title: The Character Strengths Rating Form (CSRF): Development and initial assessment of a 24-item rating scale to assess character strengths ☆

abstract: Character strengths are morally, positively, valued traits that are related to several positive life outcomes. In this study, the Character Strengths Rating Form (CSRF), a 24-item rating form of character strengths based on the classification proposed by Peterson and Seligman (2004), was developed using the data of 211 German-speaking adults. The CSRF yielded good convergence with Peterson and Seligman’s Values in Action Inventory of Strengths (VIA-IS) in terms of descriptive statistics, relationships with socio-demographic variables, and associations with life satisfaction; the means correlated .91, and standard deviations correlated .80. Correlations between corresponding strengths in the CSRF and the VIA-IS were between .41 and .77. Rank-order correlations of the correlations of both measures with age, education, and life satisfaction were .74, .76, and .84, respectively. Factor structure congruence coefficients ranged between .92 and .99. The rank-order correlation of the associations of the 5 factors with life satisfaction was .90. The CSRF proved to be a valid instrument for the assessment of character strengths. Its use is recommended for a brief measurement of character strengths when economy of instruments is at a premium (e.g., in large-scale longitudinal studies).


title: Analyzing differences between teachers when learning object affordances via guided exploration

abstract: Our work focuses on robots deployed in human environments. These robots, which will need specialized object manipulation skills, should leverage end-users to efficiently learn the affordances of objects in their environment. This approach is promising because prior work has shown that people naturally focus on showing salient aspects of objects when providing demonstrations. In our work, we use a guided exploration approach that combines self- and supervised learning. We present experimental results for a robot learning three affordances on four objects using 1219 interactions. We compare three conditions: (1) learning through self-exploration, (2) learning from supervised examples provided by 10 naive users, and (3) a combined approach of self-exploration biased by user input. Previous analysis of this data focused on aggregate performance of these different strategies across all teachers, and showed that a combined approach is the most efficient and successful. In this article, we provide additional det...


title: Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera

abstract: We present a real-time liveness detection approach against photograph spoofing in face recognition, by recognizing spontaneous eyeblinks, which is a non-intrusive manner. The approach requires no extra hardware except for a generic webcamera. Eyeblink sequences often have a complex underlying structure. We formulate blink detection as inference in an undirected conditional graphical framework, and are able to learn a compact and efficient observation and transition potentials from data. For purpose of quick and accurate recognition of the blink behavior, eye closity, an easily-computed discriminative measure derived from the adaptive boosting algorithm, is developed, and then smoothly embedded into the conditional model. An extensive set of experiments are presented to show effectiveness of our approach and how it outperforms the cascaded Adaboost and HMM in task of eyeblink detection.


title: Sentiment-Aspect Extraction based on Restricted Boltzmann Machines

abstract: Aspect extraction and sentiment analysis of reviews are both important tasks in opinion mining. We propose a novel sentiment and aspect extraction model based on Restricted Boltzmann Machines to jointly address these two tasks in an unsupervised setting. This model reflects the generation process of reviews by introducing a heterogeneous structure into the hidden layer and incorporating informative priors. Experiments show that our model outperforms previous state-of-the-art methods.


title: The LMC Skills, Confidence & Preparedness Index (SCPI): development and evaluation of a novel tool for assessing self-management in patients with diabetes

abstract: Optimal diabetes care requires a specific set of self-management behaviours. The purpose of this study was to present the development and initial psychometric evaluation of a new tool to measure three key aspects of a patient’s diabetes self-management: knowledge of the skill, confidence in being able to perform the skill and preparedness to implement the skill. A sequential exploratory mixed-methods design was used. A panel of educators, researchers and clinicians established a scale with items that would adequately capture skills, confidence and preparedness in seven core health behaviours central to diabetes care. The psychometric properties of the items were pilot tested on 120 participants with diabetes from a tertiary referral centre, and repeated 6 months later on 70 participants. Item selection was informed by factor analysis, item-total statistics and the need for brevity. Twenty five items from a pool of 36 were retained, with an excellent overall intraclass correlation (ICC) of 0.94 (95% CI 0.92–0.99; p < 0.001). Internal consistency for the subscales (skills-9 items, confidence - 8 items, preparedness – 8 items) was very good (intraclass correlation between 0.83 and 0.88), and retest reliability after 6 months was also good (r = 0.48; p < 0.01). The scale was positively correlated to established scales that assess skill (Michigan Diabetes Knowledge Test) (r = 0.21;p = 0.01), and assess skill and confidence (Diabetes Empowerment Scale) (r = 0.28;p < 0.01). The Skills, Confidence & Preparedness Index is a brief and easy to administer new scale that is more comprehensive than existing tools. It should be used to assess self-management in patients with diabetes, optimize the resources applied to each patient, and determine educational needs and direct clinical management. The scale should be further evaluated in a broader population of patients with diabetes.


title: Adaptive and efficient lane change path planning for automated vehicles

abstract: This article presents a path planning concept for automated driving. It builds upon existing approaches using quintic polynomials for path planning in structured environments. The contribution of this work are analytical solutions to evaluate selected properties of quintic polynomial based paths such as maximum acceleration and jerk or overshooting behavior. In addition, a concept for generating asymmetrical lane change trajectories is proposed. The presented concepts are evaluated in terms of computational costs and applicability in a real test vehicle.


title: Coil design for 100 KHz and 6.78 MHz WPT system :Litz and solid wires and winding methods

abstract: Coil design is one of the critical part to get high power transfer efficiency for wireless power transfer (WPT) system. There exists coil figure of merit (FOM), which is used to evaluate coil design in the aspect of WPT system efficiency. WPT coil FOM is function of coil quality factor (Q-factor) and coupling coefficient, k and higher Q and k guarantees higher coil-to-coil efficiency. Coil DC loss, skin effect, proximity effect, and displacement current between turns are key factors to determine Q-factor and its effects changes depending on wire-type, winding method, and target frequency. In this paper, we focus on transmitter coil design to maximize Q-factor in both 100 MHz and 6.78 MHz. Coil Q-factor equations are provided to explain and analyze the physics and measurement was done for the validation of the equation. Finally optimal coil winding methods are proposed to maximize coil Q-factor


title: CSI-RS design for 3D MIMO in future LTE-Advanced

abstract: Three-dimensional (3D) MIMO is a promising enhancement of MIMO technology to improve system capacity by exploiting the spatial characteristics in both azimuth dimension and elevation dimension. As one of the key aspects in practical systems, the channel state information-reference signal (CSI-RS) design specific to future LTE-Advanced are discussed, and a precoding scheme of CSI-RS considering elevation beamforming is proposed. It is shown that the proposed scheme has the advantages of better performance, small standardization efforts due to its reusability of existing codebook and CSI-RS configurations, and transparency to the legacy UEs.


title: A dual-polarity grappa kernel for the robust reconstruction of accelerated EPI data

abstract: The quality of high-resolution Echo Planar images of the human brain has improved greatly in recent years, enabled by novel multi-channel receiver coil arrays and parallel imaging. However, in regions with local field inhomogeneity, EPI artifacts limit which parts of the brain can be imaged successfully. In this work, we present evidence that certain image artifacts can be attributed to nonlinear phase errors that are present in regions of local susceptibility gradients and certain coil array elements. Because these phase errors cannot be corrected with conventional Nyquist ghost correction, we propose a new method that integrates ghost correction with parallel imaging reconstruction. The proposed Dual-Polarity GRAPPA method operates directly on raw EPI data to estimate k-space data from the under-sampled acquisition while simultaneously correcting inherent EPI phase errors. We present examples of this method successfully removing strong phase-error artifacts in high-resolution 7T EPI data.


title: PAMIR - a wideband phased array SAR/MTI system

abstract: Air- and spaceborne imaging radar systems in forthcoming surveillance and reconnaissance tasks have to meet increasingly severe demands. The next generation of top-level synthetic aperture radar (SAR) systems will comprise, among others, high resolution and long-range imaging capabilities, highly sensitive ground moving target indication and a multitude of sophisticated operational modes. The variety of tasks can be fulfilled only by the use of a reconfigurable phased array antenna together with a comprehensive wideband system design and a multichannel capability. At FGAN a new experimental X-band radar has been conceived, which will possess in its final upgrade an electronically steerable phased array consisting of 16 autonomous and reconfigurable subapertures, five independent receive channels, and a total signal bandwidth of about 1.8 GHz. The sensor is called PAMIR (Phased Array Multifunctional Imaging Radar). It is envisaged to demonstrate SAR imaging at a very high resolution and for a long range. The fine resolution will also be achieved with inverse SAR (ISAR) imaging of ground moving objects. Furthermore, the number of receive channels will allow ground-moving target indication (GMTI) by space-time adaptive processing and single-pass interferometric SAR (IfSAR) with a very high 3-D resolution. In its current stage of extension PAMIR is operable with one receive channel and a mechanically steerable antenna array. The system design and the intended capabilities of PAMIR are described. Ground-based and airborne experimental results concerning high-resolution SAR and ISAR imaging are also presented.


title: Studies of an On-Package Dual-Mode Square-Loop Band Pass Filter for Highly Integrated Wireless Transceivers Using NU-FDTD

abstract: SUMMARY An on-package dual-mode square-loop band pass ﬁlteris studied by applying a non-uniform ﬁnite diﬀerence time domain (NU-FDTD) method. The ﬁlter is integrated on a package containing atransceiver, and it is designed to operate in dual-modes, i.e., TM z100 andTM z010 , to ensure a good electric performance around the center frequencyat 5.25GHz, which is commonly allocated in wireless local area network(WLAN). This ﬁlter is also referred as a dual-mode integrated-circuit pack-age ﬁlter (DM-ICPF) based on its operational mode and integration onto anIC-package. The frequency characteristics in terms of the scattering param-eters are studied, and the results are validated against the computed resultsusing commercial software, the high frequency structure simulator (HFSS).Results show an excellent agreement between the numerical data, and theproposed DM-ICPF structure can be applied in the area of the highly inte-grated wireless transceivers. key words: DM-ICPF, NU-FDTD, wireless transceivers


title: Recent Advances and Uses of Grape Flavonoids as Nutraceuticals

abstract: Grape is one of the oldest fruit crops domesticated by humans. The numerous uses of grape in making wine, beverages, jelly, and other products, has made it one of the most economically important plants worldwide. The complex phytochemistry of the berry is characterized by a wide variety of compounds, most of which have been demonstrated to have therapeutic or health promoting properties. Among them, flavonoids are the most abundant and widely studied, and have enjoyed greater attention among grape researchers in the last century. Recent studies have shown that the beneficial health effects promoted by consumption of grape and grape products are attributed to the unique mix of polyphenolic compounds. As the largest group of grape polyphenols, flavonoids are the main candidates considered to have biological properties, including but not limited to antioxidant, anti-inflammatory, anti-cancer, antimicrobial, antiviral, cardioprotective, neuroprotective, and hepatoprotective activities. Here, we discuss the recent scientific advances supporting the beneficial health qualities of grape and grape-derived products, mechanisms of their biological activity, bioavailability, and their uses as nutraceuticals. The advantages of modern plant cell based biotechnology as an alternative method for production of grape nutraceuticals and improvement of their health qualities are also discussed.


title: Algorithms and Applications for Community Detection in Weighted Networks

abstract: Community detection is an important issue due to its wide use in designing network protocols such as data forwarding in Delay Tolerant Networks (DTN) and worm containment in Online Social Networks (OSN). However, most of the existing community detection algorithms focus on binary networks. Since most networks are naturally weighted such as DTN or OSN, in this article, we address the problems of community detection in weighted networks, exploit community for data forwarding in DTN and worm containment in OSN, and demonstrate how community can facilitate these network designs. Specifically, we propose a novel community detection algorithm, and introduce two metrics: intra-centrality and inter-centrality, to characterize nodes in communities, based on which we propose an efficient data forwarding algorithm for DTN and a worm containment strategy for OSN. Extensive trace-driven simulation results show that the proposed community detection algorithm, the data forwarding algorithm, and the worm containment strategy significantly outperform existing works.


title: CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training

abstract: We present variational generative adversarial networks, a general learning framework that combines a variational auto-encoder with a generative adversarial network, for synthesizing images in fine-grained categories, such as faces of a specific person or objects in a category. Our approach models an image as a composition of label and latent attributes in a probabilistic model. By varying the fine-grained category label fed into the resulting generative model, we can generate images in a specific category with randomly drawn values on a latent attribute vector. Our approach has two novel aspects. First, we adopt a cross entropy loss for the discriminative and classifier network, but a mean discrepancy objective for the generative network. This kind of asymmetric loss function makes the GAN training more stable. Second, we adopt an encoder network to learn the relationship between the latent space and the real image space, and use pairwise feature matching to keep the structure of generated images. We experiment with natural images of faces, flowers, and birds, and demonstrate that the proposed models are capable of generating realistic and diverse samples with fine-grained category labels. We further show that our models can be applied to other tasks, such as image inpainting, super-resolution, and data augmentation for training better face recognition models.


title: The subepithelial connective tissue graft palatal donor site: anatomic considerations for surgeons.

abstract: Surgeons must become completely familiar with the anatomy of the palatal donor site to feel confident in providing the subepithelial connective tissue graft procedure. Variations in the size and shape of the hard palate affect the dimensions of donor tissue harvested, as well as the location of the greater palatine neurovascular bundle. This article classifies palatal vaults according to height as high, average, and shallow. Illustrations and cadaver dissection are utilized to demonstrate that surgeons can gain substantial donor tissue specimens without encountering the neurovascular bundle. Actions to be followed in the unlikely event that the neurovasculature is encountered are reviewed.


title: Large-scale outdoor 3D reconstruction on a mobile device

abstract: Approach for reconstructing large-scale outdoor scenes using monocular motion stereo.Runs at interactive frame rates on a Google Project Tango Development Kit Tablet.Can use strict outlier filtering, as the user can interactively record more data.Missing parts in the live preview guide the user to places which require more data. This paper presents an approach for reconstructing large-scale outdoor scenes through monocular motion stereo at interactive frame rates on a modern mobile device (Google Project Tango Development Kit Tablet). The devices fisheye camera enables a user to reconstruct large scenes in only a few minutes by simply walking through the scene. We utilize the devices GPU to compute depth maps via plane sweep stereo. In contrast to reconstructing small objects, we observe that in large-scale scenarios using motion stereo, free-space measurements are less effective for suppressing outliers due to limited possibilities for camera placement and an unbounded reconstruction volume. Furthermore, the outlier ratio in depth maps from stereo matching is much higher compared to images from depth sensors. Consequently, we propose a set of filtering steps to detect and discard unreliable depth measurements. The remaining parts of the depth maps are then integrated into a volumetric representation of the scene using a truncated signed distance function. Ours is the first method to enable live reconstruction of large outdoor scenes on a mobile device. We extensively evaluate our approach, demonstrating the benefit of rigorously filtering depth maps.


title: Physics-of-failure-based prognostics for electronic products

abstract: This paper presents a physics-of-failure (PoF)-based prognostics and health management approach for effective reliability prediction. PoF is an approach that utilizes knowledge of a product's life cycle loading and failure mechanisms to perform reliability design and assessment. PoF-based prognostics permit the assessment of product reliability under its actual application conditions. It integrates sensor data with models that enable in situ assessment of the deviation or degradation of a product from an expected normal operating condition (ie, the product's `health') and the prediction of the future state of reliability. A formal implementation procedure, which includes failure modes, mechanisms, and effects analysis, data reduction and feature extraction from the life cycle loads, damage accumulation, and assessment of uncertainty, is presented. Then, applications of PoF-based prognostics are discussed.


title: Effects of anticipated human-robot interaction and predictability of robot behavior on perceptions of anthropomorphism

abstract: Recent research has shown that anthropomorphism represents a means to facilitate HRI. Under which conditions do people anthropomorphize robots and other nonhuman agents? This research question was investigated in an experiment that manipulated participants' anticipation of a prospective human-robot interaction (HRI) with a robot whose behavior was characterized by either low or high predictability. We examined effects of these factors on perceptions of anthropomorphism and acceptance of the robot. Innovatively, the present research demonstrates that anticipation of HRI with an unpredictable agent increased anthropomorphic inferences and acceptance of the robot. Implications for future research on psychological determinants of anthropomorphism are discussed.


Transcranial Alternating Current Stimulation (tACS) Enhances Mental Rotation Performance during and after Stimulation


title: Characterizing Instant Messaging Apps on Smartphones

abstract: Proliferation of smart devices has fueled the popularity of using mobile instant messaging (IM) apps at a rapid pace. While the IM apps on smartphones have become increasingly popular, there has only been a little research on understanding the characteristics of these apps. Because most of the IM apps use proprietary protocols, it is challenging to analyze their internal operations. In this work, we present a comprehensive characterization of mobile IM apps using experiments on LTE cellular network. We decompose the operations of an IM app into multiple independent states which allows us to systematically study them. We characterize the energy and bandwidth efficiency of each of the states and provide numerous insights. Our analysis reveals that typing notification feature of the IM apps is a major contributor to the energy consumption. We also find that the bandwidth efficiency of current IM apps are alarmingly poor compared to other applications such as email and web surfing. These, along with other findings, provided in this work can help improve the energy and network performance of IM apps.


INTRODUCTION TO FUNCTIONAL DEPENDENCY NETWORK ANALYSIS


title: Interpreting Deep Classification Models With Bayesian Inference

abstract: In this paper, we propose a novel approach to interpret a well-trained classification model through systematically investigating effects of its hidden units on prediction making. We search for the core hidden units responsible for predicting inputs as the class of interest under the generative Bayesian inference framework. We model such a process of unit selection as an Indian Buffet Process, and derive a simplified objective function via the MAP asymptotic technique. The induced binary optimization problem is efficiently solved with a continuous relaxation method by attaching a Switch Gate layer to the hidden layers of interest. The resulted interpreter model is thus end-to-end optimized via standard gradient back-propagation. Experiments are conducted with two popular deep convolutional classifiers, respectively well-trained on the MNIST dataset and the CI- FAR10 dataset. The results demonstrate that the proposed interpreter successfully finds the core hidden units most responsible for prediction making. The modified model, only with the selected units activated, can hold correct predictions at a high rate. Besides, this interpreter model is also able to extract the most informative pixels in the images by connecting a Switch Gate layer to the input layer.


Psychologically Plausible Methods for Character Behavior Design


title: Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

abstract: Recent advances in deep reinforcement learning have made significant strides in performance on applications such as Go and Atari games. However, developing practical methods to balance exploration and exploitation in complex domains remains largely unsolved. Thompson Sampling and its extension to reinforcement learning provide an elegant approach to exploration that only requires access to posterior samples of the model. At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical. Thus, it is attractive to consider approximate Bayesian neural networks in a Thompson Sampling framework. To understand the impact of using an approximate posterior on Thompson Sampling, we benchmark well-established and recently developed methods for approximate posterior sampling combined with Thompson Sampling over a series of contextual bandit problems. We found that many approaches that have been successful in the supervised learning setting underperformed in the sequential decision-making scenario. In particular, we highlight the challenge of adapting slowly converging uncertainty estimates to the online setting.


title: Data-driven Super-resolution on a Tactile Dome

abstract: While tactile sensor technology has made great strides over the past decades, applications in robotic manipulation are limited by aspects such as blind spots, difficult integration into hands, and low spatial resolution. We present a method for localizing contact with high accuracy over curved, three dimensional surfaces, with a low wire count and reduced integration complexity. To achieve this, we build a volume of soft material embedded with individual off-the-shelf pressure sensors. Using data driven techniques, we map the raw signals from these pressure sensors to known surface locations and indentation depths. Additionally, we show that a finite element model can be used to improve the placement of the pressure sensors inside the volume and to explore the design space in simulation. We validate our approach on physically implemented tactile domes which achieve high contact localization accuracy ($1.1mm$ in the best case) over a large, curved sensing area ($1,300mm^2$ hemisphere). We believe this approach can be used to deploy tactile sensing capabilities over three dimensional surfaces such as a robotic finger or palm.


title: Cognition-based MRI brain tumor segmentation technique using modified level set method

abstract: Gliomas are the most common types of brain tumors seen in adults. Generally, it starts from glioma cells and affects the adjacent tissues. Even though the analysis of glioma has well developed, the identification is still poor. In this paper, we propose an efficient modified level set method for brain tumor segmentation, in which we preprocess the image to remove the noise and then accurately segment the magnetic resonance images (MRI). Therefore, this document anticipated an innovative level set algorithm for segmenting gliomas from the MRI brain images where the segmentation is made automatically by means of selecting the initial contour automatically from the maximum intensity pixel computed from the histogram intensity plots. The proposed methodology is implemented in the working platform of MATLAB to produce 99% accuracy, and the results are analyzed by the existing methods.


title: Energy-Based Geometric Multi-model Fitting

abstract: Geometric model fitting is a typical chicken-&-egg problem: data points should be clustered based on geometric proximity to models whose unknown parameters must be estimated at the same time. Most existing methods, including generalizations of RANSAC, greedily search for models with most inliers (within a threshold) ignoring overall classification of points. We formulate geometric multi-model fitting as an optimal labeling problem with a global energy function balancing geometric errors and regularity of inlier clusters. Regularization based on spatial coherence (on some near-neighbor graph) and/or label costs is NP hard. Standard combinatorial algorithms with guaranteed approximation bounds (e.g. ?-expansion) can minimize such regularization energies over a finite set of labels, but they are not directly applicable to a continuum of labels, e.g. ${\mathcal{R}}^{2}$ in line fitting. Our proposed approach (PEaRL) combines model sampling from data points as in RANSAC with iterative re-estimation of inliers and models' parameters based on a global regularization functional. This technique efficiently explores the continuum of labels in the context of energy minimization. In practice, PEaRL converges to a good quality local minimum of the energy automatically selecting a small number of models that best explain the whole data set. Our tests demonstrate that our energy-based approach significantly improves the current state of the art in geometric model fitting currently dominated by various greedy generalizations of RANSAC.


title: Enhancement of SSD by concatenating feature maps for object detection

abstract: We propose an object detection method that improves the accuracy of the conventional SSD (Single Shot Multibox Detector), which is one of the top object detection algorithms in both aspects of accuracy and speed. The performance of a deep network is known to be improved as the number of feature maps increases. However, it is difficult to improve the performance by simply raising the number of feature maps. In this paper, we propose and analyze how to use feature maps effectively to improve the performance of the conventional SSD. The enhanced performance was obtained by changing the structure close to the classifier network, rather than growing layers close to the input data, e.g., by replacing VGGNet with ResNet. The proposed network is suitable for sharing the weights in the classifier networks, by which property, the training can be faster with better generalization power. For the Pascal VOC 2007 test set trained with VOC 2007 and VOC 2012 training sets, the proposed network with the input size of 300 x 300 achieved 78.5% mAP (mean average precision) at the speed of 35.0 FPS (frame per second), while the network with a 512 x 512 sized input achieved 80.8% mAP at 16.6 FPS using Nvidia Titan X GPU. The proposed network shows state-of-the-art mAP, which is better than those of the conventional SSD, YOLO, Faster-RCNN and RFCN. Also, it is faster than Faster-RCNN and RFCN.


title: Interpreting Anaphoric Shell Nouns using Antecedents of Cataphoric Shell Nouns as Training Data

abstract: Interpreting anaphoric shell nouns (ASNs) such as this issue and this fact is essential to understanding virtually any substantial natural language text. One obstacle in developing methods for automatically interpreting ASNs is the lack of annotated data. We tackle this challenge by exploiting cataphoric shell nouns (CSNs) whose construction makes them particularly easy to interpret (e.g., the fact that X). We propose an approach that uses automatically extracted antecedents of CSNs as training data to interpret ASNs. We achieve precisions in the range of 0.35 (baseline = 0.21) to 0.72 (baseline = 0.44), depending upon the shell noun.


title: © notice, is given to the source. Subjective Well-Being and Income: Is There Any Evidence of Satiation?

abstract: Many scholars have argued that once "basic needs" have been met, higher income is no longer associated with higher in subjective well-being. We assess the validity of this claim in comparisons of both rich and poor countries, and also of rich and poor people within a country. Analyzing multiple datasets, multiple definitions of "basic needs" and multiple questions about well-being, we find no support for this claim. The relationship between well-being and income is roughly linear-log and does not diminish as incomes rise. If there is a satiation point, we are yet to reach it.


title: PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features

abstract: To address the problem of estimating camera trajectory and to build a structural three-dimensional (3D) map based on inertial measurements and visual observations, this paper proposes point–line visual–inertial odometry (PL-VIO), a tightly-coupled monocular visual–inertial odometry system exploiting both point and line features. Compared with point features, lines provide significantly more geometrical structure information on the environment. To obtain both computation simplicity and representational compactness of a 3D spatial line, Plucker coordinates and orthonormal representation for the line are employed. To tightly and efficiently fuse the information from inertial measurement units (IMUs) and visual sensors, we optimize the states by minimizing a cost function which combines the pre-integrated IMU error term together with the point and line re-projection error terms in a sliding window optimization framework. The experiments evaluated on public datasets demonstrate that the PL-VIO method that combines point and line features outperforms several state-of-the-art VIO systems which use point features only.


Recommendation of Job Offers Using Random Forests and Support Vector Machines


title: A Security Analysis of Cyber-Physical Systems Architecture for Healthcare

abstract: This paper surveys the available system architectures for cyber-physical systems. Several candidate architectures are examined using a series of essential qualities for cyber-physical systems for healthcare. Next, diagrams detailing the expected functionality of infusion pumps in two of the architectures are analyzed. The STRIDE Threat Model is then used to decompose each to determine possible security issues and how they can be addressed. Finally, a comparison of the major security issues in each architecture is presented to help determine which is most adaptable to meet the security needs of cyber-physical systems in healthcare.


title: Development of Component Mechanisms and Novel Actuation for Origami Inspired Designs

abstract: Abstract : We propose to design and implement novel origami-inspired mechanisms that are embedded with actuators. The key research goal is to contribute toestablishing fundamental design components that can be used to develop various active engineering systems inspired by origami designs, and provideconcrete demonstrations of how origami designs can be exploited to provide a scalable mechanism that can function as a useful active system, notjust a structure. To achieve the goal, we tried to develop multilateral topics related to origami-inspired mechanisms which includes pattern design aswell as fabrication and actuation. We suggest novel design methods that solve specific design requirement, but our theme and design methodologycan be applied as design component to various applications.The research has three main thrusts:1. Innovative design and fabrication of origami-inspired functional mechanisms.2. Novel embedded actuation system for origami-inspired mechanisms3. Integration of the developed mechanisms and actuation to practical application


Aspect-based Sentiment Analysis on Hotel Reviews


title: Developing predictive models for time to failure estimation

abstract: The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management (PHM) systems. Taking advantage of advances in sensor technologies, PHM systems enable a predictive maintenance strategy through continuously monitoring the health of complex systems. The core of PHM technology is prognostic which is able to estimate time to failure (TTF) for the monitored components or systems using the built-in predictive models. In this paper, the state of the art of TTF estimation will be first reviewed. After introduction of traditional methods of TTF estimation, we will present the developed approaches for estimating TTF, including classification, regression, on-demand regression, Particle Filtering (PF)-based method, and so on. The main purpose of this paper is to summarize the work on TTF estimation technologies developed in the past decade.


title: Higher order graph centrality measures for Neo4j

abstract: Graphs are currently the epicenter of intense research as they lay the theoretical groundwork in diverse fields ranging from combinatorial optimization to computational neuroscience. Vertex centrality plays a crucial role in graph mining as it ranks them according to their contribution to overall graph communication. Specifically, within the social network analysis context centrality identifies influential indivduals, whereas in the bioinformatics field centrality locates dominant proteins in protein-to-protein interaction. In recent years graph databases, part of the rising NoSQL movement, have been added to the graph analysis toolset. An implementation of eigenvector centrality, a prominent member of the broad class of spectral centrality, in Java and NetBeans designed for use with Neo4j, a major schemaless graph database, is outlined and the findings resulting from its application to a real world social graph are discussed.


title: Revisiting why students drop CS1

abstract: This paper describes a qualitative study of the factors that contribute to a student's decision to withdraw from CS1. Individual interviews were held with 18 students in a majors-focused CS1 at a large, research-intensive North American university, and results both validate and extend previous work on the experience of students who struggle in introductory computer science. In particular, our analysis confirms the complexity of the decision to drop, with students citing a combination of interrelated factors that contribute to the decision. Lack of time, combined with ineffective study strategies or with a prioritization of other courses, were the most commonly cited combinations of factors. Interestingly, when compared to the experience of students who chose to complete the course, there is evidence that students encounter a decision point when they realize they are or soon will be behind. Students who drop speak of focusing on other priorities or being unable to catch up, while students who complete speak of understanding the need to use new techniques for learning and increasing their efforts.


title: A 79 GHz differentially fed grid array antenna

abstract: This paper presents a planar grid array antenna with a 100 Ω differential microstrip line feed on a single layer of standard soft substrate. The antenna operates in the 79 GHz frequency band for automotive radar applications. Its single row design offers a narrow beam in elevation and a wide beam in azimuth. Together with the differential microstrip line feeding, the antenna is suitable for differential multichannel MMICs in the frequency range.


title: Lubricating effect of thin films of styrene-dimethylsiloxane block copolymers

abstract: The lubricating effect of styrene-dimethylsiloxane block copolymer films spin cast on Si wafers was found to increase with increasing size of the poly(dimethylsiloxane) (PDMS) block. Friction properties of symmetrical and close-to-symmetrical block copolymers were evaluated in contact with bromobutyl rubber in a plate-on-plate configuration. X-ray photoelectron spectroscopy showed that the copolymer films were all covered by an at least a 10 nm thick layer of PDMS. The lubricating effect remained constant down to a film thickness comparable to the chain dimensions of the copolymer, R-0, after which it decreased progressively. At thicknesses below R-0, the copolymer dewetted the wafer and formed discrete domains with sizes comparable to R-0. The copolymer could further be blended with up to 70 wt % polystyrene without detecting a decrease in the lubricating effect. A mechanism is proposed for the observed frictional behavior where microflakes are abraded from lamellar copolymer surfaces and stick to the contacting rubber surface, whereafter lubrication may result from sliding of PDMS brushes against each other. (Less)


title: Agent-based computational finance

abstract: This chapter surveys research on agent-based models used in finance. It will concentrate on models where the use of computational tools is critical for the process of crafting models which give insights into the importance and dynamics of investor heterogeneity in many financial settings.


title: PEARP, the Météo‐France short‐range ensemble prediction system

abstract: Meteo-France has implemented a short-range ensemble prediction system known as Prevision d'Ensemble ARPEGE (PEARP). This system is a global ensemble performing forecasts up to 4.5 days. It uses the operational global numerical weather prediction model Action de Recherche Petite Echelle Grande Echelle (ARPEGE) and benefits from variable horizontal resolution, so that it is comparable to some limited-area mesoscale systems over France. Perturbations to the initial conditions are computed by combining an ensemble data assimilation system with singular vectors. Model uncertainties are represented through a ‘multiphysics’ approach with ten different physical parametrization sets. The article describes the set-up of the system and provides an assessment of the approaches used to represent initial conditions and model uncertainties. The positive impact of the variable horizontal resolution of PEARP is also illustrated. As a global ensemble forecast system (EFS), PEARP is also used to forecast cyclone tracks. It is shown that it has correctly predicted the landfall of hurricane Sandy. The performance of PEARP as run operationally with these features in 2014 is assessed objectively and compared with that of four operational global EFSs using classical probabilistic scores. This comparison is based on The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data. This is one of the first evaluations of EFSs for short-range forecasts. The reliability and global skill of the five EFSs are evaluated over a three-month period with scores computed against observations. PEARP shows skill comparable to or better than the other EFSs.


title: Comparison of empirical testing and walkthrough methods in user interface evaluation

abstract: We investigated the relative effectiveness of empirical usability testing and individual and team walkthrough methods in identifying usability problems in two graphical user interface office systems. The findings were replicated across the two systems and show that the empirical testing condition identified the largest number of problems, and identified a significant number of relatively severe problems that were missed by the walkthrough conditions. Team walkthroughs achieved better results than individual walkthroughs in some areas. About a third of the significant usability problems identified were common across all methods. Cost-effectiveness data show that empirical testing required the same or less time to identify each problem when compared to walkthroughs.


title: High-voltage high-frequency inverter using 3.3kV SiC MOSFETs

abstract: High voltage SiC MOSFETs enable high switching frequency operation that would otherwise be only possible with more complex architectures such as multilevel or interleaved inverters. In this paper, a full bridge inverter using 3.3kV SiC MOSFETs is presented to achieve high-voltage (2100V dc bus), high-frequency (62.5kHz) operation with a simple hard switching PWM technique. The switching characteristics of the 3.3kV SiC device are presented and the effect of the parasitic parameters is analyzed in details. The analysis shows that the tiny stray output capacitor, which is mainly introduced by the bus-bars implemented with a PCB, can have a significant impact on the switching waveforms of the SiC devices. The reported experimental results validate the performance of the proposed inverter.


MICRO-PULLWINDING – AN AUTOMATED PRODUCTION TECHNOLOGY FOR MEDICAL DEVICES


title: Kernel-predicting convolutional networks for denoising Monte Carlo renderings

abstract: Regression-based algorithms have shown to be good at denoising Monte Carlo (MC) renderings by leveraging its inexpensive by-products (e.g., feature buffers). However, when using higher-order models to handle complex cases, these techniques often overfit to noise in the input. For this reason, supervised learning methods have been proposed that train on a large collection of reference examples, but they use explicit filters that limit their denoising ability. To address these problems, we propose a novel, supervised learning approach that allows the filtering kernel to be more complex and general by leveraging a deep convolutional neural network (CNN) architecture. In one embodiment of our framework, the CNN directly predicts the final denoised pixel value as a highly non-linear combination of the input features. In a second approach, we introduce a novel, kernel-prediction network which uses the CNN to estimate the local weighting kernels used to compute each denoised pixel from its neighbors. We train and evaluate our networks on production data and observe improvements over state-of-the-art MC denoisers, showing that our methods generalize well to a variety of scenes. We conclude by analyzing various components of our architecture and identify areas of further research in deep learning for MC denoising.


title: From IoT big data to IoT big services

abstract: The large-scale deployments of Internet of Things (IoT) systems have introduced several new challenges in terms of processing their data. The massive amount of IoT-generated data requires design solutions to speed up data processing, scale up with the data volume and improve data adaptability and extensibility. Beyond existing techniques for IoT data collection, filtering, and analytics, innovative service computing technologies are required for provisioning data-centric and scalable IoT services. This paper presents a service-oriented design model and framework for realizing scalable and efficient acquisition, processing and integration of data-centric IoT services. In this approach, data-centric IoT services are organized in a service integrating tree structure, adhering to the architecture of many large-scale IoT systems, including recent fog-based IoT computing models. A service node in the tree is called a Big Service and acts as an integrator, collecting data from lower level Big Services, processing them, and delivering the result to higher level IoT Big Services. The service tree thereby encapsulates required data processing functions in a hierarchical manner in order to achieve scalable and real-time data collection and processing. We have implemented the IoT Big Services framework leveraging a popular cloud-based service and data platform called Firebase, and evaluated its performance in terms of real-time requirements.


title: Live video analytics at scale with approximation and delay-tolerance

abstract: Video cameras are pervasively deployed for security and smart city scenarios, with millions of them in large cities worldwide. Achieving the potential of these cameras requires efficiently analyzing the live videos in real-time. We describe VideoStorm, a video analytics system that processes thousands of video analytics queries on live video streams over large clusters. Given the high costs of vision processing, resource management is crucial. We consider two key characteristics of video analytics: resource-quality tradeoff with multi-dimensional configurations, and variety in quality and lag goals. VideoStorm's offline profiler generates query resource-quality profile, while its online scheduler allocates resources to queries to maximize performance on quality and lag, in contrast to the commonly used fair sharing of resources in clusters. Deployment on an Azure cluster of 101 machines shows improvement by as much as 80% in quality of real-world queries and 7× better lag, processing video from operational traffic cameras.


title: Design and implementation of a fire detection and control system for automobiles using fuzzy logic

abstract: The immense benefits of fire in road transport cannot be overemphasized. However more than two thousand vehicles are damaged by unwanted fire on a daily basis. On a global scale, incendiary losses to the automobile and insurance industries have ran into billions of dollars over the last decade. A not-so-distant contributory factor is the lack of a sophisticated fire safety system on the automobile. This has been addressed by designing and implementing fuzzy logic control system with feedback over an Arduino micro-controller system. The automatic system consisting of flame sensors, temperature sensors, smoke sensors and a re-engineered mobile carbon dioxide air-conditioning unit was tested on a medium sized physical car. Results show that the automobile fire detection and control system devoid of false alarms, detects and extinguishes fire under 20 seconds. An innovative, very promising solution module for hardware implementation in fire detection and control for automobiles has been developed by using new algorithms and fuzzy logic.


title: Differentially private grids for geospatial data

abstract: We tackle the problem of constructing a differentially private synopsis for two-dimensional datasets such as geospatial datasets. The current state-of-the-art methods work by performing recursive binary partitioning of the data domains, and constructing a hierarchy of partitions. We show that the key challenge in partition-based synopsis methods lies in choosing the right partition granularity to balance the noise error and the nonuniformity error. We study the uniform-grid approach, which applies an equi-width grid of a certain size over the data domain and then issues independent count queries on the grid cells. This method has received no attention in the literature, probably due to the fact that no good method for choosing a grid size was known. Based on an analysis of the two kinds of errors, we propose a method for choosing the grid size. Experimental results validate our method, and show that this approach performs as well as, and often times better than, the state-of-the-art methods. We further introduce a novel adaptive-grid method. The adaptive grid method lays a coarse-grained grid over the dataset, and then further partitions each cell according to its noisy count. Both levels of partitions are then used in answering queries over the dataset. This method exploits the need to have finer granularity partitioning over dense regions and, at the same time, coarse partitioning over sparse regions. Through extensive experiments on real-world datasets, we show that this approach consistently and significantly outperforms the uniform grid method and other state-of-the-art methods.


title: The Aha! Moment The Cognitive Neuroscience of Insight

abstract: A sudden comprehension that solves a problem, reinterprets a situation, explains a joke, or resolves an ambiguous percept is called an insight (i.e., the “Aha! moment”). Psychologists have studied insight using behavioral methods for nearly a century. Recently, the tools of cognitive neuroscience have been applied to this phenomenon. A series of studies have used electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to study the neural correlates of the “Aha! moment” and its antecedents. Although the experience of insight is sudden and can seem disconnected from the immediately preceding thought, these studies show that insight is the culmination of a series of brain states and processes operating at different time scales. Elucidation of these precursors suggests interventional opportunities for the facilitation of insight.


title: Bangla Speech-to-Text conversion using SAPI

abstract: Speech is the most natural form of communication and interaction between humans; whereas, text and symbols are the most common form of transaction in computer systems. Therefore, interest regarding conversion between speech and text is increasing day by day for speech oriented human-computer interaction. Microsoft Corporation developed Speech Application Program Interface (SAPI) for speech related works in its Windows operating systems that includes features for only eight languages including English. So, the aim of this study is to investigate Speech-to-Text (STT) conversion using SAPI for Bangla language. Bangla is an important language with a rich heritage; 21st February is declared as the International Mother Language day by UNESCO to respect the language martyrs for the language in Bangladesh at the year of 1952. We managed SAPI to match pronunciation from continuous Bangla speech in precompiled grammar file of SAPI and SAPI returned Bangla words in English character if matches occur. The words are then used to fetch Bangla words from database and return words in true Bangla characters and to complete the sentences. Several English words for particular Bangla word in the grammar file of SAPI is found to overcome tone variation of persons as well as pronunciation variation in language communities and shown to improve overall performance of the system. Experimental study is carried out for the technique on an article from a news paper and the recognition rate was approximately 78% on an average. Although achieved performance is promising for STT related studies, we identified several elements to improve the performance and might give better accuracy. The theme of this study will also be helpful for other languages for Speech-to-Text conversion and similar tasks.


title: Birdbrains could teach basal ganglia research a new song

abstract: Recent advances in anatomical, physiological and histochemical characterization of avian basal ganglia neurons and circuitry have revealed remarkable similarities to mammalian basal ganglia. A modern revision of the avian anatomical nomenclature has now provided a common language for studying the function of the cortical–basal-ganglia–cortical loop, enabling neuroscientists to take advantage of the specialization of basal ganglia areas in various avian species. For instance, songbirds, which learn their vocal motor behavior using sensory feedback, have specialized a portion of their cortical–basal ganglia circuitry for song learning and production. This discrete circuit dedicated to a specific sensorimotor task could be especially tractable for elucidating the interwoven sensory, motor and reward signals carried by basal ganglia, and the function of these signals in task learning and execution.


title: Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation

abstract: Topic models and neural networks can discover meaningful low-dimensional latent representations of text corpora; as such, they have become a key technology of document representation. However, such models presume all documents are non-discriminatory, resulting in latent representation dependent upon all other documents and an inability to provide discriminative document representation. To address this problem, we propose a semi-supervised manifold-inspired autoencoder to extract meaningful latent representations of documents, taking the local perspective that the latent representation of nearby documents should be correlative. We first determine the discriminative neighbors set with Euclidean distance in observation spaces. Then, the autoencoder is trained by joint minimization of the Bernoulli cross-entropy error between input and output and the sum of the square error between neighbors of input and output. The results of two widely used corpora show that our method yields at least a 15% improvement in document clustering and a nearly 7% improvement in classification tasks compared to comparative methods. The evidence demonstrates that our method can readily capture more discriminative latent representation of new documents. Moreover, some meaningful combinations of words can be efficiently discovered by activating features that promote the comprehensibility of latent representation.


title: Cloud forensics: A research perspective

abstract: Cloud computing and digital forensics are both developing topics and researching these topics requires an understanding of the main aspects of both cloud computing and digital forensics. In cloud computing it is necessary not only to understand its characteristics and the different services and deployment models but also to survey the underpinning elements of cloud computing such as virtualization and the distributed computing which are important to identify its impact on current digital forensics guidelines and procedures. Unlike papers discussing the challenges and opportunities presented by cloud computing in relation to digital forensics, in this paper, we will discuss the underpinning cloud computing elements which are required to provide forensics friendly cloud services. Furthermore, we suggest a set of questions that will aid in the process of cloud forensics analysis.


title: More about VLAD: A leap from Euclidean to Riemannian manifolds

abstract: This paper takes a step forward in image and video coding by extending the well-known Vector of Locally Aggregated Descriptors (VLAD) onto an extensive space of curved Riemannian manifolds. We provide a comprehensive mathematical framework that formulates the aggregation problem of such manifold data into an elegant solution. In particular, we consider structured descriptors from visual data, namely Region Covariance Descriptors and linear subspaces that reside on the manifold of Symmetric Positive Definite matrices and the Grassmannian manifolds, respectively. Through rigorous experimental validation, we demonstrate the superior performance of this novel Riemannian VLAD descriptor on several visual classification tasks including video-based face recognition, dynamic scene recognition, and head pose classification.


title: Batch Reinforcement Learning on the Industrial Benchmark: First Experiences

abstract: The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks in an off-policy, batch-based setting. To further investigate the properties and feasibility on real-world applications, this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a novel reinforcement learning (RL) benchmark that aims at being realistic by including a variety of aspects found in industrial applications, such as continuous state and action spaces, a high dimensional, partially observable state space, delayed effects, and complex stochasticity. The experimental results of PSO-P on IB are compared to results of closed-form control policies derived from the model-based Recurrent Control Neural Network (RCNN) and the model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not only of interest for academic benchmarks, but also for real-world industrial applications, since it also yielded the best performing policy in our IB setting. Compared to other well established RL techniques, PSO-P produced outstanding results in performance and robustness, requiring only a relatively low amount of effort in finding adequate parameters or making complex design decisions.


title: Human milk: a source of more life than we imagine

abstract: The presence of bacteria in human milk has been acknowledged since the seventies. For a long time, microbiological analysis of human milk was only performed in case of infections and therefore the presence of non-pathogenic bacteria was yet unknown. During the last decades, the use of more sophisticated culture-dependent and -independent techniques, and the steady development of the -omic approaches are opening up the new concept of the ‘milk microbiome’, a complex ecosystem with a greater diversity than previously anticipated. In this review, possible mechanisms by which bacteria can reach the mammary gland (contamination versus active migration) are discussed. In addition, the potential roles of human milk for both infant and maternal health are summarised. A better understanding of the link between the milk microbiome and health benefit, the potential factors influencing this relationship and whether or not it can be influenced by nutrition is required to open new avenues in the field of pregnancy and ...


title: Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters

abstract: As autonomous vehicles become an every-day reality, high-accuracy pedestrian detection is of paramount practical importance. Pedestrian detection is a highly researched topic with mature methods, but most datasets focus on common scenes of people engaged in typical walking poses on sidewalks. But performance is most crucial for dangerous scenarios, such as children playing in the street or people using bicycles/skateboards in unexpected ways. Such "in-the-tail" data is notoriously hard to observe, making both training and testing difficult. To analyze this problem, we have collected a novel annotated dataset of dangerous scenarios called the Precarious Pedestrian dataset. Even given a dedicated collection effort, it is relatively small by contemporary standards (around 1000 images). To allow for large-scale data-driven learning, we explore the use of synthetic data generated by a game engine. A significant challenge is selected the right "priors" or parameters for synthesis: we would like realistic data with poses and object configurations that mimic true Precarious Pedestrians. Inspired by Generative Adversarial Networks (GANs), we generate a massive amount of synthetic data and train a discriminative classifier to select a realistic subset, which we deem the Adversarial Imposters. We demonstrate that this simple pipeline allows one to synthesize realistic training data by making use of rendering/animation engines within a GAN framework. Interestingly, we also demonstrate that such data can be used to rank algorithms, suggesting that Adversarial Imposters can also be used for "in-the-tail" validation at test-time, a notoriously difficult challenge for real-world deployment.


title: A framework and theory for cyber security assessments

abstract: Information technology (IT) is critical and valuable to our society. An important type of IT system is Supervisor Control And Data Acquisition (SCADA) systems. These systems are used to control and ...


title: R.: Phish and chips: Traditional and new recipes for attacking EMV

abstract: This paper surveys existing and new security issues affect- ing the EMV electronic payments protocol. We first introduce a new price/effort point for the cost of deploying eavesdropping and relay at- tacks - a microcontroller-based interceptor costing less than $100. We look next at EMV protocol failures in the back-end security API, where we describe two new attacks based on chosen-plaintext CBC weaknesses, and on key separation failues. We then consider future modes of attack, specifically looking at combining the phenomenon of phishing (sending unsolicited messages by email, post or phone to trick users into divulging their account details) with chip card sabotage. Our proposed attacks ex- ploit covert channels through the payments network to allow sabotaged cards to signal back their PINS. We hope these new recipes will enliven the debate about the pros and cons of Chip and PIN at both technical and commercial levels.


title: Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models

abstract: Neural language models (NLMs) have recently gained a renewed interest by achieving state-of-the-art performance across many natural language processing (NLP) tasks. However, NLMs are very computationally demanding largely due to the computational cost of the softmax layer over a large vocabulary. We observe that, in decoding of many NLP tasks, only the probabilities of the top-K hypotheses need to be calculated preciously and K is often much smaller than the vocabulary size. This paper proposes a novel softmax layer approximation algorithm, called Fast Graph Decoder (FGD), which quickly identifies, for a given context, a set of K words that are most likely to occur according to a NLM. We demonstrate that FGD reduces the decoding time by an order of magnitude while attaining close to the full softmax baseline accuracy on neural machine translation and language modeling tasks. We also prove the theoretical guarantee on the softmax approximation quality.


title: Skaterbots: optimization-based design and motion synthesis for robotic creatures with legs and wheels

abstract: We propose a computation-driven approach to design optimization and motion synthesis for robotic creatures that locomote using arbitrary arrangements of legs and wheels.


Multidimensional Scaling


title: Semantics, conceptual spaces, and the meeting of minds

abstract: We present an account of semantics that is not construed as a mapping of language to the world but rather as a mapping between individual meaning spaces. The meanings of linguistic entities are established via a “meeting of minds.” The concepts in the minds of communicating individuals are modeled as convex regions in conceptual spaces. We outline a mathematical framework, based on fixpoints in continuous mappings between conceptual spaces, that can be used to model such a semantics. If concepts are convex, it will in general be possible for interactors to agree on joint meaning even if they start out from different representational spaces. Language is discrete, while mental representations tend to be continuous—posing a seeming paradox. We show that the convexity assumption allows us to address this problem. Using examples, we further show that our approach helps explain the semantic processes involved in the composition of expressions.


title: Detecting Abnormal Operating Performance: The Empirical Power and Specification of Test Statistics

abstract: This research evaluates methods used in event studies that employ accounting-based measures of operating performance. We examine the choice of an accounting-based performance measure, a statistical test, and a model of expected operating performance. We document the impact of these choices on the test statistics designed to detect abnormal operating performance. We find that commonly used research designs yield test statistics that are misspecified in cases where sample firms have performed either unusually well or poorly. In this sampling situation, the test statistics are only well specified when sample firms are matched to control firms of similar pre-event performance.


title: Lung cancer cell identification based on artificial neural network ensembles

abstract: An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed. The ensemble is built on a two-level ensemble architecture. The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks are combined by a novel method presented in this paper, i.e. full voting which judges a cell to be normal only when all the individual networks judge it is normal. The second-level ensemble is used to deal with the cells that are judged as cancer cells by the first-level ensemble, where each individual network has five outputs respectively adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and normal, among which the former four are different types of lung cancer cells. The predictions of those individual networks are combined by a prevailing method, i.e. plurality voting. Through adopting those techniques, NED achieves not only a high rate of overall identification, but also a low rate of false negative identification, i.e. a low rate of judging cancer cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.


title: A taxi business intelligence system

abstract: The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particularly promising area is to extract useful business intelligence, which can be used as guidance for reducing inefficiencies in energy consumption of transportation sectors, improving customer experiences, and increasing business performances. However, extracting business intelligence from location traces is not a trivial task. Conventional data analytic tools are usually not customized for handling large, complex, dynamic, and distributed nature of location traces. To that end, we develop a taxi business intelligence system to explore the massive taxi location traces from different business perspectives with various data mining functions. Since we implement the system using the real-world taxi GPS data, this demonstration will help taxi companies to improve their business performances by understanding the behaviors of both drivers and customers. In addition, several identified technical challenges also motivate data mining people to develop more sophisticate techniques in the future.


title: Nonlinear Coordinated Steering and Braking Control of Vision-Based Autonomous Vehicles in Emergency Obstacle Avoidance

abstract: This paper discusses dynamic control design for automated driving of vision-based autonomous vehicles, with a special focus on the coordinated steering and braking control in emergency obstacle avoidance. An autonomous vehicle is a complex multi-input and multi-output (MIMO) system, which possesses the features of parameter uncertainties and strong nonlinearities, and the coupled phenomena of longitudinal and lateral dynamics are evident in a combined cornering and braking maneuver. In this work, an effective coordinated control system for automated driving is proposed to deal with these coupled and nonlinear features and reject the disturbances. First, a vision algorithm is constructed to detect the reference path and provide the local location information between vehicles and reference path in real time. Then, a novel coordinated steering and braking control strategy is proposed based on the nonlinear backstepping control theory and the adaptive fuzzy sliding-mode control technique, and the asymptotic convergence of the proposed coordinated control system is proven by the Lyapunov theory. Finally, experimental tests manifest that the proposed control strategy possesses favorable tracking performance and enhances the riding comfort and stability of autonomous vehicles.


title: Processor Design Space Exploration via Statistical Sampling and Semi-Supervised Ensemble Learning

abstract: The design space exploration (DSE) has become a major challenge in microprocessors design due to the increasing complexity of microprocessor architecture and the extremely time-consuming software simulation technology. To more effectively and efficiently perform DSE, recently machine learning techniques are widely explored to build predictive models with a small set of simulations. However, most previous models are supervised models and the training samples are randomly selected. Thus they still suffered from high simulation cost or low prediction accuracy. In order to minimize the simulation overhead for DSE, this paper proposes an efficient DSE method which combines Latin hypercube sampling and semi-supervised ensemble learning technique. Latin hypercube sampling is first employed to select a small set of representative design points for simulation. Then a semi-supervised learning based AdaBoost model (SemiBoost) is proposed to predict the responses of the configurations that have not been simulated. We conduct extensive evaluations on the benchmarks of SPEC CPU2006 suite, and the experimental results demonstrate that the proposed SemiBoost model is superior to existing state-of-the-art models in terms of both efficiency and effectiveness.


title: Computer analysis of computed tomography scans of the lung: a survey

abstract: Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.


title: A Survey of Uncertain Data Algorithms and Applications

abstract: In recent years, a number of indirect data collection methodologies have lead to the proliferation of uncertain data. Such data points are often represented in the form of a probabilistic function, since the corresponding deterministic value is not known. This increases the challenge of mining and managing uncertain data, since the precise behavior of the underlying data is no longer known. In this paper, we provide a survey of uncertain data mining and management applications. In the field of uncertain data management, we will examine traditional methods such as join processing, query processing, selectivity estimation, OLAP queries, and indexing. In the field of uncertain data mining, we will examine traditional mining problems such as classification and clustering. We will also examine a general transform based technique for mining uncertain data. We discuss the models for uncertain data, and how they can be leveraged in a variety of applications. We discuss different methodologies to process and mine uncertain data in a variety of forms.


title: Toward supporting stories with procedurally generated game worlds

abstract: Computer role playing games engage players through interleaved story and open-ended game play. We present an approach to procedurally generating, rendering, and making playable novel games based on a priori unknown story structures. These stories may be authored by humans or by computational story generation systems. Our approach couples player, designer, and algorithm to generate a novel game using preferences for game play style, general design aesthetics, and a novel story structure. Our approach is implemented in Game Forge, a system that uses search-based optimization to find and render a novel game world configuration that supports a sequence of plot points plus play style preferences. Additionally, Game Forge supports execution of the game through reactive control of game world logic and non-player character behavior.


title: Deep Query Ranking for Question Answering over Knowledge Bases

abstract: We study question answering systems over knowledge graphs which map an input natural language question into candidate formal queries. Often, a ranking mechanism is used to discern the queries with higher similarity to the given question. Considering the intrinsic complexity of the natural language, finding the most accurate formal counter-part is a challenging task. In our recent paper [1], we leveraged Tree-LSTM to exploit the syntactical structure of input question as well as the candidate formal queries to compute the similarities. An empirical study shows that taking the structural information of the input question and candidate query into account enhances the performance, when compared to the baseline system. Code related to this paper is available at: https://github.com/AskNowQA/SQG.


title: Hierarchical Relational Networks for Group Activity Recognition and Retrieval

abstract: Modeling structured relationships between people in a scene is an important step toward visual understanding. We present a Hierarchical Relational Network that computes relational representations of people, given graph structures describing potential interactions. Each relational layer is fed individual person representations and a potential relationship graph. Relational representations of each person are created based on their connections in this particular graph. We demonstrate the efficacy of this model by applying it in both supervised and unsupervised learning paradigms. First, given a video sequence of people doing a collective activity, the relational scene representation is utilized for multi-person activity recognition. Second, we propose a Relational Autoencoder model for unsupervised learning of features for action and scene retrieval. Finally, a Denoising Autoencoder variant is presented to infer missing people in the scene from their context. Empirical results demonstrate that this approach learns relational feature representations that can effectively discriminate person and group activity classes.


title: High Performance Recovery for Parallel State Machine Replication

abstract: State machine replication is a fundamental approach to high availability. Despite the vast literature on the topic, relatively few studies have considered the issues involved in recovering faulty replicas. Recovering a replica requires (a) retrieving and installing an up-to-date replica checkpoint, and (b) restoring and re-executing the log of commands not reflected in the checkpoint. Parallel techniques to state machine replication render recovery particularly challenging since throughput under normal execution (i.e., in the absence of failures) is very high. Consequently, the log of commands that need to be applied until the replica is available is typically large, which delays recovery. In this paper, we present two techniques to optimize recovery in parallel state machine replication. The first technique allows new commands to execute concurrently with the execution of logged commands, before replicas are completely updated. The second technique introduces on-demand state recovery, which allows segments of a checkpoint to be recovered concurrently.


title: An 800 MHz 2$\,\times\,$ 1 Compact MIMO Antenna System for LTE Handsets

abstract: A compact size 2 × 1 multiple-input-multiple-output (MIMO) antenna system operating in the 800 MHz band is proposed for long term evolution (LTE) handsets. The fabricated MIMO system has a typical isolation of 12 dB and a maximum gain of 2.2 dBi. Two isolation enhancement methods are investigated; one that looks into the depth of the ground split separating the two antennas, the other introduces extra splits within the arms of the GND plane. It is found that introducing extra splits within the arms of the GND plane of the proposed geometry enhances the isolation by at least 3 dB. The proposed MIMO antenna system is based on meander line antennas, and covers the band from 760-886 MHz. The size of the antenna system is 40 × 50 mm2.


title: www.elsevier.com/locate/artint Learning and inferring transportation routines

abstract: This paper introduces a hierarchical Markov model that can learn and infer a user's daily movements through the community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user's mode of transportation or her goal. We apply Rao-Blackwellised particle filters for efficient inference both at the low level and at the higher levels of the hierarchy. Significant locations such as goals or locations where the user frequently changes mode of transportation are learned from GPS data logs without requiring any manual labeling. We show how to detect abnormal behaviors (e.g. taking a wrong bus) by concurrently tracking his activities with a trained and a prior model. Experiments show that our model is able to accurately predict the goals of a person and to recognize situations in which the user performs unknown activities.


title: Development of smartphone-based student attendance system

abstract: Student's attendance tracking is a vital issue in order to monitor students' performance in the classroom as well as in their studies. It becomes a key concern because the university authority maintains a rule that one student can only attend in the exam if his/her attendance is higher or equal to several percentages (60%,70% or 80% etc.) otherwise not. The traditional attendance system needs student's to physically sign the attendance sheet each time for the attendance of each class. This is unnecessarily time-consuming to notice and mark student's name on the attendance sheet. This also happens that some students may accidentally or willingly mark the student's name like as proxy. The hard copy of attendance sheet may get lost. Using Smartphone like as Android Technology the course teacher will be able to take attendance easily by our designed mobile application and save the attendance in the phone as well as in server and can check percentage and also can print as hard copy. Using the stored information, this system is able to mark attendance, marking intruders' entry, attendance percentage calculations, send emails, and send SMS to the guardian to keep them updated about their child's attendance at the Institute. The designed system has an online access from any place and any moment which may extraordinarily assist the course teacher with keeping track of their student's attendance.


title: Power and spreading factor control in low power wide area networks

abstract: Low power wide area networks are gaining interest to connect thousands of nodes to the internet of things. However, because the link budget in these networks is huge, nodes suffer from a near-far effect. Nodes far from the base station cannot send to the base station succesfully when closer nodes are transmitting, causing destructive collisions. LoRa, the considered technology in this paper, is a spread spectrum technology. It is known that spread spectrum is also sensitive to this effect. This paper presents a scheme to efficiently optimize the packet error rate fairness inside a LoRaWAN cell. This is achieved by optimizing the power and spreading factor for each node while avoiding near-far problems by allocating distant users to different channels. Simulations show that the packet error rate can be decreased up to 50% for edge nodes in a moderate contention scenario where 1 node per 1000m2 transmits every 10 minutes.


title: Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy

abstract: Abstract Machine learning techniques are increasingly used in neuroscience to classify brain signals. Decoding performance is reflected by how much the classification results depart from the rate achieved by purely random classification. In a 2-class or 4-class classification problem, the chance levels are thus 50% or 25% respectively. However, such thresholds hold for an infinite number of data samples but not for small data sets. While this limitation is widely recognized in the machine learning field, it is unfortunately sometimes still overlooked or ignored in the emerging field of brain signal classification. Incidentally, this field is often faced with the difficulty of low sample size. In this study we demonstrate how applying signal classification to Gaussian random signals can yield decoding accuracies of up to 70% or higher in two-class decoding with small sample sets. Most importantly, we provide a thorough quantification of the severity and the parameters affecting this limitation using simulations in which we manipulate sample size, class number, cross-validation parameters (k-fold, leave-one-out and repetition number) and classifier type (Linear-Discriminant Analysis, Naive Bayesian and Support Vector Machine). In addition to raising a red flag of caution, we illustrate the use of analytical and empirical solutions (binomial formula and permutation tests) that tackle the problem by providing statistical significance levels (p-values) for the decoding accuracy, taking sample size into account. Finally, we illustrate the relevance of our simulations and statistical tests on real brain data by assessing noise-level classifications in Magnetoencephalography (MEG) and intracranial EEG (iEEG) baseline recordings.


title: Minimal Assist-as-Needed Controller for Upper Limb Robotic Rehabilitation

abstract: Robotic rehabilitation of the upper limb following neurological injury is most successful when subjects are engaged in the rehabilitation protocol. Developing assistive control strategies that maximize subject participation is accordingly an active area of research, with aims to promote neural plasticity and, in turn, increase the potential for recovery of motor coordination. Unfortunately, state-of-the-art control strategies either ignore more complex subject capabilities or assume underlying patterns govern subject behavior and may therefore intervene suboptimally. In this paper, we present a minimal assist-as-needed (mAAN) controller for upper limb rehabilitation robots. The controller employs sensorless force estimation to dynamically determine subject inputs without any underlying assumptions as to the nature of subject capabilities and computes a corresponding assistance torque with adjustable ultimate bounds on position error. Our adaptive input estimation scheme is shown to yield fast, stable, and accurate measurements regardless of subject interaction and exceeds the performance of current approaches that estimate only position-dependent force inputs from the user. Two additional algorithms are introduced in this paper to further promote active participation of subjects with varying degrees of impairment. First, a bound modification algorithm is described, which alters allowable error. Second, a decayed disturbance rejection algorithm is presented, which encourages subjects who are capable of leading the reference trajectory. The mAAN controller and accompanying algorithms are demonstrated experimentally with healthy subjects in the RiceWrist-S exoskeleton.


title: A HMM-based mandarin chinese singing voice synthesis system

abstract: We propose a mandarin Chinese singing voice synthesis system, in which hidden Markov model (HMM)-based speech synthesis technique is used. A mandarin Chinese singing voice corpus is recorded and musical contextual features are well designed for training. F0 and spectrum of singing voice are simultaneously modeled with context-dependent HMMs. There is a new problem, F0 of singing voice is always sparse because of large amount of context, i.e., tempo and pitch of note, key, time signature and etc. So the features hardly ever appeared in the training data cannot be well obtained. To address this problem, difference between F0 of singing voice and that of musical score (DF0) is modeled by a single Viterbi training. To overcome the over-smoothing of the generated F0 contour, syllable level F0 model based on discrete cosine transforms (DCT) is applied, F0 contour is generated by integrating two-level statistical models. The experimental results demonstrate that the proposed system outperforms the baseline system in both objective and subjective evaluations. The proposed system can generate a more natural F0 contour. Furthermore, the syllable level F0 model can make singing voice more expressive.


title: A treatment planning classification for oligodontia.

abstract: PURPOSE ::: The aim of this research was to provide a classification for patients with oligodontia that could act as an aid in treatment planning. ::: ::: ::: MATERIALS AND METHODS ::: Panoramic radiograph records of 70 patients with oligodontia were used to categorize the extent of the disability and treatment modality. Patients were classified into types 1 through 3 depending on the number of missing primary and permanent teeth, as well as in relation to their prosthodontic requirements. The radiographs were then assessed independently on two separate occasions by three experienced clinicians to validate the classification. ::: ::: ::: RESULTS ::: There was a high level of intrarater consistency in allocating patients into the three different types with a Kappa (k) score of 0.77 for clinician 1, 0.87 for clinician 2, and 0.94 for clinician 3. There was also a strong interrater agreement (overall k score: 0.88). A k score greater then 0.6 is regarded as being good and greater than 0.8 as being very good. ::: ::: ::: CONCLUSIONS ::: Oligodontia is a heterogeneous condition. Patients with oligodontia can be classified as having three different types according to the extent of their disability and the complexity of their prosthodontic requirements. This classification is a reliable diagnostic tool based on the positive outcome of the inter- and intrarater consistency.


title: Root coverage in molar teeth: a comparative controlled randomized clinical trial.

abstract: AIM ::: To compare the clinical outcomes of laterally moved, coronally advanced flap (LMCAF) versus Bilaminar technique (BT) in the treatment of single gingival recession on molar teeth. ::: ::: ::: MATERIAL AND METHODS ::: Fifty patients showing Miller I and II gingival recessions at first molar teeth were treated: 25 were randomly assigned to the BT group and 25 belonged to the LMCAF group. Patient's post-operative morbidity was assessed 1 week after the surgery, while aesthetic evaluation and the clinical evaluation were made 1 year later. ::: ::: ::: RESULTS ::: No statistically significant difference was demonstrated in terms of recession and PPD reduction. Statistically greater probability of complete root coverage (CRC, Odds Ratio 22.1) and greater increase in gingival thickness were observed in the BT group. Greater increase in keratinized tissue was obtained in the LMCAF. Patient satisfaction with aesthetics was very high in both treatment groups. Better post-operative course was observed in the LMCAF, while better post-operative sensitivity and root coverage evaluation were demonstrated in patients treated with BT. ::: ::: ::: CONCLUSIONS ::: Gingival recession at first molar teeth can be successfully treated with LMCAF and BT. Better CRC was achieved with BT, while more comfortable post-operative course was associated with the LMCAF.


title: TAFC: Time and Attribute Factors Combined Access Control on Time-Sensitive Data in Public Cloud

abstract: The new paradigm of outsourcing data to the cloud is a double-edged sword. On one side, it frees up data owners from the technical management, and is easier for the data owners to share their data with intended recipients when data are stored in the cloud. On the other side, it brings about new challenges about privacy and security protection. To protect data confidentiality against the honest-but-curious cloud service provider, numerous works have been proposed to support fine-grained data access control. However, till now, no efficient schemes can provide the scenario of fine-grained access control together with the capacity of time-sensitive data publishing. In this paper, by embedding the mechanism of timed-release encryption into CP-ABE (Ciphertext-Policy Attribute-based Encryption), we propose TAFC: a new time and attribute factors combined access control on time-sensitive data stored in cloud. Extensive security and performance analysis shows that our proposed scheme is highly efficient and satisfies the security requirements for time-sensitive data storage in public cloud.


title: nvm malloc: Memory Allocation for NVRAM

abstract: Non-volatile main memory (NVRAM) has the potential to fundamentally change the persistency of software. Applications can make their state persistent by directly placing data structures on NVRAM instead of volatile DRAM. However, the persistent nature of NVRAM requires significant changes for memory allocators that are now faced with the additional tasks of data recovery and failure-atomicity. In this paper, we present nvm malloc, a general-purpose memory allocator concept for the NVRAM era as a basic building block for persistent applications. We introduce concepts for managing named allocations for simplified recovery and using volatile and non-volatile memory in combination to provide both high performance and failure-atomic allocations.


title: Getting stress out of stressed-out stress granules

abstract: Amyotrophic lateral sclerosis (ALS) pathology is linked to the aberrant aggregation of specific proteins, including TDP‐43, FUS, and SOD1, but it is not clear why these aggregation events cause ALS. In this issue of The EMBO Journal , Mateju et al (2017) report a direct link between misfolded proteins accumulating in stress granules and the phase transition of these stress granules from liquid to solid. This discovery provides a model connecting protein aggregation to stress granule dysfunction.


title: Firm Performance Impacts of Digitally Enabled Supply Chain Integration Capabilities

abstract: Best practice exemplars suggest that digital platforms play a critical role in managing supply chain activities and partnerships that generate performance gains for firms. However, there is limited academic investigation on how and why information technology can create performance gains for firms in a supply chain management (SCM) context. Grant's (1996) theoretical notion of higher-order capabilities and a hierarchy of capabilities has been used in recent information systems research by Barua et al. (2004), Sambamurthy et al. (2003), and Mithas et al. (2004) to reframe the conversation from the direct performance impacts of IT resources and investments to how and why IT shapes higher-order process capabilities that create performance gains for firms. We draw on the emerging IT-enabled organizational capabilities perspective to suggest that firms that develop IT infrastructure integration for SCM and leverage it to create a higher-order supply chain integration capability generate significant and sustainable performance gains. A research model is developed to investigate the hierarchy of IT-related capabilities and their impact on firm performance. Data were collected from 110 supply chain and logistics managers in manufacturing and retail organizations. Our results suggest that integrated IT infrastructures enable firms to develop the higher-order capability of supply chain process integration. This capability enables firms to unbundle information flows from physical flows, and to share information with their supply chain partners to create information-based approaches for superior demand planning, for the staging and movement of physical products, and for streamlining voluminous and complex financial work processes. Furthermore, IT-enabled supply chain integration capability results in significant and sustained firm performance gains, especially in operational excellence and revenue growth. Managerial initiatives should be directed at developing an integrated IT infrastructure and leveraging it to create process capabilities for the integration of resource flows between a firm and its supply chain partners.


Got Annoyed? Examining the Advertising Effectiveness and Annoyance Dynamics


title: An ultra-wideband schottky diode based envelope detector for 2.5 Gbps signals

abstract: In this paper an ultra-wideband (UWB) Schottky diode based envelope detector is reported. The detector consists of an input matching network, a Schottky diode and wideband output filtering network. The output network is tailored to demodulate ultra-wideband amplitude shift keying (ASK) signals up to 2.5 Gbps at 6–9 GHz carrier frequency. The detector uses microstrip and surface-mount device (SMD) components and it is fabricated on a Rogers 6002 substrate. Experimental results show error free transmissions up to 2.5 Gbps at an input power level of −11 dBm. The highest measured conversion gain of the detector is around −12 dB.


title: Mining frequent itemsets over distributed data streams by continuously maintaining a global synopsis

abstract: Mining frequent itemsets over data streams has attracted much research attention in recent years. In the past, we had developed a hash-based approach for mining frequent itemsets over a single data stream. In this paper, we extend that approach to mine global frequent itemsets from a collection of data streams distributed at distinct remote sites. To speed up the mining process, we make the first attempt to address a new problem on continuously maintaining a global synopsis for the union of all the distributed streams. The mining results therefore can be yielded on demand by directly processing the maintained global synopsis. Instead of collecting and processing all the data in a central server, which may waste the computation resources of remote sites, distributed computations over the data streams are performed. A distributed computation framework is proposed in this paper, including two communication strategies and one merging operation. These communication strategies are designed according to an accuracy guarantee of the mining results, determining when and what the remote sites should transmit to the central server (named coordinator). On the other hand, the merging operation is exploited to merge the information received from the remote sites into the global synopsis maintained at the coordinator. By the strategies and operation, the goal of continuously maintaining the global synopsis can be achieved. Rooted in the continuously maintained global synopsis, we propose a mining algorithm for finding global frequent itemsets. Moreover, the correctness guarantees of the communication strategies and merging operation, and the accuracy guarantee analysis of the mining algorithm are provided. Finally, a series of experiments on synthetic datasets and a real dataset are performed to show the effectiveness and efficiency of the distributed computation framework.


title: Transcatheter Percutaneous and Transapical Aortic Valve Replacement

abstract: Transcatheter aortic valve implantation has evolved rapidly since the first successful procedure over 5 years ago. The femoral arterial and the left ventricular apical approaches are currently favored. Initial skepticism has to some degree given way to acceptance that the procedure might be a reasonable alternative for selected patients who might be at high risk with conventional thoracotomy and cardiopulmonary bypass. Uncertainty remains about outcomes, implications, durability, and the appropriate role for this new therapy. It appears likely that transcatheter valve implantation will become a more widely available and accepted therapeutic option.