title: Guest Editor's Introduction: Homing Missile Guidance and Control

abstract: Homing missiles have played an increasingly important role in warfare since the end of World War II. In contrast to inertially guided long-range ballistic missiles, homing missiles guide themselves to intercept targets that can maneuver unpredictably, such as enemy aircraft or anti-ship cruise missiles. Intercepting such threats requires an ability to sense the target location in real time and respond rapidly to changes so that a target intercept can occur. Homing guidance, wherein an onboard sensor provides the target data on which guidance decisions are based, is used to accomplish this intercept. Because of the continually improving quality of target information as the missile closes in, homing guidance provides intercept accuracy that is unsurpassed by any other form of missile guidance. This article serves as the introduction to this Technical Digest issue on homing missile guidance and control. A number of basic concepts related to guided missiles are introduced in this article to provide the foundational concepts for the subsequent articles. Finally, the flight control and homing guidance concepts that are employed in such systems are discussed in the later articles in this issue.


title: Fake News Detection in Social Networks via Crowd Signals

abstract: Our work considers leveraging crowd signals for detecting fake news and is motivated by tools recently introduced by Facebook that enable users to flag fake news. By aggregating users' flags, our goal is to select a small subset of news every day, send them to an expert (e.g., via a third-party fact-checking organization), and stop the spread of news identified as fake by an expert. The main objective of our work is to minimize the spread of misinformation by stopping the propagation of fake news in the network. It is especially challenging to achieve this objective as it requires detecting fake news with high-confidence as quickly as possible. We show that in order to leverage users' flags efficiently, it is crucial to learn about users' flagging accuracy. We develop a novel algorithm, DETECTIVE, that performs Bayesian inference for detecting fake news and jointly learns about users' flagging accuracy over time. Our algorithm employs posterior sampling to actively trade off exploitation (selecting news that maximize the objective value at a given epoch) and exploration (selecting news that maximize the value of information towards learning about users' flagging accuracy). We demonstrate the effectiveness of our approach via extensive experiments and show the power of leveraging community signals for fake news detection.


title: Auction-Based Resource Allocation for Sharing Cloudlets in Mobile Cloud Computing

abstract: Driven by pervasive mobile devices and ubiquitous wireless communication networks, mobile cloud computing emerges as an appealing paradigm to accommodate demands for running power-hungry or computation-intensive applications over resource-constrained mobile devices. Cloudlets that move available resources closer to the network edge offer a promising architecture to support real-time applications, such as online gaming and speech recognition. To stimulate service provisioning by cloudlets, it is essential to design an incentive mechanism that charges mobile devices and rewards cloudlets. Although auction has been considered as a promising form for incentive, it is challenging to design an auction mechanism that holds certain desirable properties for the cloudlet scenario. In this paper, we propose an incentive-compatible auction mechanism (ICAM) for the resource trading between the mobile devices as service users (buyers) and cloudlets as service providers (sellers). ICAM can effectively allocate cloudlets to satisfy the service demands of mobile devices and determine the pricing. Both the theoretical analysis and the numerical results show that the ICAM guarantees desired properties with respect to individual rationality, budget balance and truthfulness (incentive compatibility) for both the buyers and the sellers, and computational efficiency.


title: Applying WebTables in Practice

abstract: We started investigating the collection of HTML tables on the Web and developed the WebTables system a few years ago [4]. Since then, our work has been motivated by applying WebTables in a broad set of applications at Google, resulting in several product launches. In this paper, we describe the challenges faced, lessons learned, and new insights that we gained from our eorts. The main challenges we faced in our eorts were (1) identifying tables that are likely to contain high-quality data (as opposed to tables used for navigation, layout, or formatting), and (2) recovering the semantics of these tables or signals that hint at their semantics. The result is a semantically enriched table corpus that we used to develop several services. First, we created a search engine for structured data whose index includes over a hundred million HTML tables. Second, we enabled users of Google Docs (through its Research Panel) to nd relevant data tables and to insert such data into their documents as needed. Most recently, we brought WebTables to a much broader audience by using the table corpus to provide richer tabular snippets for fact-seeking web search queries on Google.com.


title: A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

abstract: How do we learn an object detector that is invariant to occlusions and deformations? Our current solution is to use a data-driven strategy – collect large-scale datasets which have object instances under different conditions. The hope is that the final classifier can use these examples to learn invariances. But is it really possible to see all the occlusions in a dataset? We argue that like categories, occlusions and object deformations also follow a long-tail. Some occlusions and deformations are so rare that they hardly happen, yet we want to learn a model invariant to such occurrences. In this paper, we propose an alternative solution. We propose to learn an adversarial network that generates examples with occlusions and deformations. The goal of the adversary is to generate examples that are difficult for the object detector to classify. In our framework both the original detector and adversary are learned in a joint manner. Our experimental results indicate a 2.3% mAP boost on VOC07 and a 2.6% mAP boost on VOC2012 object detection challenge compared to the Fast-RCNN pipeline.


title: Separation of Foreground Text from Complex Background in Color Document Images

abstract: Reading of the foreground text is difficult in documents having multi colored complex background. Automatic foreground text separation in such document images is very much essential for smooth reading of the document contents. In this paper we propose a hybrid approach which combines connected component analysis and an unsupervised thresholding for separation of text from the complex background. The proposed approach identifies the candidate text regions based on edge detection followed by a connected component analysis. Because of background complexity it is also possible that a non text region may be identified as a text region. To overcome this problem we extract texture features of connected components and analyze the feature values. Finally the threshold value for each detected text region is derived automatically from the data of corresponding image region to perform foreground separation. The proposed approach can handle document images with varying background of multiple colors. Also it can handle foreground text of any color, font and size. Experimental results show that the proposed algorithm detects on an average 97.8% of text regions in the source document. Readability of the extracted foreground text is illustrated through OCRing.


A hybrid technique for annotating book tables.


title: Self-Esteem of Persons With Cerebral Palsy: From Adolescence to Adulthood

abstract: A longitudinal study of self-esteem in 22 adolescents with cerebral palsy is reported. The subjects were matched with nondisabled adolescents by age, sex, IQ, and school. Seven years later, 39 of the 44 subjects (mean age = 22.8 years) completed the Tennessee Self-Concept Scale (Roid & Fitts, 1988), the Social Support Inventory (McCubbin, Patterson, Rossman, & Cooke, 1982), and a demographic questionnaire with some open-ended questions. As adolescents, the girls with cerebral palsy scored significantly lower than the other groups on physical, social, and personal self-esteem; however, as adults, these subjects were no longer significantly different from the other groups. Male subjects with cerebral palsy had self-esteem scores similar to those of the nondisabled groups in both adolescence and adulthood. Demographic information is summarized. The factors that the subjects identified as leading to changes in self-esteem were relationships and experiences. The low self-esteem scores indicate that psychosocial occupational therapy intervention with adolescent girls with cerebral palsy and with some adults with cerebral palsy would be appropriate.


title: A gridded multisite weather generator and synchronization to observed weather data

abstract: [1] Procedures are described for constructing a daily multisite weather generator at a collection of arbitrary (e.g., gridded) locations and for synchronizing the gridded generator to observed weather series at a set of reference stations. The gridded generator is constructed by interpolating conventional single-station weather generator parameters using locally weighted regressions and producing coherent simulations of daily weather from them using spatial correlation functions. When implemented, the synchronization algorithm results in simulated spatial weather fields at the grid points that are consistent with daily weather observations at nearby locations for particular years. The synchronization is achieved by exploiting the latent multivariate Gaussian structure of the spatially distributed weather generator and making use of well-known statistical results that define conditional multivariate Gaussian distributions given known values for a subset of variables from the larger joint distribution. The primary focus is on precipitation, but the nonprecipitation variables in the weather generator are also amenable to gridding and to synchronization with nearby observed weather series. The motivating idea is to allow calibration of spatially distributed hydrological models consistent with the climate of the spatial weather generator, potentially allowing more realistic hydrological simulation, but the procedure may also be useful for interpolation of missing daily weather data.


title: The Science of Cycling Physiology and Training – Part 1

abstract: ing the scientific nature of cycling. Research findings are presented in a practical manner for their direct application to cycling. The two parts of this review provide


title: Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

abstract: Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and difficulties of generalization. In this work, we propose a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data. This formulation not only leads to greater expressive power but also stronger generalization capability. On two large datasets, Kinetics and NTU-RGBD, it achieves substantial improvements over mainstream methods.


title: Proactive user-centric secure data scheme using attribute-based semantic access controls for mobile clouds in financial industry

abstract: Abstract As one of the most significant issues in the financial industry, customers’ privacy information protection has been considered a challenging research over years. The constant emergence of the novel technologies often leads to dynamic threats from both internal and external service providers. We consider the implementations of mobile cloud-based financial services an important approach of service provisions, which also causes risks to privacy protections due to the data sharing with the unknown third parties. The data generated by mobility are usually associated with mobile users’ personal privacy information. This paper addresses this issue and proposes an approach proactively protect financial customers’ privacy information using Attributed-Based Access Control (ABAC) as well as data self-deterministic scheme. The proposed approach is called Proactive Dynamic Secure Data Scheme (P2DS), which aims to guarantee the unanticipated parties cannot reach the privacy data. There are two main algorithms supporting the proposed scheme, which are Attribute-based Semantic Access Control (A-SAC) Algorithm and Proactive Determinative Access (PDA) Algorithm . The main contributions of this paper have three aspects. First, we propose a semantic approach for constraining data accesses. Second, we propose a user-centric approach that proactively prevents users’ data from unexpected operations on the cloud side. Finally, the proposed scheme has a higher-level secure sustainability since it can deal with dynamic threats, including the emerging and future hazards. We have examined that our proposed scheme had a quality performance matching our expected goal.


title: Smart Field Monitoring: An Application of Cyber-Physical Systems in Agriculture (Work in Progress)

abstract: A huge amount of grain and food is lost to the pests and particularly rats, and environment is contaminated due to using pesticides. For rodent's control there has been no systematic high-tech solution until now. We aim to develop a "Smart Pest Control" solution (SPeC), particularly a "Rat Detection System" (RDS) in order to provide an infrastructure for monitoring rats in the agriculture field. We believe this system can help pest control experts in reducing tremendous costs for rat's control as well as huge amount of crop waste, and environment contamination. We will propose our solution based on cyber-physical systems (CPSs) and will elucidate enabling technologies and frontiers for this research.


The Cognitive Service Robotics Apartment A Versatile Environment for Human-Machine Interaction Research


title: Weighted Moving Average Passive Aggressive Algorithm for Online Portfolio Selection

abstract: Passive aggressive algorithms for online portfolio selection, such as PAMR, were recently shown empirically to achieve state-of-the-art performance in various stock markets. Inspired by the multi-period mean reversion principle in Antic or Algorithm, we present a passive aggressive algorithm by introducing a moving averaged loss function and achieve a novel online portfolio selection strategy named "Weighted Moving Average Mean Reversion" (WMAMR). The strategy is able to effectively exploit the power of mean reversion for online portfolio selection. Extensive experiments on various real markets demonstrate the effectiveness of our strategy in comparison with PAMR, especially with transaction cost.


title: Use of mass media campaigns to change health behaviour

abstract: Mass media campaigns are widely used to expose high proportions of large populations to messages through routine uses of existing media, such as television, radio, and newspapers. Exposure to such messages is, therefore, generally passive. Such campaigns are frequently competing with factors, such as pervasive product marketing, powerful social norms, and behaviours driven by addiction or habit. In this Review we discuss the outcomes of mass media campaigns in the context of various health-risk behaviours (eg, use of tobacco, alcohol, and other drugs, heart disease risk factors, sex-related behaviours, road safety, cancer screening and prevention, child survival, and organ or blood donation). We conclude that mass media campaigns can produce positive changes or prevent negative changes in health-related behaviours across large populations. We assess what contributes to these outcomes, such as concurrent availability of required services and products, availability of community-based programmes, and policies that support behaviour change. Finally, we propose areas for improvement, such as investment in longer better-funded campaigns to achieve adequate population exposure to media messages.


title: RENOIR - A Dataset for Real Low-Light Noise Image Reduction

abstract: Abstract Image denoising algorithms are evaluated using images corrupted by artificial noise, which may lead to incorrect conclusions about their performances on real noise. In this paper we introduce a dataset of color images corrupted by natural noise due to low-light conditions, together with spatially and intensity-aligned low noise images of the same scenes. We also introduce a method for estimating the true noise level in our images, since even the low noise images contain small amounts of noise. We evaluate the accuracy of our noise estimation method on real and artificial noise, and investigate the Poisson-Gaussian noise model. Finally, we use our dataset to evaluate six denoising algorithms: Active Random Field, BM3D, Bilevel-MRF, Multi-Layer Perceptron, and two versions of NL-means. We show that while the Multi-Layer Perceptron, Bilevel-MRF, and NL-means with soft threshold outperform BM3D on gray images with synthetic noise, they lag behind on our dataset.


title: Multi-view Scene Flow Estimation: A View Centered Variational Approach

abstract: We present a novel method for recovering the 3D structure and scene flow from calibrated multi-view sequences. We propose a 3D point cloud parametrization of the 3D structure and scene flow that allows us to directly estimate the desired unknowns. A unified global energy functional is proposed to incorporate the information from the available sequences and simultaneously recover both depth and scene flow. The functional enforces multi-view geometric consistency and imposes brightness constancy and piecewise smoothness assumptions directly on the 3D unknowns. It inherently handles the challenges of discontinuities, occlusions, and large displacements. The main contribution of this work is the fusion of a 3D representation and an advanced variational framework that directly uses the available multi-view information. This formulation allows us to advantageously bind the 3D unknowns in time and space. Different from optical flow and disparity, the proposed method results in a nonlinear mapping between the images' coordinates, thus giving rise to additional challenges in the optimization process. Our experiments on real and synthetic data demonstrate that the proposed method successfully recovers the 3D structure and scene flow despite the complicated nonconvex optimization problem.


Self-theories and goals: their role in motivation, personality, and development.


title: Development of a blood pressure monitoring system for home health application

abstract: A wireless physiological parameter monitoring system is presented in this paper for home health application. This system includes real time continuous collection and evaluation of physiological parameters like blood pressure, pulse rate and fall of an elderly at home. A reliable wireless personal area network like bluetooth has been used for this purpose. The hardware design of this system consists of sensors, PIC microcontroller, bluetooth module and mobile phone/laptop. This system provides safe and accurate monitoring. It also gives the freedom of movement.


title: A Liquid-to-Solid Phase Transition of the ALS Protein FUS Accelerated by Disease Mutation

abstract: Summary Many proteins contain disordered regions of low-sequence complexity, which cause aging-associated diseases because they are prone to aggregate. Here, we study FUS, a prion-like protein containing intrinsically disordered domains associated with the neurodegenerative disease ALS. We show that, in cells, FUS forms liquid compartments at sites of DNA damage and in the cytoplasm upon stress. We confirm this by reconstituting liquid FUS compartments in vitro. Using an in vitro "aging" experiment, we demonstrate that liquid droplets of FUS protein convert with time from a liquid to an aggregated state, and this conversion is accelerated by patient-derived mutations. We conclude that the physiological role of FUS requires forming dynamic liquid-like compartments. We propose that liquid-like compartments carry the trade-off between functionality and risk of aggregation and that aberrant phase transitions within liquid-like compartments lie at the heart of ALS and, presumably, other age-related diseases. Video Abstract


title: Predicting popular messages in twitter

abstract: Social network services have become a viable source of information for users. In Twitter, information deemed important by the community propagates through retweets. Studying the characteristics of such popular messages is important for a number of tasks, such as breaking news detection, personalized message recommendation, viral marketing and others. This paper investigates the problem of predicting the popularity of messages as measured by the number of future retweets and sheds some light on what kinds of factors influence information propagation in Twitter. We formulate the task into a classification problem and study two of its variants by investigating a wide spectrum of features based on the content of the messages, temporal information, metadata of messages and users, as well as structural properties of the users' social graph on a large scale dataset. We show that our method can successfully predict messages which will attract thousands of retweets with good performance.


title: The centrality of pivotal points in the evolution of scientific networks

abstract: In this paper, we describe the development of CiteSpace as an integrated environment for identifying and tracking thematic trends in scientific literature. The goal is to simplify the process of finding not only highly cited clusters of scientific articles, but also pivotal points and trails that are likely to characterize fundamental transitions of a knowledge domain as a whole. The trails of an advancing research field are captured through a sequence of snapshots of its intellectual structure over time in the form of Pathfinder networks. These networks are subsequently merged with a localized pruning algorithm. Pivotal points in the merged network are algorithmically identified and visualized using the betweenness centrality metric. An example of finding clinical evidence associated with reducing risks of heart diseases is included to illustrate how CiteSpace could be used. The contribution of the work is its integration of various change detection algorithms and interactive visualization capabilities to simply users' tasks.


title: Scalable triangulation-based logo recognition

abstract: We propose a scalable logo recognition approach that extends the common bag-of-words model and incorporates local geometry in the indexing process. Given a query image and a large logo database, the goal is to recognize the logo contained in the query, if any. We locally group features in triples using multi-scale Delaunay triangulation and represent triangles by signatures capturing both visual appearance and local geometry. Each class is represented by the union of such signatures over all instances in the class. We see large scale recognition as a sub-linear search problem where signatures of the query image are looked up in an inverted index structure of the class models. We evaluate our approach on a large-scale logo recognition dataset with more than four thousand classes.


title: Learning to Relate Literal and Sentimental Descriptions of Visual Properties

abstract: Language can describe our visual world at many levels, including not only what is literally there but also the sentiment that it invokes. In this paper, we study visual language, both literal and sentimental, that describes the overall appearance and style of virtual characters. Sentimental properties, including labels such as “youthful” or “country western,” must be inferred from descriptions of the more literal properties, such as facial features and clothing selection. We present a new dataset, collected to describe Xbox avatars, as well as models for learning the relationships between these avatars and their literal and sentimental descriptions. In a series of experiments, we demonstrate that such learned models can be used for a range of tasks, including predicting sentimental words and using them to rank and build avatars. Together, these results demonstrate that sentimental language provides a concise (though noisy) means of specifying low-level visual properties.


title: Improving MapReduce performance through data placement in heterogeneous Hadoop clusters

abstract: MapReduce has become an important distributed processing model for large-scale data-intensive applications like data mining and web indexing. Hadoop-an open-source implementation of MapReduce is widely used for short jobs requiring low response time. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Data locality has not been taken into account for launching speculative map tasks, because it is assumed that most maps are data-local. Unfortunately, both the homogeneity and data locality assumptions are not satisfied in virtualized data centers. We show that ignoring the data-locality issue in heterogeneous environments can noticeably reduce the MapReduce performance. In this paper, we address the problem of how to place data across nodes in a way that each node has a balanced data processing load. Given a dataintensive application running on a Hadoop MapReduce cluster, our data placement scheme adaptively balances the amount of data stored in each node to achieve improved data-processing performance. Experimental results on two real data-intensive applications show that our data placement strategy can always improve the MapReduce performance by rebalancing data across nodes before performing a data-intensive application in a heterogeneous Hadoop cluster.


title: A Quantum-based Model for Interactive Information Retrieval (extended version)

abstract: Even the best information retrieval model cannot always identify the most useful answers to a user query. This is in particular the case with web search systems, where it is known that users tend to minimise their effort to access relevant information. It is, however, believed that the interaction between users and a retrieval system, such as a web search engine, can be exploited to provide better answers to users. Interactive Information Retrieval (IR) systems, in which users access information through a series of interactions with the search system, are concerned with building models for IR, where interaction plays a central role. There are many possible interactions between a user and a search system, ranging from query (re)formulation to relevance feedback. However, capturing them within a single framework is difficult and previously proposed approaches have mostly focused on relevance feedback. In this paper, we propose a general framework for interactive IR that is able to capture the full interaction process in a principled way. Our approach relies upon a generalisation of the probability framework of quantum physics, whose strong geometric component can be a key towards a successful interactive IR model.


title: Siblings and the Coming-Out Process: A Comparative Case Study

abstract: ABSTRACTSiblings play a key, supportive role in the lives of many lesbian and gay adults. Yet siblings are rarely considered in the literature regarding the coming-out process (D’Augelli et al., 1998; Hilton & Szymanski, 2011; LaSala, 2010; Savin-Williams & Dube, 1998). To fill this gap in the research literature, we carried out a comparative case study in the country of Belgium between two sets of siblings—three Romani brothers with one sibling identifying as a gay male and three White sisters with one sibling identifying as a lesbian. These two cases were pulled from a larger qualitative study (Haxhe & D’Amore, 2014) of 102 native French-speaking Belgian participants. Findings of the present study revealed that siblings offered critical socio-emotional support in the coming out of their lesbian and gay sibling, particularly with disclosing to parents and with fostering self-acceptance.


title: Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs

abstract: Textual information is considered as significant supplement to knowledge representation learning (KRL). There are two main challenges for constructing knowledge representations from plain texts: (1) How to take full advantages of sequential contexts of entities in plain texts for KRL. (2) How to dynamically select those informative sentences of the corresponding entities for KRL. In this paper, we propose the Sequential Text-embodied Knowledge Representation Learning to build knowledge representations from multiple sentences. Given each reference sentence of an entity, we first utilize recurrent neural network with pooling or long short-term memory network to encode the semantic information of the sentence with respect to the entity. Then we further design an attention model to measure the informativeness of each sentence, and build text-based representations of entities. We evaluate our method on two tasks, including triple classification and link prediction. Experimental results demonstrate that our method outperforms other baselines on both tasks, which indicates that our method is capable of selecting informative sentences and encoding the textual information well into knowledge representations.


title: Using temporal IDF for efficient novelty detection in text streams

abstract: Novelty detection in text streams is a challenging task that emerges in quite a few different scenarios, ranging from email thread filtering to RSS news feed recommendation on a smartphone. An efficient novelty detection algorithm can save the user a great deal of time and resources when browsing through relevant yet usually previously-seen content. Most of the recent research on detection of novel documents in text streams has been building upon either geometric distances or distributional similarities, with the former typically performing better but being much slower due to the need of comparing an incoming document with all the previously-seen ones. In this paper, we propose a new approach to novelty detection in text streams. We describe a resource-aware mechanism that is able to handle massive text streams such as the ones present today thanks to the burst of social media and the emergence of the Web as the main source of information. We capitalize on the historical Inverse Document Frequency (IDF) that was known for capturing well term specificity and we show that it can be used successfully at the document level as a measure of document novelty. This enables us to avoid similarity comparisons with previous documents in the text stream, thus scaling better and leading to faster execution times. Moreover, as the collection of documents evolves over time, we use a temporal variant of IDF not only to maintain an efficient representation of what has already been seen but also to decay the document frequencies as the time goes by. We evaluate the performance of the proposed approach on a real-world news articles dataset created for this task. The results show that the proposed method outperforms all of the baselines while managing to operate efficiently in terms of time complexity and memory usage, which are of great importance in a mobile setting scenario.


title: Pattern-based process for a legacy to SOA modernization roadmap

abstract: Many organizations still operate their businessms which cause high maintenance costs and are difficult to change. After the hype about service-oriented architectures (SOAs) was gone, they were implemented successfully and helped replacing old legacy systems with flexible services, for example at large companies in the finance or logistics industry. While recent research and the pattern literature cover the technical aspects of SOA and the alignment between business and IT, defining and planning a roadmap for a SOA modernization program, which is essential for initiating such a program and getting the necessary commitment, has not been in the focus of research yet. Thus, we propose a design process for the definition of a high-level roadmap. It considers determining architectural decisions and planning decisions by selecting appropriate patterns and reference architectures. Furthermore we define a heuristic for the roadmap planning activities, because we see a lack of appropriate planning patterns. A case study explains how the roadmap design process has been applied in a large-scale industry project concerning a SOA modernization program of a global logistics provider. The pattern-based process should motivate pursuing further research about roadmap design processes and patterns and provide guidance for industry experts.


title: Big data enabled user behavior characteristics in mobile internet

abstract: Understanding user behavior is essential for source optimization in mobile networks, including urban planning, road traffic engineering, and content distribution. In this paper, we take the first step to investigate the basic characterization of user behavior over 340,000 users of a 4G mobile network in a large metropolitan area. Our study is carried on from two aspects: mobility and application usage. In particular, the relationship between the mobility pattern and the application usage is revealed. We observe that people's mobility patterns are correlated with the application they access. Furthermore, locations such as home and workplace, affects the applications accessed by users. Utilizing our Hadoop-based mobile big data processing platform together with a systematic analysis framework of user behavior from the 6 Terabyte (TB) 4G data traffic, our analysis provides a comprehensive understanding of user behavior in 4G networks, which may be used by network operators to design appropriate mechanisms in resource provision and mobility management for resource consumers based on different categories of applications.


title: Deep Learning Methods for Improved Decoding of Linear Codes

abstract: The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterations, so as to form a recurrent neural network architecture, can be implemented with comparable results. The advantage is that significantly less parameters are required. We also introduce a recurrent neural decoder architecture based on the method of successive relaxation. Improvements over standard belief propagation are also observed on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.


Title Convergence rates of spectral distributions of large samplecovariance matrices


title: Extracts from dulse (Palmaria palmata) are effective antioxidants and inhibitors of cell proliferation in vitro

abstract: Abstract Previously, we reported that a 1-butanol soluble extract of the edible red alga Palmaria palmata , known as dulse, exhibited hydroxyl and stable free radical scavenging activity as well as inhibition of lipid peroxidation, attributed to the reducing activity and polyphenol content of the dulse extract. In the present study, we evaluated the antioxidant and antiproliferative activities of two grades of dulse harvested from Canadian Maritime locations differing in UV radiation exposure (i.e. west versus east coasts of Grand Manan Island, New Brunswick). The 1-butanol soluble extract from Grade 1 dulse (reduced UV-exposure) exhibited lower reducing activity versus Grade 2 dulse (greater UV exposure) reflecting a lower requirement for endogenous antioxidant protection. Grade 1 and 2 dulse extracts both inhibited ( p ⩽ 0.03) AAPH-induced lipid peroxidation, but had no effect on AMVN-induced lipid peroxidation, demonstrating the aqueous nature of the antioxidants involved. The Grade 1 and 2 dulse extract inhibition ( p


title: Pulse width modulation circuit for ISFET drift reset

abstract: We present the simulation results for a Pulse Width Modulation (PWM) circuit (to be fabricated in a 0.5 μm CMOS process) used to cycle the electric field in an Ion Sensitive Field Effect Transistor (ISFET). Vertical electric field, which controls the inversion layer in a field effect transistors, can be used to reset the inherent drift behavior of ISFET. A PWM circuit, to cycle the vertical field, enables us to precisely monitor the pH of an electrolyte without needing to manually calibrate the ISFET. Two or more ISFETs could be used with the devices alternatively being placed in reset and measurement mode. By combining the outputs from measurement phase of the devices, we can read the pH of the electrolyte continuously. The PWM circuit is composed of a 10.9 kHz ring oscillator, five divider circuits giving a 100,000 frequency division, a 6 bit counter, and a Digital to Analog Converter (DAC) that feeds into a comparator whose output selects the mode of operation for the ISFETs.


title: Adolescent self-compassion: Associations with narcissism, self-esteem, aggression, and internalizing symptoms in at-risk males

abstract: Abstract Self-compassion is an attitude toward oneself that involves perceiving one’s experiences as an opportunity for self-awareness and improvement, as well as limited self-judgment after failure. Self-compassion has not been extensively studied in adolescence, a time when self-perception and self-appraisals regarding success and failure take on notable importance. This study considered the connection between self-compassion, narcissism, self-esteem, aggression, and internalizing problems in a sample of 251 male adolescents, ages 16–18, attending a residential program. Self-compassion was negatively correlated with aggression and vulnerable narcissism and positively correlated with self-esteem. In general, self-compassion did not exhibit the hypothesized protective effect on the relation between narcissism and aggression. Findings indicate that, as expected, self-compassion is indicative of a relatively secure, positive sense of self in adolescents.


title: Improving balance technique for high frequency common mode noise reduction in boost PFC converters

abstract: The objective of this paper is to investigate and improve the high frequency noise reduction performance of common mode (CM) noise reduction techniques. Balance technique is chosen as the research object. High frequency model of balanced boost PFC converter is derived and important parasitics and parameters in the model are studied. Effects of coupling coefficient, turns ratio and inductor impedance on high frequency CM noise are discussed in detail. According to the model, high frequency resonance due to serial LC network causes high CM noise peak. Turns ratio has significant impact on the noise peak level. Core and winding structure of boost inductors is another important aspect for high frequency CM noise. Design guidelines for implementing balance are summarized to achieve best high frequency CM noise reduction. Boost PFC prototype with CM EMI filter was built to verify the proposed CM noise model and noise reduction methods. Finally, the effect of grounding point on CM EMI filter performance is also studied.


title: A Fully Integrated 16-Element Phased-Array Transmitter in SiGe BiCMOS for 60-GHz Communications

abstract: A phased-array transmitter (TX) for multi-Gb/s non-line-of-sight links in the four frequency channels of the IEEE 802.15.3c standard (58.32 to 64.8 GHz) is fully integrated in a 0.12-μm SiGe BiCMOS process. It consists of an up-conversion core followed by a 1:16 power distribution tree, 16 phase-shifting front-ends, and a digital control unit. The TX core is a two-step sliding-IF up-conversion chain with frequency synthesizer that features 40 dB of gain programmability, I/Q balance and LO leakage correction, and a modulator for 802.15.3c common-mode signaling. The tradeoffs involved in the implementation of a 1:16 power distribution network are analyzed and a hybrid passive/active distribution tree architecture is introduced. Each of the 16 front-ends consists of a balanced passive phase shifter and a variable-gain, 3-stage PA that features oP1dB programmability through the bias control of the its final stage. All of the chip features are digitally controllable and individual memory arrays are integrated at each front-end to enable fast beam steering through a high-speed parallel interface. The IC occupies 44 mm and is fully characterized on wafer. The TX delivers 9 to 13.5 dBm oPidB per element at 60.48 GHz with a total power consumption of 3.8 to 6.2 W. Each element attains a phase-shift range >360° with an amplitude variation <;±1 dB across phase settings and adjacent elements. Measurement results from a packaged IC in an antenna chamber are also presented including the demonstration of spatial power combining up to +40 dBm EIRP and 16-element radiation patterns.


title: Evaluation of the Intel RealSense SR300 camera for image-guided interventions and application in vertebral level localization

abstract: PURPOSE: Optical pose tracking of medical instruments is often used in image-guided interventions. Unfortunately, compared to commonly used computing devices, optical trackers tend to be large, heavy, and expensive devices. Compact 3D vision systems, such as Intel RealSense cameras can capture 3D pose information at several magnitudes lower cost, size, and weight. We propose to use Intel SR300 device for applications where it is not practical or feasible to use conventional trackers and limited range and tracking accuracy is acceptable. We also put forward a vertebral level localization application utilizing the SR300 to reduce risk of wrong-level surgery. METHODS: The SR300 was utilized as an object tracker by extending the PLUS toolkit to support data collection from RealSense cameras. Accuracy of the camera was tested by comparing to a high-accuracy optical tracker. CT images of a lumbar spine phantom were obtained and used to create a 3D model in 3D Slicer. The SR300 was used to obtain a surface model of the phantom. Markers were attached to the phantom and a pointer and tracked using Intel RealSense SDK’s built-in object tracking feature. 3D Slicer was used to align CT image with phantom using landmark registration and display the CT image overlaid on the optical image. RESULTS: Accuracy of the camera yielded a median position error of 3.3mm (95th percentile 6.7mm) and orientation error of 1.6° (95th percentile 4.3°) in a 20x16x10cm workspace, constantly maintaining proper marker orientation. The model and surface correctly aligned demonstrating the vertebral level localization application. CONCLUSION: The SR300 may be usable for pose tracking in medical procedures where limited accuracy is acceptable. Initial results suggest the SR300 is suitable for vertebral level localization.


Strategies for Lean Product Development


title: Corrosion Protection of Electrically Conductive Surfaces

abstract: The basic function of the electrically conductive surface of electrical contacts is electrical conduction. The electrical conductivity of contact materials can be largely reduced by corrosion and in order to avoid corrosion, protective coatings must be used. Another phenomenon that leads to increasing contact resistance is fretting corrosion. Fretting corrosion is the degradation mechanism of surface material, which causes increasing contact resistance. Fretting corrosion occurs when there is a relative movement between electrical contacts with surfaces of ignoble metal. Avoiding fretting corrosion is therefore extremely challenging in electronic devices with pluggable electrical connections. Gold is one of the most commonly used noble plating materials for high performance electrical contacts because of its high corrosion resistance and its good and stable electrical behavior. The authors have investigated different ways to minimize the consumption of gold for electrical contacts and to improve the performance of gold plating. Other plating materials often used for corrosion protection of electrically conductive surfaces are tin, nickel, silver and palladium. This paper will deal with properties and new research results of different plating materials in addition to other means used for corrosion protection of electrically conductive surfaces and the testing of corrosion resistance of electrically conductive surfaces.


title: Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search

abstract: We propose and evaluate several triplet CNN architectures for measuring the similarity between sketches and photographs, within the context of the sketch based image retrieval (SBIR) task. In contrast to recent fine-grained SBIR work, we study the ability of our networks to generalise across diverse object categories from limited training data, and explore in detail strategies for weight sharing, pre-processing, data augmentation and dimensionality reduction. We exceed the performance of pre-existing techniques on both the Flickr15k category level SBIR benchmark by $18\%$, and the TU-Berlin SBIR benchmark by $\sim10 \mathcal{T}_b$, when trained on the 250 category TU-Berlin classification dataset augmented with 25k corresponding photographs harvested from the Internet.


title: Continuous pose estimation for stereo vision based on UV disparity applied to visual odometry in urban environments

abstract: This paper presents an autocalibration method to determine the pose of a stereo vision system based on knowing the geometry of the ground in front of the cameras. This pose changes considerably while the vehicle is driven, therefore it is good to know constantly the pose of the camera for several applications based on computer vision, such as advanced driver assistance systems, autonomous vehicles or robotics. These con- stant changes of the pose make interesting to be able to detect constantly the variations in its extrinsic parameters (height, pitch, roll). The validation of the autocalibration method is accomplished by a visual odometry implementation. A study of the improvement of the results of the visual odometry estimation taking into account the changes of the camera pose is presented, demonstrating the advantages of the autocalibration method. I. INTRODUCTION


title: Foreign direct investment and economic growth in Cameroon

abstract: Driving force of globalization, FDI represents an exceptional opportunity for third world countries to accelerate their development. This study examines the bi-directional connection between FDI and economic growth in Cameroon. The methodology is based on regressions of FDI and Cameroonian GDP for the period (1980 – 2009). The results confirm the positive link between FDI and economic growth. On the other hand the study shows that external resources (FDI and Foreign Aid) are more efficient than domestic investment for economic growth.


title: A coarse-to-fine model for airport detection from remote sensing images using target-oriented visual saliency and CRF

abstract: This paper presents a novel computational model to detect airports in optical remote sensing images (RSI). It works in a hierarchical architecture with a coarse layer and a fine layer. At the coarse layer, a target-oriented saliency model is built by combing the cues of contrast and line density to rapidly localize the airport candidate areas. Furthermore, at the fine layer, a learned condition random field (CRF) model is applied to each candidate area to perform the fine detection of the airport target. The CRF model is learned based on sparse features of local patches in a multi-scale structure and it also takes the contextual information of target into consideration. Therefore, its detection is more accurate and is robust to target scale variation. Comprehensive evaluations on RSI database from the Google Earth and comparisons with state-of-the-art approaches demonstrate the effectiveness of the proposed model.


title: Unsupervised prototype learning in an associative-memory network

abstract: Unsupervised learning in a generalized Hopfield associative-memory network is investigated in this work. First, we prove that the (generalized) Hopfield model is equivalent to a semi-restricted Boltzmann machine with a layer of visible neurons and another layer of hidden binary neurons, so it could serve as the building block for a multilayered deep-learning system. We then demonstrate that the Hopfield network can learn to form a faithful internal representation of the observed samples, with the learned memory patterns being prototypes of the input data. Furthermore, we propose a spectral method to extract a small set of concepts (idealized prototypes) as the most concise summary or abstraction of the empirical data.


title: Night-time indoor relocalization using depth image with Convolutional Neural Networks

abstract: In this work, we present a Convolutional Neural Network(CNN) with depth images as its inputs to solve the relocalization problem of a moving platform in night-time indoor environment. The developed algorithm can estimate the camera pose in an end-to-end manner with 0.40m and 7.49° errors in real time during night. It does not require any geometric computation as it directly uses a CNN for 6 DOFs pose regression. The architecture and its encoding methods of depth images are discussed. The proposed method is also evaluated on benchmark datasets collected from a motion capture system in our lab.


title: Matching User Photos to Online Products with Robust Deep Features

abstract: This paper focuses on a practically very important problem of matching a real-world product photo to exactly the same item(s) in online shopping sites. The task is extremely challenging because the user photos (i.e., the queries in this scenario) are often captured in uncontrolled environments, while the product images in online shops are mostly taken by professionals with clean backgrounds and perfect lighting conditions. To tackle the problem, we study deep network architectures and training schemes, with the goal of learning a robust deep feature representation that is able to bridge the domain gap between the user photos and the online product images. Our contributions are two-fold. First, we propose an alternative of the popular contrastive loss used in siamese deep networks, namely robust contrastive loss, where we "relax" the penalty on positive pairs to alleviate over-fitting. Second, a multi-task fine-tuning approach is introduced to learn a better feature representation, which not only incorporates knowledge from the provided training photo pairs, but also explores additional information from the large ImageNet dataset to regularize the fine-tuning procedure. Experiments on two challenging real-world datasets demonstrate that both the robust contrastive loss and the multi-task fine-tuning approach are effective, leading to very promising results with a time cost suitable for real-time retrieval.


title: Are we dependent upon coffee and caffeine? A review on human and animal data

abstract: Abstract Caffeine is the most widely used psychoactive substance and has been considered occasionally as a drug of abuse. The present paper reviews available data on caffeine dependence, tolerance, reinforcement and withdrawal. After sudden caffeine cessation, withdrawal symptoms develop in a small portion of the population but are moderate and transient. Tolerance to caffeine-induced stimulation of locomotor activity has been shown in animals. In humans, tolerance to some subjective effects of caffeine seems to occur, but most of the time complete tolerance to many effects of caffeine on the central nervous system does not occur. In animals, caffeine can act as a reinforcer, but only in a more limited range of conditions than with classical drugs of dependence. In humans, the reinforcing stimuli functions of caffeine are limited to low or rather moderate doses while high doses are usually avoided. The classical drugs of abuse lead to quite specific increases in cerebral functional activity and dopamine release in the shell of the nucleus accumbens, the key structure for reward, motivation and addiction. However, caffeine doses that reflect the daily human consumption, do not induce a release of dopamine in the shell of the nucleus accumbens but lead to a release of dopamine in the prefrontal cortex, which is consistent with caffeine reinforcing properties. Moreover, caffeine increases glucose utilization in the shell of the nucleus accumbens only at rather high doses that stimulate most brain structures, non-specifically, and likely reflect the side effects linked to high caffeine ingestion. That dose is also 5–10-fold higher than the one necessary to stimulate the caudate nucleus, which mediates motor activity and the structures regulating the sleep-wake cycle, the two functions the most sensitive to caffeine. In conclusion, it appears that although caffeine fulfils some of the criteria for drug dependence and shares with amphetamines and cocaine a certain specificity of action on the cerebral dopaminergic system, the methylxanthine does not act on the dopaminergic structures related to reward, motivation and addiction.


title: Broad expertise retrieval in sparse data environments

abstract: Expertise retrieval has been largely unexplored on data other than the W3C collection. At the same time, many intranets of universities and other knowledge-intensive organisations offer examples of relatively small but clean multilingual expertise data, covering broad ranges of expertise areas. We first present two main expertise retrieval tasks, along with a set of baseline approaches based on generative language modeling, aimed at finding expertise relations between topics and people. For our experimental evaluation, we introduce (and release) a new test set based on a crawl of a university site. Using this test set, we conduct two series of experiments. The first is aimed at determining the effectiveness of baseline expertise retrieval methods applied to the new test set. The second is aimed at assessing refined models that exploit characteristic features of the new test set, such as the organizational structure of the university, and the hierarchical structure of the topics in the test set. Expertise retrieval models are shown to be robust with respect to environments smaller than the W3C collection, and current techniques appear to be generalizable to other settings.


title: Knowledge management and business performance: Does innovation matter?

abstract: The purpose of this study was to assess the mediating effect of innovation in the relationship between knowledge management and business performance of SMEs in Rwanda. The study used a cross-sectional survey design to collect the data on a sample of 250 SMEs and bootstrap method was used to carry out mediation analysis. The findings revealed that innovation fully mediates the relationship between knowledge management and business performance of SMEs. The paper contributes to scholarly debate on the mediating role of innovation on the relationship between knowledge management and business performance of SMEs by providing evidence from a developing country. The results may help business owners of SMEs to adopt innovation as a conduit for knowledge management to boost their business performance.


title: Cable suspended planar robots with redundant cables: controllers with positive tensions

abstract: Cable-suspended robots are structurally similar to parallel actuated robots but with the fundamental difference that cables can only pull the end-effector but not push it. From a scientific point of view, this feature makes feedback control of cable-suspended robots more challenging than their counterpart parallel-actuated robots. In the case with redundant cables, feedback control laws can be designed to make all tensions positive while attaining desired control performance. This paper presents approaches to design positive tension controllers for cable suspended robots with redundant cables. Their effectiveness is demonstrated through simulations and experiments on a three degree-of-freedom cable suspended robots.


title: Hitting the memory wall: implications of the obvious

abstract: Static cache analysis characterizes a program's cache behavior by determining in a sound but approximate manner which memory accesses result in cache hits and which result in cache misses. Such information is valuable in optimizing compilers, worst-case execution time analysis, and side-channel attack quantification and mitigation.Cache analysis is usually performed as a combination of `must' and `may' abstract interpretations, classifying instructions as either `always hit', `always miss', or `unknown'. Instructions classified as `unknown' might result in a hit or a miss depending on program inputs or the initial cache state. It is equally possible that they do in fact always hit or always miss, but the cache analysis is too coarse to see it.Our approach to eliminate this uncertainty consists in (i) a novel abstract interpretation able to ascertain that a particular instruction may definitely cause a hit and a miss on different paths, and (ii) an exact analysis, removing all remaining uncertainty, based on model checking, using abstract-interpretation results to prune down the model for scalability.We evaluated our approach on a variety of examples; it notably improves precision upon classical abstract interpretation at reasonable cost.


title: A cyber-physical systems approach to energy management in data centers

abstract: This paper presents a new control strategy for data centers that aims to optimize the trade-off between maximizing the payoff from the provided quality of computational services and minimizing energy costs for computation and cooling. The data center is modeled as two interacting dynamic networks: a computational (cyber) network representing the distribution and flow of computational tasks, and a thermal (physical) network characterizing the distribution and flow of thermal energy. To make the problem tractable, the control architecture is decomposed hierarchically according to time-scales in the thermal and computational network dynamics, and spatially, reflecting weak coupling between zones in the data center. Simulation results demonstrate the effectiveness of the proposed coordinated control strategy relative to traditional approaches in which the cyber and physical resources are controlled independently.


title: The effect of threshold values on association rule based classification accuracy

abstract: Classification Association Rule Mining (CARM) systems operate by applying an Association Rule Mining (ARM) method to obtain classification rules from a training set of previously classified data. The rules thus generated will be influenced by the choice of ARM parameters employed by the algorithm (typically support and confidence threshold values). In this paper we examine the effect that this choice has on the predictive accuracy of CARM methods. We show that the accuracy can almost always be improved by a suitable choice of parameters, and describe a hill-climbing method for finding the best parameter settings. We also demonstrate that the proposed hill-climbing method is most effective when coupled with a fast CARM algorithm such as the TFPC algorithm which is also described.


title: A four-band dual-polarized cavity-backed antenna on LTCC technology for 60GHz applications

abstract: In this work, we present a 4-band dual-polarized antenna designed for 8-chanel applications. Based on LTCC technology, the antenna is a patch coupled aperture with a backed cavity. Each antenna element of a designated band contains two channels through two orthogonally polarized ports. Combining four dual-polarized antenna elements under different frequency, an 8-channel antenna for 60GHz applications can be achieved. The array antenna contains 8 feeding ports, which correspond to 8 independent channels. The isolation between each port can reach 20dB in most of the frequency band.


An ultra-low-power CMOS OTA for low-frequency G mC applications


title: Multilabeled Value Networks for Computer Go

abstract: This paper proposes a new approach to a novel value network architecture for the game Go, called a multi-labelled (ML) value network. In the ML value network, different values (win rates) are trained simultaneously for different settings of komi, a compensation given to balance the initiative of playing first. The ML value network has three advantages, (a) it outputs values for different komi, (b) it supports dynamic komi, and (c) it lowers the mean squared error (MSE). This paper also proposes a new dynamic komi method to improve game-playing strength. This paper also performs experiments to demonstrate the merits of the architecture. First, the MSE of the ML value network is generally lower than the value network alone. Second, the program based on the ML value network wins by a rate of 67.6% against the program based on the value network alone. Third, the program with the proposed dynamic komi method significantly improves the playing strength over the baseline that does not use dynamic komi, especially for handicap games. To our knowledge, up to date, no handicap games have been played openly by programs using value networks. This paper provides these programs with a useful approach to playing handicap games.


title: Affect is a form of cognition: A neurobiological analysis

abstract: In this paper, we suggest that affect meets the traditional definition of “cognition” such that the affect–cognition distinction is phenomenological, rather than ontological. We review how the affect–cognition distinction is not respected in the human brain, and discuss the neural mechanisms by which affect influences sensory processing. As a result of this sensory modulation, affect performs several basic “cognitive” functions. Affect appears to be necessary for normal conscious experience, language fluency, and memory. Finally, we suggest that understanding the differences between affect and cognition will require systematic study of how the phenomenological distinction characterising the two comes about, and why such a distinction is functional.


Literature Review of Information Technology Adoption Models at Firm Level


title: Cimple: Instruction and Memory Level Parallelism

abstract: Modern out-of-order processors have increased capacity to exploit instruction level parallelism (ILP) and memory level parallelism (MLP), e.g., by using wide superscalar pipelines and vector execution units, as well as deep buffers for in-flight memory requests. These resources, however, often exhibit poor utilization rates on workloads with large working sets, e.g., in-memory databases, key-value stores, and graph analytics, as compilers and hardware struggle to expose ILP and MLP from the instruction stream automatically. ::: In this paper, we introduce the IMLP (Instruction and Memory Level Parallelism) task programming model. IMLP tasks execute as coroutines that yield execution at annotated long-latency operations, e.g., memory accesses, divisions, or unpredictable branches. IMLP tasks are interleaved on a single thread, and integrate well with thread parallelism and vectorization. Our DSL embedded in C++, Cimple, allows exploration of task scheduling and transformations, such as buffering, vectorization, pipelining, and prefetching. ::: We demonstrate state-of-the-art performance on core algorithms used in in-memory databases that operate on arrays, hash tables, trees, and skip lists. Cimple applications reach 2.5x throughput gains over hardware multithreading on a multi-core, and 6.4x single thread speedup.


title: Position-Aided Compressive Channel Estimation and Tracking for Millimeter Wave Multi-User MIMO Air-to-Air Communications

abstract: We consider a multi-user (MU) millimeter wave (mmWave) network of aerial vehicles. One aircraft acts as an access point that communicates with multiple airborne users through mmWave communication links. We focus on the problem of estimating and tracking the multi-user mmWave frequency- selective MIMO channel, leveraging prior information about the position and trajectory of each user (aerial vehicle). We develop a channel estimation and tracking strategy based on compressed sensing tools which introduces a low overhead, even when operating in the high mobility aerial scenario. We show by simulation that, even with a reduced number of training symbols and in the low SNR regime, the new channel estimation and tracking algorithm provides channel state information (CSI) of enough quality to obtain and maintain near-optimal values of spectral efficiency.


title: A 6-bit CMOS Active Phase Shifter for Ku-Band Phased Arrays

abstract: A 6-bit high-accuracy active phase shifter is developed in 0.13- $\mu \text{m}$ CMOS technology for Ku -band phased arrays. Improved transformer balun with wriggly stub is applied to split the single input to differential and achieve high balance with reduced chip area. Two-stage polyphase filter (PPF) generates broadband I/Q signals with high accuracy. A main digital-to-analog convertor (DAC) controls the I/Q amplitude to achieve 6-bit phase resolution, while an auxiliary DAC compensates the amplitude/phase error introduced by balun and PPF. Consequently, high resolution along with low phase/gain error can be achieved. The phase shifter shows measured rms phase error of $0.75 \times 0.32$ mm2 excluding pads.


title: Integrating Surface and Abstract Features for Robust Cross-Domain Chinese Word Segmentation

abstract: Current character-based approaches are not robust for cross domain Chin ese word segmentation. In this paper, we alleviate this problem by deriving a novel enhanced ch aracter-based generative model with a new abstract aggregate candidate-feature, which indicates if th e given candidate prefers the corresponding position-tag of the longest dictionary matching wo rd. Since the distribution of the proposed feature is invariant across domains, our model thus possesses better generalization ability. Open tests on CIPS-SIGHAN-2010 show that the enhanced generative model achieves robust cross-domain performance for various OOV coverage rates and obtains the best performance on three out of four domains. The enhanced gen erative model is then further integrated with a discriminative model which also utilizes dictionary information . This integrated model is shown to be either superior or comparable to all other models repo rted in the literatur e on every domain of this task.


title: A content analysis of thinspiration, fitspiration, and bonespiration imagery on social media

abstract: BackgroundOn social media, images such as thinspiration, fitspiration, and bonespiration, are shared to inspire certain body ideals. Previous research has demonstrated that exposure to these groups of content is associated with increased body dissatisfaction and decreased self-esteem. It is therefore important that the bodies featured within these groups of content are more fully understood so that effective interventions and preventative measures can be informed, developed, and implemented.MethodA content analysis was conducted on a sample of body-focussed images with the hashtags thinspiration, fitspiration, and bonespiration from three social media platforms.ResultsThe analyses showed that thinspiration and bonespiration content contained more thin and objectified bodies, compared to fitspiration which featured a greater prevalence of muscles and muscular bodies. In addition, bonespiration content contained more bone protrusions and fewer muscles than thinspiration content.ConclusionsThe findings suggest fitspiration may be a less unhealthy type of content; however, a subgroup of imagery was identified which idealised the extremely thin body type and as such this content should also be approached with caution. Future research should utilise qualitative methods to further develop understandings of the body ideals that are constructed within these groups of content and the motivations behind posting this content.


title: Sources of variance in current sense of humor inventories: How much substance, how much method variance?

abstract: The present study investigates the relationship of self-report inventories of ''sense of humor'' and behavioral measures of humor as well as their location in the Eysenckian PEN system. 110 male and female adults ages 17 to 83 answered the following inventories: SHRQ (Martin and Lefcourt 1984), SHQZ (Ziv 1981), SHQ-3 revised (Svebak 1993), CHS (Martin and Lefcourt 1983), MSHS (Thorson and Powell 1993), HIS (Bell, McGhee, and Duffey 1986), 3 WD-K (Ruch 1983), CPPT (Kohler and Ruch 1993), TDS (Murgatroyd, Rushton, Apter, and Ray 1978), STCI-T (Ruch, Freiss, and Kohler 1993), and EPQ-R (Eysenck, Eysenck, and Barrett 1985). Reliability of the humor scales is examined and convergent and discriminant validity of homologous scales of humor appreciation and humor creation is determined Behavioral measures and self-report instruments yield only meager correlations. While humor appreciation and humor creation form distinct traits in the behavioral measures, they cannot be validly discriminated in the self-reports. Factor analysis of self-report inventories yields that the sense of humor is composed of the two orthogonal dimensions of cheerfulness and seriousness. Extraversion is predictive of cheerfulness, low seriousness, and quantity of humor production. Psychoticism is associated with low seriousness, wit and quality of humor production. Finally, emotional stability correlates with cheerfulness. All in all, the general state of the art in the assessment of the sense of humor and its components appears to be far from satisfactory.


title: Haptics issues in virtual environments

abstract: Haptics is a recent enhancement to virtual environments, allowing users to "touch" and feel the simulated objects they interact with. Current commercial products allow tactile feedback through desktop interfaces (such as the FEELIt/sup TM/ mouse or the PHANToM/sup TM/ arm) and dextrous tactile and force feedback at the fingertips through haptic gloves (such as the CyberTouch/sup TM/ and the CyberGrasp/sup TM/). Virtual reality haptic programming requires good physical modeling of user interactions, primarily through collision detection, and of object responses, such as surface deformation, hard-contact simulation, slippage, etc. It is at present difficult to simulate complex virtual environments that have a realistic behavior. This task is added to by the recent introduction of haptic toolkits (such as Ghost/sup TM/ or VPS). Current technology suffers from a number of limitations, which go beyond the higher production cost of haptic interfaces. These technical drawbacks include the limited workspace of desktop interfaces, the large weight of force-feedback gloves, the lack of force feedback to the body, safety concerns, etc. Not to be neglected is the high bandwidth requirement of haptics, which is not met by current Internet technology. As a result, it is not possible at present to have a large number of remote participants interacting haptically in a shared virtual space.


title: Influence of Image Acquisition on Radiation Dose and Image Quality: Full versus Narrow Phase Window Acquisition Using 320 MDCT

abstract: Purpose. To compare radiation dose and image quality using predefined narrow phase window versus complete phase window with dose modulation during R-R using 320-row MDCTA. Methods. 114 patients underwent coronary CTA study using 320-row MDCT scanner. 87 patients with mean age (61


title: DeepSat: a learning framework for satellite imagery

abstract: Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent in satellite data, most of the current object classification approaches are not suitable for handling satellite datasets. The progress of satellite image analytics has also been inhibited by the lack of a single labeled high-resolution dataset with multiple class labels. The contributions of this paper are twofold -- (1) first, we present two new satellite datasets called SAT-4 and SAT-6, and (2) then, we propose a classification framework that extracts features from an input image, normalizes them and feeds the normalized feature vectors to a Deep Belief Network for classification. On the SAT-4 dataset, our best network produces a classification accuracy of 97.95% and outperforms three state-of-the-art object recognition algorithms, namely - Deep Belief Networks, Convolutional Neural Networks and Stacked Denoising Autoencoders by ~11%. On SAT-6, it produces a classification accuracy of 93.9% and outperforms the other algorithms by ~15%. Comparative studies with a Random Forest classifier show the advantage of an unsupervised learning approach over traditional supervised learning techniques. A statistical analysis based on Distribution Separability Criterion and Intrinsic Dimensionality Estimation substantiates the effectiveness of our approach in learning better representations for satellite imagery.


title: FDI, Export, and Capital Structure

abstract: This study investigates the impact of foreign direct investment (FDI) and export on capital structure for firms in emerging economies. The hypotheses are developed based on an agency theory perspective and are tested using a sample of 566 Taiwanese firms. We find that the behavior of multinational corporations (MNCs) with a high debt ratio is in line with agency theory predictions. ::: ::: ::: Our findings show that: (1) MNCs in emerging economies, defined as those firms with at least one foreign subsidiary or some extent of FDI, have a higher level of debt than non-MNCs, which contrasts with the findings for MNCs based in developed countries; and (2) export intensity leads to a lower debt ratio, which has not received much attention in previous studies. We propose several factors related to the context of emerging economies to explain these contradictory findings. ::: ::: ::: We also explore the interaction effect of the extent of FDI and export intensity on the capital structure of MNCs, and find that the impact is negative, which implies that both monitoring costs and agency costs rise dramatically for creditors when firms’ international operations become overly complicated.


title: Efficient Parallelization of Motion Estimation for Super-Resolution

abstract: This paper presents an efficient parallelization of the Motion Estimation procedure, one of the core parts of Super Resolution techniques. The algorithm considered is the basic version of Block Matching Super Resolution, with a single low-resolution camera and fixed Macro Block dimensions. Two are the implementations provided, with OpenMP and in CUDA on an NVIDIA Kepler GPU. Tests have been conducted on five image sequences and the results show a considerable improvement of the CUDA solution in all cases. Consequently, it can be stated that GPUs can efficiently accelerate computational times assuring the same image quality.


title: IoT and Cloud Computing in Automation of Assembly Modeling Systems

abstract: After the technologies of integrated circuits, personal computers, and the Internet, Internet of Things (IoT) is the latest information technology (IT) that is radically changing business paradigms. However, IoT's influence in the manufacturing sector has yet been fully explored. On the other hand, existing computer-aided software tools are experiencing a bottleneck in dealing with complexity, dynamics, and uncertainties in their applications of modern enterprises. It is argued that the adoption of IoT and cloud computing in enterprise systems (ESs) would overcome the bottleneck. In this paper, the challenges in generating assembly plans of complex products are discussed. IoT and cloud computing are proposed to help a conventional assembly modeling system evolve into an advanced system, which is capable to deal with complexity and changes automatically. To achieve this goal, an assembly modeling system is automated, and the proposed system includes the following innovations: 1) the modularized architecture to make the system robust, reliable, flexible, and expandable; 2) the integrated object-oriented templates to facilitate interfaces and reuses of system components; and 3) the automated algorithms to retrieve relational assembly matrices for assembly planning. Assembly modeling for aircraft engines is used as examples to illustrate the system effectiveness.


title: On the Learnability and Design of Output Codes for Multiclass Problems

abstract: Output coding is a general framework for solving multiclass categorization problems. Previous research on output codes has focused on building multiclass machines given predefined output codes. In this paper we discuss for the first time the problem of designing output codes for multiclass problems. For the design problem of discrete codes, which have been used extensively in previous works, we present mostly negative results. We then introduce the notion of continuous codes and cast the design problem of continuous codes as a constrained optimization problem. We describe three optimization problems corresponding to three different norms of the code matrix. Interestingly, for the l2 norm our formalism results in a quadratic program whose dual does not depend on the length of the code. A special case of our formalism provides a multiclass scheme for building support vector machines which can be solved efficiently. We give a time and space efficient algorithm for solving the quadratic program. We describe preliminary experiments with synthetic data show that our algorithm is often two orders of magnitude faster than standard quadratic programming packages. We conclude with the generalization properties of the algorithm.


title: DFT Interpolation Algorithm for Kaiser–Bessel and Dolph–Chebyshev Windows

abstract: This paper describes the discrete Fourier transform (DFT) interpolation algorithm for arbitrary windows and its application and performance for optimal noncosine Kaiser-Bessel and Dolph-Chebyshev windows. The interpolation algorithm is based on the polynomial approximation of the window's spectrum that is computed numerically. Two- and three-point (2p and 3p) interpolations are considered. Systematic errors and noise sensitivity are analyzed for the chosen Kaiser-Bessel and Dolph-Chebyshev windows and compared with Rife-Vincent class I windows.


title: Deep Exponential Families

abstract: We describe deep exponential families (DEFs), a class of latent variable models that are inspired by the hidden structures used in deep neural networks. DEFs capture a hierarchy of dependencies between latent variables, and are easily generalized to many settings through exponential families. We perform inference using recent \black box" variational inference techniques. We then evaluate various DEFs on text and combine multiple DEFs into a model for pairwise recommendation data. In an extensive study, we show going beyond one layer improves predictions for DEFs. We demonstrate that DEFs nd interesting exploratory structure in large data sets, and give better predictive performance than state-of-the-art models.


title: Applying the balanced scorecard for better performance of intellectual capital

abstract: Purpose – One of the responses to criticisms of traditional forms of accounting reports for knowledge‐based firms has been the development of the balanced scorecard (BSC), a strategic performance measurement framework and methodology based on a family of performance measures. This paper aims to examine the issue of measuring performance in relation to a major Australian company, The Fosters Brewing Group, where a newly appointed CEO reversed a decline in performance by adopting, among other initiatives, the balanced scorecard approach to management.Design/methodology/approach – The paper takes the form of a case study, applying the theoretical framework of the BSC to a declining business in order to achieve a turnaround.Findings – The paper discusses how a newly appointed CEO of The Fosters Brewing Group reversed a decline in performance by adopting, among other initiatives, the balanced scorecard approach to management.Research limitations/implications – The BSC is a practical framework to deal with the ...


title: Leadership for safety: industrial experience

abstract: The importance of leadership for effective safety management has been the focus of research attention in industry for a number of years, especially in energy and manufacturing sectors. In contrast, very little research into leadership and safety has been carried out in medical settings. A selective review of the industrial safety literature for leadership research with possible application in health care was undertaken. Emerging findings show the importance of participative, transformational styles for safety performance at all levels of management. Transactional styles with attention to monitoring and reinforcement of workers’ safety behaviours have been shown to be effective at the supervisory level. Middle managers need to be involved in safety and foster open communication, while ensuring compliance with safety systems. They should allow supervisors a degree of autonomy for safety initiatives. Senior managers have a prime influence on the organisation’s safety culture. They need to continuously demonstrate a visible commitment to safety, best indicated by the time they devote to safety matters.


title: Bayesian Identification of Fixations, Saccades, and Smooth Pursuits

abstract: Smooth pursuit eye movements provide meaningful insights and information on subject's behavior and health and may, in particular situations, disturb the performance of typical fixation/saccade classification algorithms. Thus, an automatic and efficient algorithm to identify these eye movements is paramount for eye-tracking research involving dynamic stimuli. In this paper, we propose the Bayesian Decision Theory Identification (I-BDT) algorithm, a novel algorithm for ternary classification of eye movements that is able to reliably separate fixations, saccades, and smooth pursuits in an online fashion, even for low-resolution eye trackers. The proposed algorithm is evaluated on four datasets with distinct mixtures of eye movements, including fixations, saccades, as well as straight and circular smooth pursuits; data was collected with a sample rate of 30 Hz from six subjects, totaling 24 evaluation datasets. The algorithm exhibits high and consistent performance across all datasets and movements relative to a manual annotation by a domain expert (recall: μ = 91.42%, σ = 9.52%; precision: μ = 95.60%, σ = 5.29%; specificity μ = 95.41%, σ = 7.02%) and displays a significant improvement when compared to I-VDT, an state-of-the-art algorithm (recall: μ = 87.67%, σ = 14.73%; precision: μ = 89.57%, σ = 8.05%; specificity μ = 92.10%, σ = 11.21%). Algorithm implementation and annotated datasets are openly available at www.ti.uni-tuebingen.de/perception


title: Scene text extraction using stroke width transform for tourist translator on android platform

abstract: Scene text extraction is recent research area in the field of Computer Vision. Text component in an image is of particular interest, as it is easy to understand by both humans as well as machines and can be used in variety of applications like content-based image retrieval, aids for visually impaired people, translator for tourists. Although there exists a lot of research activities in this field, it is still remained as a challenging problem, mainly due to two issues: different variety of text patterns like fonts, colors, sizes, orientations; and presence of background outliers similar to text characters, such as windows, bricks. In this paper, an android application named as TravelMate is developed, which is a user-friendly application to assist the tourist navigation, while they are roaming in foreign countries. Proposed application is able to extract text from an image, which is captured by an android mobile camera. Extraction is performed using stroke width transform (SWT) approach and connected component analysis. Extracted texts are recognized using Google's open source optical character recognition (OCR) engine ‘Tesseract’ and translated to a target language using Google's translation. The entire result is displayed back onto the screen of the mobile Phone.


title: A Laplacian Framework for Option Discovery in Reinforcement Learning

abstract: Representation learning and option discovery are two of the biggest challenges in reinforcement learning (RL). Proto-value functions (PVFs) are a well-known approach for representation learning in MDPs. In this paper we address the option discovery problem by showing how PVFs implicitly define options. We do it by introducing eigenpurposes, intrinsic reward functions derived from the learned representations. The options discovered from eigenpurposes traverse the principal directions of the state space. They are useful for multiple tasks because they are discovered without taking the environment's rewards into consideration. Moreover, different options act at different time scales, making them helpful for exploration. We demonstrate features of eigenpurposes in traditional tabular domains as well as in Atari 2600 games.


title: Learning a Spatial Ensemble of Classifiers for Raster Classification: A Summary of Results

abstract: Given a spatial raster framework F, a set of explanatory feature maps, training and test samples with class labels on F, as well as a base classifier type, the problem of ensemble learning in raster classification aims to learn a collection of base classifiers to minimize classification errors. The problem has important societal applications such as land cover classification but is challenging due to existence of class ambiguity from spatial heterogeneity, i.e., Samples with the same feature values may have distinct class labels in different areas. Many existing approaches are non-spatial ensembles (e.g., Bagging, boosting, random forest), which assume that learning samples follow an identical distribution. Some spatial ensemble approaches also exist, which simply partition the raster framework into several regular sub-blocks and combine classification results on each sub-block. However, these existing approaches can not address the class ambiguity issue among pixels. In contrast, this paper proposes a new spatial ensemble approach, which partitions the spatial framework into several spatial footprints to minimize class ambiguity of training samples and then learns a base classifier for each footprint. Experimental evaluations on a real world remote sensing dataset show that the proposed spatial ensemble approach outperforms existing approaches when strong class ambiguity exists.


title: Urban 3D semantic modelling using stereo vision

abstract: In this paper we propose a robust algorithm that generates an efficient and accurate dense 3D reconstruction with associated semantic labellings. Intelligent autonomous systems require accurate 3D reconstructions for applications such as navigation and localisation. Such systems also need to recognise their surroundings in order to identify and interact with objects of interest. Considerable emphasis has been given to generating a good reconstruction but less effort has gone into generating a 3D semantic model. The inputs to our algorithm are street level stereo image pairs acquired from a camera mounted on a moving vehicle. The depth-maps, generated from the stereo pairs across time, are fused into a global 3D volume online in order to accommodate arbitrary long image sequences. The street level images are automatically labelled using a Conditional Random Field (CRF) framework exploiting stereo images, and label estimates are aggregated to annotate the 3D volume. We evaluate our approach on the KITTI odometry dataset and have manually generated ground truth for object class segmentation. Our qualitative evaluation is performed on various sequences of the dataset and we also quantify our results on a representative subset.


title: Meta-Learning via Feature-Label Memory Network

abstract: Deep learning typically requires training a very capable architecture using large datasets. However, many important learning problems demand an ability to draw valid inferences from small size datasets, and such problems pose a particular challenge for deep learning. In this regard, various researches on"meta-learning"are being actively conducted. Recent work has suggested a Memory Augmented Neural Network (MANN) for meta-learning. MANN is an implementation of a Neural Turing Machine (NTM) with the ability to rapidly assimilate new data in its memory, and use this data to make accurate predictions. In models such as MANN, the input data samples and their appropriate labels from previous step are bound together in the same memory locations. This often leads to memory interference when performing a task as these models have to retrieve a feature of an input from a certain memory location and read only the label information bound to that location. In this paper, we tried to address this issue by presenting a more robust MANN. We revisited the idea of meta-learning and proposed a new memory augmented neural network by explicitly splitting the external memory into feature and label memories. The feature memory is used to store the features of input data samples and the label memory stores their labels. Hence, when predicting the label of a given input, our model uses its feature memory unit as a reference to extract the stored feature of the input, and based on that feature, it retrieves the label information of the input from the label memory unit. In order for the network to function in this framework, a new memory-writingmodule to encode label information into the label memory in accordance with the meta-learning task structure is designed. Here, we demonstrate that our model outperforms MANN by a large margin in supervised one-shot classification tasks using Omniglot and MNIST datasets.


title: WalkSafe: a pedestrian safety app for mobile phone users who walk and talk while crossing roads

abstract: Research in social science has shown that mobile phone conversations distract users, presenting a significant impact to pedestrian safety; for example, a mobile phone user deep in conversation while crossing a street is generally more at risk than other pedestrians not engaged in such behavior. We propose WalkSafe, an Android smartphone application that aids people that walk and talk, improving the safety of pedestrian mobile phone users. WalkSafe uses the back camera of the mobile phone to detect vehicles approaching the user, alerting the user of a potentially unsafe situation; more specifically WalkSafe i) uses machine learning algorithms implemented on the phone to detect the front views and back views of moving vehicles and ii) exploits phone APIs to save energy by running the vehicle detection algorithm only during active calls. We present our initial design, implementation and evaluation of the WalkSafe App that is capable of real-time detection of the front and back views of cars, indicating cars are approaching or moving away from the user, respectively. WalkSafe is implemented on Android phones and alerts the user of unsafe conditions using sound and vibration from the phone. WalkSafe is available on Android Market.


title: The new frontier of genome engineering with CRISPR-Cas9

abstract: The advent of facile genome engineering using the bacterial RNA-guided CRISPR-Cas9 system in animals and plants is transforming biology. We review the history of CRISPR (clustered regularly interspaced palindromic repeat) biology from its initial discovery through the elucidation of the CRISPR-Cas9 enzyme mechanism, which has set the stage for remarkable developments using this technology to modify, regulate, or mark genomic loci in a wide variety of cells and organisms from all three domains of life. These results highlight a new era in which genomic manipulation is no longer a bottleneck to experiments, paving the way toward fundamental discoveries in biology, with applications in all branches of biotechnology, as well as strategies for human therapeutics. T echnologies for making and manipulating DNA have enabled many of the advances in biology over the past 60 years. This era began with the discovery of the DNA double helix and continued with the development


title: The Spinning Top Model, a New Path to Conceptualize Culture and Values: Applications to IS Research.

abstract: In this conceptual study we first categorize, from existing literature, different conceptions of culture rooted in Anthropology and Sociology. We argue that these conceptions build up the logical structure of specific theoretical and empirical tools which address human/IS interactions in a cultural-based perspective. We then propose a new model of the individual’s global culture, the Spinning Top Model. We posit theoretical proposals based on this model and define a new analytical framework which can open new paths for IS research, the study of IT-related values , IT-attitudes and IT-behaviors.


title: Voice Recognition using Dynamic Time Warping and Mel-Frequency Cepstral Coefficients Algorithms

abstract: Voice recognition is an important and active research area of the recent years. This research aims to build a system for voice recognition using dynamic time wrapping algorithm, by comparing the voice signal of the speaker with pre-stored voice signals in the database, and extracting the main features of the speaker voice signal using Mel-frequency cepstral coefficients, which is one of the most important factors in achieving high recognition accuracy. General Terms Dynamic time wrapping “DTW” algorithm, Mel-frequency Cepstral Coefficients “MFCC” algorithm, vocal signal.


title: Targeted Kernel Networks: Faster Convolutions with Attentive Regularization

abstract: We propose Attentive Regularization (AR), a method to constrain the activation maps of kernels in Convolutional Neural Networks (CNNs) to specific regions of interest (ROIs). Each kernel learns a location of specialization along with its weights through standard backpropagation. A differentiable attention mechanism requiring no additional supervision is used to optimize the ROIs. Traditional CNNs of different types and structures can be modified with this idea into equivalent Targeted Kernel Networks (TKNs), while keeping the network size nearly identical. By restricting kernel ROIs, we reduce the number of sliding convolutional operations performed throughout the network in its forward pass, speeding up both training and inference. We evaluate our proposed architecture on both synthetic and natural tasks across multiple domains. TKNs obtain significant improvements over baselines, requiring less computation (around an order of magnitude) while achieving superior performance.


title: CCCF: Improving Collaborative Filtering via Scalable User-Item Co-Clustering

abstract: Collaborative Filtering (CF) is the most popular method for recommender systems. The principal idea of CF is that users might be interested in items that are favorited by similar users, and most of the existing CF methods measure users' preferences by their behaviours over all the items. However, users might have different interests over different topics, thus might share similar preferences with different groups of users over different sets of items. In this paper, we propose a novel and scalable method CCCF which improves the performance of CF methods via user-item co-clustering. CCCF first clusters users and items into several subgroups, where each subgroup includes a set of like-minded users and a set of items in which these users share their interests. Then, traditional CF methods can be easily applied to each subgroup, and the recommendation results from all the subgroups can be easily aggregated. Compared with previous works, CCCF has several advantages including scalability, flexibility, interpretability and extensibility. Experimental results on four real world data sets demonstrate that the proposed method significantly improves the performance of several state-of-the-art recommendation algorithms.


title: PinPad: Touchpad Interaction with Fast and High-Resolution Tactile Output

abstract: We explored new interaction scenarios that can be realized when a touchpad outputs fast and high-resolution spatio-temporal tactile patterns to the touch-sensitive skin on the fingertips of a user. We first constructed a special tactile multi-touch touchpad called PinPad, which was capable of outputting fast and high-resolution tactile patterns using a 40 x 25 array of actuated pins. We then developed various interaction scenarios that could be realized using the prototype: 1) Tactile Target, 2) Guide and Constraint, 3) Multi-finger Output, and 4) Dynamic Partition. To evaluate the PinPad scenarios, we implemented demo applications, and conducted interviews with users to collect feedback about their experiences with PinPad and the PinPad scenarios. The participants confirmed the effectiveness of spatio-temporal outputs of PinPad in the scenarios. In particular, they provided diverse feedback regarding the unique tactile experiences of the fast and high-resolution outputs of PinPad.