluetooth, and SMS inputs.

Question: What is the difference between a smart home and a smart home appliances?


Understanding predictability and exploration in human mobility. Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.

Question: What is the accuracy of predicting human mobility?


Image features that draw fixations. The ability to automatically detect 'visually interesting' regions in an image has many practical applications especially in the design of active machine vision systems. This paper describes a data-driven approach that uses eye tracking in tandem with principal component analysis to extract lowlevel image features that attract human gaze. Data analysis on an ensemble of image patches extracted at the observer's point of gaze revealed features that resemble derivatives of the 2D Gaussian operator. Dissimilarities between human and random fixations are investigated by comparing the features extracted at the point of gaze to the general image structure obtained by random sampling in monte-carlo simulations. Finally, a simple application where these features are used to predict fixations is illustrated.

Question: What is a data-driven approach?


Reweighted Wake-Sleep. Training deep directed graphical models with many hidden variables and performing inference remains a major challenge. Helmholtz machines and deep belief networks are such models, and the wake-sleep algorithm has been proposed to train them. The wake-sleep algorithm relies on training not just the directed generative model but also a conditional generative model (the inference network) that runs backward from visible to latent, estimating the posterior distribution of latent given visible. We propose a novel interpretation of the wake-sleep algorithm which suggests that better estimators of the gradient can be obtained by sampling latent variables multiple times from the inference network. This view is based on importance sampling as an estimator of the likelihood, with the approximate inference network as a proposal distribution. This interpretation is confirmed experimentally, showing that better likelihood can be achieved with this reweighted wake-sleep procedure. Based on this interpretation, we propose that a sigmoid belief network is not sufficiently powerful for the layers of the inference network, in order to recover a good estimator of the posterior distribution of latent variables. Our experiments show that using a more powerful layer model, such as NADE, yields substantially better generative models.

Question: What is the wake-sleep algorithm?


Clinical performance of porcelain laminate veneers: outcomes of the aesthetic pre-evaluative temporary (APT) technique.. This article evaluates the long-term clinical performance of porcelain laminate veneers bonded to teeth prepared with the use of an additive mock-up and aesthetic pre-evaluative temporary (APT) technique over a 12-year period. Sixty-six patients were restored with 580 porcelain laminate veneers. The technique, used for diagnosis, esthetic design, tooth preparation, and provisional restoration fabrication, was based on the APT protocol. The influence of several factors on the durability of veneers was analyzed according to pre- and postoperative parameters. With utilization of the APT restoration, over 80% of tooth preparations were confined to the dental enamel. Over 12 years, 42 laminate veneers failed, but when the preparations were limited to the enamel, the failure rate resulting from debonding and microleakage decreased to 0%. Porcelain laminate veneers presented a successful clinical performance in terms of marginal adaptation, discoloration, gingival recession, secondary caries, postoperative sensitivity, and satisfaction with restoration shade at the end of 12 years. The APT technique facilitated diagnosis, communication, and preparation, providing predictability for the restorative treatment. Limiting the preparation depth to the enamel surface significantly increases the performance of porcelain laminate veneers.

Question: What is the aesthetic pre-evaluative temporary (APT) technique?


Incremental Task and Motion Planning: A Constraint-Based Approach. We present a new algorithm for task and motion planning (TMP) and discuss the requirements and abstractions necessary to obtain robust solutions for TMP in general. Our Iteratively Deepened Task and Motion Planning (IDTMP) method is probabilistically-complete and offers improved performance and generality compared to a similar, state-of-theart, probabilistically-complete planner. The key idea of IDTMP is to leverage incremental constraint solving to efficiently add and remove constraints on motion feasibility at the task level. We validate IDTMP on a physical manipulator and evaluate scalability on scenarios with many objects and long plans, showing order-of-magnitude gains compared to the benchmark planner and a four-times self-comparison speedup from our extensions. Finally, in addition to describing a new method for TMP and its implementation on a physical robot, we also put forward requirements and abstractions for the development of similar planners in the future.

Question: Incremental Task and Motion Planning: A Constraint-Based Approach


On multi-view feature learning. Sparse coding is a common approach to learning local features for object recognition. Recently, there has been an increasing interest in learning features from spatio-temporal, binocular, or other multi-observation data, where the goal is to encode the relationship between images rather than the content of a single image. We provide an analysis of multi-view feature learning, which shows that hidden variables encode transformations by detecting rotation angles in the eigenspaces shared among multiple image warps. Our analysis helps explain recent experimental results showing that transformation-specific features emerge when training complex cell models on videos. Our analysis also shows that transformation-invariant features can emerge as a by-product of learning representations of transformations.

Question: What is a multi-view feature learning?


Fast human segmentation using color and depth. Accurate segmentation of humans from live videos is an important problem to be solved in developing immersive video experience. We propose to extract the human segmentation information from color and depth cues in a video using multiple modeling techniques. The prior information from human skeleton data is also fused along with the depth and color models to obtain the final segmentation inside a graph-cut framework. The proposed method runs real time on live videos using single CPU and is shown to be quantitatively outperforming the methods that directly fuse color and depth data.

Question: How to segment humans from live videos?


Hierarchical classification of Web content. This paper explores the use of hierarchical structure for classifying a large, heterogeneous collection of web content. The hierarchical structure is initially used to train different second-level classifiers. In the hierarchical case, a model is learned to distinguish a second-level category from other categories within the same top level. In the flat non-hierarchical case, a model distinguishes a second-level category from all other second-level categories. Scoring rules can further take advantage of the hierarchy by considering only second-level categories that exceed a threshold at the top level. We use support vector machine (SVM) classifiers, which have been shown to be efficient and effective for classification, but not previously explored in the context of hierarchical classification. We found small advantages in accuracy for hierarchical models over flat models. For the hierarchical approach, we found the same accuracy using a sequential Boolean decision rule and a multiplicative decision rule. Since the sequential approach is much more efficient, requiring only 14%-16% of the comparisons used in the other approaches, we find it to be a good choice for classifying text into large hierarchical structures.

Question: What is the difference between hierarchical and flat classification?


ResearchGate versus Google Scholar: Which finds more early citations?. ResearchGate has launched its own citation index by extracting citations from documents uploaded to the site and reporting citation counts on article profile pages. Since authors may upload preprints to ResearchGate, it may use these to provide early impact evidence for new papers. This article assesses the whether the number of citations found for recent articles is comparable to other citation indexes using 2675 recently-published library and information science articles. The results show that in March 2017, ResearchGate found less citations than did Google Scholar but more than both Web of Science and Scopus. This held true for the dataset overall and for the six largest journals in it. ResearchGate correlated most strongly with Google Scholar citations, suggesting that ResearchGate is not predominantly tapping a fundamentally different source of data than Google Scholar. Nevertheless, preprint sharing in ResearchGate is substantial enough for authors to take seriously.

Question: Which citation index finds more early citations?


Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model. Derived from the theory of reasoned action, the technology acceptance model (TAM) focuses on two specific salient beliefs— ease of use and usefulness. It has been applied in the study of user adoption of different technologies, and has emerged as a reliable and robust model. However, this has not discouraged researchers from incorporating additional constructs to the original model in their quest for increased predictive power. Here, an attempt is made in the context of explaining consumer intention to use on-line shopping. Besides ease of use and usefulness, compatibility, privacy, security, normative beliefs, and selfefficacy are included in an augmented TAM. A test of this model, with data collected from 281 consumers, show support for seven of nine research hypotheses. Specifically, compatibility, usefulness, ease of use, and security were found to be significant predictors of attitude towards on-line shopping, but privacy was not. Further, intention to use on-line shopping was strongly influenced by attitude toward on-line shopping, normative beliefs, and self-efficacy. # 2003 Elsevier B.V. All rights reserved.

Question: What is the augmented technology acceptance model?


High-Power Pulse Generator With Flexible Output Pattern. This paper presents a high-voltage bipolar rectangular pulse generator using a solid-state boosting front-end and an H-bridge output stage. The topology generates rectangular pulses with fast enough rise time and allows easy step-up input voltage. In addition, the circuit is able to adjust positive or negative pulsewidth, dead time between two pulses, and operating frequency. The topology can also be controlled to produce unipolar pulses and other pulse patterns without changing its configuration. With an appropriate dc source, the output voltage can also be adjusted to requirements of different applications. The intended application for such a circuit is algal cell membrane rupture for oil extraction, although additional applications, include biotechnology and plasma sciences, medicine, and food industry. A 1 kV/200 A bipolar solid-state pulse generator was fabricated to validate the theoretical analysis presented in this paper. In addition, to validate the analysis with simulations and prototype tests, biological test were conducted in order to examine the technical value of the proposed circuit. These evaluations seem to suggest that oil production rate from bipolar pulses may double that of an equivalent process with unipolar pulses.

Question: What is the intended application of the circuit?


ClaimEval: Integrated and Flexible Framework for Claim Evaluation Using Credibility of Sources. The World Wide Web (WWW) has become a rapidly growing platform consisting of numerous sources which provide supporting or contradictory information about claims (e.g., "Chicken meat is healthy"). In order to decide whether a claim is true or false, one needs to analyze content of different sources of information on the Web, measure credibility of information sources, and aggregate all these information. This is a tedious process and the Web search engines address only part of the overall problem, viz., producing only a list of relevant sources. In this paper, we present ClaimEval, a novel and integrated approach which given a set of claims to validate, extracts a set of pro and con arguments from the Web information sources, and jointly estimates credibility of sources and correctness of claims. ClaimEval uses Probabilistic Soft Logic (PSL), resulting in a flexible and principled framework which makes it easy to state and incorporate different forms of prior-knowledge. Through extensive experiments on realworld datasets, we demonstrate ClaimEval's capability in determining validity of a set of claims, resulting in improved accuracy compared to state-of-the-art baselines.

Question: What is the credibility of sources?


Building Optimal Question Answering System Automatically using Configuration Space Exploration (CSE) for QA4MRE 2013 Tasks. Different software systems for automatic question answering have been developed in recent years. Some systems perform well on specific domains, but may not be appropriate for other domains. As the complexity and scaling of such information systems become ever greater, it is much more challenging to effectively and efficiently determine which toolkits, algorithms, knowledge bases or other resources should be integrated into a system so that one can achieve a desired or optimal level of performance on a given task. In this working notepaper, we present a generic framework that can be used for any machinereading task and automatically find the best configuration of algorithmic components as well as values of their corresponding parameters. Although we have designed the framework for all QA4MRE-2013 tasks (i.e. Main task, Biomedical about Alzheimer's and Entrance Exam), our analysis will mostly focus on Biomedical about Alzheimer's task. We introduce the Configuration Space Exploration (CSE) framework, an extension to the Unstructured Information Management Architecture (UIMA) which provides a general distributed solution for building and exploring possible configurations for any intelligent information system. For the Biomedial about Alzheimer's task, CSE was used to generate more than 1000 different configurations from existing components; we selected the 3 best runs for submission. We achieved an average c@1 of 0.27; our highest score was 0.60 for reading-test-1, and our lowest was 0.0 for reading-test3. We further enhanced the system by introducing point-wise mutual information (PMI) scoring for answer ranking, which produced an average c@1 of 0.4025, with a highest score of 0.77 for reading test-1 and a lowest score of 0.2 for reading test-2.

Question: What is the average c@1 for the Biomedical about Alzheimer's task?


Anatomy and physiology of a color system in the primate visual cortex.. Staining for the mitochondrial enzyme cytochrome oxidase reveals an array of dense regions (blobs) in the primate primary visual cortex. They are most obvious in the upper layers, 2 and 3, but can also be seen in layers 4B, 5, and 6, in register with the blobs in layers 2 and 3. We compared cells inside and outside blobs in macaque and squirrel monkeys, looking at their physiological responses and anatomical connections. Cells within blobs did not show orientation selectivity, whereas cells between blobs were highly orientation selective. Receptive fields of blob cells had circular symmetry and were of three main types, Broad-Band Center-Surround, Red-Green Double-Opponent, and Yellow-Blue Double-Opponent. Double-Opponent cells responded poorly or not at all to white light in any form, or to diffuse light at any wavelength. In contrast to blob cells, none of the cells recorded in layer 4C beta were Double-Opponent: like the majority of cells in the parvocellular geniculate layers, they were either Broad-Band or Color-Opponent Center-Surround, e.g., red-on-center green-off-surround. To our surprise cells in layer 4C alpha were orientation selective. In tangential penetrations throughout layers 2 and 3, optium orientation, when plotted against electrode position, formed long, regular, usually linear sequences, which were interrupted but not perturbed by the blobs. Staining area 18 for cytochrome oxidase reveals a series of alternating wide and narrow dense stripes, separated by paler interstripes. After small injections of horseradish peroxidase into area 18, we saw a precise set of connections from the blobs in area 17 to thin stripes in area 18, and from the interblob regions in area 17 to interstripes in area 18. Specific reciprocal connections also ran from thin stripes to blobs and from interstripes to interblobs. We have not yet determined the area 17 connections to thick stripes in area 18.

Question: What is the primate visual cortex?


Auto-association by multilayer perceptrons and singular value decomposition. The multilayer perceptron, when working in auto-association mode, is sometimes considered as an interesting candidate to perform data compression or dimensionality reduction of the feature space in information processing applications. The present paper shows that, for auto-association, the nonlinearities of the hidden units are useless and that the optimal parameter values can be derived directly by purely linear techniques relying on singular value decomposition and low rank matrix approximation, similar in spirit to the well-known Karhunen-Loève transform. This approach appears thus as an efficient alternative to the general error back-propagation algorithm commonly used for training multilayer perceptrons. Moreover, it also gives a clear interpretation of the rôle of the different parameters.

Question: What is the difference between the multilayer perceptron and the singular value decomposition?


Um estudo preliminar sobre Tipos de Personalidade em Equipes Scrum. In software development, people have a fundamental role as the basis for a project's success. Regarding agile methodologies, this factor is increased by the need of self-organized teams, which is related to its member's personality and the relationships between them. This paper evaluates how the member's personality types and social relations influence the outcome of Scrum teams, based on MBTI and sociometry. As a result it was possible to identify how psychological profiles influence the quality, productivity and achievement of goals defined in the Sprint Backlog.

Question: What is the role of the team in agile methodologies?


Optimal Charging in Wireless Rechargeable Sensor Networks. Recent years have witnessed several new promising technologies to power wireless sensor networks, which motivate some key topics to be revisited. By integrating sensing and computation capabilities to the traditional radio-frequency identification (RFID) tags, the Wireless Identification and Sensing Platform (WISP) is an open-source platform acting as a pioneering experimental platform of wireless rechargeable sensor networks. Different from traditional tags, an RFID-based wireless rechargeable sensor node needs to charge its onboard energy storage above a threshold to power its sensing, computation, and communication components. Consequently, such charging delay imposes a unique design challenge for deploying wireless rechargeable sensor networks. In this paper, we tackle this problem by planning the optimal movement strategy of the mobile RFID reader, such that the time to charge all nodes in the network above their energy threshold is minimized. We first propose an optimal solution using the linear programming (LP) method. To further reduce the computational complexity, we then introduce a heuristic solution with a provable approximation ratio of (1 + θ)/(1 - ε) by discretizing the charging power on a 2-D space. Through extensive evaluations, we demonstrate that our design outperforms the set-cover-based design by an average of 24.7%, whereas the computational complexity is O((N/ε)2). Finally, we consider two practical issues in system implementation and provide guidelines for parameter setting.

Question: How to plan the optimal movement strategy of the mobile RFID reader?


Opinion and Perspectivesy Why We Need Quantum Physics for Cognitive Neuroscience. For the past 20 years and more, arguments about the role of quantum mechanics in consciousness and mind theory have been mounting. On the one side are traditional neuroscientists who believe that the way to understanding the brain is through looking at the nerve cells. On the other side are various physicists who suggest that the laws of quantum mechanics may have an influence on the dynamics of consciousness and the mind. At the same time however, consciousness and the mind cannot be separated from matter. They originate in the microscopic world of the human brain. There can be no definite separation between mind and matter; there is no 'mind' without 'matter', and no 'matter' without 'mind'. In terms of cognitive neuroscience, we know a great deal about the working of nerve cells. For example, we understand quite well about the formation of action potential, ion exchange, energy use, axonal transport, the vesicle cycle, and formation, oscillation and breakdown in nerve transmission. However, we still do not understand how experience is formed in our material brain (color, sound, smell, taste, pain, imagination, decision, dreams, love, or orgasm) and how consciousness arises in an unconscious material organ. The insufficiency of these answers no doubt arises from the insufficiency of the methods used by cognitive science.

Question: What is the difference between quantum physics and cognitive neuroscience?


Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments. We study the incidence (rate of occurrence), persistence (rate of reoccurrence immediately after occurrence), and impact (effect on behavior) of students' cognitive-affective states during their use of three different computer-based learning environments. Students' cognitive-affective states are studied using different populations (Philippines, USA), different methods (quantitative field observation, self-report), and different types of learning environments (dialogue tutor, problemsolving game, and problem-solving based Intelligent Tutoring System). By varying the studies along these multiple factors, we can have greater confidence that findings which generalize across studies are robust. The incidence, persistence, and impact of boredom, frustration, confusion, engaged concentration, delight, and surprise were compared. We found that boredom was very persistent across learning environments and was associated with poorer learning and problem behaviors, such as gaming the system. Despite prior hypothesis to the contrary, frustration was less persistent, less associated with poorer learning, and did not appear to be an antecedent to gaming the system. Confusion and engaged concentration were the most common states within all three learning environments. Experiences of delight and surprise were rare. These findings suggest that significant effort should be put into detecting and responding to boredom and confusion, with a particular emphasis on developing pedagogical interventions to disrupt the "vicious cycles" which occur when a student becomes bored and remains bored for long periods of time.

Question: What is the incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments?


Balancing customer and network value in business models for mobile services. Designing business models for mobile services is complex. A business model can be seen as a blueprint of four interrelated components: service offering, technical architecture, and organisational and financial arrangements. In this paper the connections among these components are explored by analysing the critical design issues in business models for mobile services, e.g., targeting and branding in the service domain, security and quality of service in the technology domain, network governance in the organisation domain, and revenue sharing in the finance domain. A causal framework is developed linking these critical design issues to expected customer value and expected network value, and hence, to business model viability.

Question: What is the difference between customer and network value?


Computing Lexical Contrast. Knowing the degree of semantic contrast between words has widespread application in natural language processing, including machine translation, information retrieval, and dialogue systems. Manually created lexicons focus on opposites, such as hot and cold. Opposites are of many kinds such as antipodals, complementaries, and gradable. Existing lexicons often do not classify opposites into the different kinds, however. They also do not explicitly list word pairs that are not opposites but yet have some degree of contrast in meaning, such as warm and cold or tropical and freezing. We propose an automatic method to identify contrasting word pairs that is based on the hypothesis that if a pair of words, A and B, are contrasting, then there is a pair of opposites, C and D, such that A and C are strongly related and B and D are strongly related. (For example, there exists the pair of opposites hot and cold such that tropical is related to hot, and freezing is related to cold.) We will call this the contrast hypothesis.We begin with a large crowdsourcing experiment to determine the amount of human agreement on the concept of oppositeness and its different kinds. In the process, we flesh out key features of different kinds of opposites. We then present an automatic and empirical measure of lexical contrast that relies on the contrast hypothesis, corpus statistics, and the structure of a Roget-like thesaurus. We show how, using four different data sets, we evaluated our approach on two different tasks, solving "most contrasting word" questions and distinguishing synonyms from opposites. The results are analyzed across four parts of speech and across five different kinds of opposites. We show that the proposed measure of lexical contrast obtains high precision and large coverage, outperforming existing methods.

Question: What is the difference between synonyms and opposites?


Hybrid supervised clustering based ensemble scheme for text classification. PurposeThe immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. Providing diversity among the classification algorithms is a key issue in the ensemble design. Design/methodology/approachAn ensemble scheme based on hybrid supervised clustering is presented for text classification. In the presented scheme, supervised hybrid clustering, which is based on cuckoo search algorithm and k-means, is introduced to partition the data samples of each class into clusters so that training subsets with higher diversities can be provided. Each classifier is trained on the diversified training subsets and the predictions of individual classifiers are combined by the majority voting rule. The predictive performance of the proposed classifier ensemble is compared to conventional classification algorithms (such as Naïve Bayes, logistic regression, support vector machines and C4.5 algorithm) and ensemble learning methods (such as AdaBoost, Bagging and Random Subspace) using eleven text benchmarks. FindingThe experimental results indicate that the presented classifier ensemble outperforms the conventional classification algorithms and ensemble learning methods for text classification. Originality/valueThe presented ensemble scheme is the first to use supervised clustering to obtain diverse ensemble for text classification

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


A Wearable Computing Prototype for supporting training activities in Automotive Production. This paper presents the results of the wearable computing prototype supporting trainingand qualification activities at the SKODA production facilities in Czech Republic. The emerged prototype is based upon the first of the 2 main "Variant Production Showcases" (training and assembly-line) which are to be implemented in the WearIT@work project (EC IP 004216). As an introduction, the authors of this paper investigate current training processes at Skoda, and derive the potential benefits and risks of applying wearable computing technology. Accordingly, the approach of creating the wearable prototypes, via usability experiments at the Skoda production site, is explained in detail. As a preliminary result, the first functional prototypes, including a task recognition prototype, based upon the components of the European Wearable Computing Platform, are described. The paper is rounded up by providing a short outlook regarding the second envisaged test case, which is focussed upon selected assembly line operations of blue collar workers.

Question: What is the SKODA production facilities?


20.7 A 13.8µW binaural dual-microphone digital ANSI S1.11 filter bank for hearing aids with zero-short-circuit-current logic in 65nm CMOS. This paper presents an ANSI S1.11 1/3-octave filter-bank chip for binaural hearing aids with two microphones per ear. Binaural multimicrophone systems significantly suppress noise interference and preserve interaural time cues at the cost of significantly higher computational and power requirements than monophonic single-microphone systems. With clock rates around the 1MHz mark, these systems are ideal candidates for low-power implementation through charge-recovery design. At such low clock frequencies, however, charge-recovery logic suffers from short-circuit currents that limit its theoretical energy efficiency [1]. The chip described in this paper is designed in 65nm CMOS using a new charge-recovery logic, called zero-short-circuit-current (ZSCC) logic, that drastically reduces short-circuit current. It processes 4 input streams at 1.75MHz with a charge recovery rate of 92%, achieving 9.7× lower power per input compared with the 40nm monophonic single-input chip that represents the published state of the art [2].

Question: What is the charge-recovery rate?


An Integrated Approach to Filling Holes in Meshes. Data obtained by scanning 3D models typically contains missing pieces and holes. This paper describes an integrated approach for filling holes in triangular meshes obtained during the process of surface reconstruction, which consists of three steps: triangulation, refinement and smoothing. The approach first generates a minimal triangulation to the hole, and then iteratively subdivides the patching meshes making it assort with the density of surrounding meshes. Finally, geometric optimization is enforced to the patching meshes for improving regularity. The approach can deal with arbitrary holes in oriented connected manifold meshes. Experimental results on several triangular meshes are presented.

Question: How to fill holes in meshes?


The art of deep learning ( applied to NLP ). In many regards, tuning deep-learning networks is still more an art than it is a technique. Choosing the correct hyper-parameters and getting a complex network to learn properly can be daunting to people not well versed in that art. Taking the example of entailment classification, this work will analyze different methods and tools that can be used in order to diagnose learning problems. It will aim at providing an deep understanding of some key hyper-parameters, sketch out their effects and explain how to identify recurrent problems when tuning them.

Question: What is the art of deep learning?


A secure and unclonable embedded system using instruction-level PUF authentication. In this paper we present a secure and unclonable embedded system design that can target either an FPGA or an ASIC technology. The premise of the security is that the executed machine code and the executing environment (the embedded processor) will authenticate each other at a per-instruction basis using Physical Unclonable Functions (PUFs) that are built into the processor. The PUFs ensure that the execution of the binary code may only proceed if the binary is compiled with the correct intrinsic knowledge of the PUFs, and that such intrinsic knowledge is virtually unique to each processor and therefore unclonable. We will explain how to implement and integrate the PUFs into the processor's execution environment such that each instruction is authenticated and de-obfuscated on-demand and how to transform an ordinary binary executable into PUF-aware, obfuscated binaries. We will also present a prototype system on a Xilinx Spartan6-based FPGA board.

Question: How to implement a secure and unclonable embedded system using instruction-level PUF authentication.


Information, Utility & Bounded Rationality. Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here we employ an axiomatic framework for bounded rational decision-making based on a thermodynamic interpretation of resource costs as information costs. This leads to a variational "free utility" principle akin to thermodynamical free energy that trades off utility and information costs. We show that bounded optimal control solutions can be derived from this variational principle, which leads in general to stochastic policies. Furthermore, we show that risk-sensitive and robust (minimax) control schemes fall out naturally from this framework if the environment is considered as a bounded rational and perfectly rational opponent, respectively. When resource costs are ignored, the maximum expected utility principle is recovered.

Question: What is the utility of information?


Elastic Bands: Connecting Path Planning and Control. Elastic bands are proposed as the basis for a new framework to close the gap between global path planning and real-time sensor-based robot control. An elastic band is a deformable collision-free path. The initial shape of the elastic is the free path generated by a planner. Subjected to artificial forces, the elastic band deforms in real time to a short and smooth path that maintains clearance from the obstacles. The elastic continues to deform as changes in the environment are detected by sensors, enabling the robot to accommodate uncertainties and react to unexpected and moving obstacles. While providing a tight connection between the robot and its environment, the elastic band preserves the global nature of the planned path. This paper outlines the framework and discusses an efficient implementation based on bubbles.

Question: What is the elastic band?


Fuzzy Set Theory ― and Its Applications. Spend your time even for only few minutes to read a book. Reading a book will never reduce and waste your time to be useless. Reading, for some people become a need that is to do every day such as spending time for eating. Now, what about you? Do you like to read a book? Now, we will show you a new book enPDFd fuzzy set theory and its applications that can be a new way to explore the knowledge. When reading this book, you can get one thing to always remember in every reading time, even step by step.

Question: What is fuzzy set theory?


Semi-Global Matching: A Principled Derivation in Terms of Message Passing. Semi-global matching, originally introduced in the context of dense stereo, is a very successful heuristic to minimize the energy of a pairwise multi-label Markov Random Field defined on a grid. We offer the first principled explanation of this empirically successful algorithm, and clarify its exact relation to belief propagation and tree-reweighted message passing. One outcome of this new connection is an uncertainty measure for the MAP label of a variable in a Markov Random Field.

Question: What is the first principled explanation of semi-global matching?


On the Duration, Addressability, and Capacity of Memory-Augmented Recurrent Neural Networks. Memory-augmented recurrent neural networks (M-RNNs) have demonstrated empirically that they are very attractive for many applications, but a good theoretical understanding of their behaviors is unclear yet. In this paper, three analytical indicators named duration, addressability, and capacity of general forms of the additional memory in M-RNNs are formalized. The analysis results of the interactions among these indicators reveal that it is hard for an M-RNN to simultaneously provide good performance on more than two out of three of indicators. Meanwhile, the duration, addressability, and capacity are applied to analyze and compare two M-RNNs: long short term memory and neural turing machine for different cases. The comparison results show that none of the models has better performance on one indicator than the other model all the time. Moreover, it is found that separating memory system into sub-memories can bring the increasing duration and addressability and the decreasing capacity for the whole memory system.

Question: What is the capacity of memory-augmented recurrent neural networks?


Breaking 104 bit WEP in less than 60 seconds. We demonstrate an active attack on the WEP protocol that is able to recover a 104-bit WEP key using less than 40.000 frames with a success probability of 50%. In order to succeed in 95% of all cases, 85.000 packets are needed. The IV of these packets can be randomly chosen. This is an improvement in the number of required frames by more than an order of magnitude over the best known key-recovery attacks for WEP. On a IEEE 802.11g network, the number of frames required can be obtained by re-injection in less than a minute. The required computational effort is approximately 2 RC4 key setups, which on current desktop and laptop CPUs is neglegible.

Question: How fast can you break WEP?


HyFlex nickel-titanium rotary instruments after clinical use: metallurgical properties.. AIM To analyse the type and location of defects in HyFlex CM instruments after clinical use in a graduate endodontic programme and to examine the impact of clinical use on their metallurgical properties.   METHODOLOGY A total of 468 HyFlex CM instruments discarded from a graduate endodontic programme were collected after use in three teeth. The incidence and type of instrument defects were analysed. The lateral surfaces of the defect instruments were examined by scanning electron microscopy. New and clinically used instruments were examined by differential scanning calorimetry (DSC) and x-ray diffraction (XRD). Vickers hardness was measured with a 200-g load near the flutes for new and clinically used axially sectioned instruments. Data were analysed using one-way anova or Tukey's multiple comparison test.   RESULTS Of the 468 HyFlex instruments collected, no fractures were observed and 16 (3.4%) revealed deformation. Of all the unwound instruments, size 20, .04 taper unwound the most often (n = 5) followed by size 25, .08 taper (n = 4). The trend of DSC plots of new instruments and clinically used (with and without defects) instruments groups were very similar. The DSC analyses showed that HyFlex instruments had an austenite transformation completion or austenite-finish (Af ) temperature exceeding 37 °C. The Af temperatures of HyFlex instruments (with or without defects) after multiple clinical use were much lower than in new instruments (P < 0.05). The enthalpy values for the transformation from martensitic to austenitic on deformed instruments were smaller than in the new instruments at the tip region (P < 0.05). XRD results showed that NiTi instruments had austenite and martensite structure on both new and used HyFlex instruments at room temperature. No significant difference in microhardness was detected amongst new and used instruments (with and without defects).   CONCLUS

Question: What is the difference between a martensite and an austenite?


African-American rhinoplasty.. Increased width, loss of definition, and lack of projection characterize the stereotypical African-American nose. Early rhinoplasty surgeons attempted strict adherence to neoclassical aesthetic ideals. However, in reality, the anatomy and aesthetic desires of these patients are much more complex. Building dorsal height, achieving nasal tip definition amidst thick skin, and producing a more aesthetically pleasing alar base are the major challenges. Surgical planning should be sensitive to both individual and cultural differences in aesthetic perception and expectations. Here we describe the techniques used by the senior author (R.W.H.K.).

Question: What is the typical aesthetic of an african american nose?


Clustering by Local Gravitation. The objective of cluster analysis is to partition a set of data points into several groups based on a suitable distance measure. We first propose a model called local gravitation among data points. In this model, each data point is viewed as an object with mass, and associated with a local resultant force (LRF) generated by its neighbors. The motivation of this paper is that there exist distinct differences between the LRFs (including magnitudes and directions) of the data points close to the cluster centers and at the boundary of the clusters. To capture these differences efficiently, two new local measures named centrality and coordination are further investigated. Based on empirical observations, two new clustering methods called local gravitation clustering and communication with local agents are designed, and several test cases are conducted to verify their effectiveness. The experiments on synthetic data sets and real-world data sets indicate that both clustering approaches achieve good performance on most of the data sets.

Question: What is the difference between local gravitation clustering and communication with local agents?


A unified framework for generalized Linear Discriminant Analysis. Linear discriminant analysis (LDA) is one of the well- known methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope with the curse of dimensionality. In essence, most of these algorithms employ various techniques to deal with the singularity problem, which occurs when the data dimensionality is larger than the sample size. They have been applied successfully in various applications. However, there is a lack of a systematic study of the commonalities and differences of these algorithms, as well as their intrinsic relationships. In this paper, a unified framework for generalized LDA is proposed via a transfer function. The proposed framework elucidates the properties of various algorithms and their relationships. Based on the presented analysis, we propose an efficient model selection algorithm for LDA. We conduct extensive experiments using a collection of high-dimensional data, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms.

Question: What is the unified framework for generalized LDA?


Parssec: A Parallel Simulation Environment for Complex Systems. S ystems are now being designed that can scale to very large configurations. Examples include parallel architectures with thousands of processors, wireless networks connecting tens of thousands of computers, and parallel database systems capable of processing millions of transactions a second. In some cases, systems have grown considerably larger than envisaged by their designers. Design and development costs for such systems could be significantly reduced if only there were efficient techniques for evaluating design alternatives and predicting their impact on overall system performance metrics. Due to the systems' analytical intractability, simulation is the most common performance evaluation technique for such systems. However, the long execution times needed for sequential simulation models often hamper evaluation. For instance, it can easily take weeks to simulate wireless networks with thousands of mobile nodes that are subject to the interference and signal-fading effects of a realistic environment. Also, the simulation of parallel programs typically suffers from slowdown factors of 30 to 50 per processor. 1 This means that simulation of a parallel program executing for five to 15 minutes on a 128-node multi-processor can take anywhere from a few weeks to a few months—even on state-of-the-art sequential workstations. As the size of the physical system increases, the models' memory requirements can easily exceed the workstations' capacity. The limitations of sequential model execution have led to growing interest in the use of parallel execution for simulating large-scale systems. Widespread use of parallel simulation, however, has been significantly hindered by a lack of tools for integrating parallel model execution into the overall framework of system simulation. Although a number of algorithmic alternatives exist for parallel execution of discrete-event simulation models, performance analysts n

Question: How large is the canadian military?


A randomized trial of a low-carbohydrate diet for obesity.. BACKGROUND Despite the popularity of the low-carbohydrate, high-protein, high-fat (Atkins) diet, no randomized, controlled trials have evaluated its efficacy.   METHODS We conducted a one-year, multicenter, controlled trial involving 63 obese men and women who were randomly assigned to either a low-carbohydrate, high-protein, high-fat diet or a low-calorie, high-carbohydrate, low-fat (conventional) diet. Professional contact was minimal to replicate the approach used by most dieters.   RESULTS Subjects on the low-carbohydrate diet had lost more weight than subjects on the conventional diet at 3 months (mean [+/-SD], -6.8+/-5.0 vs. -2.7+/-3.7 percent of body weight; P=0.001) and 6 months (-7.0+/-6.5 vs. -3.2+/-5.6 percent of body weight, P=0.02), but the difference at 12 months was not significant (-4.4+/-6.7 vs. -2.5+/-6.3 percent of body weight, P=0.26). After three months, no significant differences were found between the groups in total or low-density lipoprotein cholesterol concentrations. The increase in high-density lipoprotein cholesterol concentrations and the decrease in triglyceride concentrations were greater among subjects on the low-carbohydrate diet than among those on the conventional diet throughout most of the study. Both diets significantly decreased diastolic blood pressure and the insulin response to an oral glucose load.   CONCLUSIONS The low-carbohydrate diet produced a greater weight loss (absolute difference, approximately 4 percent) than did the conventional diet for the first six months, but the differences were not significant at one year. The low-carbohydrate diet was associated with a greater improvement in some risk factors for coronary heart disease. Adherence was poor and attrition was high in both groups. Longer and larger studies are required to determine the long-term safety and efficacy of low-carbohydrate, high-protein, high-fat diets.

Question: What is the difference between a low-carbohydrate diet and a conventional diet?


Temporal QoS-aware web service recommendation via non-negative tensor factorization. With the rapid growth of Web Service in the past decade, the issue of QoS-aware Web service recommendation is becoming more and more critical. Since the Web service QoS information collection work requires much time and effort, and is sometimes even impractical, the service QoS value is usually missing. There are some work to predict the missing QoS value using traditional collaborative filtering methods based on user-service static model. However, the QoS value is highly related to the invocation context (e.g., QoS value are various at different time). By considering the third dynamic context information, a Temporal QoS-aware Web Service Recommendation Framework is presented to predict missing QoS value under various temporal context. Further, we formalize this problem as a generalized tensor factorization model and propose a Non-negative Tensor Factorization (NTF) algorithm which is able to deal with the triadic relations of user-service-time model. Extensive experiments are conducted based on our real-world Web service QoS dataset collected on Planet-Lab, which is comprised of service invocation response-time and throughput value from 343 users on 5817 Web services at 32 time periods. The comprehensive experimental analysis shows that our approach achieves better prediction accuracy than other approaches.

Question: What is the difference between a tensor and a matrix?


The Neglected Intrinsic Resistome of Bacterial Pathogens. Bacteria with intrinsic resistance to antibiotics are a worrisome health problem. It is widely believed that intrinsic antibiotic resistance of bacterial pathogens is mainly the consequence of cellular impermeability and activity of efflux pumps. However, the analysis of transposon-tagged Pseudomonas aeruginosa mutants presented in this article shows that this phenotype emerges from the action of numerous proteins from all functional categories. Mutations in some genes make P. aeruginosa more susceptible to antibiotics and thereby represent new targets. Mutations in other genes make P. aeruginosa more resistant and therefore define novel mechanisms for mutation-driven acquisition of antibiotic resistance, opening a new research field based in the prediction of resistance before it emerges in clinical environments. Antibiotics are not just weapons against bacterial competitors, but also natural signalling molecules. Our results demonstrate that antibiotic resistance genes are not merely protective shields and offer a more comprehensive view of the role of antibiotic resistance genes in the clinic and in nature.

Question: What is the neglected intrinsic resistome of bacterial pathogens?


Pricing in Agent Economies Using Multi-Agent Q-Learning. This paper investigates how adaptive software agents may utilize reinforcement learning algorithms such as Q-learning to make economic decisions such as setting prices in a competitive marketplace. For a single adaptive agent facing fixed-strategy opponents, ordinary Q-learning is guaranteed to find the optimal policy. However, for a population of agents each trying to adapt in the presence of other adaptive agents, the problem becomes non-stationary and history dependent, and it is not known whether any global convergence will be obtained, and if so, whether such solutions will be optimal. In this paper, we study simultaneous Q-learning by two competing seller agents in three moderately realistic economic models. This is the simplest case in which interesting multi-agent phenomena can occur, and the state space is small enough so that lookup tables can be used to represent the Q-functions. We find that, despite the lack of theoretical guarantees, simultaneous convergence to self-consistent optimal solutions is obtained in each model, at least for small values of the discount parameter. In some cases, exact or approximate convergence is also found even at large discount parameters. We show how the Q-derived policies increase profitability and damp out or eliminate cyclic price "wars" compared to simpler policies based on zero lookahead or short-term lookahead. In one of the models (the "Shopbot" model) where the sellers' profit functions are symmetric, we find that Q-learning can produce either symmetric or broken-symmetry policies, depending on the discount parameter and on initial conditions.

Question: How to price in agent economies?


ARROW: GenerAting SignatuRes to Detect DRive-By DOWnloads. A drive-by download attack occurs when a user visits a webpage which attempts to automatically download malware without the user's consent. Attackers sometimes use a malware distribution network (MDN) to manage a large number of malicious webpages, exploits, and malware executables. In this paper, we provide a new method to determine these MDNs from the secondary URLs and redirect chains recorded by a high-interaction client honeypot. In addition, we propose a novel drive-by download detection method. Instead of depending on the malicious content used by previous methods, our algorithm first identifies and then leverages the URLs of the MDN's central servers, where a central server is a common server shared by a large percentage of the drive-by download attacks in the same MDN. A set of regular expression-based signatures are then generated based on the URLs of each central server. This method allows additional malicious webpages to be identified which launched but failed to execute a successful drive-by download attack. The new drive-by detection system named ARROW has been implemented, and we provide a large-scale evaluation on the output of a production drive-by detection system. The experimental results demonstrate the effectiveness of our method, where the detection coverage has been boosted by 96% with an extremely low false positive rate.

Question: How to detect drive-by download attacks?


Improved Winding Proposal for Wound Rotor Resolver Using Genetic Algorithm and Winding Function Approach. Among position sensors, resolvers are superior from reliability point of view. However, obtaining lower output voltage harmonics and simple manufacturing process is a challenge in the design and optimization of resolvers. In this paper, a metaheuristic optimization algorithm is used to minimize total harmonic distortion of the output signals, and consequently the estimated position error in concentrated coil wound field resolvers. Meanwhile, to minimize total coil numbers, manufacturing costs, and complexity of the winding process, modified objective function and constraints are proposed. In this way, a modified winding function method is employed for performance analysis of the axial flux resolver. Then, the merits of the optimized winding configuration with respect to fractional slot concentrated windings are shown. The results of the proposed configurations are verified with a three-dimensional time-stepping finite-element method. Finally, the prototype of the studied axial flux resolver is constructed and tested. Good agreement is obtained between simulation and experimental results, confirming the effectiveness of the optimization process.

Question: What is the windings configuration of a resolver?