A 3-kW Wireless Power Transfer System for Sightseeing Car Supercapacitor Charge. Wireless power transfer (WPT) is the preferred charging method for electric vehicles (EVs) powered by battery and supercapacitor. In this paper, a novel WPT system with constant current charging capability for sightseeing car with supercapacitor storage is designed. First, an optimized magnetic coupler using ferrite cores and magnetic shielding structure is proposed to ensure stable power transfer and high efficiency. Compared with the traditional planar shape ferrite core coupler, the proposed magnetic coupler requires lesser ferrite material without degrading the performance of the WPT system. Second, the model of supercapacitor is applied to the WPT system and the relationship between equivalent load resistances of supercapacitor and charging time is analyzed in detail. Then, a Buck converter with proportional integral (PI) controller is implemented on the secondary side to maintain constant charging current for the variable load. Finally, the proposed design is verified by experiments. Constant charging current of 31.5 A across transfer distance of 15 cm is achieved. The peak transfer power and system efficiency are 2.86 kW and 88.05%, respectively.

Question: How to charge a supercapacitor?


Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm. In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.

Question: How to select a small number of informative genes from thousands of genes that may contribute in identifying cancers?


A Meta-Framework for Modeling the Human Reading Process in Sentiment Analysis. This article introduces a sentiment analysis approach that adopts the way humans read, interpret, and extract sentiment from text. Our motivation builds on the assumption that human interpretation should lead to the most accurate assessment of sentiment in text. We call this automated process Human Reading for Sentiment (HRS). Previous research in sentiment analysis has produced many frameworks that can fit one or more of the HRS aspects; however, none of these methods has addressed them all in one approach. HRS provides a meta-framework for developing new sentiment analysis methods or improving existing ones. The proposed framework provides a theoretical lens for zooming in and evaluating aspects of any sentiment analysis method to identify gaps for improvements towards matching the human reading process. Key steps in HRS include the automation of humans low-level and high-level cognitive text processing. This methodology paves the way towards the integration of psychology with computational linguistics and machine learning to employ models of pragmatics and discourse analysis for sentiment analysis. HRS is tested with two state-of-the-art methods; one is based on feature engineering, and the other is based on deep learning. HRS highlighted the gaps in both methods and showed improvements for both.

Question: What is the difference between feature engineering and deep learning?


Supervised Descent Method and Its Applications to Face Alignment. Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved through a nonlinear optimization method. It is generally accepted that 2nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, 2nd order descent methods have two main drawbacks: (1) The function might not be analytically differentiable and numerical approximations are impractical. (2) The Hessian might be large and not positive definite. To address these issues, this paper proposes a Supervised Descent Method (SDM) for minimizing a Non-linear Least Squares (NLS) function. During training, the SDM learns a sequence of descent directions that minimizes the mean of NLS functions sampled at different points. In testing, SDM minimizes the NLS objective using the learned descent directions without computing the Jacobian nor the Hessian. We illustrate the benefits of our approach in synthetic and real examples, and show how SDM achieves state-of-the-art performance in the problem of facial feature detection. The code is available at www.humansensing.cs. cmu.edu/intraface.

Question: What is the supervised descent method?


Input feature selection for classification problems. Feature selection plays an important role in classifying systems such as neural networks (NNs). We use a set of attributes which are relevant, irrelevant or redundant and from the viewpoint of managing a dataset which can be huge, reducing the number of attributes by selecting only the relevant ones is desirable. In doing so, higher performances with lower computational effort is expected. In this paper, we propose two feature selection algorithms. The limitation of mutual information feature selector (MIFS) is analyzed and a method to overcome this limitation is studied. One of the proposed algorithms makes more considered use of mutual information between input attributes and output classes than the MIFS. What is demonstrated is that the proposed method can provide the performance of the ideal greedy selection algorithm when information is distributed uniformly. The computational load for this algorithm is nearly the same as that of MIFS. In addition, another feature selection algorithm using the Taguchi method is proposed. This is advanced as a solution to the question as to how to identify good features with as few experiments as possible. The proposed algorithms are applied to several classification problems and compared with MIFS. These two algorithms can be combined to complement each other's limitations. The combined algorithm performed well in several experiments and should prove to be a useful method in selecting features for classification problems.

Question: What is the difference between mutual information and mutual information feature selector?


An analysis of 3D point cloud reconstruction from light field images. Current methodologies for the generation of 3D point cloud from real world scenes rely upon a set of 2D images capturing the scene from several points of view. Novel plenoptic cameras sample the light field crossing the main camera lens creating a light field image. The information available in a plenoptic image must be processed in order to render a view or create the depth map of the scene. This paper analyses a method for the reconstruction of 3D models. The reconstruction of the model is obtained from a single image shot. Exploiting the properties of plenoptic images, a point cloud is generated and compared with a point cloud of the same object but generated with a different plenoptic camera.

Question: How to reconstruct a 3D model from a single image?


Wind Turbine Structural Health Monitoring : A Short Investigation Based on SCADA Data. The use of offshore wind farms has been growing in recent years, as steadier and higher wind speeds can be generally found over water compared to land. Moreover, as human activities tend to complicate the construction of land wind farms, offshore locations, which can be found more easily near densely populated areas, can be seen as an attractive choice. However, the cost of an offshore wind farm is relatively high, and therefore their reliability is crucial if they ever need to be fully integrated into the energy arena. As wind turbines have become more complex, efficient, and expensive structures, they require more sophisticated monitoring systems, especially in offshore sites where the financial losses due to failure could be substantial. This paper presents the preliminary analysis of supervisor control and data acquisition (SCADA) extracts from the Lillgrund wind farm for the purposes of structural health monitoring. A machine learning approach is applied in order to produce individual power curves, and then predict measurements of the power produced of each wind turbine from the measurements of the other wind turbines in the farm. A comparison between neural network and Gaussian process regression is also made.

Question: What is the wind turbine structural health monitoring?


Standard Compliant Hazard and Threat Analysis for the Automotive Domain. The automotive industry has successfully collaborated to release the ISO 26262 standard for developing safe software for cars. The standard describes in detail how to conduct hazard analysis and risk assessments to determine the necessary safety measures for each feature. However, the standard does not concern threat analysis for malicious attackers or how to select appropriate security countermeasures. We propose to apply ISO 27001 for this purpose and show how it can be applied together with ISO 26262. We show how ISO 26262 documentation can be re-used and enhanced to satisfy the analysis and documentation demands of the ISO 27001 standard. We illustrate our approach based on an electronic steering column lock system.

Question: How to apply ISO 27001 to ISO 26262?


Ford Campus vision and lidar data set. This paper describes a data set collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS-LV) and consumer (Xsens MTi-G) inertial measurement unit (IMU), a Velodyne 3D-lidar scanner, two push-broom forward looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system. Here we present the time-registered data from these sensors mounted on the vehicle, collected while driving the vehicle around the Ford Research campus and downtown Dearborn, Michigan during NovemberDecember 2009. The vehicle path trajectory in these data sets contain several large and small-scale loop closures, which should be useful for testing various state of the art computer vision and simultaneous localization and mapping (SLAM) algorithms. Fig. 1. The modified Ford F-250 pickup truck.

Question: What is the size of the ford campus?


Sustainable construction: construction and demolition waste reconsidered.. Construction activity in Europe has increased substantially in the past decade. Likewise, there has also been a commensurate rise in the generation of construction and demolition waste (C&DW). This, together with the fact that in many European countries the rate of recycling and reuse of C&DW is still quite low has engendered a serious environmental problem and a motivation to develop strategies and management plans to solve it. Due to its composition, there is a significant potential to reuse and/or recycle C&DW, and thereby, contribute to improving the sustainability of construction and development, but practical procedures are not yet widely known or practiced in the construction industry. This article (a) summarizes the different applications that are presently practiced to optimize the recovery and/or application of C&DW for reuse, and (b) proposes various measures and strategies to improve the processing of this waste. The authors suggest that to enhance environmental effectiveness, a conscious and comprehensive C&DW management plan should be implemented in each jurisdiction. More precisely, this study presents a holistic approach towards C&DW management, through which environmental benefits can be achieved through the application of new construction methods that can contribute to sustainable growth.

Question: What is the difference between sustainable construction and sustainable development?


The Multidimensional Work Motivation Scale : Validation evidence in seven languages and nine countries. The Multidimensional Work Motivation Scale: Validation evidence in seven languages and nine countries Marylène Gagné, Jacques Forest, Maarten Vansteenkiste, Laurence Crevier-Braud, Anja Van den Broeck, Ann Kristin Aspeli, Jenny Bellerose, Charles Benabou, Emanuela Chemolli, Stefan Tomas Güntert, Hallgeir Halvari, Devani Laksmi Indiyastuti, Peter A. Johnson, Marianne Hauan Molstad, Mathias Naudin, Assane Ndao, Anja Hagen Olafsen, Patrice Roussel, Zheni Wang & Cathrine Westbye a School of Psychology, University of Western Australia, Perth, Australia b Department of Organization and Human Resource Management, UQAM School of Management Sciences, Université du Québec à Montréal, Montréal, Canada c Department of Developmental, Personality and Social Psychology, University of Gent, Gent, Belgium d Department of Psychology, Université du Québec à Montréal, Montréal, Canada e Department of Economy and Management, Hogeschool Universiteit Brussel, Brussels, and KU Leuven, Leuven, Belgium f Faculty of Economics and Social Science, Høgskolen i Buskerud og Vestfold, Kongsberg, Norway g Department of Psychology, Université de Montréal, Montréal, Canada h Psychology of Work in Organization and Society Research Group, Swiss Federal Institute of Technology, Zurich, Switzerland i Faculty of Economics and Business, Universitas Gadjah Mada, Yogyakarta, Indonesia j Institute of Work Psychology, University of Sheffield, Sheffield, United Kingdom k International Institute of Commerce and Development, Université Panthéon Assas — Paris II, Paris, France l Faculty of Economics and Management Sciences, Université Cheikh Anta DIOP, Dakkar, Sénégal m Department of Management and Human Resources, Université Toulouse 1, Toulouse, France Published online: 05 Feb 2014.

Question: What is the multidimensional work motivation scale?


Semi-Supervised Nonnegative Matrix Factorization. Nonnegative matrix factorization (NMF) is a popular method for low-rank approximation of nonnegative matrix, providing a useful tool for representation learning that is valuable for clustering and classification. When a portion of data are labeled, the performance of clustering or classification is improved if the information on class labels is incorporated into NMF. To this end, we present semi-supervised NMF (SSNMF), where we jointly incorporate the data matrix and the (partial) class label matrix into NMF. We develop multiplicative updates for SSNMF to minimize a sum of weighted residuals, each of which involves the nonnegative 2-factor decomposition of the data matrix or the label matrix, sharing a common factor matrix. Experiments on document datasets and EEG datasets in BCI competition confirm that our method improves clustering as well as classification performance, compared to the standard NMF, stressing that semi-supervised NMF yields semi-supervised feature extraction.

Question: What is a nonnegative matrix factorization?


Apprenticeship learning for motion planning with application to parking lot navigation. Motion and path-planning algorithms often use complex cost functions for both global navigation and local smoothing of trajectories. Obtaining good results typically requires carefully hand-engineering the trade-offs between different terms in the cost function. In practice, it is often much easier to demonstrate a few good trajectories. In this paper, we describe an efficient algorithm which - when given access to a few trajectory demonstrations - can automatically infer good trade-offs between the different costs. In our experiments, we apply our algorithm to the problem of navigating a robotic car in a parking lot.

Question: How to navigate a car in a parking lot?


Compact and power-efficient 5 GHz full-duplex design utilizing the 180° ring hybrid coupler. This paper presents a compact and power-efficient 5 Ghz in-band full-duplex (FD) design in ANSYS HFSS using the 180-degree ring hybrid coupler. The proposed design achieves an excellent isolation of 57dB by taking advantage of destructive interference between two radiating antennas attached to the coupler, leading to a large reduction in self-interference. The design is passive and hence overcomes additional power requirement for adaptive channel estimation. In addition, it has a very workable physical size for the desired frequency of operation. The proposed FD design is therefore compact and power-efficient, which can be used in mobile devices, such as cell phones or tablet/phablet devices for a more flexible and greater contention of scarce RF resources.

Question: What is the frequency of operation?


Friendlink: Link prediction in social networks via bounded local path traversal. Online social networks (OSNs) like Facebook, Myspace, and Hi5 have become popular, because they allow users to easily share content or expand their social circle. OSNs recommend new friends to registered users based on local graph features (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a bounded length, based on the "algorithmic small world hypothesis". As a result, we are able to provide more accurate and faster friend recommendations. We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using two real data sets (Hi5 and Epinions). Our experimental results show that our FriendLink algorithm outperforms other approaches in terms of effectiveness and efficiency in both real data sets.

Question: What is the "algorithmic small world hypothesis"?


Dynamic 2D/3D registration for the Kinect. Image and geometry registration algorithms are essential components of many computer graphics and computer vision systems. With recent technological advances in RGB-D sensors, robust algorithms that combine 2D image and 3D geometry registration have become an active area of research.  This course introduces the basics of 2D/3D registration algorithms and provides theoretical explanations and practical tools for designing computer vision and computer graphics systems based on RGB-D devices such as the Microsoft Kinect or Asus Xtion Live. To illustrate the theory and demonstrate practical relevance, the course briefly discusses three applications: rigid scanning, non-rigid modeling, and real-time face tracking.

Question: What is the Kinect?


A New Distance-weighted k-nearest Neighbor Classifier. In this paper, we develop a novel Distance-weighted k -nearest Neighbor rule (DWKNN), using the dual distance-weighted function. The proposed DWKNN is motivated by the sensitivity problem of the selection of the neighborhood size k that exists in k -nearest Neighbor rule (KNN), with the aim of improving classification performance. The experiment results on twelve real data sets demonstrate that our proposed classifier is robust to different choices of k to some degree, and yields good performance with a larger optimal k, compared to the other state-of-art KNN-based methods.

Question: What is the distance-weighted k-nearest neighbor classifier?


Syntax-Directed Variational Autoencoder for Structured Data. Deep generative models have been enjoying success in modeling continuous data. However it remains challenging to capture the representations for discrete structures with formal grammars and semantics, e.g., computer programs and molecular structures. How to generate both syntactically and semantically correct data still remains largely an open problem. Inspired by the theory of compiler where the syntax and semantics check is done via syntax-directed translation (SDT), we propose a novel syntax-directed variational autoencoder (SD-VAE) by introducing stochastic lazy attributes. This approach converts the offline SDT check into on-the-fly generated guidance for constraining the decoder. Comparing to the state-of-the-art methods, our approach enforces constraints on the output space so that the output will be not only syntactically valid, but also semantically reasonable. We evaluate the proposed model with applications in programming language and molecules, including reconstruction and program/molecule optimization. The results demonstrate the effectiveness in incorporating syntactic and semantic constraints in discrete generative models, which is significantly better than current state-of-the-art approaches.

Question: What is the difference between a variational autoencoder and a generative adversarial network?


Broadband Reflectarray Antenna Using Subwavelength Elements Based on Double Square Meander-Line Rings. A linearly polarized broadband reflectarray is presented employing a novel single layer subwavelength phase shifting element. The size of the element is a fifth of a wavelength at the center frequency of 10 GHz and the element consists of double concentric square rings of meander lines. By changing the length of the meander line, a 420° phase variation range is achieved at the center frequency. This characteristic makes the proposed configuration unique, as most of the reported subwavelength reflectarray elements can only realize a phase range far less than 360°. In addition, the slope of the phase response remains almost constant from 9 to 11 GHz, demonstrating a broadband property. A $48 \times 48$-element reflectarray antenna is simulated, fabricated, and measured. Good agreement is obtained between simulated and measured results. A measured 1.5-dB gain bandwidth of 18% and 56.5% aperture efficiency is achieved.

Question: What is a reflectarray antenna?


Trajectory pattern mining. The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we move towards this direction and develop an extension of the sequential pattern mining paradigm that analyzes the trajectories of moving objects. We introduce trajectory patterns as concise descriptions of frequent behaviours, in terms of both space (i.e., the regions of space visited during movements) and time (i.e., the duration of movements). In this setting, we provide a general formal statement of the novel mining problem and then study several different instantiations of different complexity. The various approaches are then empirically evaluated over real data and synthetic benchmarks, comparing their strengths and weaknesses.

Question: What is trajectory pattern mining?


Learning Filter Banks Using Deep Learning For Acoustic Signals. Designing appropriate features for acoustic event recognition tasks is an active field of research. Expressive features should both improve the performance of the tasks and also be interpret-able. Currently, heuristically designed features based on the domain knowledge requires tremendous effort in hand-crafting, while features extracted through deep network are difficult for human to interpret. In this work, we explore the experience guided learning method for designing acoustic features. This is a novel hybrid approach combining both domain knowledge and purely data driven feature designing. Based on the procedure of log Mel-filter banks, we design a filter bank learning layer. We concatenate this layer with a convolutional neural network (CNN) model. After training the network, the weight of the filter bank learning layer is extracted to facilitate the design of acoustic features. We smooth the trained weight of the learning layer and re-initialize it in filter bank learning layer as audio feature extractor. For the environmental sound recognition task based on the Urbansound8K dataset [1], the experience guided learning leads to a 2% accuracy improvement compared with the fixed feature extractors (the log Mel-filter bank). The shape of the new filter banks are visualized and explained to prove the effectiveness of the feature design process.

Question: What is the urbansound8k dataset?


Bartendr: a practical approach to energy-aware cellular data scheduling. Cellular radios consume more power and suffer reduced data rate when the signal is weak. According to our measurements, the communication energy per bit can be as much as 6x higher when the signal is weak than when it is strong. To realize energy savings, applications must preferentially communicate when the signal is strong, either by deferring non-urgent communication or by advancing anticipated communication to coincide with periods of strong signal. Allowing applications to perform such scheduling requires predicting signal strength, so that opportunities for energy-efficient communication can be anticipated. Furthermore, such prediction must be performed at little energy cost.  In this paper, we make several contributions towards a practical system for energy-aware cellular data scheduling called Bartendr. First, we establish, via measurements, the relationship between signal strength and power consumption. Second, we show that location alone is not sufficient to predict signal strength and motivate the use of tracks to enable effective prediction. Finally, we develop energy-aware scheduling algorithms for different workloads - syncing and streaming - and evaluate these via simulation driven by traces obtained during actual drives, demonstrating energy savings of up to 60%. Our experiments have been performed on four cellular networks across two large metropolitan areas, one in India and the other in the U.S.

Question: How much energy does it take to sync a phone?


Question: What is the process of human desire inference?


True Random Number Generator Embedded in Reconfigurable Hardware. This paper presents a new True Random Number Generator (TRNG) based on an analog Phase-Locked Loop (PLL) implemented in a digital Altera Field Programmable Logic Device (FPLD). Starting with an analysis of the one available on chip source of randomness the PLL synthesized low jitter clock signal, a new simple and reliable method of true randomness extraction is proposed. Basic assumptions about statistical properties of jitter signal are confirmed by testing of mean value of the TRNG output signal. The quality of generated true random numbers is confirmed by passing standard NIST statistical tests. The described TRNG is tailored for embedded System-On-a-ProgrammableChip (SOPC) cryptographic applications and can provide a good quality true random bit-stream with throughput of several tens of kilobits per second. The possibility of including the proposed TRNG into a SOPC design significantly increases the system security of embedded cryptographic hardware.

Question: What is a reconfigurable hardware?


Low-power energy harvester for wiegand transducers. This paper discusses the use of Wiegand magnetic sensors as energy harvesters for powering low-power electronic equipments. A Wiegand device typically releases a ~10 μs voltage pulse several volts wide when subject to an external, time-varying magnetic field. Due to the sharpness of the magnetic transition, pulse generation occurs regardless of how slow the magnetic field variation is, an attractive feature which enables its use in energy harvesting scenarios even when low-frequency sources are considered. The paper first identifies the theoretical conditions for maximum energy extraction. An efficient energy harvesting circuit is then proposed which interfaces the Wiegand source to a fixed DC voltage provided, for instance, by a rechargeable Lithium-Ion battery. Simulations and experimental results are provided supporting the developed theoretical framework and the effectiveness of the proposed implementation.

Question: What is a wiegand transducer?


Using deep learning for short text understanding. Classifying short texts to one category or clustering semantically related texts is challenging, and the importance of both is growing due to the rise of microblogging platforms, digital news feeds, and the like. We can accomplish this classifying and clustering with the help of a deep neural network which produces compact binary representations of a short text, and can assign the same category to texts that have similar binary representations. But problems arise when there is little contextual information on the short texts, which makes it difficult for the deep neural network to produce similar binary codes for semantically related texts. We propose to address this issue using semantic enrichment. This is accomplished by taking the nouns, and verbs used in the short texts and generating the concepts and co-occurring words with the help of those terms. The nouns are used to generate concepts within the given short text, whereas the verbs are used to prune the ambiguous context (if any) present in the text. The enriched text then goes through a deep neural network to produce a prediction label for that short text representing it's category.

Question: How to classify short texts using deep learning?


Block-Based Neural Networks for Personalized ECG Signal Classification. This paper presents evolvable block-based neural networks (BbNNs) for personalized ECG heartbeat pattern classification. A BbNN consists of a 2-D array of modular component NNs with flexible structures and internal configurations that can be implemented using reconfigurable digital hardware such as field-programmable gate arrays (FPGAs). Signal flow between the blocks determines the internal configuration of a block as well as the overall structure of the BbNN. Network structure and the weights are optimized using local gradient-based search and evolutionary operators with the rates changing adaptively according to their effectiveness in the previous evolution period. Such adaptive operator rate update scheme ensures higher fitness on average compared to predetermined fixed operator rates. The Hermite transform coefficients and the time interval between two neighboring R-peaks of ECG signals are used as inputs to the BbNN. A BbNN optimized with the proposed evolutionary algorithm (EA) makes a personalized heartbeat pattern classifier that copes with changing operating environments caused by individual difference and time-varying characteristics of ECG signals. Simulation results using the Massachusetts Institute of Technology/Beth Israel Hospital (MIT-BIH) arrhythmia database demonstrate high average detection accuracies of ventricular ectopic beats (98.1%) and supraventricular ectopic beats (96.6%) patterns for heartbeat monitoring, being a significant improvement over previously reported electrocardiogram (ECG) classification results.

Question: What is the hermite transform?


Accelerometer-based tilt estimation of a rigid body with only rotational degrees of freedom. An estimation algorithm is developed for determining pitch and roll angles (tilt) of a rigid body fixed at a pivot point using multiple accelerometers. The estimate is independent of the rigid body dynamics; the method is applicable both in static conditions and for any dynamic motion of the body. No dynamic model is required for the estimator; only the mounting positions of the sensors need to be known. The proposed estimator is the optimal linear estimate in a least-squares sense if knowledge of the system dynamics is not used. The estimate may be used as a basis for further filtering and fusion techniques, such as sensor fusion with rate gyro data. The estimation algorithm is applied to the problem of state estimation for the Balancing Cube, a rigid structure that can actively balance on its corners. Experimental results are provided.

Question: How to estimate the tilt of a rigid body?


An Improved Neural Segmentation Method Based on U-NET. 摘要:局部麻醉技术作为现代社会最为常见的麻醉技 术,具有安全性高,副作用小等优势。通过分析超声 图像,分割图像中的神经区域,有助于提升局部麻醉 手术的成功率。卷积神经网络作为目前最为高效的图 像处理方法之一,具有准确性高,预处理少等优势。 通过卷积神经网络来对超声图像中的神经区域进行分 割,速度更快,准确性更高。目前已有的图像分割网 络结构主要有U-NET[1],SegNet[2]。U-NET网络训练 时间短,训练参数较少,但深度略有不足。SegNet 网 络层次较深,训练时间过长,但对训练样本需求较多 由于医学样本数量有限,会对模型训练产生一定影响。 本文我们将采用一种改进后的 U-NET 网络结构来对超 声图像中的神经区域进行分割,改进后的 U-NET 网络 结构加入的残差网络(residual network)[3],并对每一层 结果进行规范化(batch normalization)处理[4]。实验结 果表明,与传统的U-NET网络结构相比,改进后的UNET 网络分割效果具有显著提升,训练时间略有增加。 同时,将改进后的 U-NET 网络与 SegNet 网络结构进 行对比,发现改进后的 U-net 无论从训练速度还是从 分割效果均高于 SegNet 网络结构。改进后的 U-net 网 络结构在神经识别方面具有很好的应用场景。

Question: What is the difference between U-net and SegNet?


Molecular assembly on two-dimensional materials.. Molecular self-assembly is a well-known technique to create highly functional nanostructures on surfaces. Self-assembly on two-dimensional (2D) materials is a developing field driven by the interest in functionalization of 2D materials in order to tune their electronic properties. This has resulted in the discovery of several rich and interesting phenomena. Here, we review this progress with an emphasis on the electronic properties of the adsorbates and the substrate in well-defined systems, as unveiled by scanning tunneling microscopy. The review covers three aspects of the self-assembly. The first one focuses on non-covalent self-assembly dealing with site-selectivity due to inherent moiré pattern present on 2D materials grown on substrates. We also see that modification of intermolecular interactions and molecule-substrate interactions influences the assembly drastically and that 2D materials can also be used as a platform to carry out covalent and metal-coordinated assembly. The second part deals with the electronic properties of molecules adsorbed on 2D materials. By virtue of being inert and possessing low density of states near the Fermi level, 2D materials decouple molecules electronically from the underlying metal substrate and allow high-resolution spectroscopy and imaging of molecular orbitals. The moiré pattern on the 2D materials causes site-selective gating and charging of molecules in some cases. The last section covers the effects of self-assembled, acceptor and donor type, organic molecules on the electronic properties of graphene as revealed by spectroscopy and electrical transport measurements. Non-covalent functionalization of 2D materials has already been applied for their application as catalysts and sensors. With the current surge of activity on building van der Waals heterostructures from atomically thin crystals, molecular self-assembly has the potential to add an extra level of flexibility a

Question: What is the difference between a 2D material and a 2D material?


Automatic Array Alignment in Data-Parallel Programs. Data-parallel languages like Fortran 90 express parallelism in the form of operations on data aggregates such as arrays. Misalignment of the operands of an array operation can reduce program performance on a distributed-memory parallel machine by requiring nonlocal data accesses. Determining array alignments that reduce communication is therefore a key issue in compiling such languages. We present a framework for the automatic determination of array alignments in data-parallel languages such as Fortran 90. Our language model handles array sectioning, reductions, spreads, transpositions, and masked operations. We decompose alignment functions into three constituents: axis, stride, and offset. For each of these subproblems, we show how to solve the alignment problem for a basic block of code, possibly containing common subexpressions. Alignments are generated for all array objects in the code, both named program variables and intermediate results. The alignments obtained by our algorithms are more general than those provided by the "owner-computes" rule. Finally, we present some ideas for dealing with control flow, replication, and dynamic alignments that depend on loop induction variables.

Question: How to determine array alignments in data-parallel languages?


Privacy-Preserving Online Task Assignment in Spatial Crowdsourcing with Untrusted Server. With spatial crowdsourcing (SC), requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. However, current solutions require the locations of the workers and/or the tasks to be disclosed to untrusted parties (SC server) for effective assignments of tasks to workers. In this paper we propose a framework for assigning tasks to workers in an online manner without compromising the location privacy of workers and tasks. We perturb the locations of both tasks and workers based on geo-indistinguishability and then devise techniques to quantify the probability of reachability between a task and a worker, given their perturbed locations. We investigate both analytical and empirical models for quantifying the worker-task pair reachability and propose task assignment strategies that strike a balance among various metrics such as the number of completed tasks, worker travel distance and system overhead. Extensive experiments on real-world datasets show that our proposed techniques result in minimal disclosure of task locations and no disclosure of worker locations without significantly sacrificing the total number of assigned tasks.

Question: How to assign tasks to workers in an online manner without compromising the location privacy of workers and tasks?


The Advanced Health and Disaster Aid Network: A Light-Weight Wireless Medical System for Triage. Advances in semiconductor technology have resulted in the creation of miniature medical embedded systems that can wirelessly monitor the vital signs of patients. These lightweight medical systems can aid providers in large disasters who become overwhelmed with the large number of patients, limited resources, and insufficient information. In a mass casualty incident, small embedded medical systems facilitate patient care, resource allocation, and real-time communication in the advanced health and disaster aid network (AID-N). We present the design of electronic triage tags on lightweight, embedded systems with limited memory and computational power. These electronic triage tags use noninvasive, biomedical sensors (pulse oximeter, electrocardiogram, and blood pressure cuff) to continuously monitor the vital signs of a patient and deliver pertinent information to first responders. This electronic triage system facilitates the seamless collection and dissemination of data from the incident site to key members of the distributed emergency response community. The real-time collection of data through a mesh network in a mass casualty drill was shown to approximately triple the number of times patients that were triaged compared with the traditional paper triage system.

Question: What is a mesh network?


Informing Pedagogical Action: Aligning Learning Analytics With Learning Design. This article considers the developing field of learning analytics and argues that to move from small-scale practice to broad scale applicability, there is a need to establish a contextual framework that helps teachers interpret the information that analytics provides. The article presents learning design as a form of documentation of pedagogical intent that can provide the context for making sense of diverse sets of analytic data. We investigate one example of learning design to explore how broad categories of analytics—which we call checkpoint and process analytics—can inform the interpretation of outcomes from a learning design and facilitate pedagogical action.

Question: What is learning design?


Learning Pulse: Using Wearable Biosensors and Learning Analytics to Investigate and Predict Learning Success in Self-regulated Learning. The Learning Pulse study aims to explore whether physiological data such as heart rate and step count correlate with learning activity data and whether they are good predictors for learning success during self-regulated learning. To verify this hypothesis an experiment was set up involving eight doctoral students at the Open University of the Netherlands. Through wearable sensors, heart rate and step count were constantly monitored and learning activity data were collected. All data were stored in a Learning Record Store in xAPI format. Additionally, with an Activity Rating Tool, the participants rated their learning and working experience by indicating the perceived levels of productivity, stress, challenge and abilities along with the type of activity. These human annotated labels can be used for supervising machine learning algorithms to discriminate the successful learning moments from the unsuccessful ones and eventually discover the attributes that most influence the learning process.

Question: What is the Learning Pulse study?


Video enhancement using per-pixel virtual exposures. We enhance underexposed, low dynamic range videos by adaptively and independently varying the exposure at each photoreceptor in a post-process. This virtual exposure is a dynamic function of both the spatial neighborhood and temporal history at each pixel. Temporal integration enables us to expand the image's dynamic range while simultaneously reducing noise. Our non-linear exposure variation and denoising filters smoothly transition from temporal to spatial for moving scene elements. Our virtual exposure framework also supports temporally coherent per frame tone mapping. Our system outputs restored video sequences with significantly reduced noise, increased exposure time of dark pixels, intact motion, and improved details.

Question: How do you enhance videos?


Novel microstrip patch antenna design employing flexible PVC substrate suitable for defence and radio-determination applications. This paper propounds a new flexible material, which has been designed by employing flexible Poly Vinyl Chloride (PVC) having dielectric constant εr= 2.7 as substrate. The projected antenna design states that a circular shaped copper patch with flag shaped slot on it has been deployed over the hexagonal PVC substrate of thickness 1 mm. Then ground plane is partially minimized and slotted to revamp the antenna performance. The proposed antenna operates within frequency range of 7.2032GHz to 7.9035GHz while resonating at 7.55GHz. The proposed antenna design has minimal return loss of −55.226dB with the impedance bandwidth of 700 MHz, gain of 4.379dB and directivity of 4.111dBi. The antenna has VSWR less than maximum acceptable value of 2. The projected antenna design can be suitably employed for UWB, radio-determination applications (7.235GHz to 7.25GHz), naval, defence systems (7.25GHz – 7.3GHz) and weather satellite applications (7.75GHz to 7.90GHz). The antenna has been designed in CST Microwave Studio 2014. The proposed antenna has been fabricated and tested using E5071C Network Analyser and anechoic chamber. It has been observed that the stimulated results closely match with the experimental results.

Question: What is the frequency range of the proposed antenna?


The structure of musical preferences: a five-factor model.. Music is a cross-cultural universal, a ubiquitous activity found in every known human culture. Individuals demonstrate manifestly different preferences in music, and yet relatively little is known about the underlying structure of those preferences. Here, we introduce a model of musical preferences based on listeners' affective reactions to excerpts of music from a wide variety of musical genres. The findings from 3 independent studies converged to suggest that there exists a latent 5-factor structure underlying music preferences that is genre free and reflects primarily emotional/affective responses to music. We have interpreted and labeled these factors as (a) a Mellow factor comprising smooth and relaxing styles; (b) an Unpretentious factor comprising a variety of different styles of sincere and rootsy music such as is often found in country and singer-songwriter genres; (c) a Sophisticated factor that includes classical, operatic, world, and jazz; (d) an Intense factor defined by loud, forceful, and energetic music; and (e) a Contemporary factor defined largely by rhythmic and percussive music, such as is found in rap, funk, and acid jazz. The findings from a fourth study suggest that preferences for the MUSIC factors are affected by both the social and the auditory characteristics of the music.

Question: What is the structure of musical preferences?


Caraoke: An E-Toll Transponder Network for Smart Cities. Electronic toll collection transponders, e.g., E-ZPass, are a widely-used wireless technology. About 70% to 89% of the cars in US have these devices, and some states plan to make them mandatory. As wireless devices however, they lack a basic function: a MAC protocol that prevents collisions. Hence, today, they can be queried only with directional antennas in isolated spots. However, if one could interact with e-toll transponders anywhere in the city despite collisions, it would enable many smart applications. For example, the city can query the transponders to estimate the vehicle flow at every intersection. It can also localize the cars using their wireless signals, and detect those that run a red-light. The same infrastructure can also deliver smart street-parking, where a user parks anywhere on the street, the city localizes his car, and automatically charges his account. This paper presents Caraoke, a networked system for delivering smart services using e-toll transponders. Our design operates with existing unmodified transponders, allowing for applications that communicate with, localize, and count transponders, despite wireless collisions. To do so, Caraoke exploits the structure of the transponders' signal and its properties in the frequency domain. We built Caraoke reader into a small PCB that harvests solar energy and can be easily deployed on street lamps. We also evaluated Caraoke on four streets on our campus and demonstrated its capabilities.

Question: How does caraoke work?


CHARADE: Remote Control of Objects Using Free-Hand Gestures. This paper presents an application that uses hand gesture input to control a computer while giving a presentation. In order to develop a prototype of this application, we have defined an interaction model, a notation for gestures, and a set of guidelines to design gestural command sets. This works aims to define interaction styles that work in computerized reality environments. In our application, gestures are used for interacting with the computer as well as for communicating with other people or operating other devices.

Question: What is a gesture?


CAPTCHA Design: Color, Usability, and Security. Most user interfaces use color, which can greatly enhance their design. Because the use of color is typically a usability issue, it rarely causes security failures. However, using color when designing CAPTCHAs, a standard security technology that many commercial websites apply widely, can have an impact on usability and interesting but critical implications for security. Here, the authors examine some CAPTCHAs to determine whether their use of color negatively affects their usability, security, or both.

Question: What is a CAPTCHA?


Estimating Fingerprint Deformation. Fingerprint matching is affected by the nonlinear distortion introduced in fingerprint impressions during the image acquisition process. This nonlinear deformation causes fingerprint features such as minutiae points and ridge curves to be distorted in a complex manner. In this paper we develop an average deformation model for a fingerprint impression (baseline impression) by observing its relative distortion with respect to several other impressions of the same finger. The deformation is computed using a Thin Plate Spline (TPS) model that relies on ridge curve correspondences between image pairs. The estimated average deformation is used to distort the minutiae template of the baseline impression prior to matching. An index of deformation has been proposed to select the average deformation model with the least variability corresponding to a finger. Preliminary results indicate that the average deformation model can improve the matching performance of a fingerprint matcher.

Question: How to estimate fingerprint deformation?


Learning a convolutional neural network for non-uniform motion blur removal. In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.

Question: How to remove non-uniform motion blur?


DSP Implementation in the Loop of the Vector Control Drive of a Permanent Magnet Synchronous Machine. The aim of this work is to design and implement the FOC (Field Oriented Control) of the permanent magnet synchronous machine (PMSM) on DSP in Hardware In the Loop (HIL). The MATLAB /Simulink, the IDE Code Composer Studio and the DSP TMS320F28335 platform will be used to perform the simulation, the code generation and the HIL.  The obtained performances of this command (a speed response time of 10 ms and a zero static error vis-à-vis the setpoint and disruption) converge to those obtained with conventional simulation on SIMULINK

Question: How to implement a command in the loop of a PMSM?


Multi-Product Utility Maximization for Economic Recommendation. Basic economic relations such as substitutability and complementarity between products are crucial for recommendation tasks, since the utility of one product may depend on whether or not other products are purchased. For example, the utility of a camera lens could be high if the user possesses the right camera (complementarity), while the utility of another camera could be low because the user has already purchased one (substitutability). We propose \emph{multi-product utility maximization} (MPUM) as a general approach to recommendation driven by economic principles. MPUM integrates the economic theory of consumer choice with personalized recommendation, and focuses on the utility of \textit{sets} of products for individual users. MPUM considers what the users already have when recommending additional products. We evaluate MPUM against several popular recommendation algorithms on two real-world E-commerce datasets. Results confirm the underlying economic intuition, and show that MPUM significantly outperforms the comparison algorithms under top-K evaluation metrics.

Question: What is the economic theory of consumer choice?


Urban Computing: Concepts, Methodologies, and Applications. Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

Question: What is urban computing?


The Use of Alternative Social Networking Sites in Higher Educational Settings: A Case Study of the E-Learning Benefits of Ning in Education. Distance education as a primary means of instruction is expanding significantly at the college and university level. Simultaneously, the growth of social networking sites (SNS) including Facebook, LinkedIn, and MySpace is also rising among today's college students. An increasing number of higher education instructors are beginning to combine distance education delivery with SNSs. However, there is currently little research detailing the educational benefits associated with the use of SNSs. Non-commercial, education-based SNSs, such as Ning in Education, have been recently shown to build communities of practice and facilitate social presence for students enrolled in distance education courses. In order to evaluate the largely unexplored educational benefits of SNSs, we surveyed graduate students enrolled in distance education courses using Ning in Education, an education-based SNS, based on their attitudes toward SNSs as productive online tools for teaching and learning. The results of our study suggest that education-based SNSs can be used most effectively in distance education courses as a technological tool for improved online communications among students in higher distance education courses. Introduction The use of distance education courses as a primary instructional delivery option, especially in the higher education community, is expanding at an unprecedented rate. The 9.7% growth rate in the number of college and university students enrolled in at least one online class reported by Allen and Seaman (2007) significantly exceeded the 1.5% growth rate in the overall higher education student population during the same period. Simultaneously, the emergence and growth of commercial social networking sites (SNSs) such as Facebook, Friendster, LinkedIn, LiveJournal, and MySpace has been extensive and widespread (Boyd & Ellison,

Question: What is the growth rate of the number of college and university students enrolled in at least one online class?


Robust high-dimensional precision matrix estimation. The dependency structure of multivariate data can be analyzed using the covariance matrix Σ. In many fields the precision matrix Σ −1 is even more informative. As the sample covariance estimator is singular in high-dimensions, it cannot be used to obtain a precision matrix estimator. A popular high-dimensional estimator is the graphical lasso, but it lacks robustness. We consider the high-dimensional independent contamination model. Here, even a small percentage of contaminated cells in the data matrix may lead to a high percentage of contaminated rows. Downweighting entire observations, which is done by traditional robust procedures, would then results in a loss of information. In this paper, we formally prove that replacing the sample covariance matrix in the graphical lasso with an elementwise robust covariance matrix leads to an elementwise robust, sparse precision matrix estimator computable in high-dimensions. Examples of such elementwise robust covariance estimators are given. The final precision matrix estimator is positive definite, has a high breakdown point under elementwise contamination and can be computed fast.

Question: How to estimate a precision matrix in high-dimensions?


Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems. The study on human-computer conversation systems is a hot research topic nowadays. One of the prevailing methods to build the system is using the generative Sequence-to-Sequence (Seq2Seq) model through neural networks. However, the standard Seq2Seq model is prone to generate trivial responses. In this paper, we aim to generate a more meaningful and informative reply when answering a given question. We propose an implicit content-introducing method which incorporates additional information into the Seq2Seq model in a flexible way. Specifically, we fuse the general decoding and the auxiliary cue word information through our proposed hierarchical gated fusion unit. Experiments on real-life data demonstrate that our model consistently outperforms a set of competitive baselines in terms of BLEU scores and human evaluation.

Question: What is the purpose of this paper?


A tensorized logic programming language for large-scale data. We introduce a new logic programming language T-PRISM based on tensor embeddings. Our embedding scheme is a modification of the distribution semantics in PRISM, one of the state-of-the-art probabilistic logic programming languages, by replacing distribution functions with multidimensional arrays, i.e., tensors. T-PRISM consists of two parts: logic programming part and numerical computation part. The former provides flexible and interpretable modeling at the level of first order logic, and the latter part provides scalable computation utilizing parallelization and hardware acceleration with GPUs. Combing these two parts provides a remarkably wide range of high-level declarative modeling from symbolic reasoning to deep learning. To embody this programming language, we also introduce a new semantics, termed tensorized semantics, which combines the traditional least model semantics in logic programming with the embeddings of tensors. In T-PRISM, we first derive a set of equations related to tensors from a given program using logical inference, i.e., Prolog execution in a symbolic space and then solve the derived equations in a continuous space by TensorFlow. Using our preliminary implementation of T-PRISM, we have successfully dealt with a wide range of modeling. We have succeeded in dealing with real large-scale data in the declarative modeling. This paper presents a DistMult model for knowledge graphs using the FB15k and WN18 datasets.

Question: What is a tensor?


A novel booster antenna on flexible substrates for metal proximity NFC applications. In this article a novel booster antenna is proposed in order to increase the read range of a small (1.1×10-4 m2) metal mounted HF (13.56 MHz) RFID. The highly inductive coupling between the reader and booster, and between the RFID and booster is the main propagation method. The communication between the reader and RFID realized through the booster antenna. For this purpose two coils are connected in parallel to each other resonating at the same frequency. The greater coil is coupled to the reader antenna and the smaller coil is coupled to the RFID. The simulation and measurement results show the reliability of the proposed structure.

Question: How to increase the read range of a small metal mounted HF RFID?


An integrated approach to characterize genetic interaction networks in yeast metabolism. Although experimental and theoretical efforts have been applied to globally map genetic interactions, we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we i, quantitatively measured genetic interactions between ∼185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii, superposed the data on a detailed systems biology model of metabolism and iii, introduced a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigated the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy and gene dispensability. Last, we show the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.

Question: How do we quantify the genetic interactions in yeast?


Vehicle Tracking, Monitoring and Alerting System: A Review. The goal of this paper is to review the past work of vehicle tracking, monitoring and alerting system, to categorize various methodologies and identify new trends. Vehicle tracking, monitoring and alerting system is challenging problem. There are various challenges encounter in vehicle tracking, monitoring and alerting due to deficiency in proper real time vehicle location and problem of alerting system. GPS (Global Positioning System) is most widely used technology for vehicle tracking and keep regular monitoring of vehicle. The objective of tracking system is to manage and control the transport using GPS transreceiver to know the current location of vehicle. In number of system, RFID (Radio Frequency Identification) is chosen as one of technology implemented for bus monitoring system. GSM (Global System for Mobile Communication) is most widely used for alerting system. Alerting system is essential for providing the location and information about vehicle to passenger, owner or user.

Question: What is the difference between GPS and GSM?


Tracking-based deer vehicle collision detection using thermal imaging. Deer vehicle collision (DVC) is constantly a major safety issue for the driving on rural road. It is estimated that there are over 35,000 DVCs yearly in the US resulting in about 200 deaths and close to 4,000 reported property damages of one thousand dollars or more. This justifies many attempts trying to detect deer on road. However, very little success has been achieved. In order to reduce the number of DVCs, this work focused on the study of using an infrared thermal camera with tracking system to detect the presence of deer to avoid DVCs. The prototype consists of an infrared thermal temperature image grabbing and processing system, which includes an infrared thermal camera, a frame grabber, an image processing system and a motion tracking system, which includes two motors with their motion control system. By analyzing the infrared thermal images which are independent of visible light, the presence of an animal can be determined in either night or day time through pattern recognition and matching.

Question: How to detect deer using infrared thermal camera?


The Propulsion Committee Final Report and Recommendations to the 25 th ITTC 1.

Question: What is the 25 th ITTC?


Multiple Trench Split-gate SOI LDMOS Integrated With Schottky Rectifier. In this paper, a multiple trench split-gate silicon-on-insulator (SOI) lateral double-diffused MOSFET with a Schottky rectifier (MTS-SG-LDMOS) is proposed and its characteristics are studied using 2-D simulations. The new structure features double oxide trenches, a floating polysilicon split-gate, and a Schottky rectifier. Each oxide trench includes a vertical field plate which enhances the depletion of the drift region and modulates the bulk electric field. As the simulation results, when compared to the conventional SOI LDMOS (C-LDMOS), the breakdown voltage in the MTS-SG-LDMOS increases from 297 to 350 V, the specific on-state resistance (<inline-formula> <tex-math notation="LaTeX">${R} _{ \mathrm{\scriptscriptstyle ON},\mathsf {sp}}$ </tex-math></inline-formula>) decreases from 142.9 to 48.2 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot \mathsf {cm}^{{\mathsf {2}}}$ </tex-math></inline-formula>, and the gate–drain charge (<inline-formula> <tex-math notation="LaTeX">${Q} _{{\mathsf {gd}}}$ </tex-math></inline-formula>) decreases from 19 to 9 pC. Moreover, the reverse recovery time of the proposed structure shows a 73.6% reduction as compared to the C-LDMOS device.

Question: What is the breakdown voltage of the proposed structure?


A Compositional Interpretation of Biomedical Event Factuality. We propose a compositional method to assess the factuality of biomedical events extracted from the literature. The composition procedure relies on the notion of semantic embedding and a fine-grained classification of extrapropositional phenomena, including modality and valence shifting, and a dictionary based on this classification. The event factuality is computed as a product of the extra-propositional operators that have scope over the event. We evaluate our approach on the GENIA event corpus enriched with certainty level and polarity annotations. The results indicate that our approach is effective in identifying the certainty level component of factuality and is less successful in recognizing the other element, negative polarity.

Question: What is the compositional method to assess the factuality of biomedical events?


Comparing and Aligning Process Representations. Processes within organizations can be highly complex chains of inter-related steps, involving numerous stakeholders and information systems. Due to this complexity, having access to the right information is vital to the proper execution and effective management of an organization's business processes. A major challenge in this regard is that information on a single process is often spread out over numerous models, documents, and systems. This phenomenon results from efforts to provide a variety of process stakeholders with the information that is relevant to them, in a suitable format. However, this disintegration of process information also has considerable disadvantages for organizations. In particular, it can lead to severe maintenance issues, reduced execution efficiency, and negative effects on the quality of process results. Against this background, this doctoral thesis focuses on the spread of process information in organizations and, in particular, on the mitigation of the negative aspects associated with this phenomenon. The main contributions of this thesis are five techniques that focus on the alignment and comparison of process information from different informational artifacts. Each of these techniques tackles a specific scenario involving multiple informational artifacts that contain process information in different representation formats. Among others, we present automated techniques for the detection of inconsistencies between process models and textual process descriptions, the alignment of process performance measurements to process models, conformance-checking in the context of uncertainty, and the matching of process models through the analysis of event-log information. We demonstrate the efficacy and usefulness of these techniques through quantitative evaluations involving data obtained from real-world settings. Altogether, the presented work provides important contributions for the analysis, compa

Question: How can we compare and align process representations?


Effects of communication styles on acceptance of recommendations in intercultural collaboration. The objective of this study is to investigate the impact of culture and communication style (explicit versus implicit) on people's reactions on recommendations in intercultural collaboration. The experimental results from three intercultural collaboration teams were studied: Chinese-American, Chinese-German, and Chinese-Korean. The results indicate that Chinese participants showed more positive evaluations (i.e., higher trust, higher satisfaction, and more future collaboration intention) on the implicit advisor than American and German participants. Compared with Chinese participants, Korean participants accepted explicit recommendations more often and showed more positive evaluations on the explicit advisor. The results also show that when Chinese express recommendations in an explicit way, their recommendations were accepted more often and were more positively evaluated by cross-cultural partners.

Question: What is the impact of culture and communication style on acceptance of recommendations in intercultural collaboration?


Real-time lane detection and forward collision warning system based on stereo vision. This paper presents a real-time and robust lane detection and forward collision warning technique based on stereo cameras. First, obstacles image is obtained through stereo matching and UV-disparity segmentation algorithm. Then, Inverse Perspective Mapping(IPM) and Sobel filtering are conducted to generate a low-noise top view of the road by fusing the obstacles image and the original image. Next, Hough Transformation for the top view map is completed and the extreme points(poles) are calculated as the detected lanes according to the traffic lanes model. Besides, the host lane is selected or supplemented among all the detected lanes and the nearest obstacle in this host lane is detected for the forward collision warning. Experimental results on the public data set indicate that our method can work effectively and real-timely in the normal structured environment.

Question: What is the nearest obstacle in the host lane?


Monophyly of Primary Photosynthetic Eukaryotes: Green Plants, Red Algae, and Glaucophytes. Between 1 and 1.5 billion years ago, eukaryotic organisms acquired the ability to convert light into chemical energy through endosymbiosis with a Cyanobacterium (e.g.,). This event gave rise to "primary" plastids, which are present in green plants, red algae, and glaucophytes ("Plantae" sensu Cavalier-Smith). The widely accepted view that primary plastids arose only once implies two predictions: (1) all plastids form a monophyletic group, as do (2) primary photosynthetic eukaryotes. Nonetheless, unequivocal support for both predictions is lacking (e.g.,). In this report, we present two phylogenomic analyses, with 50 genes from 16 plastid and 15 cyanobacterial genomes and with 143 nuclear genes from 34 eukaryotic species, respectively. The nuclear dataset includes new sequences from glaucophytes, the less-studied group of primary photosynthetic eukaryotes. We find significant support for both predictions. Taken together, our analyses provide the first strong support for a single endosymbiotic event that gave rise to primary photosynthetic eukaryotes, the Plantae. Because our dataset does not cover the entire eukaryotic diversity (but only four of six major groups in), further testing of the monophyly of Plantae should include representatives from eukaryotic lineages for which currently insufficient sequence information is available.

Question: What is the monophyly of primary photosynthetic eukaryotes?


A deep neuro-fuzzy method for multi-label malware classification and fuzzy rules extraction. Modern malware represents an emerging threat not only to individual privacy, but also to organizational security. Over the last few decades, a number of malware families have been codified, differentiated by their behaviour and functionality. The majority of machine learning methods work well with the general benign-malicious classification yet fail to distinguish among many classes. Deep learning is known to be successful for such applications, Deep Neural Networks in particular. There is no way however to extract an understandable model in order to characterize each malware family separately. Thus, the goal of this paper is many-fold: we investigated multinomial malware categorization using static and dynamic behavioural features extracted from Windows Portable Executables (PE32) malware collection as of the end of 2015. Furthermore, we proposed a novel Deep Neuro-Fuzzy architecture, a Hybrid Intelligence approach capable of boosting the performance of conventional Neuro-Fuzzy method targeting rules extraction. It proved capable of achieving a 49.335% better result than classical Neuro-Fuzzy and 7.347% improvement over the same complexity Deep Neural Network (DNN). Moreover, we studied the Deep Neural Network behaviour when handling such non-trivial problems as multinomial malware categorization. We believe that this model contributes to Windows PE32 malware categorization by improving the similarity-based detection of zero-days malware. It can serve as a stepping stone for malware analysts to speed up the detection and dissemination.

Question: What is the difference between a deep neural network and a deep neuro-fuzzy architecture?


An Evaluation of Space Time Cube Representation of Spatiotemporal Patterns. Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a data set to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap, we report on a between-subjects experiment comparing novice users' error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions, the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the data set, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users analyzing complex spatiotemporal patterns.

Question: What is the space time cube representation?


Complications during root canal irrigation--literature review and case reports.. LITERATURE REVIEW AND CASE REPORTS: The literature concerning the aetiology, symptomatology and therapy of complications during root canal irrigation is reviewed. Three cases of inadvertent injection of sodium hypochlorite and hydrogen peroxide beyond the root apex are presented. Clinical symptoms are discussed, as well as preventive and therapeutic considerations.

Question: What are the complications of root canal irrigation?


Some Simple Effective Approximations to the 2-Poisson Model for Probabilistic Weighted Retrieval. The 2-Poisson model for term frequencies is used to suggest ways of incorporating certain variables in probabilistic models for information retrieval. The variables concerned are within-document term tkequency, document length, and within-query term frequency. Simple weighting functions are developed, and tested on the TREC test collection. Considerable performance improvements (over simple inverse collection frequency weighting) are demonstrated.

Question: What is the 2-poisson model for term frequencies?


Detail-preserving median based filters in image processing.

Question: What is a detail-preserving median based filter?


FMRI Visualization of Brain Activity during a Monetary Incentive Delay Task. Comparative studies have implicated striatal and mesial forebrain circuitry in the generation of autonomic, endocrine, and behavioral responses for incentives. Using blood oxygen level-dependent functional magnetic resonance imaging, we sought to visualize functional activation of these regions in 12 normal volunteers as they anticipated and responded for monetary incentives. Both individual and group analyses of time-series data revealed significant activation of striatal and mesial forebrain structures (including insula, caudate, putamen, and mesial prefrontal cortex) during trials involving both monetary rewards and punishments. In addition to these areas, during trials involving punishment, group analysis revealed activation foci in the anterior cingulate and thalamus. These results corroborate comparative studies which implicate striatal and mesial forebrain circuitry in the elaboration of incentive-driven behavior. This report also introduces a new paradigm for probing the functional integrity of this circuitry in humans.

Question: What is the difference between a reward and a punishment?


Coding of pleasant touch by unmyelinated afferents in humans. Pleasant touch sensations may begin with neural coding in the periphery by specific afferents. We found that during soft brush stroking, low-threshold unmyelinated mechanoreceptors (C-tactile), but not myelinated afferents, responded most vigorously at intermediate brushing velocities (1−10 cm s−1), which were perceived by subjects as being the most pleasant. Our results indicate that C-tactile afferents constitute a privileged peripheral pathway for pleasant tactile stimulation that is likely to signal affiliative social body contact.

Question: What is the most pleasant touch?


The UJI librarian robot. This paper describes the UJI Librarian Robot, a mobile manipulator that is able to autonomously locate a book in an ordinary library, and grasp it from a bookshelf, by using eye-in-hand stereo vision and force sensing. The robot is only provided with the book code, a library map and some knowledge about its logical structure and takes advantage of the spatio-temporal constraints and regularities of the environment by applying disparate techniques such as stereo vision, visual tracking, probabilistic matching, motion estimation, multisensor-based grasping, visual servoing and hybrid control, in such a way that it exhibits a robust and dependable performance. The system has been tested, and experimental results show how it is able to robustly locate and grasp a book in a reasonable time without human intervention.

Question: What is the name of the robot?


How useful are your comments?: analyzing and predicting youtube comments and comment ratings. An analysis of the social video sharing platform YouTube reveals a high amount of community feedback through comments for published videos as well as through meta ratings for these comments. In this paper, we present an in-depth study of commenting and comment rating behavior on a sample of more than 6 million comments on 67,000 YouTube videos for which we analyzed dependencies between comments, views, comment ratings and topic categories. In addition, we studied the influence of sentiment expressed in comments on the ratings for these comments using the SentiWordNet thesaurus, a lexical WordNet-based resource containing sentiment annotations. Finally, to predict community acceptance for comments not yet rated, we built different classifiers for the estimation of ratings for these comments. The results of our large-scale evaluations are promising and indicate that community feedback on already rated comments can help to filter new unrated comments or suggest particularly useful but still unrated comments.

Question: How useful are your comments?


Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms. The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms. Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized at 50 micron pixels, 12 bits deep. Film images with no CLAHE applied were compared to film images with nine different combinations of clip levels and region sizes applied. A simulated spiculation was embedded in a background of dense breast tissue, with the orientation of the spiculation varied. The key variables involved in each trial included the orientation of the spiculation, contrast level of the spiculation and the CLAHE settings applied to the image. Combining the 10 CLAHE conditions, 4 contrast levels and 4 orientations gave 160 combinations. The trials were constructed by pairing 160 combinations of key variables with 40 backgrounds. Twenty student observers were asked to detect the orientation of the spiculation in the image. There was a statistically significant improvement in detection performance for spiculations with CLAHE over unenhanced images when the region size was set at 32 with a clip level of 2, and when the region size was set at 32 with a clip level of 4. The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved.

Question: What is the purpose of this project?


Number 693 December 2000 ON THE EFFECT OF THE INTERNET ON INTERNATIONAL TRADE. The Internet stimulates trade. Using a gravity equation of trade among 56 countries, we find no evidence of an effect of the Internet on total trade flows in 1995 and only weak evidence of an effect in 1996. However, we find an increasing and significant impact from 1997 to 1999. Specifically, our results imply that a 10 percent increase in the relative number of web hosts in one country would have led to about 1 percent greater trade in 1998 and 1999. Surprisingly, we find that the effect of the Internet on trade has been stronger for poor countries than for rich countries, and that there is little evidence that the Internet has reduced the impact of distance on trade. The evidence is consistent with a model in which the Internet creates a global exchange for goods, thereby reducing market-specific sunk costs of exporting.

Question: What is the effect of the internet on international trade?


Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval. This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) cells. The proposed LSTM-RNN model sequentially takes each word in a sentence, extracts its information, and embeds it into a semantic vector. Due to its ability to capture long term memory, the LSTM-RNN accumulates increasingly richer information as it goes through the sentence, and when it reaches the last word, the hidden layer of the network provides a semantic representation of the whole sentence. In this paper, the LSTM-RNN is trained in a weakly supervised manner on user click-through data logged by a commercial web search engine. Visualization and analysis are performed to understand how the embedding process works. The model is found to automatically attenuate the unimportant words and detect the salient keywords in the sentence. Furthermore, these detected keywords are found to automatically activate different cells of the LSTM-RNN, where words belonging to a similar topic activate the same cell. As a semantic representation of the sentence, the embedding vector can be used in many different applications. These automatic keyword detection and topic allocation abilities enabled by the LSTM-RNN allow the network to perform document retrieval, a difficult language processing task, where the similarity between the query and documents can be measured by the distance between their corresponding sentence embedding vectors computed by the LSTM-RNN. On a web search task, the LSTM-RNN embedding is shown to significantly outperform several existing state of the art methods. We emphasize that the proposed model generates sentence embedding vectors that are specially useful for web document retrieval tasks. A comparison with a well known general sentence embedding

Question: What is the difference between a weakly supervised and a supervised learning?


Early sensitivity to arguments: how preschoolers weight circular arguments.. Observational studies suggest that children as young as 2 years can evaluate some of the arguments people offer them. However, experimental studies of sensitivity to different arguments have not yet targeted children younger than 5 years. The current study aimed at bridging this gap by testing the ability of preschoolers (3-, 4-, and 5-year-olds) to weight arguments. To do so, it focused on a common type of fallacy-circularity-to which 5-year-olds are sensitive. The current experiment asked children-and, as a group control, adults-to choose between the contradictory opinions of two speakers. In the first task, participants of all age groups favored an opinion supported by a strong argument over an opinion supported by a circular argument. In the second task, 4- and 5-year-olds, but not 3-year-olds or adults, favored the opinion supported by a circular argument over an unsupported opinion. We suggest that the results of these tasks in 3- to 5-year-olds are best interpreted as resulting from the combination of two mechanisms: (a) basic skills of argument evaluations that process the content of arguments, allowing children as young as 3 years to favor non-circular arguments over circular arguments, and (b) a heuristic that leads older children (4- and 5-year-olds) to give some weight to circular arguments, possibly by interpreting these arguments as a cue to speaker dominance.

Question: How do preschoolers weigh circular arguments?


Transferable Semi-Supervised Semantic Segmentation. The performance of deep learning based semantic segmentation models heavily depends on sufficient data with careful annotations. However, even the largest public datasets only provide samples with pixel-level annotations for rather limited semantic categories. Such data scarcity critically limits scalability and applicability of semantic segmentation models in real applications. In this paper, we propose a novel transferable semi-supervised semantic segmentation model that can transfer the learned segmentation knowledge from a few strong categories with pixel-level annotations to unseen weak categories with only image-level annotations, significantly broadening the applicable territory of deep segmentation models. In particular, the proposed model consists of two complementary and learnable components: a Label transfer Network (L-Net) and a Prediction transfer Network (PNet). The L-Net learns to transfer the segmentation knowledge from strong categories to the images in the weak categories and produces coarse pixel-level semantic maps, by effectively exploiting the similar appearance shared across categories. Meanwhile, the P-Net tailors the transferred knowledge through a carefully designed adversarial learning strategy and produces refined segmentation results with better details. Integrating the L-Net and P-Net achieves 96.5% and 89.4% performance of the fully-supervised baseline using 50% and 0% categories with pixel-level annotations respectively on PASCAL VOC 2012. With such a novel transfer mechanism, our proposed model is easily generalizable to a variety of new categories, only requiring image-level annotations, and offers appealing scalability in real applications.

Question: What is the largest public dataset?


The inconspicuous penis in children. The term 'inconspicuous penis' refers to a group of anatomical abnormalities in which the penis looks smaller than is expected. Micropenis can be defined as 'true micropenis'—which results from a defect in the hypothalamic–pituitary–gonadal axis—and 'micropenis secondary to congenital anatomical anomalies of the surrounding and overlying structures'—also known as 'concealed penis'. The different forms of concealed penis include webbed penis, congenital megaprepuce and partially hidden penis caused by prepubic adiposity. This disorder can also have iatrogenic causes resulting from adhesions that are secondary to circumcision—this type of concealed penis is known as 'trapped penis'. However, in both groups, micropenis is defined as a stretched penile length that is at least 2.5 SD below the mean for the patient's age, but without any other penile defects. Patients with true micropenis can be managed with testosterone, which has demonstrated good penile elongation results in the long term. Surgery also has a pivotal role in reconstruction for elongating the penis and for correction of anatomical abnormalities in concealed penis.

Question: What is the inconspicuous penis in children?


Design and Implementation of a Wearable Gas Sensor Network for Oil and Gas Industry Workers. Industrial environment usually involves some types of hazardous substances including toxic and/or flammable gases. Accidental gas leakage can cause potential dangers to a plant, its employees and surrounding neighborhoods. Around 64% of accidents that happen in the oil fields are due to combustibles and/or toxic gases. The safety plan of most industries includes measures to reduce risk to humans and plants by incorporating early-warning devices, such as gas detectors. Most existing tools for monitoring gases are stationary and incapable of accurately measuring individual exposures that depend on personal lifestyles and environment. This paper provides a design and implementation of a wearable gas sensor network by building sensor nodes with wireless communication modules which communicate their data along the network. The system is designed to be flexible, low cost, low maintenance and with accurate performance to detect toxic gases in a timely fashion to warn employees before an existence of a disaster.

Question: What is the gas sensor network?


An acute bout of self-myofascial release increases range of motion without a subsequent decrease in muscle activation or force.. Foam rolling is thought to improve muscular function, performance, overuse, and joint range of motion (ROM); however, there is no empirical evidence demonstrating this. Thus, the objective of the study was to determine the effect of self-myofascial release (SMR) via foam roller application on knee extensor force and activation and knee joint ROM. Eleven healthy male (height 178.9 ± 3.5 cm, mass 86.3 ± 7.4 kg, age 22.3 ± 3.8 years) subjects who were physically active participated. Subjects' quadriceps maximum voluntary contraction force, evoked force and activation, and knee joint ROM were measured before, 2 minutes, and 10 minutes after 2 conditions: (a) 2, 1-minute trials of SMR of the quadriceps via a foam roller and (b) no SMR (Control). A 2-way analysis of variance (condition × time) with repeated measures was performed on all dependent variables recorded in the precondition and postcondition tests. There were no significant differences between conditions for any of the neuromuscular dependent variables. However, after foam rolling, subjects' ROM significantly (p < 0.001) increased by 10° and 8° at 2 and 10 minutes, respectively. There was a significant (p < 0.01) negative correlation between subjects' force and ROM before foam rolling, which no longer existed after foam rolling. In conclusion, an acute bout of SMR of the quadriceps was an effective treatment to acutely enhance knee joint ROM without a concomitant deficit in muscle performance.

Question: What is the effect of foam rolling on muscle performance?


Development of the radiographic union score for tibial fractures for the assessment of tibial fracture healing after intramedullary fixation.. BACKGROUND : The objective was to evaluate the newly developed Radiographic Union Score for Tibial fractures (RUST). Because there is no "gold standard," it was hypothesized that the RUST score would provide substantial improvements compared with previous scores presented in the literature.   METHODS : Forty-five sets of X-rays of tibial shaft fractures treated with intramedullary fixation were selected. Seven orthopedic reviewers independently scored bony union using RUST. Radiographs were reassessed at 9 weeks. Intraclass correlation coefficients (ICC) with 95% confidence intervals (CI) measured agreement.   RESULTS : Overall agreement was substantial (ICC, 0.86; 95% CI, 0.79-0.91). There was improved reliability among traumatologists compared with others (ICC = 0.86, 0.81, and 0.83, respectively). Overall intraobserver reliability was also substantial (ICC, 0.88; 95% CI, 0.80-0.96).   CONCLUSIONS : The RUST score exhibits substantial improvements in reliability from previously published scores and produces equally reproducible results among a variety of orthopedic specialties and experience levels. Because no "gold standards" currently exist against which RUST can be compared, this study provides only the initial step in the score's full validation for use in a clinical context.

Question: How do you measure the reliability of a radiographic union score?


A highly area-efficient controller for capacitive touch screen panel systems. In this paper, a highly area-efficient controller for capacitive touch screen panels (TSPs) is proposed. The proposed controller uses a 10-bit successive approximation register analog-to-digital converter (SAR ADC) with an adder to compensate for the capacitance variation in the TSP and for the offset voltage variation in the charge amplifier of the sensing circuit. By using the proposed compensation method, the area of the controller can be reduced by 90.3% of the area of the conventional controllers. The measurement results showed that the signal-to-noise ratio (SNR) of the controller increases from 12.5 to 21.3 dB after compensation. Also, its spatial jitter decreases from ±1.5 to ±0.46 mm, which is 7% of the sensor pitch of 8 mm.

Question: What is the area of the controller?


Types of Participant Behavior in a Massive Open Online Course. In recent years there has been a proliferation of massive open online courses (MOOCs), which provide unprecedented opportunities for lifelong learning. Registrants approach these courses with a variety of motivations for participation. Characterizing the different types of participation in MOOCs is fundamental in order to be able to better evaluate the phenomenon and to support MOOCs developers and instructors in devising courses which are adapted for different learners' needs. Thus, the purpose of this study was to characterize the different types of participant behavior in a MOOC. Using a data mining methodology, 21,889 participants of a MOOC were classified into clusters, based on their activity in the main learning resources of the course: video lectures, discussion forums, and assessments. Thereafter, the participants in each cluster were characterized in regard to demographics, course participation, and course achievement characteristics. Seven types of participant behavior were identified: Tasters (64.8%), Downloaders (8.5%), Disengagers (11.5%), Offline Engagers (3.6%), Online Engagers (7.4%), Moderately Social Engagers (3.7%), and Social Engagers (0.6%). A significant number of 1,020 participants were found to be engaged in the course, but did not achieve a certificate. The types are discussed according to the established research questions. The results provide further evidence regarding the utilization of the flexibility, which is offered in MOOCs, by the participants according to their needs. Furthermore, this study supports the claim that MOOCs' impact should not be evaluated solely based on certification rates but rather based on learning behaviors.

Question: What types of participant behavior are in a MOOC?


Design and Optimization of OpenFOAM-based CFD Applications for Modern Hybrid and Heterogeneous HPC Platforms. Design and Optimization of OpenFOAM-based CFD Applications for Modern Hybrid and Heterogeneous HPC Platforms Amani AlOnazi The progress of high performance computing platforms is dramatic, and most of the simulations carried out on these platforms result in improvements on one level, yet expose shortcomings of current CFD packages capabilities. Therefore, hardwareaware design and optimizations are crucial towards exploiting modern computing resources. This thesis proposes optimizations aimed at accelerating numerical simulations, which are illustrated in OpenFOAM solvers. A hybrid MPI and GPGPU parallel conjugate gradient linear solver has been designed and implemented to solve the sparse linear algebraic kernel that derives from two CFD solver: icoFoam, which is an incompressible flow solver, and laplacianFoam, which solves the Poisson equation, for e.g., thermal diffusion. A load-balancing step is applied using heterogeneous decomposition, which decomposes the computations taking into account the performance of each computing device and seeking to minimize communication. In addition, we implemented the recently developed pipeline conjugate gradient as an algorithmic improvement, and parallelized it using MPI, GPGPU, and a hybrid technique. While many questions of ultimately attainable per node performance and multi-node scaling remain, the experimental results show that the hybrid implementation of both solvers significantly outperforms state-of-the-art implementations of a widely used open source package.

Question: What is the hybrid implementation of the pipeline conjugate gradient?