title: The Development of the Scale of Protective Factors: Resilience in a Violent Trauma Sample.

abstract: This research developed the Scale of Protective Factors (SPF-24) to measure protective factors contributing to resilience. We investigated the factor structure of 35 items. After exploratory factor analysis, we subjected 25 items representing 2 social-interpersonal and 2 cognitive-individual factors to confirmatory factor analysis. The sample consisted of 942 college students from 3 studies and 2 institutions. To examine the diagnostic function of the SPF, we used clinical criteria to identify a subsample of participants who had experienced violent trauma and scored low, moderate, or high on an established resilience scale. RESULTS showed that the low-resilient group scored significantly lower on all subscales of the SPF with marked differences in prioritizing/planning behavior. Implications for the research and clinical settings are discussed. Language: en


title: Recent Advances in Radio Resource Management for Heterogeneous LTE/LTE-A Networks

abstract: As heterogeneous networks (HetNets) emerge as one of the most promising developments toward realizing the target specifications of Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, radio resource management (RRM) research for such networks has, in recent times, been intensively pursued. Clearly, recent research mainly concentrates on the aspect of interference mitigation. Other RRM aspects, such as radio resource utilization, fairness, complexity, and QoS, have not been given much attention. In this paper, we aim to provide an overview of the key challenges arising from HetNets and highlight their importance. Subsequently, we present a comprehensive survey of the RRM schemes that have been studied in recent years for LTE/LTE-A HetNets, with a particular focus on those for femtocells and relay nodes. Furthermore, we classify these RRM schemes according to their underlying approaches. In addition, these RRM schemes are qualitatively analyzed and compared to each other. We also identify a number of potential research directions for future RRM development. Finally, we discuss the lack of current RRM research and the importance of multi-objective RRM studies.


Toward Sustainable Insights, or Why Polygamy is Bad for You


Out-Of-Core Algorithms for Scientific Visualization and Computer Graphics Course


title: On Computing the Diameter of a Point Set in High Dimensional Euclidean Space

abstract: We consider the problem of computing the diameter of a set of n points in d-dimensional Euclidean space under Euclidean distance function. We describe an algorithm that in time O(dnlogn + n2) finds with high probability an arbitrarily close approximation of the diameter. For large values of d the complexity bound of our algorithm is a substantial improvement over the complexity bounds of previously known exact algorithms. Computing and approximating the diameter are fundamental primitives in high dimensional computational geometry and find practical application, for example, in clustering operations for image databases.


title: Smart Home Communication Technologies and Applications: Wireless Protocol Assessment for Home Area Network Resources

abstract: The paper discusses Home Area Networks (HAN) communication technologies for smart home and domestic application integration. The work is initiated by identifying the application areas that can benefit from this integration. A broad and inclusive home communication interface is analysed utilizing as a key piece a Gateway based on machine-to-machine (M2M) communications that interacts with the surrounding environment. Then, the main wireless networks are thoroughly assessed, and later, their suitability to the requirements of HAN considering the application area is analysed. Finally, a qualitative analysis is portrayed.


title: Quantification of private information leakage from phenotype-genotype data: linking attacks

abstract: Studies on genomic privacy have traditionally focused on identifying individuals using DNA variants. In contrast, molecular phenotype data, such as gene expression levels, are generally assumed to be free of such identifying information. Although there is no explicit genotypic information in phenotype data, adversaries can statistically link phenotypes to genotypes using publicly available genotype-phenotype correlations such as expression quantitative trait loci (eQTLs). This linking can be accurate when high-dimensional data (i.e., many expression levels) are used, and the resulting links can then reveal sensitive information (for example, the fact that an individual has cancer). Here we develop frameworks for quantifying the leakage of characterizing information from phenotype data sets. These frameworks can be used to estimate the leakage from large data sets before release. We also present a general three-step procedure for practically instantiating linking attacks and a specific attack using outlier gene expression levels that is simple yet accurate. Finally, we describe the effectiveness of this outlier attack under different scenarios.


title: An efficient synthesis of glycosyl esters exploiting the mitsunobu reaction.

abstract: Abstract The anomeric hydroxyl group of various pyranose hemiacetals can be esterified with inversion of configuration, conveniently, mildly and on large-scale using triphenylphosphine (TPP), with either diisopropylazodicarboxylate (DIAD) or diethylazodicarboxylate (DEAD) and a carboxylic acid in THF at either −50° C or at room temperature.


title: Reliability of Reviews on the Internet: The Case of TripAdvisor

abstract: Given the rampant growth of travel-related user-generated content on the Internet, this paper seeks to investigate the reliability of reviews in TripAdvisor, a popular travel review site. To support the goal, two objectives are submitted. The first is to locate clusters of highly-interlocked hotels that have been evaluated by a common pool of reviewers. This enables review baselines to be established, which in turn facilitates detection of anomalies. The second is to determine the inter-reviewer and intra-reviewer reliability of reviews. Results suggest that reviews in TripAdvisor could be largely reliable. The findings gleaned from the results are discussed.


title: Combining Rhythm-Based and Pitch-Based Methods for Background and Melody Separation

abstract: Musical works are often composed of two characteristic components: the background (typically the musical accompaniment), which generally exhibits a strong rhythmic structure with distinctive repeating time elements, and the melody (typically the singing voice or a solo instrument), which generally exhibits a strong harmonic structure with a distinctive predominant pitch contour. Drawing from findings in cognitive psychology, we propose to investigate the simple combination of two dedicated approaches for separating those two components: a rhythm-based method that focuses on extracting the background via a rhythmic mask derived from identifying the repeating time elements in the mixture and a pitch-based method that focuses on extracting the melody via a harmonic mask derived from identifying the predominant pitch contour in the mixture. Evaluation on a data set of song clips showed that combining such two contrasting yet complementary methods can help to improve separation performance--from the point of view of both components--compared with using only one of those methods, and also compared with two other state-of-the-art approaches.


title: The Mobile Sensing Platform: An Embedded Activity Recognition System

abstract: Activity-aware systems have inspired novel user interfaces and new applications in smart environments, surveillance, emergency response, and military missions. Systems that recognize human activities from body-worn sensors can further open the door to a world of healthcare applications, such as fitness monitoring, eldercare support, long-term preventive and chronic care, and cognitive assistance. Wearable systems have the advantage of being with the user continuously. So, for example, a fitness application could use real-time activity information to encourage users to perform opportunistic activities. Furthermore, the general public is more likely to accept such activity recognition systems because they are usually easy to turn off or remove.


title: Post-antibiotic effect of isepamicin compared to that of other aminoglycosides.

abstract: Isepamicin is a new aminoglycoside drug derived from gentamicin, which is more stable than other aminoglycosides to inactivating enzymes and experimentally is less nephrotoxic than gentamicin or amikacin. In this work a comparative study was carried out on the antibacterial activity (MIC and lethality curves) and the post-antibiotic effect (PAE) of different concentrations of isepamicin, netilmicin and gentamicin on pure cultures of S. aureus and E. coli. The MIC and curves of lethality were determined by conventional methods. The PAE was determined after exposure of the bacterial culture to the antimicrobial one for 1 h at 37 degrees C in a bath with stirring. Elimination of the drug was made by the dilution method. Isepamicin was found to have a MIC four times greater than netilmicin and gentamicin. Its activity over time was similar to that exhibited by other aminoglycosides. At concentrations higher than the MIC, a bactericidal effect was found with the three antibiotics, although isepamicin produced it at lower concentrations. The post-antibiotic effect induced by isepamicin, the same as that of netilmicin and gentamicin, was extensive and depended on the concentration and the strain used. Isepamicin shows antibiotic activity over time and a PAE similar to the other aminoglycosides tested but, unlike them, it has a faster activity, less toxicity and better resistance to inactivating enzymes. For this reason it makes a considerable contribution to antibacterial therapies in severe conditions, including immunodepressed patients who require long-term treatment.


title: Uplift Random Forests

abstract: Conventional supervised statistical learning models aim to achieve high accuracy in predicting the value of an outcome measure based on a number of input measures. However, in many applications, some type of action is randomized on the observational units. This is the case, for example, in treatment/control settings, such as those usually encountered in marketing and clinical trial applications. In these situations, we may not necessarily be interested in predicting the outcome itself, but in estimating the expected change in the outcome as a result of the action. This is precisely the idea behind uplift models, which, despite their many practical applications, have received little attention in the literature. In this article, we extend the state-of-the-art research in this area by proposing a new approach based on Random Forests. We perform carefully designed experiments using simple simulation models to illustrate some of the properties of the proposed method. In addition, we present evidence on a dataset pertaining to a large Canadian insurer on a customer retention case. The results confirm the effectiveness of the proposed method and show favorable performance relative to other existing uplift modeling approaches.


title: An Approach for Dynamic Identification of Online Radicalization in Social Networks

abstract: The Online Social Network OSN has evolved as a popular platform enabling rich topic-centric interactions and serving as a medium to facilitate online radicalization Behr et al. 2013. Keeping in view the growing need of uncovering online radicalization, we focus on the information network of Twitter and present an approach for identifying dynamic communities, which arise due to “radical” topic-centric user interactions. The approach at successive timestamps deploys evolving topic-entity maps along with evolving interaction graphs. We propose “Rate of Overlap ROAct” to determine the similarity among successive community timestamps. We further validate our approach using an open dataset of criminal offences in the city of Denver, Colorado. The approach presented is simple, fast, and effective for dynamic identification of topic-centric communities and, thus, will enable law enforcement agencies to identify hidden radicalization.


Process of Victimisation: Investigating Risk, Reporting and Service Use


title: Evaluation of the Performance of Adaptive HTTP Streaming Systems

abstract: Adaptive video streaming over HTTP is becoming omnipresent in our daily life. In the past, dozens of research papers have proposed novel approaches to address different aspects of adaptive streaming and a decent amount of player implementations (commercial and open source) are available. However, state of the art evaluations are sometimes superficial as many proposals only investigate a certain aspect of the problem or focus on a specific platform - player implementations used in actual services are rarely considered. HTML5 is now available on many platforms and foster the deployment of adaptive media streaming applications. We propose a common evaluation framework for adaptive HTML5 players and demonstrate its applicability by evaluating eight different players which are actually deployed in real-world services.


title: Is there another way to take account of noncompliance in randomized controlled trials

abstract: Noncompliance is an important issue in the design and conduct of randomized controlled trials (RCTs). It arises when participants do not receive the treatment or intervention to which they were randomly allocated. For example, some participants invited to go through a screening program may not


title: PARAFAC algorithms for large-scale problems

abstract: Parallel factor analysis (PARAFAC) is a tensor (multiway array) factorization method which allows to find hidden factors (component matrices) from a multidimensional data. Most of the existing algorithms for the PARAFAC, especially the alternating least squares (ALS) algorithm need to compute Khatri-Rao products of tall factors and multiplication of large matrices, and due to this require high computational cost and large memory and are not suitable for very large-scale-problems. Hence, PARAFAC for large-scale data tensors is still a challenging problem. In this paper, we propose a new approach based on a modified ALS algorithm which computes Hadamard products, instead Khatri-Rao products, and employs relatively small matrices. The new algorithms are able to process extremely large-scale tensors with billions of entries. Extensive experiments confirm the validity and high performance of the developed algorithm in comparison with other well-known algorithms.


title: An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems

abstract: Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.


title: Electrical machines for aerospace applications

abstract: This paper presents an overview of high performance electrical machines for aerospace applications. The drive towards aircraft electrification is highly dependent on the performance and reliability of electrical machine drive systems. A review of the advancements of electrical machine constituent materials which are being applied in the aerospace sector is outlined followed by key advancements in electrical machine technologies. Case studies demonstrating advances in electrical machine technology will then be presented highlighting key performance indices achieved.


title: Product Fit Uncertainty in Online Markets: Nature, Effects, and Antecedents

abstract: Product fit uncertainty defined as the degree to which a consumer cannot assess whether a product's attributes match her preference is proposed to be a major impediment to online markets with costly product returns and lack of consumer satisfaction. We conceptualize the nature of product fit uncertainty as an information problem and theorize its distinct effect on product returns and consumer satisfaction versus product quality uncertainty, particularly for experience versus search goods without product familiarity. To reduce product fit uncertainty, we propose two Internet-enabled systems---website media visualization systems and online product forums collaborative shopping systems---that are hypothesized to attenuate the effect of product type experience versus search goods on product fit uncertainty. ::: ::: Hypotheses that link experience goods to product returns through the mediating role of product fit uncertainty are tested with analyses of a unique data set composed of secondary data matched with primary direct data from numerous consumers who had recently participated in buy-it-now auctions. The results show the distinction between product fit uncertainty and quality uncertainty as two distinct dimensions of product uncertainty and interestingly show that, relative to product quality uncertainty, product fit uncertainty has a significantly stronger effect on product returns. Notably, whereas product quality uncertainty is mainly driven by the experience attributes of a product, product fit uncertainty is mainly driven by both experience attributes and lack of product familiarity. The results also suggest that Internet-enabled systems are differentially used to reduce product fit and quality uncertainty. Notably, the use of online product forums is shown to moderate the effect of experience goods on product fit uncertainty, and website media are shown to attenuate the effect of experience goods on product quality uncertainty. The results are robust to econometric specifications and estimation methods. The paper concludes by stressing the importance of reducing the increasingly prevalent information problem of product fit uncertainty in online markets with the aid of Internet-enabled systems.


title: CORE STABILITY TRAINING: APPLICATIONS TO SPORTS CONDITIONING PROGRAMS

abstract: Willardson, J.M. Core stability training: Applica- tions to sports conditioning programs. J. Strength Cond. Res. 21(3):979-985. 2007.—In recent years, fitness practitioners have increasingly recommended core stability exercises in sports con- ditioning programs. Greater core stability may benefit sports performance by providing a foundation for greater force produc- tion in the upper and lower extremities. Traditional resistance exercises have been modified to emphasize core stability. Such modifications have included performing exercises on unstable rather than stable surfaces, performing exercises while standing rather than seated, performing exercises with free weights rath- er than machines, and performing exercises unilaterally rather than bilaterally. Despite the popularity of core stability training, relatively little scientific research has been conducted to dem- onstrate the benefits for healthy athletes. Therefore, the purpose of this review was to critically examine core stability training and other issues related to this topic to determine useful appli- cations for sports conditioning programs. Based on the current literature, prescription of core stability exercises should vary based on the phase of training and the health status ofthe ath- lete. During preseason and in-season mesocycles, free weight ex- ercises performed while standing on a stable surface are rec- ommended for increases in core strength and power. Free weight exercises performed in this manner are specific to the core sta- bility requirements of sports-related skills due to moderate lev- els of instability and high levels of force production. Conversely, during postseason and off-season mesocycles, Swiss ball exercis- es involving isometric muscle actions, small loads, and long ten- sion times are recommended for increases in core endurance. Furthermore, balance board and stability disc exercises, per- formed in conjunction with plyometric exercises, are recom- mended to improve proprioceptive and reactive capabilities, which may reduce the likelihood of lower extremity injuries.


title: Market Basket Analysis with Data Mining methods

abstract: This paper describes the way of Market Basket Analysis implementation to Six Sigma methodology. Data Mining methods provide a lot of opportunities in the market sector. Basket Market Analysis is one of them. Six Sigma methodology uses several statistical methods. With implementation of Market Basket Analysis (as a part of Data Mining) to Six Sigma (to one of its phase), we can improve the results and change the Sigma performance level of the process. In our research we used GRI (General Rule Induction) algorithm to produce association rules between products in the market basket. These associations show a variety between the products. To show the dependence between the products we used a Web plot. The last algorithm in analysis was C5.0. This algorithm was used to build rule-based profiles.


title: Beyond the hedonic treadmill: Revisions to the adaptation theory of well-being

abstract: According to the hedonic treadmill model, good and bad events temporarily affect happiness, but people quickly adapt back to hedonic neutrality. The theory, which has gained widespread acceptance in recent years, implies that individual and societal efforts to increase happiness are doomed to failure. The recent empirical work outlined here indicates that 5 important revisions to the treadmill model are needed. First, individuals’ set points are not hedonically neutral. Second, people have different set points, which are partly dependent on their temperaments. Third, a single person may have multiple happiness set points: Different components of well-being such as pleasant emotions, unpleasant emotions, and life satisfaction can move in different directions. Fourth, and perhaps most important, well-being set points can change under some conditions. Finally, individuals differ in their adaptation to events, with some individuals changing their set point and others not changing in reaction to some external event. These revisions offer hope for psychologists and policymakers who aim to decrease human misery and increase happiness.


title: Duration of load and probability of failure in wood. Part I. Modelling creep rupture

abstract: Two models are considered for the study of wood creep rupture behaviour and the associated load duration effects on strength. Both are damage accumulation models. In model I, the damage rate is expressed as the product of a stress-dependent factor and a damage-dependent factor. In model II, the damage rate is given as the sum of a stress-dependent term and a damage-dependent term. Both models consider a stress threshold below which the damage rate vanishes. For each model, stress histories corresponding to two types of tests are considered: a step function load test and a ramp load test. The predictions of the models for these two cases are used to obtain model parameters by fitting constant and ramp load test data. Data used correspond to small, clear specimens of Douglas-fir in bending. As an application, the problem of residual strength in a ramp load test after a time of constant load is discussed.


title: Restoring 2D Content from Distorted Documents

abstract: This paper presents a framework to restore the 2D content printed on documents in the presence of geometric distortion and nonuniform illumination. Compared with text-based document imaging approaches that correct distortion to a level necessary to obtain sufficiently readable text or to facilitate optical character recognition (OCR), our work targets nontextual documents where the original printed content is desired. To achieve this goal, our framework acquires a 3D scan of the document's surface together with a high-resolution image. Conformal mapping is used to rectify geometric distortion by mapping the 3D surface back to a plane while minimizing angular distortion. This conformal "deskewing" assumes no parametric model of the document's surface and is suitable for arbitrary distortions. Illumination correction is performed by using the 3D shape to distinguish content gradient edges from illumination gradient edges in the high-resolution image. Integration is performed using only the content edges to obtain a reflectance image with significantly less illumination artifacts. This approach makes no assumptions about light sources and their positions. The results from the geometric and photometric correction are combined to produce the final output.


Bringing Model-based Systems Engineering Capabilities to Project Management: an Application to PRINCE2


title: Human mismatch repair system corrects errors produced during lagging strand replication more effectively

abstract: Mismatch repair (MMR) is one of the main systems maintaining fidelity of replication. Different effectiveness in correction of errors produced during replication of the leading and the lagging DNA strands was reported in yeast, but this effect is poorly studied in humans. Here, we use MMR-deficient (MSI) and MMR-proficient (MSS) cancer samples to investigate properties of the human MMR. MSI, but not MSS, cancers demonstrate unequal mutation rates between the leading and the lagging strands. The direction of strand asymmetry in MSI cancers matches that observed in cancers with mutated exonuclease domain of polymerase δ, indicating that polymerase δ contributes more mutations than its leading-strand counterpart, polymerase ε. As polymerase δ primarily synthesizes DNA during the lagging strand replication, this implies that mutations produced in wild type cells during lagging strand replication are repaired by the MMR ~3 times more effectively, compared to those produced on the leading strand.


title: Optimizing oxide charge balanced devices for Unclamped Inductive Switching (UIS)

abstract: Power MOSFET designs have been moving to higher performance particularly in the medium voltage area. (60V to 300V) New designs require lower Specific On-resistance while not sacrificing Unclamped Inductive Switching (UIS) capability or increasing turn-off losses. Two charge balance technologies currently address these needs, the PN junction and the Shielded Gate Charge Balance device topologies. This paper will study the impact of drift region as well as other design parameters that influence the shielded gate class of charge balance devices. The optimum design for maximizing UIS capability and minimizing the impact on other design parameters such as R DSON and switching performance are addressed. It will be shown through TCAD simulation one can design devices to have a stable avalanche point that is not influenced by small variations within a die or die-to-die that result from normal processing. Finally, measured and simulated data will be presented showing a fabricated device with near theoretical UIS capability.


title: Radio frequency energy harvesting technology

abstract: Radio frequency (RF) energy harvesting is a promising technique to energize low power electronic devices due to the sustainability it could offer resulting from the surge in ambient wireless signals it could utilize. In this paper, a concise literature survey on this technique is presented. Firstly, the architecture of a RF energy harvesting network is briefly introduced. Secondly, background relating to the antenna and rectifier designs is provided. Finally, some state-of-the-art designs from recent years are presented.


title: Predicting query execution time: Are optimizer cost models really unusable?

abstract: Predicting query execution time is useful in many database management issues including admission control, query scheduling, progress monitoring, and system sizing. Recently the research community has been exploring the use of statistical machine learning approaches to build predictive models for this task. An implicit assumption behind this work is that the cost models used by query optimizers are insufficient for query execution time prediction. In this paper we challenge this assumption and show while the simple approach of scaling the optimizer's estimated cost indeed fails, a properly calibrated optimizer cost model is surprisingly effective. However, even a well-tuned optimizer cost model will fail in the presence of errors in cardinality estimates. Accordingly we investigate the novel idea of spending extra resources to refine estimates for the query plan after it has been chosen by the optimizer but before execution. In our experiments we find that a well calibrated query optimizer model along with cardinality estimation refinement provides a low overhead way to provide estimates that are always competitive and often much better than the best reported numbers from the machine learning approaches.


title: CORTISOL DECREASES AND SEROTONIN AND DOPAMINE INCREASE FOLLOWING MASSAGE THERAPY

abstract: In this article the positive effects of massage therapy on biochemistry are reviewed including decreased levels of cortisol and increased levels of serotonin and dopamine. The research reviewed includes studies on depression (including sex abuse and eating disorder studies), pain syndrome studies, research on autoimmune conditions (including asthma and chronic fatigue), immune studies (including HIV and breast cancer), and studies on the reduction of stress on the job, the stress of aging, and pregnancy stress. In studies in which cortisol was assayed either in saliva or in urine, significant decreases were noted in cortisol


title: A Bidirectional-Switch-Based Wide-Input Range High-Efficiency Isolated Resonant Converter for Photovoltaic Applications

abstract: Modular photovoltaic (PV) power conditioning systems (PCSs) require a high-efficiency dc-dc converter stage capable of regulation over a wide input voltage range for maximum power point tracking. In order to mitigate ground leakage currents and to be able to use a high-efficiency, nonisolated grid-tied inverter, it is also desirable for this microconverter to provide galvanic isolation between the PV module and the inverter. This paper presents a novel, isolated topology that is able to meet the high efficiency over a wide input voltage range requirement. This topology yields high efficiency through low circulating currents, zero-voltage switching (ZVS) and low-current switching of the primary side devices, ZCS of the output diodes, and direct power transfer to the load for the majority of switching cycle. This topology is also able to provide voltage regulation through basic fixed-frequency pulsewidth modulated (PWM) control. These features are able to be achieved with the simple addition of a secondary-side bidirectional ac switch to the isolated series resonant converter. Detailed analysis of the operation of this converter is discussed along with a detailed design procedure. Experimental results of a 300-W prototype are given. The prototype reached a peak power stage efficiency of 98.3% and a California Energy Commission (CEC) weighted power stage efficiency of 98.0% at the nominal input voltage.


title: Lightweight Pyramid Networks for Image Deraining

abstract: Existing deep convolutional neural networks (CNNs) have found major success in image deraining, but at the expense of an enormous number of parameters. This limits their potential applications, e.g., in mobile devices. In this paper, we propose a lightweight pyramid networt (LPNet) for single-image deraining. Instead of designing a complex network structure, we use domain-specific knowledge to simplify the learning process. In particular, we find that by introducing the mature Gaussian-Laplacian image pyramid decomposition technology to the neural network, the learning problem at each pyramid level is greatly simplified and can be handled by a relatively shallow network with few parameters. We adopt recursive and residual network structures to build the proposed LPNet, which has less than 8K parameters while still achieving the state-of-the-art performance on rain removal. We also discuss the potential value of LPNet for other low- and high-level vision tasks.


title: Robot-Human Agreements and Financial Transactions Enabled by a Blockchain and Smart Contracts

abstract: This paper explores the design of a robot and interaction model that enables a robot to engage in human-like financial transactions, and to enter into agreements with a human counterpart. More explicitly, (1) we bestow the agent with a cryptocurrency wallet and (2) define bilateral and multilateral agreements that can be automated as smart contracts in a distributed ledger. As a use case of a robot with such features, we describe roBU - a traveling robot, that can enter into financial agreements in exchange for assistance in traveling the world. With this effort, we expect to validate the idea of near-future scenarios where autonomous or semi-autonomous agents are endowed with, a type of, social autonomy and the ability to engage in financial transactions. We believe the latter can improve task completion and enable further exploration of robot-human relationships, dependencies and trust.


title: A practical approach to Sentiment Analysis of hindi tweets

abstract: Sentiment Analysis(SA) is a combination of emotions, opinions and subjectivity of text. Today, social networking sites like Twitter are tremendously used in expressing the opinions about a particular entity in the form of tweets which are limited to 140 characters. Reviews and opinions play a very important role in understanding peoples satisfaction regarding a particular entity. Such opinions have high potential for knowledge discovery. The main target of SA is to find opinions from tweets, extract sentiments from them and then define their polarity, i.e, positive, negative or neutral. Most of the work in this domain has been done for English Language. In this paper, we discuss and propose sentiment analysis using Hindi language. We will discuss an unsupervised lexicon method for classification.


IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases


title: Voltage Stability Prediction Using Active Machine Learning

abstract: An active machine learning technique for monitoring the voltage stability in transmission systems is presented. It has been shown that machine learning algorithms may be used to supplement the traditional simulation approach, but they suffer from the difficulties of online machine learning model update and offline training data preparation. We propose an active learning solution to enhance existing machine learning applications by actively interacting with the online prediction and offline training process. The technique identifies operating points where machine learning predictions based on power system measurements contradict with actual system conditions. By creating the training set around the identified operating points, it is possible to improve the capability of machine learning tools to predict future power system states. The technique also accelerates the offline training process by reducing the amount of simulations on a detailed power system model around operating points where correct predictions are made. Experiments show a significant advantage in relation to the training time, prediction time, and number of measurements that need to be queried to achieve high prediction accuracy.


title: Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network

abstract: Introduction Cone beam computed tomography (CBCT) plays an important role in image-guided radiation therapy (IGRT), while having disadvantages of severe shading artifact caused by the reconstruction using scatter contaminated and truncated projections. The purpose of this study is to develop a deep convolutional neural network (DCNN) method for improving CBCT image quality. Methods CBCT and planning computed tomography (pCT) image pairs from 20 prostate cancer patients were selected. Subsequently, each pCT volume was pre-aligned to the corresponding CBCT volume by image registration, thereby leading to registered pCT data (pCTr). Next, a 39-layer DCNN model was trained to learn a direct mapping from the CBCT to the corresponding pCTr images. The trained model was applied to a new CBCT data set to obtain improved CBCT (i-CBCT) images. The resulting i-CBCT images were compared to pCTr using the spatial non-uniformity (SNU), the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). Results The image quality of the i-CBCT has shown a substantial improvement on spatial uniformity compared to that of the original CBCT, and a significant improvement on the PSNR and the SSIM compared to that of the original CBCT and the enhanced CBCT by the existing pCT-based correction method. Conclusion We have developed a DCNN method for improving CBCT image quality. The proposed method may be directly applicable to CBCT images acquired by any commercial CBCT scanner.


title: Kalman filter based observer design for real-time frequency identification in motion control systems

abstract: The paper deals with a systematic observer design for a robust frequency identification of electromechanical systems. The observer estimates the frequency response of the system which is excited by a swept harmonic signal with a variable frequency, amplitude and DC component. The paper presents a computationally efficient algorithm based on the recursive discretization of a Kalman filter. An application of the proposed method is demonstrated on a problem of motion control of a compliant mechanism.


title: Efficient Heart Sound Segmentation and Extraction Using Ensemble Empirical Mode Decomposition and Kurtosis Features

abstract: An efficient heart sound segmentation (HSS) method that automatically detects the location of first ( S 1 ) and second ( S 2 ) heart sound and extracts them from heart auscultatory raw data is presented here. The heart phonocardiogram is analyzed by employing ensemble empirical mode decomposition (EEMD) combined with kurtosis features to locate the presence of S 1 , S 2 , and extract them from the recorded data, forming the proposed HSS scheme, namely HSS-EEMD/K. Its performance is evaluated on an experimental dataset of 43 heart sound recordings performed in a real clinical environment, drawn from 11 normal subjects, 16 patients with aortic stenosis, and 16 ones with mitral regurgitation of different degrees of severity, producing 2608 S 1 and S 2 sequences without and with murmurs, respectively. Experimental results have shown that, overall, the HSS-EEMD/K approach determines the heart sound locations in a percentage of 94.56% and segments heart cycles correctly for the 83.05% of the cases. Moreover, results from a noise stress test with additive Gaussian noise and respiratory noises justify the noise robustness of the HSS-EEMD/K. When compared with four other efficient methods that mainly employ wavelet transform, energy, simplicity, and frequency measures, respectively, using the same experimental database, the HSS-EEMD/K scheme exhibits increased accuracy and prediction power over all others at the level of 7-19% and 4-9%, respectively, both in controls and pathological cases. The promising performance of the HSS-EEMD/K paves the way for further exploitation of the diagnostic value of heart sounds in everyday clinical practice.


title: TOWARDS A FORMAL MODEL OF KNOWLEDGE ACQUISITION VIA COOEPERATIVE DIALOGUE

abstract: A liquid dispenser which includes a flexible tube having a relatively heavy intake fitting engageable with the lower end of the flexible tube, the relatively heavy intake fitting having a longitudinally extending passage through it containing a stop engageable with the lower end of the tube and a constriction engaging the outer periphery of the tube at a distance spaced above the stop, the intake fitting and the liquid contents both being displaced by gravity to the same region within the container when the container is tilted.


title: Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism

abstract: We propose multi-way, multilingual neural machine translation. The proposed approach enables a single neural translation model to translate between multiple languages, with a number of parameters that grows only linearly with the number of languages. This is made possible by having a single attention mechanism that is shared across all language pairs. We train the proposed multi-way, multilingual model on ten language pairs from WMT'15 simultaneously and observe clear performance improvements over models trained on only one language pair. In particular, we observe that the proposed model significantly improves the translation quality of low-resource language pairs.


title: Ultralow-Loss CMOS Copper Plasmonic Waveguides

abstract: Surface plasmon polaritons can give a unique opportunity to manipulate light at a scale well below the diffraction limit reducing the size of optical components down to that of nanoelectronic circuits. At the same time, plasmonics is mostly based on noble metals, which are not compatible with microelectronics manufacturing technologies. This prevents plasmonic components from integration with both silicon photonics and silicon microelectronics. Here, we demonstrate ultralow-loss copper plasmonic waveguides fabricated in a simple complementary metal-oxide semiconductor (CMOS) compatible process, which can outperform gold plasmonic waveguides simultaneously providing long (>40 μm) propagation length and deep subwavelength (∼λ2/50, where λ is the free-space wavelength) mode confinement in the telecommunication spectral range. These results create the backbone for the development of a CMOS plasmonic platform and its integration in future electronic chips.


title: A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews

abstract: Online physician reviews are a massive and potentially rich source of information capturing patient sentiment regarding healthcare. We analyze a corpus comprising nearly 60 000 such reviews with a state-of-the-art probabilistic model of text. We describe a probabilistic generative model that captures latent sentiment across aspects of care (eg, interpersonal manner ). We target specific aspects by leveraging a small set of manually annotated reviews. We perform regression analysis to assess whether model output improves correlation with state-level measures of healthcare. We report both qualitative and quantitative results. Model output correlates with state-level measures of quality healthcare, including patient likelihood of visiting their primary care physician within 14 days of discharge (p=0.03), and using the proposed model better predicts this outcome (p=0.10). We find similar results for healthcare expenditure. Generative models of text can recover important information from online physician reviews, facilitating large-scale analyses of such reviews.


title: An investigation into the transference and survivability of human DNA following simulated manual strangulation with consideration of the problem of third party contamination

abstract: Amplification was performed on human DNA material transferred during a model of manual strangulation. A total of 29 separate experiments were performed using a single male offender-female victim combination to observe whether DNA was transferred both from the offender’s fingers to the victim’s neck and vice versa and to consider the period of time after the event during which the material could potentially be recovered and amplified. DNA was amplified from either the victim’s neck or the offender’s fingers for at least 10 days after the contact although it is discussed whether this is potentially due to primary contact or a secondary/tertiary transfer event. The study highlights the problem of contamination of the offender’s hands and victim’s neck with third party DNA, the presence of which could have a significant outcome for both the investigating authority and the third party.


title: Changing your mind: On the contributions of top-down and bottom-up guidance in visual search for feature singletons

abstract: Observers, searching for targets among distractor items, guide attention with a mix of top-down information— based on observers’ knowledge—and bottom-up information—stimulus-based and largely independent of that knowledge. There are 2 types of top-down guidance: explicit information (e.g., verbal description) and implicit priming by preceding targets (top-down because it implies knowledge of previous searches). Experiments 1 and 2 separate bottom-up and top-down contributions to singleton search. Experiment 3 shows that priming effects are based more strongly on target than on distractor identity. Experiments 4 and 5 show that more difficult search for one type of target (color) can impair search for other types (size, orientation). Experiment 6 shows that priming guides attention and does not just modulate response.


Incorporating the Irrelevant : Anchors in Judgments of Belief and Value


title: Parallel processing techniques for high performance image processing applications

abstract: The complexity of many image processing applications and their stringent performance requirements have come to a point where they can no longer meet the real time deadlines, if implemented on conventional architectures based on a single general-purpose processor. Acceleration of these algorithms can be done by parallel computing. Parallelism can be accomplished both at hardware and software levels by various tools and methodologies. The various methods hence discussed prove to be helpful and thus a combination of both the custom hardware and software tool helps in speeding up the image processing algorithm. Different methodologies that can be used for parallel computation are discussed.


title: A swarm intelligent method for traffic light scheduling: application to real urban traffic networks

abstract: Traffic lights play an important role nowadays for solving complex and serious urban traffic problems. How to optimize the schedule of hundreds of traffic lights has become a challenging and exciting problem. This paper proposes an inner and outer cellular automaton mechanism combined with particle swarm optimization (IOCA-PSO) method to achieve a dynamic and real-time optimization scheduling of urban traffic lights. The IOCA-PSO method includes the inner cellular model (ICM), the outer cellular model (OCM), and the fitness function. Our work can be divided into following parts: (1) Concise basic transition rules and affiliated transition rules are proposed in ICM, which can help the proposed phase cycle planning (PCP) algorithm achieve a globally sophisticated scheduling and offer effective solutions for different traffic problems; (2) Benefited from the combination of cellular automaton (CA) and particle swarm optimization (PSO), the proposed inner and outer cellular PSO (IOPSO) algorithm in OCM offers a strong search ability to find out the optimal timing control; (3) The proposed fitness function can evaluate and conduct the optimization of traffic lights' scheduling dynamically for different aims by adjusting parameters. Extensive experiments show that, compared with the PSO method, the genetic algorithm method and the RANDOM method in real cases, IOCA-PSO presents distinct improvements under different traffic conditions, which shows a high adaptability of the proposed method in urban traffic network scales under different traffic flow states, intersection numbers, and vehicle numbers.


title: Villard's legacy : studies in medieval technology, science and art in memory of Jean Gimpel

abstract: Contents: Introduction, Marie-Therese Zenner In Memoriam Jean Gimpel, 1918-96, Carl F. Barnes, Jr. Essay: Jean Gimpel and the legacy of medieval technology, Charles Stegeman Bibliography of Jean Gimpel Portfolio of Villard de Honnecourt, selected folios. Part I Art And Architecture: Architectural geometry and the portfolio of Villard de Honnecourt, Nigel Hiscock Reims cathedral in the portfolio of Villard de Honnecourt, William W. Clark Villard de Honnecourt and masons' marks, Jennifer S. Alexander Costumes in the portfolio of Villard de Honnecourt, Janet Snyder The oral tradition and Villard de Honnecourt, Renaud Beffeyte The saracen's sepulchre: an interpretation of folio 6r in the portfolio of Villard de Honnecourt, Roland Bechmann Rock, spires, paper: technical aspects of gothic spire construction, Robert Bork The use of tufa webbing and wattle centering in English vaults down to 1340, Malcolm Thurlby. Part II Science And Technology: The medieval horse harness: revolution or evolution? A case study in technological change, Paul J. Gans Technological innovation and control at the medieval Venetian mint, Alan M. Stahl Astronomy, time, and churches in the early middle ages, Stephen C. McCluskey The art of radically coherent geometry, Robert D. Stevick Euclidean geometry in the early middle ages: a preliminary reassessment, Wesley M. Stevens Diagram and thought in medieval science, John North Index.


title: Sensor Discovery and Configuration Framework for The Internet of Things Paradigm

abstract: Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. The data generated by the Internet of Things are valuable and have the potential to drive innovative and novel applications. The data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. One of the most challenging tasks before collecting and processing data from these devices (e.g. sensors) is discovering and configuring the sensors and the associated data streams. In this paper, we propose a tool called SmartLink that can be used to discover and configure sensors. Specifically, SmartLink, is capable of discovering sensors deployed in a particular location despite their heterogeneity (e.g. different communication protocols, communication sequences, capabilities). SmartLink establishes the direct communication between the sensor hardware and cloud-based IoT middleware. We address the challenge of heterogeneity using a plugin architecture. Our prototype tool is developed on the Android platform. We evaluate the significance of our approach by discovering and configuring 52 different types of Libelium sensors.


title: Sleep-States-Transition Model by Body Movement and Estimation of Sleep-Stage-Appearance Probabilities by Kalman Filter

abstract: The judgment standards of R-K method include ambiguities and are thus compensated by subjective interpretations of sleep-stage scorers. This paper presents a novel method to compensate uncertainties in judgments by the subjective interpretations by the sleep-model estimation approach and by describing the judgments in probabilistic terms. Kalman filter based on the two sleep models with no body movement and with body movement was designed. Sleep stages judged by three different scorers were rejudged by the filter. The two sleep models were stochastically estimated from biosignals from 15 nights' data and the rejudged scores by the filter were evaluated by the data from 5 nights. The average values of kappa statistics, which show the degree of agreement, were 0.85, 0.89, and 0.81, respectively, for the original sleep stages. Because the new method provides probabilities on how surely the sleep belongs to each sleep stage, we were able to determine the most, second most, and third most probable sleep stage. The kappa statistics between the most probable sleep stages were improved to 0.90, 0.93, and 0.84, respectively. Those of sleep stages determined from the most and second most probable were 0.92, 0.94, and 0.89 and those from the most, second most, and third most probable were 0.95, 0.97, and 0.92. The sleep stages estimated by the filter are expressed by probabilistic manner, which are more reasonable in expression than those given by deterministic manner. The expression could compensate the uncertainties in each judgments and thus were more accurate than the direct judgments.


title: Accelerating Haskell array codes with multicore GPUs

abstract: Current GPUs are massively parallel multicore processors optimised for workloads with a large degree of SIMD parallelism. Good performance requires highly idiomatic programs, whose development is work intensive and requires expert knowledge. To raise the level of abstraction, we propose a domain-specific high-level language of array computations that captures appropriate idioms in the form of collective array operations. We embed this purely functional array language in Haskell with an online code generator for NVIDIA's CUDA GPGPU programming environment. We regard the embedded language's collective array operations as algorithmic skeletons; our code generator instantiates CUDA implementations of those skeletons to execute embedded array programs. This paper outlines our embedding in Haskell, details the design and implementation of the dynamic code generator, and reports on initial benchmark results. These results suggest that we can compete with moderately optimised native CUDA code, while enabling much simpler source programs.


title: Tactical Approaches for Alleviating Distance inGlobal software Development

abstract: To overcome the problem of distance in global software development, various managers are experimenting and quickly adjusting their tactical approaches. We discuss some emerging approaches and explain their motivations from conceptual and practical perspectives. The most intuitive approach for alleviating distance is to apply communication technologies, but this is not our focus. Rather, we examine tactics that go beyond communication technologies, tactics aimed at reducing intensive collaboration, national and organizational cultural differences, and temporal distance.


title: The Dichromatic Reflection Model - Future Research Directions and Applications.

abstract: The dichromatic reflection model (DRM) predicts that color distributions form a parallelogram in color space, whose shape is defined by the body reflectance and the illuminant color. In this paper we resume the assumptions which led to the DRM and shortly recall two of its main applications domains: color image segmentation and photometric invariant feature computation. After having introduced the model we discuss several limitations of the theory, especially those which are raised once working on real-world uncalibrated images. In addition, we summerize recent extensions of the model which allow to handle more complicated light interactions. Finally, we suggest some future research directions which would further extend its applicability.


title: Trusset: Parallel Development of Software and Construction Systems for Space-Truss Structures

abstract: This paper documents our current progress on the parallel development of a building system and corresponding agent-based software design tools; together the two produce a seamless pipeline from design to fabrication and assembly. The building system is a clad differential space-truss designed for fabrication entirely with computer numerically controlled (CNC) linear cutting devices such as CNC laser cutters or two-axis mills. The software component is a set of agent-based design tools for developing surfaces and envelopes formally suitable to be built using our space-truss system.


title: Low-cost antenna-in-package solution for 122 GHz radar module

abstract: In this paper, a glob-top packaging solution using thermally conductive adhesive Polytec TC 430-T is demonstrated which can be adapted for mass production using standard assembly effort. The packaging concept is first tested on two dummy chips connected by λ/2 wirebonds. Thereafter, the packaging concept is implemented on a 122 GHz wirebond based Antenna-in-Package (AiP). The off-chip circuitry of the AiP contains a monostatic circularly polarized (CP) microstrip patch antenna and a branchline coupler. Finally, the feasibility of realizing a 122 GHz low-cost, fully integrated, surface mountable monostatic radar sensor package is evaluated on the basis of simulations and probe based measurements carried out in the frequency range of 110 GHz and 170 GHz.


title: Embassies: Radically refactoring the web

abstract: Web browsers ostensibly provide strong isolation for the client-side components of web applications. Unfortunately, this isolation is weak in practice; as browsers add increasingly rich APIs to please developers, these complex interfaces bloat the trusted computing base and erode cross-app isolation boundaries. ::: ::: We reenvision the web interface based on the notion of a pico-datacenter, the client-side version of a shared server datacenter. Mutually untrusting vendors run their code on the user's computer in low-level native code containers that communicate with the outside world only via IP. Just as in the cloud datacenter, the simple semantics makes isolation tractable, yet native code gives vendors the freedom to run any software stack. Since the datacenter model is designed to be robust to malicious tenants, it is never dangerous for the user to click a link and invite a possibly-hostile party onto the client.


title: Atypical Prosody in Asperger Syndrome: Perceptual and Acoustic Measurements

abstract: It is known that individuals with Asperger syndrome (AS) may show no problems with regard to what is said (e.g., lexical content) but tend to have difficulties in how utterances are produced, i.e., they may show prosodic impairments. In the present study, we focus on the use of prosodic features to express grammatical meaning. Specifically, we explored the sentence type difference between statements and questions that is conveyed by intonation, using perceptual and acoustic measurements. Children aged 8 and 9 years with AS (n = 12) were matched according to age and nonverbal intelligence with typically developing peers (n = 17). Although children with AS could produce categorically accurate prosodic patterns, their prosodic contours were perceived as odd by adult listeners, and acoustic measurements showed alterations in duration and pitch. Additionally, children with AS had greater variability in fundamental frequency contours compared to typically developing peers.


title: A Hierarchical Phrase-Based Model For Statistical Machine Translation

abstract: We present a statistical phrase-based translation model that uses hierarchical phrases---phrases that contain subphrases. The model is formally a synchronous context-free grammar but is learned from a bitext without any syntactic information. Thus it can be seen as a shift to the formal machinery of syntax-based translation systems without any linguistic commitment. In our experiments using BLEU as a metric, the hierarchical phrase-based model achieves a relative improvement of 7.5% over Pharaoh, a state-of-the-art phrase-based system.


Enhanced e-Learning System for e-Courses Based on Cloud Computing


title: Automated Monitoring System for the Fish Farm Aquaculture Environment

abstract: This panel will establish an automated monitoring system of wireless sensor networks for a fish farm Environment Simulation. This system allows a user with a mobile device to monitor the fish farm Environmental Data with Instant mastery and control over the various environmental data. Temperature, dissolved oxygen, PH value and water level sensing modules are incorporated in this monitoring system. MCU processing is used to capture the physical sensing signal. The ZigBee wireless sensor network brings the data to a central processing core. A WIFI interface transfers the data to the user terminal device. The user can control the entire fish farm environment through the terminal device. Android software was used to design the terminal device user interface. A low power MSP430 series MCU is the core of each sensing terminal and the central terminal. The power supply can be battery-powered, standard electricity supply and/or solar battery powered. UPS makes the whole system more secure with low-cost, low energy consumption, easy operating features with a high degree of freedom for this wireless breeding environment monitoring system.


title: Agent-based Simulation Platforms: Review and Development Recommendations

abstract: Five software platforms for scientific agent-based models (ABMs) were reviewed by implementing example models in each. NetLogo is the highest-level platform, providing a simple yet powerful programming language, built-in graphical interfaces, and comprehensive documentation. It is designed primarily for ABMs of mobile individuals with local interactions in a grid space, but not necessarily clumsy for others. NetLogo is highly recommended, even for prototyping complex models. MASON, Repast, and Swarm are "framework and library" platforms, providing a conceptual framework for organizing and designing ABMs and corresponding software libraries. MASON is least mature and designed with execution speed a high priority. The Objective-C version of Swarm is the most mature library platform and is stable and well organized. Objective-C seems more natural than Java for ABMs but weak error-handling and the lack of developer tools are drawbacks. Java Swarm allows Swarm's Objective-C libraries to be called from Java; it does not seem to combine the advantages of the two languages well. Repast provides Swarm-like functions in a Java library and is a good choice for many, but parts of its organization and design could be improved. A rough comparison of execution speed found MASON and Repast usually fastest (MASON 1-35% faster than Repast), Swarm (including Objective-C) fastest for simple models but slowest for complex ones, and NetLogo intermediate. Recommendations include completing the documentation (for all platforms except NetLogo), strengthening conceptual frameworks, providing better tools for statistical output and automating simulation experiments, simplifying common tasks, and researching technologies for understanding how simulation results arise.


title: See through walls with WiFi!

abstract: Wi-Fi signals are typically information carriers between a transmitter and a receiver. In this paper, we show that Wi-Fi can also extend our senses, enabling us to see moving objects through walls and behind closed doors. In particular, we can use such signals to identify the number of people in a closed room and their relative locations. We can also identify simple gestures made behind a wall, and combine a sequence of gestures to communicate messages to a wireless receiver without carrying any transmitting device. The paper introduces two main innovations. First, it shows how one can use MIMO interference nulling to eliminate reflections off static objects and focus the receiver on a moving target. Second, it shows how one can track a human by treating the motion of a human body as an antenna array and tracking the resulting RF beam. We demonstrate the validity of our design by building it into USRP software radios and testing it in office buildings.


title: Improving efficiency and accuracy in multilingual entity extraction

abstract: There has recently been an increased interest in named entity recognition and disambiguation systems at major conferences such as WWW, SIGIR, ACL, KDD, etc. However, most work has focused on algorithms and evaluations, leaving little space for implementation details. In this paper, we discuss some implementation and data processing challenges we encountered while developing a new multilingual version of DBpedia Spotlight that is faster, more accurate and easier to configure. We compare our solution to the previous system, considering time performance, space requirements and accuracy in the context of the Dutch and English languages. Additionally, we report results for 9 additional languages among the largest Wikipedias. Finally, we present challenges and experiences to foment the discussion with other developers interested in recognition and disambiguation of entities in natural language text.


title: Social networking site use by mothers of young children

abstract: In this paper, we present the first formal study of how mothers of young children (aged three and under) use social networking sites, particularly Facebook and Twitter, including mothers' perceptions of which SNSes are appropriate for sharing information about their children, changes in post style and frequency after birth, and the volume and nature of child-related content shared in these venues. Our findings have implications for improving the utility and usability of SNS tools for mothers of young children, as well as for creating and improving sociotechnical systems related to maternal and child health.


The Structure of Transmedia Storytelling: A Case Study of 19 Reinos


An Integrative Review of Family Interventions based on a Philosophy of Family-Centered Care in Neonatal Intensive Care Units


Case Studies and Organisational Sustainability Modelling Presented by Cloud Computing Business Framework


title: Top-$K$ Ranking from Pairwise Comparisons: When Spectral Ranking is Optimal

abstract: We explore the top-$K$ rank aggregation problem. Suppose a collection of items is compared in pairs repeatedly, and we aim to recover a consistent ordering that focuses on the top-$K$ ranked items based on partially revealed preference information. We investigate the Bradley-Terry-Luce model in which one ranks items according to their perceived utilities modeled as noisy observations of their underlying true utilities. Our main contributions are two-fold. First, in a general comparison model where item pairs to compare are given a priori, we attain an upper and lower bound on the sample size for reliable recovery of the top-$K$ ranked items. Second, more importantly, extending the result to a random comparison model where item pairs to compare are chosen independently with some probability, we show that in slightly restricted regimes, the gap between the derived bounds reduces to a constant factor, hence reveals that a spectral method can achieve the minimax optimality on the (order-wise) sample size required for top-$K$ ranking. That is to say, we demonstrate a spectral method alone to be sufficient to achieve the optimality and advantageous in terms of computational complexity, as it does not require an additional stage of maximum likelihood estimation that a state-of-the-art scheme employs to achieve the optimality. We corroborate our main results by numerical experiments.


title: Text Summarization Techniques: A Brief Survey

abstract: In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.


title: The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants

abstract: Because human activities impact the timing, location, and degree of pollutant exposure, they play a key role in explaining exposure variation. This fact has motivated the collection of activity pattern data for their specific use in exposure assessments. The largest of these recent efforts is the National Human Activity Pattern Survey (NHAPS), a 2-year probability-based telephone survey (n=9386) of exposure-related human activities in the United States (U.S.) sponsored by the U.S. Environmental Protection Agency (EPA). The primary purpose of NHAPS was to provide comprehensive and current exposure information over broad geographical and temporal scales, particularly for use in probabilistic population exposure models. NHAPS was conducted on a virtually daily basis from late September 1992 through September 1994 by the University of Maryland's Survey Research Center using a computer-assisted telephone interview instrument (CATI) to collect 24-h retrospective diaries and answers to a number of personal and exposure-related questions from each respondent. The resulting diary records contain beginning and ending times for each distinct combination of location and activity occurring on the diary day (i.e., each microenvironment). Between 340 and 1713 respondents of all ages were interviewed in each of the 10 EPA regions across the 48 contiguous states. Interviews were completed in 63% of the households contacted. NHAPS respondents reported spending an average of 87% of their time in enclosed buildings and about 6% of their time in enclosed vehicles. These proportions are fairly constant across the various regions of the U.S. and Canada and for the California population between the late 1980s, when the California Air Resources Board (CARB) sponsored a state-wide activity pattern study, and the mid-1990s, when NHAPS was conducted. However, the number of people exposed to environmental tobacco smoke (ETS) in California seems to have decreased over the same time period, where exposure is determined by the reported time spent with a smoker. In both California and the entire nation, the most time spent exposed to ETS was reported to take place in residential locations.


Lengths are coordinates for convex structures


title: A Review of Charging Algorithms for Nickel and Lithium Battery Chargers

abstract: Battery-charging algorithms can be used for either single- or multiple-battery chemistries. In general, single-chemistry chargers have the advantages of simplicity and reliability. On the other hand, multichemistry chargers, or “universal battery chargers,” provide a practical option for multichemistry battery systems, particularly for portable appliances, but they have some limitations. This paper presents a review of some charging algorithms for major batteries, i.e., nickel-cadmium, nickel-metal-hydride, and lithium-ion batteries for single- and multiple-chemistry chargers. A comparison between these algorithms in terms of their charging schemes and charge termination techniques is included. In addition, some trends of recent chargers development are presented.


title: Autocalibration with the Minimum Number of Cameras with Known Pixel Shape

abstract: In 3D reconstruction, the recovery of the calibration parameters of the cameras is paramount since it provides metric information about the observed scene, e.g., measures of angles and ratios of distances. Autocalibration enables the estimation of the camera parameters without using a calibration device, but by enforcing simple constraints on the camera parameters. In the absence of information about the internal camera parameters such as the focal length and the principal point, the knowledge of the camera pixel shape is usually the only available constraint. Given a projective reconstruction of a rigid scene, we address the problem of the autocalibration of a minimal set of cameras with known pixel shape and otherwise arbitrarily varying intrinsic and extrinsic parameters. We propose an algorithm that only requires 5 cameras (the theoretical minimum), thus halving the number of cameras required by previous algorithms based on the same constraint. To this purpose, we introduce as our basic geometric tool the six-line conic variety (SLCV), consisting in the set of planes intersecting six given lines of 3D space in points of a conic. We show that the set of solutions of the Euclidean upgrading problem for three cameras with known pixel shape can be parameterized in a computationally efficient way. This parameterization is then used to solve autocalibration from five or more cameras, reducing the three-dimensional search space to a two-dimensional one. We provide experiments with real images showing the good performance of the technique.


title: Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task

abstract: We introduce a new multi-modal task for computer systems, posed as a combined vision-language comprehension challenge: identifying the most suitable text describing a scene, given several similar options. Accomplishing the task entails demonstrating comprehension beyond just recognizing"keywords"(or key-phrases) and their corresponding visual concepts. Instead, it requires an alignment between the representations of the two modalities that achieves a visually-grounded"understanding"of various linguistic elements and their dependencies. This new task also admits an easy-to-compute and well-studied metric: the accuracy in detecting the true target among the decoys. The paper makes several contributions: an effective and extensible mechanism for generating decoys from (human-created) image captions; an instance of applying this mechanism, yielding a large-scale machine comprehension dataset (based on the COCO images and captions) that we make publicly available; human evaluation results on this dataset, informing a performance upper-bound; and several baseline and competitive learning approaches that illustrate the utility of the proposed task and dataset in advancing both image and language comprehension. We also show that, in a multi-task learning setting, the performance on the proposed task is positively correlated with the end-to-end task of image captioning.


title: Mining YouTube to Discover Extremist Videos, Users and Hidden Communities

abstract: We describe a semi-automated system to assist law enforcement and intelligence agencies dealing with cyber-crime related to promotion of hate and radicalization on the Internet. The focus of this work is on mining YouTube to discover hate videos, users and virtual hidden communities. Finding precise information on YouTube is a challenging task because of the huge size of the YouTube repository and a large subscriber base. We present a solution based on data mining and social network analysis (using a variety of relationships such as friends, subscriptions, favorites and related videos) to aid an analyst in discovering insightful and actionable information. Furthermore, we performed a systematic study of the features and properties of the data and hidden social networks which has implications in understanding extremism on Internet. We take a case study based approach and perform empirical validation of the proposed hypothesis. Our approach succeeded in finding hate videos which were validated manually.


title: Machine learning aided cognitive RAT selection for 5G heterogeneous networks

abstract: The starring role of the Heterogeneous Networks (HetNet) strategy as the key Radio Access Network (RAN) architecture for future 5G networks poses serious challenges to the current user association (cell selection) mechanisms used in cellular networks. The max-SINR algorithm, although historically effective for performing this function, is inefficient at best and obsolete at worst in 5G HetNets. The foreseen embarrassment of riches and diversified propagation characteristics of network attachment points spanning multiple Radio Access Technologies (RAT) requires novel and creative context-aware system designs that optimize the association and routing decisions in the context of single-RAT and multi-RAT connections, respectively. This paper proposes a framework under these guidelines that relies on Machine Learning techniques at the terminal device level for Cognitive RAT Selection and presents simulation results to suppport it.


title: Multitenancy - Security Risks and Countermeasures

abstract: Security within the cloud is of paramount importance as the interest and indeed utilization of cloud computing increase. Multitenancy in particular introduces unique security risks to cloud computing as a result of more than one tenant utilizing the same physical computer hardware and sharing the same software and data. The purpose of this paper is to explore the specific risks in cloud computing due to Multitenancy and the measures that can be taken to mitigate those risks.


title: The habitual consumer

abstract: Consumers sometimes act like creatures of habit, automatically repeating past behavior with little regard to current goals and valued outcomes. To explain this phenomenon, we show that habits are a specific form of automaticity in which responses are directly cued by the contexts (e.g., locations, preceding actions) that consistently covaried with past performance. Habits are prepotent responses that are quick to activate in memory over alternatives and that have a slow-to-modify memory trace. In daily life, the tendency to act on habits is compounded by everyday demands, including time pressures, distraction, and self-control depletion. However, habits are not immune to deliberative processes. Habits are learned largely as people pursue goals in daily life, and habits are broken through the strategic deployment of effortful self-control. Also, habits influence the post hoc inferences that people make about their behavior.


title: Fibrous dysplasia for radiologists: beyond ground glass bone matrix

abstract: Fibrous dysplasia (FD) is a congenital disorder arising from sporadic mutation of the α-subunit of the Gs stimulatory protein. Osseous changes are characterised by the replacement and distortion of normal bone with poorly organised, structurally unsound, fibrous tissue. The disease process may be localised to a single or multiple bones. In McCune-Albright syndrome (MAS), fibrous dysplasia is associated with hyperfunction of endocrine organs and overproduction of melanin in the skin, while Mazabraud syndrome FD is associated with intramuscular myxomas. In radiology, FD is very often automatically associated with the term “ground glass matrix”. However, FD is a complex disease, and knowledge of its unique pathogenesis and course are crucial to understanding imaging findings and potential complications. This article aims to not only summarise the spectrum of radiological findings of osseous and extra-osseous abnormalities associated with FD but also to highlight the pathological base of the disease evolution, corresponding imaging changes and complications based on the disease distribution. We also have provided current recommendations for clinical management and follow-up of patients with FD. • FD is often a part of complex disease, involving not only bone but also multiple other organs. • FD lesions are characterised by age-related histological, radiographical and clinical transformations. • Radiologists play a crucial role in the identification of osseous complications associated with FD. • The craniofacial form of the disease is the most common type of FD and the most difficult form to manage. • Patients with McCune-Albright syndrome may have different extra-skeletal abnormalities, which often require follow-up.


title: Detecting Duplicate Pull-requests in GitHub

abstract: The widespread use of pull-requests boosts the development and evolution for many open source software projects. However, due to the parallel and uncoordinated nature of development process in GitHub, duplicate pull-requests may be submitted by different contributors to solve the same problem. Duplicate pull-requests increase the maintenance cost of GitHub, result in the waste of time spent on the redundant effort of code review, and even frustrate developers' willing to offer continuous contribution. In this paper, we investigate using text information to automatically detect duplicate pull-requests in GitHub. For a new-arriving pull-request, we compare the textual similarity between it and other existing pull-requests, and then return a candidate list of the most similar ones. We evaluate our approach on three popular projects hosted in GitHub, namely Rails, Elasticsearch and Angular.JS. The evaluation shows that about 55.3% -- 71.0% of the duplicates can be found when we use the combination of title similarity and description similarity.


title: Speech recognition using neural networks

abstract: This paper presents investigation on speech recognition classification performance when using different standard neural networks structures as a classifier. Those cases include usage of a Feed-forward Neural Network (NN) with back propagation algorithm and a Radial Basis Functions (RBF) Neural Network.


A Requirements-to-Implementation Mapping Tool for Requirements Traceability


title: An Automatic Infants Cry Detection Using Linear Frequency Cepstrum Coefficients(LFCC)

abstract: In this paper, we mainly focused on automation of Infant's Cry. For this implementation we use LFCC for feature extraction and VQ codebook for matching samples using LBG algorithm. The baby crying samples collected from various crying baby having 0-6months age. There are 150 baby's sound as training data, each of which represents the 30 hungry infant cries, 30 sleepy infant cries, 30 wanted to burp infant cries, 30 in pain infant cries, and 30 uncomfortable infant cries (could be because his/her diaper is wet/too hot/cold air or anything else).The testing data is 40, respectively 8 infant cries for each type of infant cry. The identification of infant cries based the minimum distance of Euclidean distance. The, classification of the cry in five classes neh - hunger owh - sleepy, heh - discomfort ,eair - lower gas, eh - burp.Here for classification of the cry our system is divided into two phases. First, in training phase, in which LFCC is applied for feature extraction, and then VQ codebooks are generated to compress the feature vectors. Second, is the testing phase in which features extraction and codebook generation of samples are repeated. Here, comparison of the codebook template of samples to the all the available templates in the database are carried based on Euclidian distance between them. LFCC effectively capture the lower as well as higher frequency characteristics than MFCC, hence we will get good results over MFCC.


title: Random Semantic Tensor Ensemble for Scalable Knowledge Graph Link Prediction

abstract: Link prediction on knowledge graphs is useful in numerous application areas such as semantic search, question answering, entity disambiguation, enterprise decision support, recommender systems and so on. While many of these applications require a reasonably quick response and may operate on data that is constantly changing, existing methods often lack speed and adaptability to cope with these requirements. This is aggravated by the fact that knowledge graphs are often extremely large and may easily contain millions of entities rendering many of these methods impractical. In this paper, we address the weaknesses of current methods by proposing Random Semantic Tensor Ensemble (RSTE), a scalable ensemble-enabled framework based on tensor factorization. Our proposed approach samples a knowledge graph tensor in its graph representation and performs link prediction via ensembles of tensor factorization. Our experiments on both publicly available datasets and real world enterprise/sales knowledge bases have shown that our approach is not only highly scalable, parallelizable and memory efficient, but also able to increase the prediction accuracy significantly across all datasets.


title: Matlab and GNU radio-based SFCW radar for range detection

abstract: In this paper, a radar based on stepped frequency continues wave (SFCW) method is proposed to gain higher range resolution. The method is intended to makes the bandwidth becoming wider without affecting the pulse width to be shorter. Here, the bandwidth equals to the step frequency and the number of generated pulses. The simulation is performed in Matlab® and GNU Radio to measure range detection and its spectrum. It shows that the wide bandwidth of 1.25GHz can be achieve with the range resolution of 0.125m at start frequency of 1.5GHz and step frequency of 10MHz. Moreover, the SFCW radar simulation is also simulated with audio frequency in voice spectrum by using the speaker as transmitter and the microphone as receiver. Although the performance of range resolution is undeterminable accurately due to the echoed signal, however it can show the capability for range detection with the error of 5.35%.


title: Flip-Invariant Motion Representation

abstract: In action recognition, local motion descriptors contribute to effectively representing video sequences where target actions appear in localized spatio-temporal regions. For robust recognition, those fundamental descriptors are required to be invariant against horizontal (mirror) flipping in video frames which frequently occurs due to changes of camera viewpoints and action directions, deteriorating classification performance. In this paper, we propose methods to render flip invariance to the local motion descriptors by two approaches. One method leverages local motion flows to ensure the invariance on input patches where the descriptors are computed. The other derives a invariant form theoretically from the flipping transformation applied to hand-crafted descriptors. The method is also extended so as to deal with ConvNet descriptors through learning the invariant form based on data. The experimental results on human action classification show that the proposed methods favorably improve performance both of the handcrafted and the ConvNet descriptors.


title: Microplastics in bivalves cultured for human consumption.

abstract: Microplastics are present throughout the marine environment and ingestion of these plastic particles (<1 mm) has been demonstrated in a laboratory setting for a wide array of marine organisms. Here, we investigate the presence of microplastics in two species of commercially grown bivalves: Mytilus edulis and Crassostrea gigas. Microplastics were recovered from the soft tissues of both species. At time of human consumption, M. edulis contains on average 0.36 ± 0.07 particles g(-1) (wet weight), while a plastic load of 0.47 ± 0.16 particles g(-1) ww was detected in C. gigas. As a result, the annual dietary exposure for European shellfish consumers can amount to 11,000 microplastics per year. The presence of marine microplastics in seafood could pose a threat to food safety, however, due to the complexity of estimating microplastic toxicity, estimations of the potential risks for human health posed by microplastics in food stuffs is not (yet) possible.


title: Neural basis of quasi-rational decision making

abstract: Standard economic theories conceive homo economicus as a rational decision maker capable of maximizing utility. In reality, however, people tend to approximate optimal decision-making strategies through a collection of heuristic routines. Some of these routines are driven by emotional processes, and others are adjusted iteratively through experience. In addition, routines specialized for social decision making, such as inference about the mental states of other decision makers, might share their origins and neural mechanisms with the ability to simulate or imagine outcomes expected from alternative actions that an individual can take. A recent surge of collaborations across economics, psychology and neuroscience has provided new insights into how such multiple elements of decision making interact in the brain.


title: Review of IEEE 802.22 and IEC 61850 for real-time communication in Smart Grid

abstract: Real time data communication in Smart Grid is a big challenge due to its vastness and complexities. Wireless Regional Area Network (WRAN) - IEEE 802.22 permits opportunistic use of television white space (TVWS) by secondary user, if it is not being utilized by primary user. This standard has the potential to transfer big data of smart grid in real time to control center. The IEC 61850 is the standard for electrical substation automation that uses TCP/IP for data transfer among different ‘intelligent electronic devices’ (IEDs). IEC 61850 consists of different message types which belong to different performance classes that are mapped to different communication protocols. The different performance classes are GOOSE, SV, GSSE (Generic substation status event) and MMS (Manufacturing Message Specification). GOOSE and SV are directly mapped on Ethernet for fast data transfer. The performance classes of Generic Substation State Event (GSSE) and Manufacturing Message Specification (MMS) can be used for teleprotection and transmitting synchrophasors over Wide Area Network (WAN). This paper reviews IEC 61850 and explores the possibility of IEEE 802.22 for teleprotection and transmitting synchrophasors over Wide Area Network (WAN) using GSSE and MMS. The superframe structure of IEEE 802.22 consists of 16 frames each of 10 ms. This 10 ms sub-frame in IEEE 802.22 is capable to send data on uplink frequency using upstream burst.


title: Determination of Nonpoint-Source Pollution Using GIS and Numerical Models

abstract: A geographic information system (GIS) was utilized to apply a modified DRASTIC method to the assessment of groundwater contamination sensitivity in Goshen County, Wyoming. Several basic environmental characteristics, identified as influencing contaminant transport through the vadose zone to groundwater systems, were mapped, automated, and analyzed. These characteristics include : depth to groundwater, net recharge, hydrogeologic setting, vadose zone soil properties, land surface slope, and saturated hydraulic conductivity. Sensitivity ratings were developed for each parameter based on a combination of mathematical functions and the inherent capacity of each characteristic to influence transport of contaminants. A raster-based overlay analysis was performed to derive a map that portrays cumulative aquifer sensitivity ratings across the county, providing a relative indication of groundwater vulnerability to contamination. A process-based numerical model was used to simulate water flow and solute transport in the vadose zone and groundwater systems. The model incorporated soil and hydraulic properties produced with the GIS into the simulations. Numerical simulations described the time and spatial distributions of contaminants. Chemical mass stored in the soil and leaching out from the vadose zone were computed to characterize groundwater contamination. Groundwater sensitivity indexes, which were developed based on the numerical modeling results, were compared with the GIS sensitivity map and used to verify the reliability of the map.


title: Bronchial Erosion by an Indwelling Central Venous Catheter

abstract: Despite improved technology, central venous catheters are associated with many complications that occur usually within 48 h of placement. We report a 42-year-old man with a rare erosion of a venous catheter (Silastic) into a bronchus 2 years after its insertion.


title: Community detection in a large real-world social network

abstract: Identifying meaningful community structure in social networks is a hard problem, and extreme network size or sparseness of the network compound the difficulty of the task.With a proliferation of real-world network datasets there has been an increasing demand for algorithms that work effectively and efficiently. Existing methods are limited by their computational requirements and rely heavily on the network topology, which fails in scale-free networks. Yet, in addition to the network connectivity, many datasets also include attributes of individual nodes, but current methods are unable to incorporate this data. Cognizant of these requirements we propose a simple approach that stirs away from complex algorithms, focusing instead on the edge weights; more specifically, we leverage the node attributes to compute better weights. Our experimental results on a real-world social network show that a simple thresholding method with edge weights based on node attributes is sufficient to identify a very strong community structure.


title: Proposal of Backtracked Tabu Search (BTS) Algorithm

abstract: There are several heuristic search techniques, Tabu Search (TS) is one of them. TS based on generating the neighbor states but it has several problems in its work. This paper presents a new version of TS based on backtracking techniques (called BTS) to reduce the effect of these problems. Two case studies have been solved using BTS, 4-Color Map and Traveling Salesman Problem. The proposed algorithm gives good results compare with the original, the iteration numbers are less and the local minimum or non-optimal solutions are less.


title: One-Two-One Networks for Compression Artifacts Reduction in Remote Sensing

abstract: Compression artifacts reduction (CAR) is a challenging problem in the field of remote sensing. Most recent deep learning based methods have demonstrated superior performance over the previous hand-crafted methods. In this paper, we propose an end-to-end one-two-one (OTO) network, to combine different deep models, i.e., summation and difference models, to solve the CAR problem. Particularly, the difference model motivated by the Laplacian pyramid is designed to obtain the high frequency information, while the summation model aggregates the low frequency information. We provide an in-depth investigation into our OTO architecture based on the Taylor expansion, which shows that these two kinds of information can be fused in a nonlinear scheme to gain more capacity of handling complicated image compression artifacts, especially the blocking effect in compression. Extensive experiments are conducted to demonstrate the superior performance of the OTO networks, as compared to the state-of-the-arts on remote sensing datasets and other benchmark datasets. The source code will be available here: https://github.com/bczhangbczhang/.


title: Data Fusion-Based Multi-Object Tracking for Unconstrained Visual Sensor Networks

abstract: Camera node perception capability is one of the crucial issues for visual sensor networks, which belongs to the field of Internet of Things. Multi-object tracking is an important feature in analyzing object trajectories across multiple cameras, thus allowing synthesis of data and security analysis of images in various scenarios. Despite intensive research in the last decades, it remains challenging for tracking systems to perform in real-world situations. We therefore focus on key issues of multi-object state estimation for unconstrained multi-camera systems, e.g., data fusion of multiple sensors and data association. Unlike previous work that rely on camera network topology inference, we construct a graph from 2-D observations of all camera pairs without any assumption of network configuration. We apply the shortest path algorithm to the graph to find fused 3-D observation groups. Our approach is thus able to reject false positive reconstructions automatically, and also minimize computational complexity to guarantee feasible data fusion. Particle filters are applied as the 3-D tracker to form tracklets that integrate local features. These tracklets are modeled by a graph and linked into full tracks incorporating global spatial-temporal features. Experiments on the real-world PETS2009 S2/L1 sequence show the accuracy of our approach. Analyses of the different components of our approach provide meaningful insights for object tracking using multiple cameras. Evidence is provided for selecting the best view for a visual sensor network.


title: Wave Menus: Improving the Novice Mode of Hierarchical Marking Menus

abstract: We present Wave menus, a variant of multi-stroke marking menus designed for improving the novice mode of marking while preserving their efficiency in the expert mode of marking. Focusing on the novice mode, a criteria-based analysis of existing marking menus motivates the design of Wave menus. Moreover a user experiment is presented that compares four hierarchical marking menus in novice mode. Results show that Wave and compound-stroke menus are significantly faster and more accurate than multi-stroke menus in novice mode, while it has been shown that in expert mode the multistroke menus and therefore the Wave menus outperform the compound-stroke menus. Wave menus also require significantly less screen space than compound-stroke menus. As a conclusion, Wave menus offer the best performance for both novice and expert modes in comparison with existing multi-level marking menus, while requiring less screen space than compound-stroke menus.


title: Microstrip-based ultra-wideband composite BPF with short-circuited stubs

abstract: In this paper, the design and realization of microstrip-based ultra-wideband (UWB) composite bandpass filter (BPF) with short-circuited stubs is presented. The BPF is compositely constructed from the step impedance lowpass filter (LPF) and the optimum distributed highpass filter (HPF). Prior to the realization, the performances of filter and its physical dimension are investigated numerically to obtain the optimum design. The BPF is deployed on a grounded FR4 Epoxy dielectric substrate with the thickness of 0.8mm and the dimension of 25mm x 25mm. From the measurement result, it shows that the realized UWB composite BPF has 3dB bandwidth response of 10.03GHz ranges from 1.86GHz to 11.89GHz.