Cartesian K-Means. A fundamental limitation of quantization techniques like the k-means clustering algorithm is the storage and run-time cost associated with the large numbers of clusters required to keep quantization errors small and model fidelity high. We develop new models with a compositional parameterization of cluster centers, so representational capacity increases super-linearly in the number of parameters. This allows one to effectively quantize data using billions or trillions of centers. We formulate two such models, Orthogonal k-means and Cartesian k-means. They are closely related to one another, to k-means, to methods for binary hash function optimization like ITQ (Gong and Lazebnik, 2011), and to Product Quantization for vector quantization (Jegou et al., 2011). The models are tested on large-scale ANN retrieval tasks (1M GIST, 1B SIFT features), and on codebook learning for object recognition (CIFAR-10).

Question: What is the storage and run-time cost associated with the large numbers of clusters required to keep quantization errors small and model fidelity high?


Chinese Word Segmentation and Named Entity Recognition: A Pragmatic Approach. This article presents a pragmatic approach to Chinese word segmentation. It differs from most previous approaches mainly in three respects. First, while theoretical linguists have defined Chinese words using various linguistic criteria, Chinese words in this study are defined pragmatically as segmentation units whose definition depends on how they are used and processed in realistic computer applications. Second, we propose a pragmatic mathematical framework in which segmenting known words and detecting unknown words of different types (i.e., morphologically derived words, factoids, named entities, and other unlisted words) can be performed simultaneously in a unified way. These tasks are usually conducted separately in other systems. Finally, we do not assume the existence of a universal word segmentation standard that is application-independent. Instead, we argue for the necessity of multiple segmentation standards due to the pragmatic fact that different natural language processing applications might require different granularities of Chinese words. These pragmatic approaches have been implemented in an adaptive Chinese word segmenter, called MSRSeg, which will be described in detail. It consists of two components: (1) a generic segmenter that is based on the framework of linear mixture models and provides a unified approach to the five fundamental features of word-level Chinese language processing: lexicon word processing, morphological analysis, factoid detection, named entity recognition, and new word identification; and (2) a set of output adaptors for adapting the output of (1) to different application-specific standards. Evaluation on five test sets with different standards shows that the adaptive system achieves state-of-the-art performance on all the test sets.

Question: What is the difference between a word segmenter and a word recognizer?


An analytical method for diseases prediction using machine learning techniques.

Question: What is a machine learning technique?


Movie-DiC: a Movie Dialogue Corpus for Research and Development. This paper describes Movie-DiC a Movie Dialogue Corpus recently collected for research and development purposes. The collected dataset comprises 132,229 dialogues containing a total of 764,146 turns that have been extracted from 753 movies. Details on how the data collection has been created and how it is structured are provided along with its main statistics and characteristics.

Question: What is a Movie Dialogue Corpus?


Comparison of Fixed and Variable Pitch Actuators for Agile Quadrotors. This paper presents the design, analysis and experimental testing of a variablepitch quadrotor. A custom in-lab built quadrotor with on-board attitude stabilization is developed and tested. An analysis of the dynamic differences in thrust output between a fixed-pitch and variable-pitch propeller is given and validated with simulation and experimental results. It is shown that variable-pitch actuation has significant advantages over the conventional fixed-pitch configuration, including increased thrust rate of change, decreased control saturation, and the ability to quickly and efficiently reverse thrust. These advantages result in improved quadrotor tracking of linear and angular acceleration command inputs in both simulation and hardware testing. The benefits should enable more aggressive and aerobatic flying with the variable-pitch quadrotor than with standard fixed-pitch actuation, while retaining much of the mechanical simplicity and robustness of the fixed-pitch quadrotor.

Question: What is the difference between a fixed and variable pitch propeller?


A Novel Wideband Microstrip Line to Ridge Gap Waveguide Transition Using Defected Ground Slot. In this letter design of a novel, wideband and low loss microstrip line transition to ridge gap waveguide is described. The transition is made up of two sections. The first section is a microstrip line that feeds a slot on the ground plane. The microstrip line is placed inside a metal box. The second part is the slot to ridge gap waveguide matching section. The proposed design has a broadband coupling to ridge gap waveguide and covers the whole stop-band of the periodic gap structure. The design optimization is performed for the microstrip line, slot size and ridge gap waveguide matching ridges. A prototype in Ku-band is manufactured and measured that shows a return loss less than 14 dB and a back-to-back insertion loss smaller than 0.5 dB for the given bandwidth. The microstrip transition enables integration of monolithic microwave integrated circuits (MMICs) in ridge gap waveguide technology.

Question: What is a novel wideband microstrip line to ridge gap waveguide transition?


A Survey and Typology of Human Computation Games. Human computation games (HCGs) can be seen as a paradigm that makes use of human brain power to solve computational problems as a byproduct of game play. In recent years, HCGs are increasing in popularity within various application areas which aim to harvest human problem solving skills by seamlessly integrating computational tasks into games. Thus, a proper system of describing such games is necessary in order to obtain a better understanding of the current body of work and identify new opportunities for future research directions. As a starting point, in this paper, we review existing literature on HCGs and present a typology consisting of 12 dimensions based on the key features of HCGs. With this typology, all HCGs could be classified and compared in a systematic manner.

Question: What is a typology of human computation games?


Information systems in supply chain integration and management. Supply chain management (SCM) is the 21st century global operations strategy for achieving organizational competitiveness. Companies are attempting to find ways to improve their flexibility and responsiveness and in turn competitiveness by changing their operations strategy, methods and technologies that include the implementation of SCM paradigm and information technology (IT). However, a thorough and critical review of literature is yet to be carried out with the objective of bringing out pertinent factors and useful insights into the role and implications of IT in SCM. In this paper, the literature available on IT in SCM have been classified using suitable criteria and then critically reviewed to develop a framework for studying the applications of IT in SCM. Based on this review and analysis, recommendations have been made regarding the application of IT in SCM and some future research directions are indicated. 2003 Elsevier B.V. All rights reserved.

Question: What is the role of information technology in supply chain management?


Sex differences in intrinsic aptitude for mathematics and science?: a critical review.. This article considers 3 claims that cognitive sex differences account for the differential representation of men and women in high-level careers in mathematics and science: (a) males are more focused on objects from the beginning of life and therefore are predisposed to better learning about mechanical systems; (b) males have a profile of spatial and numerical abilities producing greater aptitude for mathematics; and (c) males are more variable in their cognitive abilities and therefore predominate at the upper reaches of mathematical talent. Research on cognitive development in human infants, preschool children, and students at all levels fails to support these claims. Instead, it provides evidence that mathematical and scientific reasoning develop from a set of biologically based cognitive capacities that males and females share. These capacities lead men and women to develop equal talent for mathematics and science.

Question: What is the difference between males and females?


Development and Evaluation of a Bluetooth EKG Monitoring Sensor. With the adoption of 2G and 3G cellular network technologies, mobile phones now have the bandwidth capability to stream data back to monitoring stations in real-time. Our paper describes the design and evaluation of a Bluetooth electrocardiogram sensor that transmits medical data to a cell phone. This data is displayed and stored on the phone. Future development of the system will relay this data over a cellular GPRS network. The current system provides a low cost and lightweight alternative to existing EKG event monitors. The final GPRS connected system will provide continuous monitoring of a patient 's heart anywhere cellular coverage is available

Question: How does the system work?


Log-Euclidean metrics for fast and simple calculus on diffusion tensors.. Diffusion tensor imaging (DT-MRI or DTI) is an emerging imaging modality whose importance has been growing considerably. However, the processing of this type of data (i.e., symmetric positive-definite matrices), called "tensors" here, has proved difficult in recent years. Usual Euclidean operations on matrices suffer from many defects on tensors, which have led to the use of many ad hoc methods. Recently, affine-invariant Riemannian metrics have been proposed as a rigorous and general framework in which these defects are corrected. These metrics have excellent theoretical properties and provide powerful processing tools, but also lead in practice to complex and slow algorithms. To remedy this limitation, a new family of Riemannian metrics called Log-Euclidean is proposed in this article. They also have excellent theoretical properties and yield similar results in practice, but with much simpler and faster computations. This new approach is based on a novel vector space structure for tensors. In this framework, Riemannian computations can be converted into Euclidean ones once tensors have been transformed into their matrix logarithms. Theoretical aspects are presented and the Euclidean, affine-invariant, and Log-Euclidean frameworks are compared experimentally. The comparison is carried out on interpolation and regularization tasks on synthetic and clinical 3D DTI data.

Question: What is the difference between the affine-invariant and the Log-Euclidean metrics?


Inference in Probabilistic Logic Programs using Weighted CNF's. Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. Several classical probabilistic inference tasks (such as MAP and computing marginals) have not yet received a lot of attention for this formalism. The contribution of this paper is that we develop efficient inference algorithms for these tasks. This is based on a conversion of the probabilistic logic program and the query and evidence to a weighted CNF formula. This allows us to reduce the inference tasks to wellstudied tasks such as weighted model counting. To solve such tasks, we employ state-ofthe-art methods. We consider multiple methods for the conversion of the programs as well as for inference on the weighted CNF. The resulting approach is evaluated experimentally and shown to improve upon the state-of-theart in probabilistic logic programming.

Question: What is the inference algorithm for probabilistic logic programs?


Modelling and Simulation of Four Quadrant Operation of Three Phase Brushless DC Motor With Hysteresis Current. Brushless DC (BLDC) motor drives are becoming more popular in industrial, traction applications. This makes the control of BLDC motor in all the four quadrants very vitalThe motor is operated in four steady state operating modes of torque-speed plane. To control a BLDC machine it is generally required to measure the speed and position of rotor by using the sensor because the inverter phases, acting at any time, must be commutated depending on the rotor position. Simulation of the proposed model was done using MATLAB/ SIMULINK.

Question: How to model a BLDC motor?


Hidden Voice Commands. Voice interfaces are becoming more ubiquitous and are now the primary input method for many devices. We explore in this paper how they can be attacked with hidden voice commands that are unintelligible to human listeners but which are interpreted as commands by devices. We evaluate these attacks under two different threat models. In the black-box model, an attacker uses the speech recognition system as an opaque oracle. We show that the adversary can produce difficult to understand commands that are effective against existing systems in the black-box model. Under the white-box model, the attacker has full knowledge of the internals of the speech recognition system and uses it to create attack commands that we demonstrate through user testing are not understandable by humans. We then evaluate several defenses, including notifying the user when a voice command is accepted; a verbal challenge-response protocol; and a machine learning approach that can detect our attacks with 99.8% accuracy.

Question: What is the difference between a black-box and a white-box model?


Highway long short-term memory RNNS for distant speech recognition. In this paper, we extend the deep long short-term memory (DL-STM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers. These direct links, called highway connections, enable unimpeded information flow across different layers and thus alleviate the gradient vanishing problem when building deeper LSTMs. We further introduce the latency-controlled bidirectional LSTMs (BLSTMs) which can exploit the whole history while keeping the latency under control. Efficient algorithms are proposed to train these novel networks using both frame and sequence discriminative criteria. Experiments on the AMI distant speech recognition (DSR) task indicate that we can train deeper LSTMs and achieve better improvement from sequence training with highway LSTMs (HLSTMs). Our novel model obtains 43.9/47.7% WER on AMI (SDM) dev and eval sets, outperforming all previous works. It beats the strong DNN and DLSTM baselines with 15.7% and 5.3% relative improvement respectively.

Question: How to train a deep LSTM?


Utility-Preserving Differentially Private Data Releases Via Individual Ranking Microaggregation. Being able to release and exploit open data gathered in information systems is crucial for researchers, enterprises and the overall society. Yet, these data must be anonymized before release to protect the privacy of the subjects to whom the records relate. Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as k-anonymity and its extensions. However, it is often disregarded that the utility of differentially private outputs is quite limited, either because of the amount of noise that needs to be added to obtain them or because utility is only preserved for a restricted type and/or a limited number of queries. On the contrary, k-anonymity-like data releases make no assumptions on the uses of the protected data and, thus, do not restrict the number and type of doable analyses. Recently, some authors have proposed mechanisms to offer general-purpose differentially private data releases. This paper extends such works with a specific focus on the preservation of the utility of the protected data. Our proposal builds on microaggregation-based anonymization, which is more flexible and utility-preserving than alternative anonymization methods used in the literature, in order to reduce the amount of noise needed to satisfy differential privacy. In this way, we improve the utility of differentially private data releases. Moreover, the noise reduction we achieve does not depend on the size of the data set, but just on the number of attributes to be protected, which is a more desirable behavior for large data sets. The utility benefits brought by our proposal are empirically evaluated and compared with related works for several data sets and metrics.

Question: What is the utility of differentially private data releases?


LITMUS: Landslide detection by integrating multiple sources. Disasters often lead to other kinds of disasters, forming multi-hazards such as landslides, which may be caused by earthquakes, rainfalls, water erosion, among other reasons. Effective detection and management of multihazards cannot rely only on one information source. In this paper, we evaluate a landslide detection system LITMUS, which combines multiple physical sensors and social media to handle the inherent varied origins and composition of multi-hazards. LITMUS integrates near real-time data from USGS seismic network, NASA TRMM rainfall network, Twitter, YouTube, and Instagram. The landslide detection process consists of several stages of social media filtering and integration with physical sensor data, with a final ranking of relevance by integrated signal strength. Applying LITMUS to data collected in October 2013, we analyzed and filtered 34.5k tweets, 2.5k video descriptions and 1.6k image captions containing landslide keywords followed by integration with physical sources based on a Bayesian model strategy. It resulted in detection of all 11 landslides reported by USGS and 31 more landslides unreported by USGS. An illustrative example is provided to demonstrate how LITMUS' functionality can be used to determine landslides related to the recent Typhoon Haiyan.

Question: How does LITMUS work?


UWB antipodal vivaldi antennas with protruded dielectric rods for higher gain, symmetric patterns and minimal phase center variations. In this paper, the authors proposed a technique to improve the gain, narrow the H-plane beamwidth, and minimize the phase center variations with frequency by utilizing Vivaldi antenna with a protruded dielectric rod. A sample antenna was fabricated and measured, and preliminary measured results are very promising, and in good agreement with the simulation results.

Question: What is the gain of the antenna?


A capability-based security approach to manage access control in the Internet of Things. Resource and information protection plays a relevant role in distributed systems like the ones present in the Internet of Things (IoT). Authorization frameworks like RBAC and ABAC do not provide scalable, manageable, effective, and efficient mechanisms to support distributed systems with many interacting services and are not able to effectively support the dynamicity and scaling needs of IoT contexts that envisage a potentially unbound number of sensors, actuators and related resources, services and subjects, as well as a more relevance of short-lived, unplanned and dynamic interaction patterns. Furthermore, as more end-users start using smart devices (e.g. smart phones, smart home appliances, etc.) the need to have more scalable, manageable, understandable and easy to use access control mechanisms increases. This paper describes a capability based access control system that enterprises, or even individuals, can use to manage their own access control processes to services and information. The proposed mechanism supports rights delegation and a more sophisticated access control customization. The proposed approach is being developed within the European FP7 IoT@Work project to manage access control to some of the project's services deployed in the shop floor.

Question: What is a capability based access control system?


A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback. We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

Question: What is the name of the algorithm?


An interleaving and load sharing method for multiphase LLC converters. Interleaving frequency-controlled LLC resonant converters will encounter load-sharing problem due to the tolerance of the resonant components. In this paper, full-wave and half-wave switch-controlled capacitors (SCCs) are used in LLC stages to solve this problem. By using resonant capacitance as a control variable, the output current can be modulated even when all the LLC stages are synchronized at the same switching frequency. A design procedure is developed. A 600W prototype is built to verify the feasibility.

Question: How to design a LLC converter?


Increasing ATM Efficiency withAssistant Based Speech Recognition. Initiatives to integrate Automatic Speech Recognition into Air Traffic Management (ATM) exists at least since the late 90s. Some success to replace pseudo pilots have been reported, but its integration into controller assistant tools is missing. German Aerospace Center (DLR) and Saarland University developed Assistant Based Speech Recognition (ABSR) enabling command recognition rates better than 95%. However, good recognition rates are no convincing argument for decision makers. Therefore, we conducted an ABSR validation study with eight air traffic controllers to quantify the benefits with respect to workload and efficiency. The study validates that ABSR does not just reduce controllers' workload, which would already be a lot, but this paper presents that ABSR significantly increases ATM efficiency. Fuel reductions of 60 liters (16 gallons) per flight and a throughput increase by two arrivals per hour are possible. Keywords—AcListant®, Automatic Speech Recognition (ASR), Assistant Based Speech Recognition (ABSR), ATM Efficiency, Electronic Flight Strips, Aircraft Label

Question: What is the difference between a pseudo pilot and an assistant based speech recognition?


Learning deep representations by mutual information estimation and maximization. This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation's suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals.

Question: What is the canadian military?


A Problem of Dimensionality: A Simple Example. In pattern recognition problems it has been noted that beyond a certain point the inclusion of additional parameters (that have been estimated) leads to higher probabilities of error. A simple problem has been formulated where the probability of error approaches zero as the dimensionality increases and all the parameters are known; on the other hand, the probability of error approaches one-half as the dimensionality increases and parameters are estimated.

Question: What is the probability of error?


Building Natural-Language Generation Systems. Reiter and Dale's book is about natural language generation (NLG), and describes methods for building systems of this kind. It offers a comprehensive overview of the field of NLG, from a perspective that has rarely been described elsewhere. It is very valuable for people with little or even no knowledge about NLG particulars who intend to investigate applications where NLG technology might play a certain role, as well as for people who want to obtain an application-oriented overview of the field. In this sense, all communities intended to be served by the book—students, academics in related areas, and software developers—are well addressed. In addition, some sections provide material that is probably new even to researchers with considerable experience in the field, such as the discussions on corpus analysis and domain modeling. In the introduction, the field of NLG is briefly characterized from researchand application-oriented perspectives and is illustrated by screen shots produced by several systems. Then conditions for effective uses of this technology are elaborated and contrasted with conditions where other techniques are more appropriate, and methods for determining the intended functionality of a system to be built are discussed. The main sections of the book are devoted to the prototypical architecture of applicationoriented NLG systems and their major processing phases: document planning, microplanning, and surface realization. Each of these three phases is illustrated by a number of detailed examples, demonstrating the successive refinements of utterance specifications in the course of processing. In the final section, the embedding of natural language processing technology is discussed, featuring typography, combined uses with graphics and hypertext, and integration with speech. The methods are illustrated by a large number of examples—the book contains more than 120 figures on its 248 pages. At the end of ea

Question: What is the difference between a natural language generation system and a natural language processing system?


Pattern Recognition for Downhole Dynamometer Card in Oil Rod Pump System using Artificial Neural Networks. This paper presents the development of an Artificial Neural Network system for Dynamometer Card pattern recognition in oil well rod pump systems. It covers the establishment of pattern classes and a set of standards for training and validation, the study of descriptors which allow the design and the implementation of features extractor, training, analysis and finally the validation and performance test with

Question: What is the difference between a dynamometer card and a dynamometer?


Library Anxiety: A Grounded Theory and Its Development. This qualitative study explored the feelings of students about using the library for research. Personal writing, collected in beginning composition courses over a two-year period, was analyzed for recurrent themes. It was found that 75 to 85 percent of the students in these courses described their initial response to library research in terms of fear. Three concepts emerged from these descriptions: (1) students generally feel that their own library-use skills are inadequate while the skills of other students are adequate, (2) the inadequacy is shameful and should be hidden, and (3) the inadequacy would be revealed by asking questions. A grounded theory of library anxiety was constructed from these data.

Question: What is the grounded theory of library anxiety?


Analysis of Statistical Question Classification for Fact-Based Questions. Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.

Question: What is the role of statistical question classification in question answering systems?


Hyracks: A flexible and extensible foundation for data-intensive computing. Hyracks is a new partitioned-parallel software platform designed to run data-intensive computations on large shared-nothing clusters of computers. Hyracks allows users to express a computation as a DAG of data operators and connectors. Operators operate on partitions of input data and produce partitions of output data, while connectors repartition operators' outputs to make the newly produced partitions available at the consuming operators. We describe the Hyracks end user model, for authors of dataflow jobs, and the extension model for users who wish to augment Hyracks' built-in library with new operator and/or connector types. We also describe our initial Hyracks implementation. Since Hyracks is in roughly the same space as the open source Hadoop platform, we compare Hyracks with Hadoop experimentally for several different kinds of use cases. The initial results demonstrate that Hyracks has significant promise as a next-generation platform for data-intensive applications.

Question: What is a data-intensive computing platform?


Abstract Cryptography. Cryptography Ueli Maurer ETH Zurich FOSAD 2009, Bertinoro, Aug./Sept. 2009. Abstract CryptographyCryptography Ueli Maurer ETH Zurich FOSAD 2009, Bertinoro, Aug./Sept. 2009. "I can only understand simple things." JAMES MASSEY Abstraction Abstraction: eliminate irrelevant details from consideration Examples: group, field, vector space, relation, graph, .... Goals of abstraction: • simpler definitions • generality of results • simpler proofs • elegance • didactic suitabilityion Abstraction: eliminate irrelevant details from consideration Examples: group, field, vector space, relation, graph, .... Goals of abstraction: • simpler definitions • generality of results • simpler proofs • elegance • didactic suitability Abstraction Abstraction: eliminate irrelevant details from consideration Examples: group, field, vector space, relation, graph, .... Goals of abstraction: • simpler definitions • generality of results • simpler proofs • elegance • didactic suitability • understandingion Abstraction: eliminate irrelevant details from consideration Examples: group, field, vector space, relation, graph, .... Goals of abstraction: • simpler definitions • generality of results • simpler proofs • elegance • didactic suitability • understanding Abstraction Abstraction: eliminate irrelevant details from consideration Examples: group, field, vector space, relation, graph, .... Goals of abstraction: • simpler definitions • generality of results • simpler proofs • elegance • didactic suitability • understanding Goals of this talk: • Introduce layers of abstraction in cryptography. • Ex mples of abstract definitions and proofs. • Announce new security framework "abstract cryptography" (with Renato Renner).ion Abstraction: eliminate irrelevant details from consideration Examples: group, field, vector space, relation, graph, .... Goals of abstraction: • simpler definitions • generality of results • simpler proofs • elegance • didactic suitability • understanding Goals

Question: What is the difference between abstract cryptography and cryptography?


Maintaining Views Incrementally. We present incremental evaluation algorithms to compute changes to materialized views in relational and deductive database systems, in response to changes (insertions, deletions, and updates) to the relations. The view definitions can be in SQL or Datalog, and may use UNION, negation, aggregation (e.g. SUM, MIN), linear recursion, and general recursion. We first present a counting algorithm that tracks the number of alternative derivations (counts) for each derived tuple in a view. The algorithm works with both set and duplicate semantics. We present the algorithm for nonrecursive views (with negation and aggregation), and show that the count for a tuple can be computed at little or no cost above the cost of deriving the tuple. The algorithm is optimal in that it computes exactly those view tuples that are inserted or deleted. Note that we store only the number of derivations, not the derivations themselves. We then present the Delete and Rederive algorithm, DRed, for incremental maintenance of recursive views (negation and aggregation are permitted). The algorithm works by first deleting a superset of the tuples that need to be deleted, and then rederiving some of them. The algorithm can also be used when the view definition is itself altered.

Question: What is the difference between a view and a materialized view?


Structure of the tendon connective tissue.. Tendons consist of collagen (mostly type I collagen) and elastin embedded in a proteoglycan-water matrix with collagen accounting for 65-80% and elastin approximately 1-2% of the dry mass of the tendon. These elements are produced by tenoblasts and tenocytes, which are the elongated fibroblasts and fibrocytes that lie between the collagen fibers, and are organized in a complex hierarchical scheme to form the tendon proper. Soluble tropocollagen molecules form cross-links to create insoluble collagen molecules which then aggregate progressively into microfibrils and then into electronmicroscopically clearly visible units, the collagen fibrils. A bunch of collagen fibrils forms a collagen fiber, which is the basic unit of a tendon. A fine sheath of connective tissue called endotenon invests each collagen fiber and binds fibers together. A bunch of collagen fibers forms a primary fiber bundle, and a group of primary fiber bundles forms a secondary fiber bundle. A group of secondary fiber bundles, in turn, forms a tertiary bundle, and the tertiary bundles make up the tendon. The entire tendon is surrounded by a fine connective tissue sheath called epitenon. The three-dimensional ultrastructure of tendon fibers and fiber bundles is complex. Within one collagen fiber, the fibrils are oriented not only longitudinally but also transversely and horizontally. The longitudinal fibers do not run only parallel but also cross each other, forming spirals. Some of the individual fibrils and fibril groups form spiral-type plaits. The basic function of the tendon is to transmit the force created by the muscle to the bone, and, in this way, make joint movement possible. The complex macro- and microstructure of tendons and tendon fibers make this possible. During various phases of movements, the tendons are exposed not only to longitudinal but also to transversal and rotational forces. In addition, they must be prepared to withstand direct co

Question: What is the structure of the tendon connective tissue?


On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task. Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing e cient deep architectures to analyse volumetric medical images remains challenging. This work investigates e cient and flexible elements of modern convolutional networks such as dilated convolution and residual connection. With these essential building blocks, we propose a high-resolution, compact convolutional network for volumetric image segmentation. To illustrate its e ciency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images. Our experiments show that the proposed network architecture compares favourably with state-of-the-art volumetric segmentation networks while being an order of magnitude more compact. We consider the brain parcellation task as a pretext task for volumetric image segmentation; our trained network potentially provides a good starting point for transfer learning. Additionally, we show the feasibility of voxel-level uncertainty estimation using a sampling approximation through dropout.

Question: What is the size of the brain?


A two-view based multilayer feature graph for robot navigation. To facilitate scene understanding and robot navigation in a modern urban area, we design a multilayer feature graph (MFG) based on two views from an on-board camera. The nodes of an MFG are features such as scale invariant feature transformation (SIFT) feature points, line segments, lines, and planes while edges of the MFG represent different geometric relationships such as adjacency, parallelism, collinearity, and coplanarity. MFG also connects the features in two views and the corresponding 3D coordinate system. Building on SIFT feature points and line segments, MFG is constructed using feature fusion which incrementally, iteratively, and extensively verifies the aforementioned geometric relationships using random sample consensus (RANSAC) framework. Physical experiments show that MFG can be successfully constructed in urban area and the construction method is demonstrated to be very robust in identifying feature correspondence.

Question: What is a feature graph?


A Compact GPS Antenna for Artillery Projectile Applications. This letter presents a compact antenna suitable for the reception of GPS signals on artillery projectiles over 1.57-1.60 GHz. Four inverted-F-type elements are excited by a series feed network in equal magnitude and successive 90° phase difference. The shape and form factor of the antenna is tailored so that the antenna can be easily installed inside an artillery fuze. Measurements show that the proposed antenna has a gain of 2.90-3.77 dBic, an axial ratio of 1.9-2.86 dB, and a reflection coefficient of less than -10 dB over 1.57-1.62 GHz.

Question: What is the gain of the antenna?


Parameter Space Noise for Exploration. Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both offand on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually.

Question: What is the difference between parameter noise and action noise?


A performance comparison of polar codes and Reed-Muller codes. Polar coding is a code construction method that can be used to construct capacity-achieving codes for binary-input channels with certain symmetries. Polar coding may be considered as a generalization of Reed-Muller (RM) coding. Here, we demonstrate the performance advantages of polar codes over RM codes under belief-propagation decoding.

Question: What is the performance of polar codes?


Spell Checking Techniques in NLP: A Survey. Spell checkers in Indian languages are the basic tools that need to be developed. A spell checker is a software tool that identifies and corrects any spelling mistakes in a text. Spell checkers can be combined with other applications or they can be distributed individually. In this paper the authors are discussing both the approaches and their roles in various applications.

Question: What is the role of spell checkers in NLP?


Interactive Plan Explicability in Human-Robot Teaming. Human-robot teaming is one of the most important applications of artificial intelligence in the fast-growing field of robotics. For effective teaming, a robot must not only maintain a behavioral model of its human teammates to project the team status, but also be aware of its human teammates' expectation of itself. Being aware of the human teammates' expectation leads to robot behaviors that better align with the human expectation, thus facilitating more efficient and potentially safer teams. Our work addresses the problem of human-robot interaction with the consideration of such teammate models in sequential domains by leveraging the concept of plan explicability. In plan explicability, however, the human is considered solely as an observer. In this paper, we extend plan explicability to consider interactive settings where the human and robot's behaviors can influence each other. We term this new measure Interactive Plan Explicability (IPE). We compare the joint plan generated by our approach with the consideration of this measure using the fast forward (FF) planner, with the plan generated by FF without such consideration, as well as with the plan created with human subjects interacting with a robot running an FF planner. Since the human subject is expected to adapt to the robot's behavior dynamically when it deviates from her expectation, the plan created with human subjects is expected to be more explicable than the FF plan, and comparable to the explicable plan generated by our approach. Results indicate that the explicability score of plans generated by our algorithm is indeed closer to the human interactive plan than the plan generated by FF, implying that the plans generated by our algorithms align better with the expected plans of the human during execution. This can lead to more efficient collaboration in practice.

Question: What is the difference between the human-robot teaming and the human-human teaming?


A realist review of mobile phone-based health interventions for non-communicable disease management in sub-Saharan Africa. BACKGROUND The prevalence of non-communicable diseases (NCDs) is increasing in sub-Saharan Africa. At the same time, the use of mobile phones is rising, expanding the opportunities for the implementation of mobile phone-based health (mHealth) interventions. This review aims to understand how, why, for whom, and in what circumstances mHealth interventions against NCDs improve treatment and care in sub-Saharan Africa.   METHODS Four main databases (PubMed, Cochrane Library, Web of Science, and Google Scholar) and references of included articles were searched for studies reporting effects of mHealth interventions on patients with NCDs in sub-Saharan Africa. All studies published up until May 2015 were included in the review. Following a realist review approach, middle-range theories were identified and integrated into a Framework for Understanding the Contribution of mHealth Interventions to Improved Access to Care for patients with NCDs in sub-Saharan Africa. The main indicators of the framework consist of predisposing characteristics, needs, enabling resources, perceived usefulness, and perceived ease of use. Studies were analyzed in depth to populate the framework.   RESULTS The search identified 6137 titles for screening, of which 20 were retained for the realist synthesis. The contribution of mHealth interventions to improved treatment and care is that they facilitate (remote) access to previously unavailable (specialized) services. Three contextual factors (predisposing characteristics, needs, and enabling resources) influence if patients and providers believe that mHealth interventions are useful and easy to use. Only if they believe mHealth to be useful and easy to use, will mHealth ultimately contribute to improved access to care. The analysis of included studies showed that the most important predisposing characteristics are a positive at

Question: What is a realist review?


Programming by choice: urban youth learning programming with scratch. This paper describes Scratch, a visual, block-based programming language designed to facilitate media manipulation for novice programmers. We report on the Scratch programming experiences of urban youth ages 8-18 at a Computer Clubhouse 'an after school center' over an 18-month period. Our analyses of 536 Scratch projects collected during this time documents the learning of key programming concepts even in the absence of instructional interventions or experienced mentors. We discuss the motivations of urban youth who choose to program in Scratch rather than using one of the many other software packages available to them and the implications for introducing programming at after school settings in underserved communities.

Question: What is the motivation of urban youth to learn programming?


Cocaine cues and dopamine in dorsal striatum: mechanism of craving in cocaine addiction.. The ability of drugs of abuse to increase dopamine in nucleus accumbens underlies their reinforcing effects. However, preclinical studies have shown that with repeated drug exposure neutral stimuli paired with the drug (conditioned stimuli) start to increase dopamine by themselves, which is an effect that could underlie drug-seeking behavior. Here we test whether dopamine increases occur to conditioned stimuli in human subjects addicted to cocaine and whether this is associated with drug craving. We tested eighteen cocaine-addicted subjects using positron emission tomography and [11C]raclopride (dopamine D2 receptor radioligand sensitive to competition with endogenous dopamine). We measured changes in dopamine by comparing the specific binding of [11C]raclopride when subjects watched a neutral video (nature scenes) versus when they watched a cocaine-cue video (scenes of subjects smoking cocaine). The specific binding of [11C]raclopride in dorsal (caudate and putamen) but not in ventral striatum (in which nucleus accumbens is located) was significantly reduced in the cocaine-cue condition and the magnitude of this reduction correlated with self-reports of craving. Moreover, subjects with the highest scores on measures of withdrawal symptoms and of addiction severity that have been shown to predict treatment outcomes, had the largest dopamine changes in dorsal striatum. This provides evidence that dopamine in the dorsal striatum (region implicated in habit learning and in action initiation) is involved with craving and is a fundamental component of addiction. Because craving is a key contributor to relapse, strategies aimed at inhibiting dopamine increases from conditioned responses are likely to be therapeutically beneficial in cocaine addiction.

Question: What is the mechanism of craving in cocaine addiction?


A Monolithic CMOS-MEMS 3-Axis Accelerometer With a Low-Noise, Low-Power Dual-Chopper Amplifier. This paper reports a monolithically integrated CMOS-MEMS three-axis capacitive accelerometer with a single proof mass. An improved DRIE post-CMOS MEMS process has been developed, which provides robust single-crystal silicon (SCS) structures in all three axes and greatly reduces undercut of comb fingers. The sensing electrodes are also composed of the thick SCS layer, resulting in high resolution and large sensing capacitance. Due to the high wiring flexibility provided by the fabrication process, fully differential capacitive sensing and common-centroid configurations are realized in all three axes. A low-noise, low- power dual-chopper amplifier is designed for each axis, which consumes only 1 mW power. With 44.5 dB on-chip amplification, the measured sensitivities of x-, y-, and z-axis accelerometers are 520 mV/g, 460 mV/g, and 320 mV/g, respectively, which can be tuned by simply changing the amplitude of the modulation signal. Accordingly, the overall noise floors of the x-, y-, and z-axis are 12 mug/radicHz , 14 mug/radicHz, and 110 mug/radicHz, respectively, when tested at around 200 Hz.

Question: What is a monolithic CMOS-MEMS accelerometer?


Episodic Training for Domain Generalization. Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domain with different statistics than a set of known training domains. The simple approach of aggregating data from all source domains and training a single deep neural network end-to-end on all the data provides a surprisingly strong baseline that surpasses many prior published methods. In this paper we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime. Specifically, we decompose a deep network into feature extractor and classifier components, and then train each component by simulating it interacting with a partner who is badly tuned for the current domain. This makes both components more robust, ultimately leading to our networks producing state-of-the-art performance on three DG benchmarks. As a demonstration, we consider the pervasive workflow of using an ImageNet trained CNN as a fixed feature extractor for downstream recognition tasks. Using the Visual Decathlon benchmark, we demonstrate that our episodic-DG training improves the performance of such a general purpose feature extractor by explicitly training it for robustness to novel problems. This provides the largest-scale demonstration of heterogeneous DG to date.

Question: How does the training procedure work?


Abstract Meaning Representation for Multi-Document Summarization. Generating an abstract from a collection of documents is a desirable capability for many realworld applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.

Question: What is the abstract meaning representation?


Metal-oxide-semiconductor field-effect transistor with a vacuum channel.. High-speed electronic devices rely on short carrier transport times, which are usually achieved by decreasing the channel length and/or increasing the carrier velocity. Ideally, the carriers enter into a ballistic transport regime in which they are not scattered. However, it is difficult to achieve ballistic transport in a solid-state medium because the high electric fields used to increase the carrier velocity also increase scattering. Vacuum is an ideal medium for ballistic transport, but vacuum electronic devices commonly suffer from low emission currents and high operating voltages. Here, we report the fabrication of a low-voltage field-effect transistor with a vertical vacuum channel (channel length of ~20 nm) etched into a metal-oxide-semiconductor substrate. We measure a transconductance of 20 nS µm(-1), an on/off ratio of 500 and a turn-on gate voltage of 0.5 V under ambient conditions. Coulombic repulsion in the two-dimensional electron system at the interface between the oxide and the metal or the semiconductor reduces the energy barrier to electron emission, leading to a high emission current density (~1 × 10(5) A cm(-2)) under a bias of only 1 V. The emission of two-dimensional electron systems into vacuum channels could enable a new class of low-power, high-speed transistors.

Question: What is the difference between a vacuum channel and a vacuum channel?


Grail: context-aware fixing of concurrency bugs. Writing efficient synchronization for multithreaded programs is notoriously hard. The resulting code often contains subtle concurrency bugs. Even worse, many bug fixes introduce new bugs. A classic example, seen widely in practice, is deadlocks resulting from fixing of an atomicity violation. These complexities have motivated the development of automated fixing techniques. Current techniques generate fixes that are typically conservative, giving up on available parallelism. Moreover, some of the techniques cannot guarantee the correctness of a fix, and may introduce deadlocks similarly to manual fix, whereas techniques that ensure correctness do so at the expense of even greater performance loss. We present Grail, a novel fixing algorithm that departs from previous techniques by simultaneously providing both correctness and optimality guarantees. Grail synthesizes bug-free yet optimal lock-based synchronization. To achieve this, Grail builds an analysis model of the buggy code that is both contextual, distinguishing different aliasing contexts to ensure efficiency, and global, accounting for the entire synchronization behavior of the involved threads to ensure correctness. Evaluation of Grail on 12 bugs from popular codebases confirms its practical advantages, especially compared with existing techniques: Grail patches are, in general, >=40% more efficient than the patches produced by other techniques, and incur only 2% overhead.

Question: What is the context-aware fixing of concurrency bugs?


Automatic Recognition of Deceptive Facial Expressions of Emotion. Humans modify facial expressions in order to mislead observers regarding their true emotional states. Being able to recognize the authenticity of emotional displays is notoriously difficult for human observers. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of genuine and deceptive facial expressions of emotions for automatic recognition1 . We show that overall the problem of recognizing deceptive facial expressions can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt feature space. Interesting additional results show that on average it is easier to distinguish among genuine expressions than deceptive ones and that certain emotion pairs are more difficult to distinguish than others.

Question: How can we recognize deceptive facial expressions?


A Circuit-Compatible SPICE model for Enhancement Mode Carbon Nanotube Field Effect Transistors. This paper presents a circuit-compatible compact model for short channel length (5 nm~100 nm), quasi-ballistic single wall carbon nanotube field-effect transistors (CNFETs). For the first time, a universal circuit-compatible CNFET model was implemented with HSPICE. This model includes practical device non-idealities, e.g. the quantum confinement effects in both circumferential and channel length direction, the acoustical/optical phonon scattering in channel region and the resistive source/drain, as well as the real time dynamic response with a transcapacitance array. This model is valid for CNFET for a wide diameter range and various chiralities as long as the carbon nanotube (CNT) is semiconducting

Question: What is a circuit-compatible SPICE model?


Benefits from Using Continuous Rating Scales in Online Survey Research. The usage of Likert-type scales has become widespread practice in current IS research. Those scales require individuals to choose between a limited number of choices, and have been criticized in the literature for causing loss of information, allowing the researcher to affect responses by determining the range, and being ordinal in nature. The use of online surveys allows for the easy implementation of continuous rating scales, which have a long history in psychophysical measurement but were rarely used in IS surveys. This type of measurement requires survey participants to express their opinion in a visual form, i.e. to place a mark at an appropriate position on a continuous line. That not only solves the problems of information loss, but also allows for applying advanced robust statistical analyses. In this 1 Augasse 2-6, A-1090 Vienna, Austria Tel.: +43/1/31336/4480 Fax: +43/1/31336/746 E-Mail: Horst.Treiblmaier@wu.ac.at

Question: What is the difference between a Likert scale and a continuous rating scale?


A General Divide and Conquer Approach for Process Mining. Operational processes leave trails in the information systems supporting them. Such event data are the starting point for process mining – an emerging scientific discipline relating modeled and observed behavior. The relevance of process mining is increasing as more and more event data become available. The increasing volume of such data ("Big Data") provides both opportunities and challenges for process mining. In this paper we focus on two particular types of process mining: process discovery (learning a process model from example behavior recorded in an event log) and conformance checking (diagnosing and quantifying discrepancies between observed behavior and modeled behavior). These tasks become challenging when there are hundreds or even thousands of different activities and millions of cases. Typically, process mining algorithms are linear in the number of cases and exponential in the number of different activities. This paper proposes a very general divide-and-conquer approach that decomposes the event log based on a partitioning of activities. Unlike existing approaches, this paper does not assume a particular process representation (e.g., Petri nets or BPMN) and allows for various decomposition strategies (e.g., SESEor passage-based decomposition). Moreover, the generic divide-andconquer approach reveals the core requirements for decomposing process discovery and conformance checking problems.

Question: What is a divide and conquer approach?


A DTC-Based Subsampling PLL Capable of Self-Calibrated Fractional Synthesis and Two-Point Modulation. We present an analog subsampling PLL based on a digital-to-time converter (DTC), which operates with almost no performance gap (176/198 fs RMS jitter) between the integer and the worst case fractional operation, achieving -246.6 dB FOM in the worst case fractional mode. The PLL is capable of two-point, 10 Mbit/s GMSK modulation with -40.5 dB EVM around a 10.24 GHz fractional carrier. The analog nonidealities-DTC gain, DTC nonlinearity, modulating VCO bank gain, and nonlinearity-are calibrated in the background while the system operates normally. This results in ~15 dB fractional spur improvement (from -41 dBc to -56.5 dBc) during synthesis and ~15 dB EVM improvement (from -25 dB to -40.5 dB) during modulation. The paper provides an overview of the mechanisms that contribute to performance degradation in DTC-based PLL/phase modulators and presents ways to mitigate them. We demonstrate state-of-the-art performance in nanoscale CMOS for fractional-N synthesis and phase modulation.

Question: What is the difference between a DTC and a DDS?


"Is it Weird to Still Be a Virgin": Anonymous, Locally Targeted Questions on Facebook Confession Boards. People have long sought answers to questions online, typically using either anonymous or pseudonymous forums or social network platforms that primarily use real names. Systems that allow anonymous communication afford freedom to explore identity and discuss taboo topics, but can result in negative disinhibited behavior such as cyberbullying. Identifiable communication systems allows one to reach a known audience and avoid negative disinhibition, but can constrain behavior with concerns about privacy and reputation. One persistent design issue is understanding how to leverage the benefits of anonymity without suffering its drawbacks. This paper presents a case study analysis of question asking on Facebook confession boards (FCBs), a tool popular on some college campuses. FCBs present a unique configuration in which members of an offline community (e.g., a university) anonymously submit content to a moderator who posts it to a Facebook page where others in the community can view it and respond. Response is via identifiable Facebook comments and likes. Our results show users asking about taboo and stigmatized topics with local others, and receiving relevant responses with little cyberbullying or negativity.

Question: What is a confession board?


Progressive Stochastic Learning for Noisy Labels. Large-scale learning problems require a plethora of labels that can be efficiently collected from crowdsourcing services at low cost. However, labels annotated by crowdsourced workers are often noisy, which inevitably degrades the performance of large-scale optimizations including the prevalent stochastic gradient descent (SGD). Specifically, these noisy labels adversely affect updates of the primal variable in conventional SGD. To solve this challenge, we propose a robust SGD mechanism called progressive stochastic learning (POSTAL), which naturally integrates the learning regime of curriculum learning (CL) with the update process of vanilla SGD. Our inspiration comes from the progressive learning process of CL, namely learning from "easy" tasks to "complex" tasks. Through the robust learning process of CL, POSTAL aims to yield robust updates of the primal variable on an ordered label sequence, namely, from "reliable" labels to "noisy" labels. To realize POSTAL mechanism, we design a cluster of "screening losses," which sorts all labels from the reliable region to the noisy region. To sum up, POSTAL using screening losses ensures robust updates of the primal variable on reliable labels first, then on noisy labels incrementally until convergence. In theory, we derive the convergence rate of POSTAL realized by screening losses. Meanwhile, we provide the robustness analysis of representative screening losses. Experimental results on UCI1 simulated and Amazon Mechanical Turk crowdsourcing data sets show that the POSTAL using screening losses is more effective and robust than several existing baselines.1UCI is the abbreviation of University of California Irvine.

Question: How large is the canadian military?


Applications of Data Mining Techniques in TelecomChurn Prediction. In this competitive world, business becomes highly saturated. Especially, the field of telecommunication faces complex challenges due to a number of vibrant competitive service providers. Therefore it has become very difficult for them to retain existing customers. Since the cost of acquiring new customers is much higher than the cost of retaining the existing customers, it is the time for the telecom industries to take necessary steps to retain the customers to stabilize their market value. This paper explores the application of data mining techniques in predicting likely churners and the impact of attribute selection on identifying the churn. It also compares the efficiency of Decision tree and Neural Network classifiers and lists their performances.

Question: What is churn prediction?


Modularized Morphing of Neural Networks. In this work we study the problem of network morphism, an effective learning scheme to morph a well-trained neural network to a new one with the network function completely preserved. Different from existing work where basic morphing types on the layer level were addressed, we target at the central problem of network morphism at a higher level, i.e., how a convolutional layer can be morphed into an arbitrary module of a neural network. To simplify the representation of a network, we abstract a module as a graph with blobs as vertices and convolutional layers as edges, based on which the morphing process is able to be formulated as a graph transformation problem. Two atomic morphing operations are introduced to compose the graphs, based on which modules are classified into two families, i.e., simple morphable modules and complex modules. We present practical morphing solutions for both of these two families, and prove that any reasonable module can be morphed from a single convolutional layer. Extensive experiments have been conducted based on the state-of-the-art ResNet on benchmark datasets, and the effectiveness of the proposed solution has been verified.

Question: What is a neural network?


RMiner: a tool set for role mining. Recently, there are many approaches proposed for mining roles using automated technologies. However, it lacks a tool set that can be used to aid the application of role mining approaches and update role states. In this demonstration, we introduce a tool set, RMiner, which is based on the core of WEKA, an open source data mining tool. RMiner implements most of the classic and latest role mining algorithms and provides interactive tools for administrator to update role states. The running examples of RMiner are presented to demonstrate the effectiveness of the tool set.

Question: What is a role mining tool set?


Video Super-Resolution via Bidirectional Recurrent Convolutional Networks. Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.

Question: How to super resolve a low-resolution video?


Quality-biased ranking for queries with commercial intent. Modern search engines are good enough to answer popular commercial queries with mainly highly relevant documents. However, our experiments show that users behavior on such relevant commercial sites may differ from one to another web-site with the same relevance label. Thus search engines face the challenge of ranking results that are equally relevant from the perspective of the traditional relevance grading approach. To solve this problem we propose to consider additional facets of relevance, such as trustability, usability, design quality and the quality of service. In order to let a ranking algorithm take these facets in account, we proposed a number of features, capturing the quality of a web page along the proposed dimensions. We aggregated new facets into the single label, commercial relevance, that represents cumulative quality of the site. We extrapolated commercial relevance labels for the entire learning-to-rank dataset and used weighted sum of commercial and topical relevance instead of default relevance labels. For evaluating our method we created new DCG-like metrics and conducted off-line evaluation as well as on-line interleaving experiments demonstrating that a ranking algorithm taking the proposed facets of relevance into account is better aligned with user preferences.

Question: What is the quality of a web page?


Basic Concepts of Lexical Resource Semantics. Semanticists use a range of highly expressive logical langu ges to characterize the meaning of natural language expressions. The logical languages are usually taken from an i nventory of standard mathematical systems, with which generative linguists are familiar. They are, thus, easily a ccessible beyond the borders of a given framework such as Categorial Grammar, Lexical Functional Grammar, or Govern me t and Binding Theory. Linguists working in the HPSG framework, on the other hand, often use rather idiosync ratic and specialized semantic representations. Their choice is sometimes motivated by computational applicatio ns n parsing, generation, or machine translation. Natural ly, the intended areas of application influence the design of sem antic representations. A typical property of semantic representations in HPSG that is concerned with computational a pplications is underspecification, and other properties come from the particular unification or constraint solving a l orithms that are used for processing grammars. While the resulting semantic representations have properties th at are motivated by, and are adequate for, certain practical applications, their relationship to standard languages is sometimes left on an intuitive level. In addition, the theor etical and ontological status of the semantic representations is o ften neglected. This vagueness tends to be unsatisfying to many semanticists, and the idiosyncratic shape of the seman tic representations confines their usage to HPSG. Since their entire architecture is highly dependent on HPSG, hard ly anyone working outside of that framework is interested in studying them. With our work on Lexical Resource Semantics (LRS), we want to contribute to the investigation of a number of important theoretical issues surrounding semantic repres ntations and possible ways of underspecification. While LR S is formulated in a constraint-based grammar environment an d t kes adv

Question: What is underspecification?


Enhancing computer vision to detect face spoofing attack utilizing a single frame from a replay video attack using deep learning. Recently, automatic face recognition has been applied in many web and mobile applications. Developers integrate and implement face recognition as an access control into these applications. However, face recognition authentication is vulnerable to several attacks especially when an attacker presents a 2-D printed image or recorded video frames in front of the face sensor system to gain access as a legitimate user. This paper introduces a non-intrusive method to detect face spoofing attacks that utilize a single frame of sequenced frames. We propose a specialized deep convolution neural network to extract complex and high features of the input diffused frame. We tested our method on the Replay Attack dataset which consists of 1200 short videos of both real-access and spoofing attacks. An extensive experimental analysis was conducted that demonstrated better results when compared to previous static algorithms results.

Question: How to detect face spoofing attack using deep learning?


Detecting Structural Similarities between XML Documents. In this paper we propose a technique for detecting the similarity in the structure of XML documents. The technique is based on the idea of representing the structure of an XML document as a time series in which each occurrence of a tag corresponds to a given impulse. By analyzing the frequencies of the corresponding Fourier transform, we can hence state the degree of similarity between documents. The efficiency and effectiveness of this approach are compelling when compared with traditional ones.

Question: How to detect the similarity between XML documents?


A PPG sensor for continuous cuffless blood pressure monitoring with self-adaptive signal processing. A new portable PPG-BP device designed for continuously measuring blood pressure (BP) without a cuff is proposed in this study. This continuous and long-time BP monitoring enabled herein by the proposed portable cuffless BP sensor. Towards the aforementioned goal, the sensor is designed capable of detecting in real time, non-invasively and continuously the temporal intravascular blood volume change based on the principle of photoplethysmograph (PPG) for estimating BP. The hardware of the sensor consists mainly of light emitting diodes (LEDs) in wavelengths of 660 nm, a photo detectors (PD), and also signal processing chips to read output signals of the PD. The PD readout circuit includes a PD pre-amplifier, a band-pass filter, a programmable gain amplifier (PGA), a microcontroller unit for calculation and a wireless module for communication. A laptop is also used to display continuous BPs and conducts statistical analysis and displaying results. 27 subjects participated in the experimental validation, in which the obtained BPs are calibrated by and then compared with the results from a commercial blood pressure monitor by OMRON. The resultant signal-to-noise ratio (SNR) is capable of rising more than 15%, correlation coefficient, R2, for systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 0.89 and 0.98, respectively.

Question: How does the PPG sensor work?


Transferring a semantic representation for person re-identification and search. Learning semantic attributes for person re-identification and description-based person search has gained increasing interest due to attributes' great potential as a pose and view-invariant representation. However, existing attribute-centric approaches have thus far underperformed state-of-the-art conventional approaches. This is due to their nonscalable need for extensive domain (camera) specific annotation. In this paper we present a new semantic attribute learning approach for person re-identification and search. Our model is trained on existing fashion photography datasets - either weakly or strongly labelled. It can then be transferred and adapted to provide a powerful semantic description of surveillance person detections, without requiring any surveillance domain supervision. The resulting representation is useful for both unsupervised and supervised person re-identification, achieving state-of-the-art and near state-of-the-art performance respectively. Furthermore, as a semantic representation it allows description-based person search to be integrated within the same framework.

Question: What is a semantic representation for person re-identification and search?


When moderation is mediated and mediation is moderated.. Procedures for examining whether treatment effects on an outcome are mediated and/or moderated have been well developed and are routinely applied. The mediation question focuses on the intervening mechanism that produces the treatment effect. The moderation question focuses on factors that affect the magnitude of the treatment effect. It is important to note that these two processes may be combined in informative ways, such that moderation is mediated or mediation is moderated. Although some prior literature has discussed these possibilities, their exact definitions and analytic procedures have not been completely articulated. The purpose of this article is to define precisely both mediated moderation and moderated mediation and provide analytic strategies for assessing each.

Question: What is the difference between mediation and moderation?


Hidden in Plain Sight: Storing and Managing Secrets on a Public Ledger. Current blockchain systems are incapable of holding sensitive data securely on their public ledger while supporting accountability of data access requests and revocability of data access rights. Instead, they either keep the sensitive data off-chain as a semi-centralized solution or they just publish the data on the ledger ignoring the problem altogether. In this work, we introduce SCARAB the first secure decentralized access control mechanism for blockchain systems that addresses the challenges of accountability, by publicly logging each request before granting data access, and of revocability, by introducing collectively managed data access policies. SCARAB introduces, therefore, on-chain secrets, which utilize verifiable secret sharing to enable collectively managed secrets under a Byzantine adversary, and identity skipchains, which enable the dynamic management of identities and of access control policies. The evaluation of our SCARAB implementation shows that the latency of a single read/write request scales linearly with the number of access-securing trustees and is in the range of 200 ms to 8 seconds for 16 to 128 trustees.

Question: How does SCARAB work?


Effects of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, and prosocial behavior: a meta-analytic review of the scientific literature.. Research on exposure to television and movie violence suggests that playing violent video games will increase aggressive behavior. A metaanalytic review of the video-game research literature reveals that violent video games increase aggressive behavior in children and young adults. Experimental and nonexperimental studies with males and females in laboratory and field settings support this conclusion. Analyses also reveal that exposure to violent video games increases physiological arousal and aggression-related thoughts and feelings. Playing violent video games also decreases prosocial behavior.

Question: What is the effect of violent video games on aggressive behavior?


The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults.. The objective of this study was to refine the APACHE (Acute Physiology, Age, Chronic Health Evaluation) methodology in order to more accurately predict hospital mortality risk for critically ill hospitalized adults. We prospectively collected data on 17,440 unselected adult medical/surgical intensive care unit (ICU) admissions at 40 US hospitals (14 volunteer tertiary-care institutions and 26 hospitals randomly chosen to represent intensive care services nationwide). We analyzed the relationship between the patient's likelihood of surviving to hospital discharge and the following predictive variables: major medical and surgical disease categories, acute physiologic abnormalities, age, preexisting functional limitations, major comorbidities, and treatment location immediately prior to ICU admission. The APACHE III prognostic system consists of two options: (1) an APACHE III score, which can provide initial risk stratification for severely ill hospitalized patients within independently defined patient groups; and (2) an APACHE III predictive equation, which uses APACHE III score and reference data on major disease categories and treatment location immediately prior to ICU admission to provide risk estimates for hospital mortality for individual ICU patients. A five-point increase in APACHE III score (range, 0 to 299) is independently associated with a statistically significant increase in the relative risk of hospital death (odds ratio, 1.10 to 1.78) within each of 78 major medical and surgical disease categories. The overall predictive accuracy of the first-day APACHE III equation was such that, within 24 h of ICU admission, 95 percent of ICU admissions could be given a risk estimate for hospital death that was within 3 percent of that actually observed (r2 = 0.41; receiver operating characteristic = 0.90). Recording changes in the APACHE III score on each

Question: What is the APACHE III prognostic system?


Transimpedance Amplifier ( TIA ) Design for 400 Gb / s Optical Fiber Communications. (ABSTRACT) Analogcircuit/IC design for high speed optical fiber communication is a fairly new research area in Dr. Ha's group. In the first project sponsored by ETRI (Electronics and Telecommunication Research Institute) we started to design the building blocks of receiver for next generation 400 Gb/s optical fiber communication. In this thesis research a transceiver architecture based on 4x100 Gb/s parallel communication is proposed. As part of the receiver, a transimpedance amplifier for 100 Gb/s optical communication is designed, analyzed and simulated. Simulation results demonstrate the excellent feasibility of proposed architecture. always dominated the high speed optical transceiver design because of their inherent properties of high mobility and low noise. But they are power hungry and bulky in size which made them less attractive for highly integrated circuit design. On the contrary, CMOS technology always drew attraction because of low cost, low power dissipation and high level of integration facility. But their notorious parasitic characteristic and inferior noise performance makes high speed transceiver design very challenging. The emergence of nano-scale CMOS offer highly scaled feature sized transistors with transition frequencies exceeding 200 GHz and can improve optical receiver performance significantly. Increasing bandwidth to meet the target data rate is the most challenging task of TIA design especially in CMOS technology. Several CMOS TIA architectures have been published recently [6]-[11] for 40 Gb/s data rate having bandwidth no more than 40 GHz. In contrast to existing works, the goal of this research is to step further and design a single channel stand-alone iii TIA compatible in serial 100 Gb/s data rate with enhanced bandwidth and optimized transimpedance gain, input referred noise and group delay variation. A 100 Gb/s transimpedance amplifier (TIA) for opt

Question: What is the difference between a transimpedance amplifier and a transimpedance amplifier?


Real-time decompression and visualization of animated volume data. Interactive exploration of animated volume data is required by many application, but the huge amount of computational time and storage space needed for rendering does not allow the visualization of animated volumes by now. In this paper we introduce an algorithm running at interactive frame rates using 3d wavelet transforms that allows for any wavelet, motion compensation techniques and various encoding schemes of the resulting wavelet coefficients to be used. We analyze different families and orders of wavelets for compression ratio and the introduced error. We use a quantization that has been optimized for the visual impression of the reconstructed volume independent of the viewing. This enables us to achieve very high compression ratios while still being able to reconstruct the volume with as few visual artifacts as possible. A further improvement of the compression ratio has been achieved by applying a motion compensation scheme to exploit temporal coherency. Using these scheme we are capable of decompressing each volume of our animation at interactive frame rates, while visualizing these decompressed volumes on a single PC. We also present a number of improved visualization algorithms for high quality display using OpenGL hardware running at interactive frame rates on a standard PC.

Question: What is the compression ratio of this algorithm?


Bayesian Gaussian process models : PAC-Bayesian generalisation error bounds and sparse approximations. Non-parametric models and techniques enjoy a growing popularity in the field of machine learning, and among these Bayesian inference for Gaussian process (GP) models has recently received significant attention. We feel that GP priors should be part of the standard toolbox for constructing models relevant to machine learning in the same way as parametric linear models are, and the results in this thesis help to remove some obstacles on the way towards this goal. In the first main chapter, we provide a distribution-free finite sample bound on the difference between generalisation and empirical (training) error for GP classification methods. While the general theorem (the PAC-Bayesian bound) is not new, we give a much simplified and somewhat generalised derivation and point out the underlying core technique (convex duality) explicitly. Furthermore, the application to GP models is novel (to our knowledge). A central feature of this bound is that its quality depends crucially on task knowledge being encoded faithfully in the model and prior distributions, so there is a mutual benefit between a sharp theoretical guarantee and empirically well-established statistical practices. Extensive simulations on real-world classification tasks indicate an impressive tightness of the bound, in spite of the fact that many previous bounds for related kernel machines fail to give non-trivial guarantees in this practically relevant regime. In the second main chapter, sparse approximations are developed to address the problem of the unfavourable scaling of most GP techniques with large training sets. Due to its high importance in practice, this problem has received a lot of attention recently. We demonstrate the tractability and usefulness of simple greedy forward selection with information-theoretic criteria previously used in active learning (or sequential design) and develop generic s

Question: What is the difference between a bayesian and a non-bayesian model?


Impacts of IoT and big data to automotive industry. With the recent advancement in technologies such as embedded system, wireless distributed sensor, light weight material, smart cognitive radio networks, cloud computing, higher efficiency and ultra-low emission internal combustion engines, intelligent converter, high performance battery and fuel cell technology, the production of smarter, safer, energy efficient and zero emission vehicles is possible in near future. Apart from vehicle technologies, other factors such as road users', well maintained road infrastructure, well maintained vehicles, drivers' attitudes and law and enforcement are also important to be considered and they should work together in order to make our world natural resources can be preserved and maintain cleaner environment and produce sustainable mobility. This paper will discuss the impacts of IoT and Big Data and other emerging technologies mentioned above to the automotive industry. It will include discussion on education, economy, advanced technology, environment, safety and energy.

Question: What are the impacts of IoT and big data to automotive industry?


Millimeter Wave MIMO With Lens Antenna Array: A New Path Division Multiplexing Paradigm. Millimeter wave (mmWave) communication is a promising technology for future wireless systems, while one practical challenge is to achieve its large-antenna gains with only limited radio frequency (RF) chains for cost-effective implementation. To this end, we study in this paper a new lens antenna array enabled mmWave multiple-input multiple-output (MIMO) communication system. We first show that the array response of lens antenna arrays follows a "sinc" function, where the antenna element with the peak response is determined by the angle of arrival (AoA)/departure (AoD) of the received/transmitted signal. By exploiting this unique property along with the multi-path sparsity of mmWave channels, we propose a novel low-cost and capacity-achieving spatial multiplexing scheme for both narrow-band and wide-band mmWave communications, termed path division multiplexing (PDM), where parallel data streams are transmitted over different propagation paths with simple per-path processing. We further propose a simple path grouping technique with group-based small-scale MIMO processing to effectively mitigate the inter-stream interference due to similar AoAs/AoDs. Numerical results are provided to compare the performance of the proposed mmWave lens MIMO against the conventional MIMO with uniform planar arrays (UPAs) and hybrid analog/digital processing. It is shown that the proposed design achieves significant throughput gains as well as complexity and cost reductions, thus leading to a promising new paradigm for mmWave MIMO communications.

Question: What is the difference between a lens antenna array and a UPA?


Further Optimal Regret Bounds for Thompson Sampling. Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized algorithm based on Bayesian ideas, and has recently generated significant interest after several studies demonstrated it to have comparable or better empirical performance compared to the state of the art methods. In this paper, we provide a novel regret analysis for Thompson Sampling that proves the first near-optimal problem-independent bound of O( √ NT lnT ) on the expected regret of this algorithm. Our novel martingalebased analysis techniques are conceptually simple, and easily extend to distributions other than the Beta distribution. For the version of Thompson Sampling that uses Gaussian priors, we prove a problemindependent bound of O( √ NT lnN) on the expected regret, and demonstrate the optimality of this bound by providing a matching lower bound. This lower bound of Ω( √ NT lnN) is the first lower bound on the performance of a natural version of Thompson Sampling that is away from the optimal bound (O( √ NT )) achievable for the multi-armed bandit problem by another algorithm [4]. Our near-optimal problem-independent bounds for Thompson Sampling solve a COLT 2012 open problem of Chapelle and Li. Additionally, our techniques simultaneously provide the optimal problem-dependent bound of (1 + ) ∑ i lnT d(μi,μ1) +O( 2 ) on the expected regret. The optimal problem-dependent regret bound for this problem was first Preliminary work. Under review by AISTATS 2013. Do not distribute. proven recently by Kaufmann et al. [15].

Question: What is the optimal regret bound for Thompson sampling?


Web-scale knowledge extraction from semi-structured tables. A wealth of knowledge is encoded in the form of tables on the World Wide Web. We propose a classification algorithm and a rich feature set for automatically recognizing layout tables and attribute/value tables. We report the frequencies of these table types over a large analysis of the Web and propose open challenges for extracting from attribute/value tables semantic triples (knowledge). We then describe a solution to a key problem in extracting semantic triples: protagonist detection, i.e., finding the subject of the table that often is not present in the table itself. In 79% of our Web tables, our method finds the correct protagonist in its top three returned candidates.

Question: What is the frequency of tables on the web?


Bernoulli Embeddings for Graphs. Just as semantic hashing (Salakhutdinov and Hinton 2009) can accelerate information retrieval, binary valued embeddings can significantly reduce latency in the retrieval of graphical data. We introduce a simple but effective model for learning such binary vectors for nodes in a graph. By imagining the embeddings as independent coin flips of varying bias, continuous optimization techniques can be applied to the approximate expected loss. Embeddings optimized in this fashion consistently outperform the quantization of both spectral graph embeddings and various learned real-valued embeddings, on both ranking and pre-ranking tasks for a variety of datasets.

Question: What is a binary valued embedding?


On the statistics of natural stochastic textures and their application in image processing. Statistics of natural images has become an important subject of research in recent years. The highly kurtotic, non-Gaussian, statistics known to be characteristic of many natural images are exploited in various image processing tasks. In this paper, we focus on natural stochastic textures (NST) and substantiate our finding that NST have Gaussian statistics. Using the well-known statistical self-similarity property of natural images, exhibited even more profoundly in NST, we exploit a Gaussian self-similar process known as the fractional Brownian motion, to derive a fBm-PDE-based singleimage superresolution scheme for textured images. Using the same process as a prior, we also apply it in denoising of NST.

Question: What is the fractional Brownian motion?


Person Re-identification: What Features Are Important?. State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with a single vector of global weights, which are assumed to be universally good for all individuals, independent to their different appearances. In this study, we show that certain features play more important role than others under different circumstances. Consequently, we propose a novel unsupervised approach for learning a bottom-up feature importance, so features extracted from different individuals are weighted adaptively driven by their unique and inherent appearance attributes. Extensive experiments on two public datasets demonstrate that attribute-sensitive feature importance facilitates more accurate person matching when it is fused together with global weights obtained using existing methods.

Question: What is the difference between a person re-identification method and a person re-identification system?


HERB: a home exploring robotic butler. We describe the architecture, algorithms, and experiments with HERB, an autonomous mobile manipulator that performs useful manipulation tasks in the home. We present new algorithms for searching for objects, learning to navigate in cluttered dynamic indoor scenes, recognizing and registering objects accurately in high clutter using vision, manipulating doors and other constrained objects using caging grasps, grasp planning and execution in clutter, and manipulation on pose and torque constraint manifolds. S.S. Srinivasa ( ) · D. Ferguson · C.J. Helfrich Intel Research Pittsburgh, 4720 Forbes Avenue, Suite 410, Pittsburgh, PA 15213, USA e-mail: siddhartha.srinivasa@intel.com C.J. Helfrich e-mail: casey.j.helfrich@intel.com D. Berenson · A. Collet · R. Diankov · G. Gallagher · G. Hollinger · J. Kuffner · M.V. Weghe The Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA D. Berenson e-mail: dberenso@ri.cmu.edu A. Collet e-mail: acollet@ri.cmu.edu R. Diankov e-mail: rdiankov@ri.cmu.edu G. Gallagher e-mail: ggallagh@ri.cmu.edu G. Hollinger e-mail: gholling@ri.cmu.edu J. Kuffner e-mail: kuffner@ri.cmu.edu M.V. Weghe e-mail: vandeweg@ri.cmu.edu We also present numerous severe real-world test results from the integration of these algorithms into a single mobile manipulator.

Question: What is the canadian military?


Graph SLAM based mapping for AGV localization in large-scale warehouses. The operation of industrial Automated Guided Vehicles (AGV) today requires designated infrastructure and readily available maps for their localization. In logistics, high effort and investment is necessary to enable the introduction of AGVs. Within the SICK AG coordinated EU-funded research project PAN-Robots we aim to reduce the installation time and costs dramatically by semi-automated plant exploration and localization based on natural landmarks. In this paper, we present our current mapping and localization results based on measurement data acquired at the site of our project partner Coca-Cola Iberian Partners in Bilbao, Spain. We evaluate our solution in terms of accuracy of the map, i.e. comparing landmark position estimates with a ground truth map of millimeter accuracy. The localization results are shown based on artificial landmarks as well as natural landmarks (gridmaps) based on the same graph based optimization solution.

Question: What is the accuracy of the map?


Efficient SIMD code generation for runtime alignment and length conversion. When generating codes for today's multimedia extensions, one of the major challenges is to deal with memory alignment issues. While hand programming still yields best performing SIMD codes, it is both time consuming and error prone. Compiler technology has greatly improved, including techniques that simdize loops with misaligned accesses by automatically rearranging mis-aligned memory streams in registers. Current techniques are applicable to runtime alignments, but they aggressively reduce the alignment overhead only when all alignments are known at compile time. This paper presents two major enhancements to the state of the art, improving both performance and coverage. First, we propose a novel technique to simdize loops with runtime alignment nearly as efficiently as those with compile-time misalignment. Runtime alignment is pervasive in real applications because it is either part of the algorithms, or it is an artifact of the compiler's inability to extract accurate alignment information from complex applications. Second, we incorporate length conversion operations, e.g., conversions between data of different sizes, into the alignment handling framework. Length conversions are pervasive in multimedia applications where mixed integer types are often used. Supporting length conversion can greatly improve the coverage of simdizable loops. Experimental results indicate that our runtime alignment technique achieves a 19% to 32% speedup increase over prior art for a benchmark stressing the impact of misaligned data. We also demonstrate speedup factors of up to 8.11 for real benchmarks over sequential execution.

Question: How to generate SIMD codes for runtime alignment?