title: Interactive technical illustration

abstract: A rendering is an abstraction that favors, preserves, or even emphasizes some qualities while sacrificing, suppressing, or omitting other characteristics that are not the focus of attention. Most computer graphics rendering activities have been concerned with photorealism, i.e., trying to emulate an image that looks like a highquality photograph. This laudable goal is useful and appropriate in many applications, but not in technical illustration where elucidation of structure and technical information is the preeminent motivation. This calls for a different kind of abstraction in which technical communication is central, but art and appearance are still essential instruments toward this end. Work that has been done on computer generated technical illustrations has focused on static images, and has not included all of the techniques used to hand draw technical illustrations. A paradigm for the display of technical illustrations in a dynamic environment is presented. This display environment includes all of the benefits of computer generated technical illustrations, such as a clearer picture of shape, structure, and material composition than traditional computer graphics methods. It also includes the three-dimensional interactive strength of modern display systems. This is accomplished by using new algorithms for real time drawing of silhouette curves, algorithms which solve a number of the problems inherent in previous methods. We incorporate current non-photorealistic lighting methods, and augment them with new shadowing algorithms based on accepted techniques used by artists and studies carried out in human perception. This paper, all of the images, and a mpeg video clip are available at http://www.cs.utah.edu/ bgooch/ITI/. CR Categories: I.3.0 [Computer Graphics]: General; I.3.6 [Computer Graphics]: Methodology and Techniques.


title: Adaptive radio for multimedia wireless links

abstract: The quality of wireless links suffers from time-varying channel degradations such as interference, flat-fading, and frequency-selective fading. Current radios are limited in their ability to adapt to these channel variations because they are designed with fixed values for most system parameters such as frame length, error control, and processing gain. The values for these parameters are usually a compromise between the requirements for worst-case channel conditions and the need for low implementation cost. Therefore, in benign channel conditions these commercial radios can consume more battery energy than needed to maintain a desired link quality, while in a severely degraded channel they can consume energy without providing any quality-of-service (QoS). While techniques for adapting radio parameters to channel variations have been studied to improve link performance, in this paper they are applied to minimize battery energy. Specifically, an adaptive radio is being designed that adapts the frame length, error control, processing gain, and equalization to different channel conditions, while minimizing battery energy consumption. Experimental measurements and simulation results are presented to illustrate the adaptive radio's energy savings.


Machine-Translated Knowledge Transfer for Commonsense Causal Reasoning


title: Ultra-dense networks in millimeter-wave frequencies

abstract: Demands for very high system capacity and end-user data rates of the order of 10 Gb/s can be met in localized environments by Ultra-Dense Networks (UDN), characterized as networks with very short inter-site distances capable of ensuring low interference levels during communications. UDNs are expected to operate in the millimeter-wave band, where wide bandwidth signals needed for such high data rates can be designed, and will rely on high-gain beamforming to mitigate path loss and ensure low interference. The dense deployment of infrastructure nodes will make traditional wire-based backhaul provisioning challenging. Wireless self-backhauling over multiple hops is proposed to enhance flexibility in deployment. A description of the architecture and a concept based on separation of mobility, radio resource coordination among multiple nodes, and data plane handling, as well as on integration with wide-area networks, is introduced. A simulation of a multi-node office environment is used to demonstrate the performance of wireless self-backhauling at various loads.


title: Medium-duration modulation cepstral feature for robust speech recognition

abstract: Studies have shown that the performance of state-of-the-art automatic speech recognition (ASR) systems significantly deteriorate with increased noise levels and channel degradations, when compared to human speech recognition capability. Traditionally, noise-robust acoustic features are deployed to improve speech recognition performance under varying background conditions to compensate for the performance degradations. In this paper, we present the Modulation of Medium Duration Speech Amplitude (MMeDuSA) feature, which is a composite feature capturing subband speech modulations and a summary modulation. We analyze MMeDuSA's speech recognition performance using SRI International's DECIPHER ::: ® ::: large vocabulary continuous speech recognition (LVCSR) system, on noise and channel degraded Levantine Arabic speech distributed through the Defense Advance Research Projects Agency (DARPA) Robust Automatic Speech Transcription (RATS) program. We also analyzed MMeDuSA's performance against the Aurora-4 noise-and-channel degraded English corpus. Our results from all these experiments suggest that the proposed MMeDuSA feature improved recognition performance under both noisy and channel degraded conditions in almost all the recognition tasks.


title: Are software startups applying agile practices? The state of the practice from a large survey

abstract: Software startups operate under various uncertainties and the demand on their ability to deal with change is high. Agile methods are considered a suitable and viable development approach for them. However, the competing needs for speed and quality may render certain agile practices less suitable than others in the startup context. The adoption of agile practices can be further complicated in software startups that adopt the Lean Startup approach. To make the best of agile practices, it is necessary to first understand whether and how they are used in software startups. This study targets at a better understanding of the use of agile practices in software startups, with a particular focus on lean startups. Based on a large survey of 1526 software startups, we examined the use of five agile practices, including quality related (regular refactoring and test first), speed related (frequent release and agile planning) and communication practice (daily standup meeting). The findings show that speed related agile practices are used to a greater extent in comparison to quality practices. Daily standup meeting is least used. Software startups who adopt the Lean Startup approach do not sacrifice quality for speed more than other startups do.


title: Who Will Reply to/Retweet This Tweet?: The Dynamics of Intimacy from Online Social Interactions

abstract: Friendships are dynamic. Previous studies have converged to suggest that social interactions, in both online and offline social networks, are diagnostic reflections of friendship relations (also called social ties). However, most existing approaches consider a social tie as either a binary relation, or a fixed value (named tie strength). In this paper, we investigate the dynamics of dyadic friend relationships through online social interactions, in terms of a variety of aspects, such as reciprocity, temporality, and contextuality. In turn, we propose a model to predict repliers and retweeters given a particular tweet posted at a certain time in a microblog-based social network. More specifically, we have devised a learning-to-rank approach to train a ranker that considers elaborate user-level and tweet-level features (like sentiment, self-disclosure, and responsiveness) to address these dynamics. In the prediction phase, a tweet posted by a user is deemed a query and the predicted repliers/retweeters are retrieved using the learned ranker. We have collected a large dataset containing 73.3 million dyadic relationships with their interactions (replies and retweets). Extensive experimental results based on this dataset show that by incorporating the dynamics of friendship relations, our approach significantly outperforms state-of-the-art models in terms of multiple evaluation metrics, such as MAP, NDCG and Topmost Accuracy. In particular, the advantage of our model is even more promising in predicting the exact sequence of repliers/retweeters considering their orders. Furthermore, the proposed approach provides emerging implications for many high-value applications in online social networks.


title: A new variable step size INC MPPT method for PV systems

abstract: Maximum power points tracking (MPPT) techniques are widely utilized in photovoltaic (PV) systems to operate at the peak power of PV array which depends on solar and ambient temperature. Between all the MPPT strategies, the incremental conductance (INC) algorithm is extensively employed due to the high tracking accuracy at steady state and good adaptability to the rapidly changing atmospheric conditions. In this paper, a novel variable step-size incremental conductance MPPT algorithm is used in solar array power systems with direct control method, which automatically adjusts the step size to track MPP without the proportional-integral control loop. Compared with conventional INC and variable step-size INC method, the proposed method can largely improve the MPPT response speed and accuracy at steady state simultaneously without steady-state oscillation. The proposed algorithm was tested in MATLAB/SIMULINK environment. Results show the feasibility, a faster response speed and higher tracking accuracy in changing irradiance.


title: Functional neuroanatomy of human rapid-eye-movement sleep and dreaming

abstract: Rapid-eye-movement (REM) sleep is associated with intense neuronal activity, ocular saccades, muscular atonia and dreaming. The function of REM sleep remains elusive and its neural correlates have not been characterized precisely in man. Here we use positron emission tomography and statistical parametric mapping to study the brain state associated with REM sleep in humans. We report a group study of seven subjects who maintained steady REM sleep during brain scanning and recalled dreams upon awakening. The results show that regional cerebral blood flow is positively correlated with REM sleep in pontine tegmentum, left thalamus, both amygdaloid complexes, anterior cingulate cortex and right parietal operculum. Negative correlations between regional cerebral blood flow and REM sleep are observed bilaterally, in a vast area of dorsolateral prefrontal cortex, in parietal cortex (supramarginal gyrus) as well as in posterior cingulate cortex and precuneus. Given the role of the amygdaloid complexes in the acquisition of emotionally influenced memories, the pattern of activation in the amygdala and the cortical areas provides a biological basis for the processing of some types of memory during REM sleep.


title: A 475–511GHz radiating source with SIW-based harmonic power extractor in 40 nm CMOS

abstract: This paper presents a 0.49 terahertz (THz) radiating source in 40 nm CMOS. The radiating source is composed of a cross-coupled oscillator, a differential tripler, a substrate integrated waveguide (SIW) based harmonic power extractor (HPE) and a folded dipole antenna. The HPE can optimize third harmonic power extraction and provide suppression of unwanted lower order harmonic leakage. The measured equivalent isotropically radiated power (EIRP) of the radiating source is −4.1 dBm. According to simulated antenna gain of 11.2 dB, the output power and DC-to-THz efficiency of the signal source can be calculated as −15.3 dBm and 0.173%, respectively. The output frequency can be tuned from 475 to 511 GHz within 10 dB EIRP variation.


PUFFT—The Purdue University fast FORTRAN translator


title: Intelligent tagging of online texts using fuzzy logic

abstract: We propose four fuzzy-logic based models of tag recommendation, which are based on the interpretation of word frequency as a fuzzy membership function, and provide experimental results of tag recommendation for a variety of text datasets using different fuzzy logic operators. The novelty of the proposed models is the use of fuzzy logic modeling concepts to define a set of tags, the use of an existing set of tags for the selection of tags to strengthen the selection of most relevant (i.e., commonly used) tags, and the possibility to use an ontology to select semantically generalized tags. A system developed using the proposed models is adaptive (adapts the recommended tags to the existing set of tags), has a feedback (after each tagging, the set of tags and the dictionary are updated), is personalized (each user develops its own set of tags), and is semantics-aware (uses an ontology to refine tags). The models are validated using five sets of texts with different topics (technology, cooking, carrier, scientific, nature) and different length.


title: A Comparison of Low-Cost Monocular Vision Techniques for Pothole Distance Estimation

abstract: Consider a single camera mounted on the inside of a vehicle's windscreen used for detecting potholes and other obstacles on the road surface. This paper outlines three approaches to the depth estimation problem of determining the distance to these obstacles in the range of 5 m to 30 m. We provide an empirical evaluation of the accuracy of these approaches under various conditions, and make recommendations for when each approach is most suitable. The approaches are based on the pinhole camera model: the simplest approach is based on the geometry of similar triangles, another employs the cross-ratio of a set of collinear points, and the final approach relies on calibration of the camera matrix. We recommend the use of the cross ratio approach for a fixed camera setup and depth estimation almost directly ahead, and an approach using similar triangles when predicting distances at wide angles or adjusting the camera height may be necessary.


title: Surveying Port Scans and Their Detection Methodologies

abstract: Scanning of ports on a computer occurs frequently on the Internet. An attacker performs port scans of Internet protocol addresses to find vulnerable hosts to compromise. However, it is also useful for system administrators and other network defenders to detect port scans as possible preliminaries to more serious attacks. It is a very difficult task to recognize instances of malicious port scanning. In general, a port scan may be an instance of a scan by attackers or an instance of a scan by network defenders. In this survey, we present research and development trends in this area. Our presentation includes a discussion of common port scan attacks. We provide a comparison of port scan methods based on type, mode of detection, mechanism used for detection and other characteristics. This survey also reports on the available data sets and evaluation criteria for port scan detection approaches.


title: Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence.

abstract: Prior research indicates that Facebook usage predicts declines in subjective well-being over time. How does this come about? We examined this issue in 2 studies using experimental and field methods. In Study 1, cueing people in the laboratory to use Facebook passively (rather than actively) led to declines in affective well-being over time. Study 2 replicated these findings in the field using experience-sampling techniques. It also demonstrated how passive Facebook usage leads to declines in affective well-being: by increasing envy. Critically, the relationship between passive Facebook usage and changes in affective well-being remained significant when controlling for active Facebook use, non-Facebook online social network usage, and direct social interactions, highlighting the specificity of this result. These findings demonstrate that passive Facebook usage undermines affective well-being.


title: MaxiNet: Distributed emulation of software-defined networks

abstract: Network emulations are widely used for testing novel network protocols and routing algorithms in realistic scenarios. Up to now, there is no emulation tool that is able to emulate large software-defined data center networks that consist of several thousand nodes. Mininet is the most common tool to emulate Software-Defined Networks of several hundred nodes. We extend Mininet to span an emulated network over several physical machines, making it possible to emulate networks of several thousand nodes on just a handful of physical machines. This enables us to emulate, e.g., large data center networks. To test this approach, we additionally introduce a traffic generator for data center traffic. Since there are no data center traffic traces publicly available we use the results of two recent traffic studies to create synthetic traffic. We show the design and discuss some challenges we had in building our traffic generator. As a showcase for our work we emulated a data center consisting of 3200 hosts on a cluster of only 12 physical machines. We show the resulting workloads and the trade-offs involved.


title: Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output

abstract: State-of-the-art image sensors suffer from significant limitations imposed by their very principle of operation. These sensors acquire the visual information as a series of “snapshot” images, recorded at discrete points in time. Visual information gets time quantized at a predetermined frame rate which has no relation to the dynamics present in the scene. Furthermore, each recorded frame conveys the information from all pixels, regardless of whether this information, or a part of it, has changed since the last frame had been acquired. This acquisition method limits the temporal resolution, potentially missing important information, and leads to redundancy in the recorded image data, unnecessarily inflating data rate and volume. Biology is leading the way to a more efficient style of image acquisition. Biological vision systems are driven by events happening within the scene in view, and not, like image sensors, by artificially created timing and control signals. Translating the frameless paradigm of biological vision to artificial imaging systems implies that control over the acquisition of visual information is no longer being imposed externally to an array of pixels but the decision making is transferred to the single pixel that handles its own information individually. In this paper, recent developments in bioinspired, neuromorphic optical sensing and artificial vision are presented and discussed. It is suggested that bioinspired vision systems have the potential to outperform conventional, frame-based vision systems in many application fields and to establish new benchmarks in terms of redundancy suppression and data compression, dynamic range, temporal resolution, and power efficiency. Demanding vision tasks such as real-time 3-D mapping, complex multiobject tracking, or fast visual feedback loops for sensory-motor action, tasks that often pose severe, sometimes insurmountable, challenges to conventional artificial vision systems, are in reach using bioinspired vision sensing and processing techniques.


title: Constraint Programming : In Pursuit of the Holy Grail

abstract: Constraint programming (CP) is an emergent software technology for declarative description and effective solving of large, particularly combinatorial, problems especially in areas of planning and scheduling. It represents the most exciting developments in programming languages of the last decade and, not surprisingly, it has recently been identified by the ACM (Association for Computing Machinery) as one of the strategic directions in computer research. Not only it is based on a strong theoretical foundation but it is attracting widespread commercial interest as well, in particular, in areas of modelling heterogeneous optimisation and satisfaction problems. In the paper, we give a survey of constraint programming technology and its applications starting from the history context and interdisciplinary nature of CP. The central part of the paper is dedicated to the description of main constraint satisfaction techniques and industrial applications. We conclude with the overview of limitations of current CP tools and with outlook of future directions.


title: Automated detection of centromere in G banded chromosomes

abstract: The centromere position is an important feature in analyzing chromosomes and to make karyogram. In the field of chromosome analysis the accurate determination centromere from the segmented chromosome image is a challenging task. Karyogram is an arrangement of 46 chromosomes, for finding out many genetic disorders, various abnormalities and cancers. There exist so many algorithms to detect centromere positions, but most of the algorithms cannot apply for all chromosomes because of their orientation in metaphase. Here we propose a novel algorithm that associates with some rules based on morphological features of chromosome, a GLM mask and rotation procedure. The algorithm is tested on publically available database (LK1) and images collected from RCC Trivandrum.


title: Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images

abstract: Abstract When approaching the semantic segmentation of overhead imagery in the decimeter spatial resolution range, successful strategies usually combine powerful methods to learn the visual appearance of the semantic classes (e.g. convolutional neural networks) with strategies for spatial regularization (e.g. graphical models such as conditional random fields). In this paper, we propose a method to learn evidence in the form of semantic class likelihoods, semantic boundaries across classes and shallow-to-deep visual features, each one modeled by a multi-task convolutional neural network architecture. We combine this bottom-up information with top-down spatial regularization encoded by a conditional random field model optimizing the label space across a hierarchy of segments with constraints related to structural, spatial and data-dependent pairwise relationships between regions. Our results show that such strategy provide better regularization than a series of strong baselines reflecting state-of-the-art technologies. The proposed strategy offers a flexible and principled framework to include several sources of visual and structural information, while allowing for different degrees of spatial regularization accounting for priors about the expected output structures.


title: Fuzzy sets across the natural language generation pipeline

abstract: We explore the implications of using fuzzy techniques (mainly those commonly used in the linguistic description/summarization of data discipline) from a natural language generation perspective. For this, we provide an extensive discussion of some general convergence points and an exploration of the relationship between the different tasks involved in the standard NLG system pipeline architecture and the most common fuzzy approaches used in linguistic summarization/description of data, such as fuzzy quantified statements, evaluation criteria or aggregation operators. Each individual discussion is illustrated with a related use case. Recent work made in the context of cross-fertilization of both research fields is also referenced. This paper encompasses general ideas that emerged as part of the PhD thesis "Application of fuzzy sets in data-to-text systems". It does not present a specific application or a formal approach, but rather discusses current high-level issues and potential usages of fuzzy sets (focused on linguistic summarization of data) in natural language generation.


title: Hitting Back-Spin Balls by Robotic Table Tennis System based on Physical Models of Ball Motion

abstract: Abstract In this paper, we develop a robotic table tennis system in the case of the back spin with the same measurement method and the ball models of the aerodynamics and the rebounds as in the study of Nakashima et al. (2011). First, the aerodynamics model is improved for precise prediction of the ball trajectory with data of the flying back-spin ball. Second, a method to determine the racket motion is shown where approximated inverse problems of the models are solved with optimizations. Third, a motion planning of the robot to achieve the racket motion is proposed with the velocity limitation of the robot joints. Experimental results are shown to verify the effectiveness of the proposed method.


title: Glenohumeral Internal Rotation Deficit in the Asymptomatic Professional Pitcher and Its Relationship to Humeral Retroversion

abstract: The purpose of this study was to determine if glenohumeral internal rotation deficits (GIRD) exist in an asymptomatic population of professional pitchers, and to assess whether these changes are primarily a bony or soft tissue adaptation. Twenty three, active, asymptomatic professional (Major League Baseball) pitchers volunteered for the study. Clinical measures of glenohumeral ranges of motion, laxity, GIRD, as well as radiographic measures of humeral retroversion were taken by two independent orthopaedic surgeons. Data comparing side to side differences in range of motion, laxity, and humeral retroversion were analyzed for statistical significance using a paired t-test for continuous data and a Chi-squared test for ordinal data, with a significance set at 0.05. Evaluations of statistical correlations between different measurement parameters were accomplished using a Pearson product moment correlation. We hypothesized GIRD will be positively correlated with humeral retroversion (HR) in the pitching arm. All clinical and radiographic measures were made in the field, at spring training, by physicians of both private and institutional based sports medicine practices. For the entire group, significant differences were exhibited for HR, external rotation at 90° and internal rotation at 90°, for dominant vs. non-dominant arms. GIRD of greater than 25° was noted in 10/23 of pitchers. In this group, HR was significantly increased and correlated to GIRD. No such increase or correlation was noted for the non-GIRD group. GIRD is a common finding in asymptomatic professional pitchers, and is related to humeral retroversion. Thus internal rotation deficits should not be used as the sole screening tool to diagnose the disabled throwing shoulder. ::: ::: ::: ::: Key points ::: ::: ::: GIRD is relatively common in asymptomatic baseball pitchers (35-43%). ::: ::: ::: Large ranges (-45 to 5°) and a large standard deviation (±16°) were noted suggesting that GIRD is quite variable in this population. ::: ::: ::: GIRD is a variable measure in the asymptomatic population, and therefore should not be used as sole proof for the disabled throwing shoulder.


title: Mobile Collaboration for Young Children

abstract: Social interaction and collaboration are essential to the emotional and cognitive development of young children [40]. Constructionism [32] is a learning theory where children learn as they build or construct a public artifact. Creative activities that promote collaboration, especially those based on principles of constructionism, provide enhanced learning opportunities for young children. Mobile devices can support the learning experience as children can create artifacts in various contexts. The proposed research incorporates collaboration, constructionism, children, stories and mobile technologies; specifically investigating developmentally appropriate interfaces to support mobile collaboration for young children.


title: Occlusions and binocular stereo

abstract: Binocular stereo is the process of obtaining depth information from a pair of cameras. In the past, stereo algorithms have had problems at occlusions and have tended to fail there (though sometimes post-processing has been added to mitigate the worst effects). We show that, on the contrary, occlusions can help stereo computation by providing cues for depth discontinuities.


title: Human vs. Computer Go: Review and Prospect

abstract: The Google DeepMind challenge match in March 2016 was a historic achievement for computer Go development. This article discusses the development of computational intelligence (CI) and its relative strength in comparison with human intelligence for the game of Go. We first summarize the milestones achieved for computer Go from 1998 to 2016. Then, the computer Go programs that have participated in previous IEEE CIS competitions as well as methods and techniques used in AlphaGo are briefly introduced. Commentaries from three high-level professional Go players on the five AlphaGo versus Lee Sedol games are also included. We conclude that AlphaGo beating Lee Sedol is a huge achievement in artificial intelligence (AI) based largely on CI methods. In the future, powerful computer Go programs such as AlphaGo are expected to be instrumental in promoting Go education and AI real-world applications.


title: Effects of stabilization exercise using a ball on mutifidus cross-sectional area in patients with chronic low back pain.

abstract: The purpose of this study was to compare the effects of lumbar stabilization exercises using balls to the effects of general lumbar stabilization exercises with respect to changes in the cross section of the multifidus (MF), weight bearing, pain, and functional disorders in patients with non-specific chronic low back pain. Twelve patients participated in either a 8 week (3 days per week) stabilization exercise program using balls and control group (n = 12). The computer tomography (CT) was used to analyze MF cross-sectional areas (CSA) and Tetrax balancing scale was used to analyze left and right weight bearing differences. Both groups had significant changes in the CSA of the MF by segment after training (p < 0.05) and the experimental group showed greater increases at the L4 (F = 9.854, p = 0.005) and L5 (F = 39. 266, p = 0.000). Both groups showed significant decreases in weight bearing, from 9.25% to 5.83% in the experimental group and from 9.33% to 4.25% in the control group (p < 0.05), but did not differ significantly between the two groups. These results suggests that stabilization exercises using ball can increases in the CSA of the MF segments, improvement in weight bearing, pain relief, and recovery from functional disorders, and the increases in the CSA of the MF of the L4 and L5 segments for patients with low back pain. Key PointsCompared with the stabilization exercise using a ball and general stabilization exercise increased the CSA of the MF, weight bearing, pain, and functional ability in patients with low back pain.We verified that increases in the CSA of the MF of the L4 and L5 segments and functional ability during the stabilization exercise using a ball.The stabilization exercise using a ball was shown to be an effective exercise method for patients with low back pain in a rehabilitation program by increasing functional ability and the CSA of the MF.


title: The Ins and Outs of the Probabilistic Model Checker MRMC

abstract: The Markov Reward Model Checker (MRMC) is a software toolfor verifying properties over probabilistic models. It supports PCTL and CSL model checking, and their rewardextensions. Distinguishing features of MRMC are its support for computing time- and reward-bounded reachability probabilities, (property-driven) bisimulation minimization, and precise on-the-fly steady-state detection. Recent tool features include time-bounded reachability analysis for uniform CTMDPs and CSL model checking by discrete-event simulation. This paper presents the tool's current status and its implementation details.


title: User-Based Active Learning

abstract: Active learning has been proven a reliable strategy to reduce manual efforts in training data labeling. Such strategies incorporate the user as oracle: the classifier selects the most appropriate example and the user provides the label. While this approach is tailored towards the classifier, more intelligent input from the user may be beneficial. For instance, given only one example at a time users are hardly able to determine whether this example is an outlier or not. In this paper we propose user-based visually-supported active learning strategies that allow the user to do both, selecting and labeling examples given a trained classifier. While labeling is straightforward, selection takes place using a interactive visualization of the classifier's a-posteriori output probabilities. By simulating different user selection strategies we show, that user-based active learning outperforms uncertainty based sampling methods and yields a more robust approach on different data sets. The obtained results point towards the potential of combining active learning strategies with results from the field of information visualization.


title: Three-Dimensional Printing of High-Content Graphene Scaffolds for Electronic and Biomedical Applications

abstract: The exceptional properties of graphene enable applications in electronics, optoelectronics, energy storage, and structural composites. Here we demonstrate a 3D printable graphene (3DG) composite consisting of majority graphene and minority polylactide-co-glycolide, a biocompatible elastomer, 3D-printed from a liquid ink. This ink can be utilized under ambient conditions via extrusion-based 3D printing to create graphene structures with features as small as 100 μm composed of as few as two layers ( 10 cm thick object). The resulting 3DG material is mechanically robust and flexible while retaining electrical conductivities greater than 800 S/m, an order of magnitude increase over previously reported 3D-printed carbon materials. In vitro experiments in simple growth medium, in the absence of neurogenic stimuli, reveal that 3DG supports human mesenchymal stem cell (hMSC) adhesion, viability, proliferation, and neurogenic differentiation with significant upregu...


title: A Neuro-fuzzy System for Fraud Detection in Electricity Distribution

abstract: The volume of energy loss that Brazilian electrical utilities have to deal with has been ever increasing. Electricity distribution companies have suffered significant and increasing losses in the last years, due to theft, measurement errors and other irregularities. Therefore there is a great concern to identify the profile of irregular customers, in order to reduce the volume of such losses. This paper presents a combined approach of a neural networks committee and a neuro-fuzzy hierarchical system intended to increase the level of accuracy in the identification of irregularities among low voltage consumers. The data used to test the proposed system are from Light S.A., the distribution company of Rio de Janeiro. The results obtained presented a significant increase in the identification of irregular customers when compared to the current methodology employed by the company. Keywords— neural nets, hierarchical neuro-fuzzy systems, binary space partition, electricity distribution, fraud detection.


title: Virtual Remote Center of Motion Control for Needle Placement Robots

abstract: Surgical robots, including those with remote center of motion (RCM) mechanisms, have demonstrated utility in image-guided percutaneous needle placement procedures. However, widespread clinical application of these robots is hindered by not only complicated mechanical design but also the need for calibration and registration of the robot to the medical imager prior to each use. In response, we propose a Virtual RCM algorithm that requires only online tracking or registering the surgical tool to the imager, and a five degree-of-freedom (DOF) robot comprised of three prismatic DOF decoupled from two rotational DOF. The robot can be unencoded, uncalibrated, and does not require pre-operative registration. An incremental adaptive motion control cycle both guides the needle to the insertion point and orients it to align with the target. The robot executes RCM motion “virtually” without having a physically constrained fulcrum point. The proof-of-concept prototype system achieved 0.78 mm translation and 1.4 degrees rotational accuracy (within the tracker accuracy), within 17 iterative steps (0.5–1s).


title: Texture descriptors based on co-occurrence matrices

abstract: This paper focuses on the problem of texture classification using statistical descriptors based on the co-occurrence matrices. A major part of the paper is dedicated to the derivation of a general model for analysis and interpretation of experimental results in texture analysis when individual and groups of classifiers are being used, and a technique for evaluating their performance. Using six representative classifiers; that is, second angular moment f1, contrast f2, inverse difference moment f5, entropy f9 , and information measures of correlation I and II, f12 and f13 , we give a systematic study of the discrimination power of all 63 combination of these classifiers on 13 samples of Brodatz textures. The conclusion that can be drawn from our study is that it is useful to combine classifiers up to a certain order. Here it turned out that groups of four classifiers are optimal.


title: A Fast Semantic Web Services Matchmaker for OWL-S Services

abstract: Bringing semantics to Web Services description and matching remarkably improves the recall and precision ratios of Service discovery. Though there are many effective semantic based Web Services matchmakers, they have poor time efficiency for consuming time in the semantic reasoning step. To shorten service matching time, this paper presents a fast matchmaker for OWL-S services, called XServices Semantic Service Discovery (XSSD). It executes the Semantics Pretreatment Algorithm (SPA) in service publishing phase and implements a multi-feature based Semantic Web services matching method when queries coming. SPA extracts part of semantic description in the OWL-S files and creates matrixes which we call service feature matrixes to express service semantics. By shifting part of the reasoning process from service matching stage to service publishing stage, XSSD reduces the waiting time for the matchmaker clients. The results of the evaluation on a well-annotated Semantic Web Services set OWLS-TC4 show that while XSSD offers the comparable recall and precision ratios, the time efficiency of XSSD outperforms existing similar matchmakers.


title: Sanguinarine, inhibitor of Na-K dependent ATP'ase.

abstract: Sanguinarine, a benzophenanthridine alkaloid, has been found to be an inhibitor of the Na+, K+-ATPase isolated from guinea pig brain. It has a pI50 of 5.25 (5.62 μM) and exhibits uncompetitive kinetics with respect to [Na+] and [K+] and is non-competitive with respect to [ATP]. Preincubation with sanguinarine causes increased inhibition. ATP partially protects the enzyme against this preincubation effect, while Na+ protects to a lesser extent and K+ has no effect.


Development of a Mobile Robot Test Platform and Methods for Validation of Prognostics-Enabled Decision Making Algorithms


title: A 2.45-GHz low cost and efficient rectenna

abstract: This paper reports a novel rectifying antenna (rectenna) based on modified bridge rectifier with four Schottky diodes. The 2.45-GHz microwave rectifier has been developed and optimized using a global simulation technique which associates electromagnetic and circuit approaches in order to accurately predict the rectifier performances. The presented device doesn't need neither input HF filter nor bypass capacitor. This makes the structure more compact and low cost. A 2.45GHz linearly polarized patch antenna has been designed and associated to the microwave rectifier to obtain the full rectenna. The rectifier achieves an RF-to-dc conversion efficiency of 61% at 10 mW input power. When the power density is 0.15 mW/cm², the full rectenna circuit shows an efficiency of 52% over an optimal resistive load of 1050Ω.


title: REAL TIME WIRELESS AIR POLLUTION MONITORING SYSTEM

abstract: Air pollution has ::: significant influence on the concentration of ::: constituents in the atmosphere leading to effects like global warming ::: and acid rains. To avoid such adverse imbalances in the nature, an ::: air pollution monitoring system is utmost important. This paper ::: attempts ::: to develop an effective solution for pollution monitoring ::: using wireless sensor networks (WSN) on a real time basis namely ::: real time wireless air pollution monitoring system. Commercially ::: available discrete gas sensors for sensing concentration of gases l ::: ike ::: CO ::: 2 ::: , NO ::: 2, ::: CO and O ::: 2 ::: are calibrated using appropriate calibration ::: technologies. These pre ::: - ::: calibrated gas sensors are then integrated ::: with the wireless sensor motes for field deployment at the campus and ::: the Hyderabad city using multi hop data aggregatio ::: n algorithm. A ::: light weight middleware and a web interface to view the live pollution ::: data in the form of numbers and charts from the test beds was ::: developed and made available from anywhere on the internet. Other ::: parameters like temperature and humidity w ::: ere also sensed along ::: with gas concentrations to enable data analysis through data fusion ::: techniques. Experimentation carried out using the developed wireless ::: air pollution monitoring system under different physical conditions ::: show that the system collects ::: reliable source of real time fine ::: - ::: grain ::: pollution data.


title: * Manuscript Differences in Effect of Robot and Screen Agent Recommendations on Human Decision-Making

abstract: This paper compares the effect of a robot's and on-screen agent's recommendations on human decision-making using a quantitative evaluation method. We are interested in whether a robot's physical body produces some differences in the effect or not. Previous research investigated the advantage of a physical body; however, the advantage was not clarified quantitatively and there was not enough evidence to give the results credibility. Our method based on quantitative evaluation clarifies the effect of a robot's and on-screen agent's behavior on user decision-making. Comparing a robot's behavior with an on-screen agent's, we show that the degree of the effect firmly depends on the interaction environment and that geometrical consistency between the interaction environment and embodied social agents (ESAs), which include robots and on-screen agents, is important in the recommendation situation.


title: MonoSLAM: Real-Time Single Camera SLAM

abstract: We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to structure from motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera


title: Fuzzy expert system approach for coronary artery disease screening using clinical parameters

abstract: Coronary artery disease (CAD) affects millions of people all over the world including a major portion in India every year. Although much progress has been done in medical science, but the early detection of this disease is still a challenge for prevention. The objective of this paper is to describe developing of a screening expert system that will help to detect CAD at an early stage. Rules were formulated from the doctors and fuzzy expert system approach was taken to cope with uncertainty present in medical domain. This work describes the risk factors responsible for CAD, knowledge acquisition and knowledge representation techniques, method of rule organisation, fuzzification of clinical parameters and defuzzification of fuzzy output to crisp value. The system implementation is done using object oriented analysis and design. The proposed methodology is developed to assist the medical practitioners in predicting the patient's risk status of CAD from rules provided by medical experts. The present paper focuses on rule organisation using the concept of modules, meta-rule base, rule address storage in tree representation and rule consistency checking for efficient search of large number of rules in rule base. The developed system leads to 95.85% sensitivity and 83.33% specificity in CAD risk computation.


Improving Wellness through Fighting Overweight: Towards an Efficient Social Strategy


title: Efficient lazy elimination for averaged one-dependence estimators

abstract: Semi-naive Bayesian classifiers seek to retain the numerous strengths of naive Bayes while reducing error by relaxing the attribute independence assumption. Backwards Sequential Elimination (BSE) is a wrapper technique for attribute elimination that has proved effective at this task. We explore a new technique, Lazy Elimination (LE), which eliminates highly related attribute-values at classification time without the computational overheads inherent in wrapper techniques. We analyze the effect of LE and BSE on a state-of-the-art semi-naive Bayesian algorithm Averaged One-Dependence Estimators (AODE). Our experiments show that LE significantly reduces bias and error without undue computation, while BSE significantly reduces bias but not error, with high training time complexity. In the context of AODE, LE has a significant advantage over BSE in both computational efficiency and error.


title: Self-Organizing Quantum Neural Network

abstract: This paper combines quantum computation with conventional artificial neural network theory to present a novel network - self-organizing quantum neural network (SOQNN) that can perform pattern classification self-organizationally and automatically through quantum competitive process. It does not need to prestore some given patterns but can classify input patterns with higher classification rapidity than that of classical neural networks(CNN).Further, SOQNN make the analogical pattern win easily in the next quantum competitive process by modifying the value of the quantum register(QR) corresponding to the pattern classified.


title: Two-level volume rendering

abstract: Presents a two-level approach for volume rendering, which allows for selectively using different rendering techniques for different subsets of a 3D data set. Different structures within the data set are rendered locally on an object-by-object basis by either direct volume rendering (DVR), maximum-intensity projection (MIP), surface rendering, value integration (X-ray-like images) or non-photorealistic rendering (NPR). All the results of subsequent object renderings are combined globally in a merging step (usually compositing in our case). This allows us to selectively choose the most suitable technique for depicting each object within the data while keeping the amount of information contained in the image at a reasonable level. This is especially useful when inner structures should be visualized together with semi-transparent outer parts, similar to the focus+context approach known from information visualization. We also present an implementation of our approach which allows us to explore volumetric data using two-level rendering at interactive frame rates.


title: Self-ensembling for visual domain adaptation

abstract: This paper explores the use of self-ensembling for visual domain adaptation problems. Our technique is derived from the mean teacher variant (Tarvainen et al., 2017) of temporal ensembling (Laine et al;, 2017), a technique that achieved state of the art results in the area of semi-supervised learning. We introduce a number of modifications to their approach for challenging domain adaptation scenarios and evaluate its effectiveness. Our approach achieves state of the art results in a variety of benchmarks, including our winning entry in the VISDA-2017 visual domain adaptation challenge. In small image benchmarks, our algorithm not only outperforms prior art, but can also achieve accuracy that is close to that of a classifier trained in a supervised fashion.


title: A Facile and Green Synthesis Route to Mesoporous Spinel-type Zn−Al Complex Oxide

abstract: A facile and green synthesis route to a mesoporous solid solution of Zn−Al spinel-type complex oxide (ZnAl2O4) without using organic template has been successfully established. This approach involves the formation of precursor utilizing urea hydrolysis under hydrothermal conditions followed by calcination at high temperatures. The formation of precursor with a wormholelike micro−mesoporous framework is a result of concurrent urea hydrolysis and homogeneous coprecipitation induced spontaneously under hydrothermal conditions. The following calcination at higher temperatures give ZnAl2O4 crystallines with a lathlike mesoporous framework. The kind of structural transformation has been confirmed by X-ray diffraction (XRD) measurements, thermogravimetric analysis (TGA), 27Al solid-state magic-angle spinning (MAS) NMR, electron microscope (TEM/SEM) observations, and low-temperature N2 adsorption−desorption experiments. By changing the synthesis parameters, the structural and textural properties of mesoporous ZnA...


title: When diagnosing ADHD in young adults emphasize informant reports, DSM items, and impairment.

abstract: It is clear that when children with attention-deficit/hyperactivity disorder (ADHD) become adults, many continue to display manifestations of inattention, hyperactivity, and impulsivity (Barkley, Fischer, Smallish, & Fletcher, 2006; Mannuzza, Gittelman-Klein, Bessler, Malloy, & LaPadula, 1993; Weiss & Hechtman, 1993). As such, ADHD in adults is characterized by a range of impairments in daily life functioning (Barkley, Murphy, & Fischer, 2008). For example, compared with their peers, young adults who were diagnosed with ADHD in childhood are far less likely to pursue higher education, hold a steady job, responsibly manage their finances, and maintain adaptive social relationships (Barkley et al., 2006, 2008; Weiss & Hechtman (1993). These individuals also are more likely to experience dangerous problems with driving, risky sexual behavior, substance abuse, intimate partner violence, and criminal behavior (Barkley et al., 2008; Derefinko & Pelham, in press; Flory, Molina, Pelham, Gnagy, & Smith, 2006; Mannuzza, Klein, & Moulton, 2008; Thompson, Molina, Pelham, & Gnagy, 2007; Weiss & Hechtman, 1993; Wymbs et al., in press). Despite these poor outcomes, ADHD in adulthood is poorly defined and somewhat controversial (Barkley, 2006). A major reason for this confusion is that the field lacks clear evidence-based methods for identifying ADHD in adulthood. ::: ::: Studies applying strictly interpretation of Diagnostic and Statistical Manuel of Mental Disorders (DSM; American Psychiatric Association, 1980–2000)) diagnostic criteria report low to moderate persistence rates (4%-42%; Barkley, Fischer, Smallish, & Fletcher, 2002; Kessler et al., 2005; Mannuzza, Klein, Bessler, Malloy, & LaPadula, 1998; Mannuzza, Klein, & Moulton, 2003). Therefore, experts initially believed that ADHD tends to remit after adolescence (Barkley et al., 2002; Mannuzza et al., 1998). However, other studies report higher persistence rates (49%–66%) by defining diagnostic threshold according to the presence of significant impairment or elevated symptomatology (as compared with control-group norms; Barkley et al., 2002; Weiss & Hechtman, 1993). These data suggest that, as adults, individuals with childhood ADHD display the core symptoms of the disorder and serious dysfunction (Biederman, Mick, & Faraone, 2000) but meet criteria for fewer of the DSM ADHD symptoms than children. ::: ::: Recent work on ADHD in adults aims to better characterize its symptom expression. However, the typical adult with ADHD may not be well represented in these samples. Namely, most diagnostic research involves self-identified clinical samples of adults with ADHD (Barkley et al., 2008; Biederman et al., 2006). Typically, up to 50% of adults in these samples are women (e.g., Biederman et al., 2006), and sample participants often are not required to possess a childhood history of significant ADHD-related impairment (e.g., Barkley et al., 2008), making their composition different from samples identified in childhood, who possess a standard ADHD diagnosis. Namely, adults with a lifetime history of ADHD tend to underestimate their problems (Barkley et al., 2002), rarely present for ADHD treatment in adulthood, and therefore are unlikely to be included in adult-referred clinical samples. As a result, confusion over the expression of ADHD in adulthood may stem from research with somewhat unrepresentative samples. ::: ::: Beyond “who” is included in the aforementioned samples, correct characterization of ADHD in adulthood hinges upon “how” information about these individuals is obtained. As mentioned previously, the typical adult with a childhood history of ADHD tends to dramatically underreport his or her own problems (Barkley et al., 2002; Sibley et al., 2010). Yet, most research with adult-diagnosed samples relies solely on self-report, which only appears valid for these self-referred individuals (Barkley, Knouse, & Murphy, 2011). Specifically, reports from informants such as parents (Barkley et al., 2002), siblings (Loney, Ledolter, Kramer, & Volpe, 2007), and other adults (Barkley et al., 2008) appear to offer more valid ratings of adults with an established childhood history of ADHD. Furthermore, most adult-diagnosed samples either do not require the DSM “B” criterion be met (ADHD symptoms in childhood; Barkley et al., 2008; Biederman et al., 2006) or rely solely on retrospective self-report to assess childhood functioning (Faraone et al., 2006; Kessler et al., 2010). Research is mixed with regard to the ability of adults with ADHD to provide accurate retrospective report of their childhood functioning (Mannuzza, Klein, Klein, Bessler, & Shrout, 2002; Miller, Newcorn, & Halpern, 2010). Therefore, further work is needed in this area. ::: ::: The recent studies with adult-diagnosed samples universally suggest that the DSM needs new developmentally appropriate items for ADHD in adulthood (Barkley et al., 2008; Faraone, Biederman, & Spencer, 2010; Kessler et al., 2010). Undoubtedly, some of the DSM–IV (DSM–4th ed., American Psychiatric Association, 1994) symptoms are inappropriate descriptors of adults (e.g., difficulty playing quietly, inappropriate running and/or climbing). A string of studies asserts that combining adult-specific items with several developmentally ubiquitous ones (e.g., easily distracted, difficulty organizing tasks, difficulty sustaining attention) creates an adult-ADHD algorithm that improves upon the DSM's diagnostic utility (Barkley et al., 2008; Faraone et al., 2010; Fedele, Hartung, Canu, & Wilkowski, 2010; Kessler et al., 2010). However, these findings are limited by factors discussed previously, namely, the use of adult-referred samples and self-report information. Consequently, there are now several recommended sets of adult-specific ADHD items that possess very little overlap with each other (Barkley et al., 2008; Faraone et al., 2010; Kessler et al., 2010; Wender, 1985). To date, adult-specific ADHD items have not been examined using a sample of adults with established ADHD in childhood that provides both self- and informant-report of functioning. Using these methods might elucidate the expression of ADHD in adulthood and the utility of adult-specific items. ::: ::: In sum, further work is needed to understand and standardize the diagnosis of ADHD in adulthood. Thus, in the current study, we aimed to develop recommendations for an adult-ADHD diagnostic protocol by examining the symptoms and functioning of young adults in the Pittsburgh ADHD Longitudinal Study (PALS; Molina, Pelham, Gnagy, Thompson, & Marshal, 2007). The PALS includes a sample of young adults who were well diagnosed with ADHD in childhood using standard DSM criteria applied in a specialty clinic setting. We first compared estimates of ADHD persistence into young adulthood by examining rates of DSM–IV–TR (4th ed., text rev.; American Psychiatric Association, 2000) diagnosis (A criteria), elevated ADHD symptomatology, and clinically significant functional impairment. We hypothesized that a majority of the sample would continue to display elevated ADHD symptomatology and clinically significant functional impairment in young adulthood, but that fewer would meet DSM criteria for ADHD. With regard to the utility of informant report, we hypothesized that young adults with ADHD would underreport their current and childhood symptomatology and that parent report alone would be the most useful method of assessing ADHD. Next, we evaluated the performance of adult-specific item sets posited by several research teams (Barkley et al., 2008; Faraone et al., 2010; Kessler et al., 2010) relative to item sets based on DSM-IV criteria. To do so, we compared symptom endorsement rates, parent- and self-report agreement, and convergent validity for each of these item sets within the PALS ADHD and non-ADHD control groups. We hypothesized that across these indices, the adult-specific items would possess greater diagnostic utility than the DSM items.


title: 60-GHz Air Substrate Leaky-Wave Antenna Based on MEMS Micromachining Technology

abstract: In this paper, a 60-GHz air substrate leaky-wave antenna based on microelectromechanical system (MEMS) bulk micromachining is proposed. Using penetration dry etching and gold plating processes, the air-filled leaky-wave slots, waveguide, and coupling slot, which are located in the top, middle, and bottom silicon-based layers, respectively, are generated. The three layers are stacked and clamped together using two location pins and five metal screws. The most distinctive feature of the proposed antenna is that the electromagnetic wave cannot see any substrates, except for the air medium, leading to good antenna performance. Good agreement between simulation and measurement demonstrates that the MEMS micromachining process fulfills the millimeter-wave (mm-wave) accuracy. To the best of the authors’ knowledge, this is the first time that an air substrate mm-wave antenna is fabricated using the MEMS bulk micromachining technology. The fabricated prototype with a bandwidth of 17.3% and a peak gain of 11.4 dBi is attractive for mm-wave wireless communication applications. Validity of the feasibility provides the possibility to realize the system-in-package solutions.


title: Wireless communications from high altitude platforms: Applications, deployment and development

abstract: In this paper, an overview of the HAP concept development and HAP trails is introduced to show the worldwide interest and development in the emerging novel technology. A comparison of the HAP, terrestrial and satellite system characteristics is given. Main advantages of HAPs for wireless communication applications are large coverage area, high capacity and cost-effective deployment. Three applicable scenarios of HAP communication are shown.


title: Scrap your boilerplate: a practical design pattern for generic programming

abstract: textabstractWe describe a design pattern for writing programs that traverse data structures built from rich mutually-recursive data types. Such programs often have a great deal of 'boilerplate' code that simply walks the structure, hiding a small amount of 'real' code that constitutes the reason for the traversal. Our technique allows most of this boilerplate to be written once and for all, or even generated mechanically, leaving the programmer free to concentrate on the important part of the algorithm. These generic programs are much more adaptive when faced with data structure evolution because they contain many fewer lines of type-specific code. Our approach is simple to understand, reasonably efficient, and it handles all the data types found in conventional functional programming languages. It makes essential use of rank-2 polymorphism, an extension found in some implementations of Haskell. Further it relies on a simple type-safe cast operator.


Religious beliefs and climate change adaptation: A study of three rural South African communities


title: Can electrocardiogram classification be applied to phonocardiogram data? — An analysis using recurrent neural networks

abstract: Both a Phonocardiogram (PCG) and an Electrocardiogram (ECG) are sequential measurements of heart activity used to distinguish normal from abnormal heart function. Although they measure different physical quantities, we show that training a long short-term memory network on the Physionet challenge using only the ECG data available for the MIT heart sounds database still yields a score of 0.74 compared to the reference score of 0.82 for a similar net trained on the PCG data. This finding suggests that it may be valuable to train a transformational neural network to produce an artificial ECG from a PCG. Such a transformational net would allow to harness the know-how of decades of research on ECG classification to improve PCG classification. Unfortunately, this task seems too hard for current state-of-the art architectures for neural networks given the data of the Physionet challenge 2016. However, it may be worthwhile to further pursue this approach using data with less variance in the ECG signals or a specialized network architecture.


title: KnowMe and ShareMe: understanding automatically discovered personality traits from social media and user sharing preferences

abstract: There is much recent work on using the digital footprints left by people on social media to predict personal traits and gain a deeper understanding of individuals. Due to the veracity of social media, imperfections in prediction algorithms, and the sensitive nature of one's personal traits, much research is still needed to better understand the effectiveness of this line of work, including users' preferences of sharing their computationally derived traits. In this paper, we report a two- part study involving 256 participants, which (1) examines the feasibility and effectiveness of automatically deriving three types of personality traits from Twitter, including Big 5 personality, basic human values, and fundamental needs, and (2) investigates users' opinions of using and sharing these traits. Our findings show there is a potential feasibility of automatically deriving one's personality traits from social media with various factors impacting the accuracy of models. The results also indicate over 61.5% users are willing to share their derived traits in the workplace and that a number of factors significantly influence their sharing preferences. Since our findings demonstrate the feasibility of automatically inferring a user's personal traits from social media, we discuss their implications for designing a new generation of privacy-preserving, hyper-personalized systems.


THE THEORY OF USER ACCEPTANCE AND USE OF TECHNOLOGY ( UTAUT ) : A META-ANALYTIC REVIEW OF EMPIRICAL FINDINGS 1


title: Infants of depressed mothers show "depressed" behavior even with nondepressed adults.

abstract: To determine whether the "depressed" behavior (e.g., less positive affect and lower activity level) of infants noted during interactions with their "depressed" mothers generalizes to their interactions with nondepressed adults, 74 3-6-month-old infants of "depressed" and nondepressed mothers were videotaped in face-to-face interactions with their mothers and with nondepressed female strangers. "Depressed" mothers and their infants received lower ratings on all behaviors than nondepressed mothers and infants. Although the infants of "depressed" versus nondepressed mothers also received lower ratings with the stranger adult, very few differences were noted between those infants' ratings when interacting with their mother versus the stranger, suggesting that their "depressed" style of interacting is not specific to their interactions with depressed mothers but generalizes to their interactions with nondepressed adults as early as 3 months of age.


title: PET and PVC Separation with Hyperspectral Imagery

abstract: Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density). Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers—polyethylene terephthalate (PET) and polyvinyl chloride (PVC)—in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials) to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900–1700 nm) reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry.


title: Principles of Persuasion in Social Engineering and Their Use in Phishing

abstract: Research on marketing and deception has identified principles of persuasion that influence human decisions. However, this research is scattered: it focuses on specific contexts and produces different taxonomies. In regard to frauds and scams, three taxonomies are often referred in the literature: Cialdini's principles of influence, Gragg's psychological triggers, and Stajano et al. principles of scams. It is unclear whether these relate but clearly some of their principles seem overlapping whereas others look complementary. We propose a way to connect those principles and present a merged and reviewed list for them. Then, we analyse various phishing emails and show that our principles are used therein in specific combinations. Our analysis of phishing is based on peer review and further research is needed to make it automatic, but the approach we follow, together with principles we propose, can be applied more consistently and more comprehensively than the original taxonomies.


title: Computer Graphics and Geometric Ornamental Design

abstract: Throughout history, geometric patterns have formed an important part of art and ornamental design. Today we have unprecedented ability to understand ornamental styles of the past, to recreate traditional designs, and to innovate with new interpretations of old styles and with new styles altogether. ::: The power to further the study and practice of ornament stems from three sources. We have new mathematical tools: a modern conception of geometry that enables us to describe with precision what designers of the past could only hint at. We have new algorithmic tools: computers and the abstract mathematical processing they enable allow us to perform calculations that were intractable in previous generations. Finally, we have technological tools: manufacturing devices that can turn a synthetic description provided by a computer into a real-world artifact. Taken together, these three sets of tools provide new opportunities for the application of computers to the analysis and creation of ornament. ::: In this dissertation, I present my research in the area of computer-generated geometric art and ornament. I focus on two projects in particular. First I develop a collection of tools and methods for producing traditional Islamic star patterns. Then I examine the tesselations of M. C. Escher, developing an “Escherization” algorithm that can derive novel Escher-like tesselations of the plane from arbitrary user-supplied shapes. Throughout, I show how modern mathematics, algorithms, and technology can be applied to the study of these ornamental styles.


title: Investigating cyber-physical attacks against IEC 61850 photovoltaic inverter installations

abstract: Cyber-attacks against Smart Grids have been found in the real world. Malware such as Havex and BlackEnergy have been found targeting industrial control systems (ICS) and researchers have shown that cyber-attacks can exploit vulnerabilities in widely used Smart Grid communication standards. This paper addresses a deep investigation of attacks against the manufacturing message specification of IEC 61850, which is expected to become one of the most widely used communication services in Smart Grids. We investigate how an attacker can build a custom tool to execute man-in-the-middle attacks, manipulate data, and affect the physical system. Attack capabilities are demonstrated based on NESCOR scenarios to make it possible to thoroughly test these scenarios in a real system. The goal is to help understand the potential for such attacks, and to aid the development and testing of cyber security solutions. An attack use-case is presented that focuses on the standard for power utility automation, IEC 61850 in the context of inverter-based distributed energy resource devices; especially photovoltaics (PV) generators.


title: Constructing risk measures from uncertainty sets

abstract: We illustrate the correspondence between uncertainty sets in robust optimization and some popular risk measures in finance and show how robust optimization can be used to generalize the concepts of these risk measures. We also show that by using properly defined uncertainty sets in robust optimization models, one can construct coherent risk measures and address the issue of the computational tractability of the resulting formulations. Our results have implications for efficient portfolio optimization under different measures of risk.


The Transferable Belief Model


title: IoT Security: Ongoing Challenges and Research Opportunities

abstract: The Internet of Things (IoT) opens opportunities for wearable devices, home appliances, and software to share and communicate information on the Internet. Given that the shared data contains a large amount of private information, preserving information security on the shared data is an important issue that cannot be neglected. In this paper, we begin with general information security background of IoT and continue on with information security related challenges that IoT will encountered. Finally, we will also point out research directions that could be the future work for the solutions to the security challenges that IoT encounters.


title: Deep Graph Laplacian Regularization for Robust Denoising of Real Images

abstract: Recent developments in deep learning have revolutionized the paradigm of image restoration. However, its applications on real image denoising are still limited, due to its sensitivity to training data and the complex nature of real image noise. In this work, we combine the robustness merit of model-based approaches and the learning power of data-driven approaches for real image denoising. Specifically, by integrating graph Laplacian regularization as a trainable module into a deep learning framework, we are less susceptible to overfitting than pure CNN-based approaches, achieving higher robustness to small datasets and cross-domain denoising. First, a sparse neighborhood graph is built from the output of a convolutional neural network (CNN). Then the image is restored by solving an unconstrained quadratic programming problem, using a corresponding graph Laplacian regularizer as a prior term. The proposed restoration pipeline is fully differentiable and hence can be end-to-end trained. Experimental results demonstrate that our work is less prone to overfitting given small training data. It is also endowed with strong cross-domain generalization power, outperforming the state-of-the-art approaches by a remarkable margin.


title: Challenges and opportunities: from big data to knowledge in AI 2.0

abstract: In this paper, we review recent emerging theoretical and technological advances of artificial intelligence (AI) in the big data settings. We conclude that integrating data-driven machine learning with human knowledge (common priors or implicit intuitions) can effectively lead to explainable, robust, and general AI, as follows: from shallow computation to deep neural reasoning; from merely data-driven model to data-driven with structured logic rules models; from task-oriented (domain-specific) intelligence (adherence to explicit instructions) to artificial general intelligence in a general context (the capability to learn from experience). Motivated by such endeavors, the next generation of AI, namely AI 2.0, is positioned to reinvent computing itself, to transform big data into structured knowledge, and to enable better decision-making for our society.


title: The self to other model of empathy: Providing a new framework for understanding empathy impairments in psychopathy, autism, and alexithymia

abstract: Abstract Despite increasing empirical and theoretical work on empathy, particularly on the content of empathic representations, there is a relative lack of consensus regarding the information processing necessary for empathy to occur. Here we attempt to delineate a mechanistic cognitive model of empathy in order to provide a framework within which neuroimaging work on empathy can be located, and which may be used in order to understand various disorders characterised by atypical levels of empathy. To this end data from individuals with psychopathy, autism, and alexithymia inform the model, and the model is used to provide a unifying framework for any empathy impairments seen in these disorders. The model adopts a developmental framework and tries to address the four difficult questions of empathy: How do we know what another is feeling? What is the role of theory of mind in empathy? How does the state of another cause a corresponding state in the self? How do we represent another's emotion once emotional contagion has taken place?


title: Maximum-likelihood expectation-maximization reconstruction of sinograms with arbitrary noise distribution using NEC-transformations

abstract: The maximum-likelihood (ML) expectation-maximization (EM) [ML-EM] algorithm is being widely used for image reconstruction in positron emission tomography. The algorithm is strictly valid if the data are Poisson distributed. However, it is also often applied to processed sinograms that do not meet this requirement. This may sometimes lead to suboptimal results: streak artifacts appear and the algorithm converges toward a lower likelihood value. As a remedy, the authors propose two simple pixel-by-pixel methods [noise equivalent counts (NEC)-scaling and NEC-shifting] in order to transform arbitrary sinogram noise into noise which is approximately Poisson distributed (the first and second moments of the distribution match those of the Poisson distribution). The convergence speed associated with both transformation methods is compared, and the NEC-scaling method is validated with both simulations and clinical data. These new methods extend the ML-EM algorithm to a general purpose nonnegative reconstruction algorithm.


title: Gold-solid polymer electrolyte sensor for detecting dissolved oxygen in water

abstract: Abstract The gold-solid polymer electrolyte (Au-SPE) sensor for detecting dissolved oxygen (DO) in water was studied. The results indicated that the prepared Au-SPE sensor has a good performance in sensing DO in water. The preparation of electrode was optimized to obtain the best sensitivity. The potential windows of DO reduction on Au/Nafion® electrode in aqueous solutions were determined. The interference of NH 4 + , NO 3 − and Cl − were also examined. The Au-SPE sensor shows a high selectivity for DO detection.


title: Emotional modulation of attention affects time perception: evidence from event-related potentials

abstract: Abstract Emotional effects on human time perception are generally attributed to arousal speeding up or slowing down the internal clock. The aim of the present study is to investigate the less frequently considered role of attention as an alternative mediator of these effects with the help of event-related potentials (ERPs). Participants produced short intervals (0.9, 1.5, 2.7, and 3.3 s) while viewing high arousal images with pleasant and unpleasant contents in comparison to neutral images. Behavioral results revealed that durations were overproduced for the 0.9 s interval whereas, for 2.7 and 3.3 s intervals, underproduction was observed. The effect of affective valence was present for the shorter durations and decreased as the target intervals became longer. More specifically, the durations for unpleasant images were less overproduced in the 0.9 s intervals, and for the 1.5 s trials, durations for unpleasant images were slightly underproduced, compared to pleasant images, which were overproduced. The analysis of different ERP components suggests possible attention processes related to the timing of affective images in addition to changes in pacemaker speed. Early Posterior Negativity (EPN) was larger for positive than for negative images, indicating valence-specific differences in activation of early attention mechanisms. Within the early P1 and the Late Positive Potential (LPP) components, both pleasant and unpleasant stimuli exhibited equal affective modulation. Contingent Negative Variation (CNV) remained independent of both timing performance and affective modulation. This pattern suggests that both pleasant and unpleasant stimuli enhanced arousal and captured attention, but the latter effect was more pronounced for pleasant stimuli. The valence-specificity of affective attention revealed by ERPs combined with behavioral timing results suggests that attention processes indeed contribute to emotion-induced temporal distortions, especially for longer target intervals.


title: Composition, Host Range and Host Suitability of Vegetable-Infesting Tephritids on Cucurbits Cultivated in Kenya

abstract: Damage incidence and interactions between native and invasive vegetable-infesting fruit flies (Diptera: Tephritidae) on high-value crops have rarely been studied, despite their threats to the horticultural industries in Africa and quarantine implications.We established the composition and host range of tephritid flies in the field and carried out comparative host suitability studies of major fruit flies: Dacus bivittatus (Bigot), Dacus ciliatus Loew and Zeugodacus cucurbitae (Coquillet) on watermelon [Citrullus lanatus (Thunb.)], cucumber (Cucumis sativus L.), courgette (Cucurbita pepo L.) and butternut (Cucurbita moschata Duchesne) in the laboratory. Five new host species were recorded. Zeugodacus cucurbitae was the most destructive fruit fly, infesting 17 hosts from five families, followed by D. ciliatus and D. bivittatus. Direct field damage inflicted on immature to mature crops on the plants ranged from 2.1%on eggplant (Solanum melongena L.) to 66.8%on bitter gourd (Momordica charantia L.). The most preferred hosts of Z. cucurbitae were bitter gourd, watermelon and tomato (Solanum lycopersicum L.) (16.2, 13.3 and 12.1 flies/kg fruit, respectively). Mango (Mangifera indica L.) (24.1 flies/kg fruit) and guava (Psidium guajava L.) (14.7 flies/kg fruit) were recorded for the first time as hosts of D. ciliatus. The relative abundance index showed high variation among host species, suggesting a differential use of these hosts by the different fruit fly species. Choice versus no-choice experiments revealed a significant variation in pupal recovery, pupal size, adult emergence, percentage females, adult wing deformity and body size. These results are significant for the decision-making process for effective monitoring and management of fruit fly species on cucurbitaceous vegetable crops.


Towards embedded control for resonant scanning MEMS micromirror


Bearing condition monitoring based on shock pulse method and improved redundant lifting scheme


title: Trajectories of science and technology and their co-evolution in BRICS: Insights from publication and patent analysis

abstract: By empirical demonstration, this study extends the assessments of BRICS countries (Brazil, Russia, India, China and South Africa) in performing science and technology in previous studies by exploring their cumulative patterns of science and technology (proxied by publications and patents respectively). Projections of cumulative production in science and technology are made using logistic growth function. Our analyses show that – though having different growth trajectories in science production – the BRICS countries exhibit similar patterns in pursuing technology. This embodies the strong commitment of BRICS to improve their technological capabilities in the process of industrial development. Inspired by the Relative Impact Index (RII) proposed by Nesta and Patel, we propose the Relative Science Impact Index (RSII) to evaluate the relative impact of science and technology on the process of technological catching-up in emerging economies and examine the co-evolution between science-based patents and patent citations. Our correlation analysis between forward citation and RSII marks some distinctive pursuits of BRICS countries in science-based patenting activities.


title: Dry anaerobic digestion of food waste and cardboard at different substrate loads, solid contents and co-digestion proportions.

abstract: The increasing food waste production calls for developing efficient technologies for its treatment. Anaerobic processes provide an effective waste valorization. The influence of the initial substrate load on the performance of batch dry anaerobic co-digestion reactors treating food waste and cardboard was investigated. The load was varied by modifying the substrate to inoculum ratio (S/X), the total solids content and the co-digestion proportions. The results showed that the S/X was a crucial parameter. Within the tested values (0.25, 1 and 4gVS·gVS-1), only the reactors working at 0.25 produced methane. Methanosarcina was the main archaea, indicating its importance for efficient methanogenesis. Acidogenic fermentation was predominant at higher S/X, producing hydrogen and other metabolites. Higher substrate conversions (≤48%) and hydrogen yields (≤62mL·gVS-1) were achieved at low loads. This study suggests that different value-added compounds can be produced in dry conditions, with the initial substrate load as easy-to-control operational parameter.


title: A ranking-based approach for hierarchical classification

abstract: With the growth of information organized in hierarchical databases, it is essential to develop automated approaches for classifying data instances (e.g., documents, proteins and images) into hierarchies. Several classification approaches have been developed that exploit the hierarchical structure prevalent within these underlying databases. One commonly used approach is to train local one-versus-rest classifiers for each of the nodes within the hierarchy and then make a prediction using a combination of these several trained classifiers. In this paper, we develop a hierarchical classification approach that utilizes a rank-based loss function to differentiate between the examples from different nodes across the hierarchies. The intuition behind our approach is that it allows for the training examples to have an ordering based on their class membership within the hierarchy. We also add regularization constraints that force the learned weight vectors associated with the parent and children nodes within the hierarchy to be similar to each other. To further improve the model performance, we extend our formulation to include a hierarchy based cost-sensitive loss during training. To ensure the scalability of our approach to datasets with large number of classes, instances and feature space, we implemented a distributed map-reduce based algorithm for training the different per-node classifiers. Our empirical results on a diverse set of image and text hierarchical databases shows an improved performance with the use of rank-based models in comparison to other baseline approaches.


title: Cancer-Related Fatigue: Scientific Progress Has Been Made in 40 Years

abstract: Cancer-related fatigue (CRF) is a distressing, persistent symptom that is experienced by survivors during and after treatment. Unsurprisingly, many early CRF studies were conducted by nurses. These studies included a look at patients receiving localized radiation treatment (Haylock & Hart, 1979); an exploration of fatigue as a conceptual approach to a clinical problem (Aistars, 1987); the development of a nursing theory focused on fatigue mechanisms (Piper, Lindsey, & Dodd, 1987); an examination of fatigue mechanisms (St Pierre, Kasper, & Lindsey, 1992), as well as of fatigue in advanced cancer (Bruera & MacDonald, 1988) and in non-small cell lung cancer (Sarna, 1993); and a description of fatigue and potential nursing interventions (Nail & King, 1987). Winningham et al. (1994) wrote a state-of-the-science article about fatigue in the cancer experience for the Oncology Nursing Forum, and Mock et al. (1997) was one of the first to conduct an exercise study regarding the effects of exercise on fatigue, physical functioning, and emotional distress during radiation therapy for breast cancer. Nurse scholars from the 1970s-2000s were pivotal in advancing the science of fatigue in various cancers and have provided a scientific foundation for those four decades. .


The Effect of Celebrity Endorsement on Creating Brand Loyalty : An Application on the Lebanese Cosmetic Sector ’ s Demand


title: Review TRENDS in Cognitive Sciences Vol.8 No.1 January 2004 The cognitive foundations of cultural stability and diversity

abstract: The existence and diversity of human cultures are made possible by our species-specific cognitive capacities. But how? Do cultures emerge and diverge as a result of the deployment, over generations and in different populations, of general abilities to learn, imitate and communicate? What role if any do domain-specific evolved cognitive abilities play in the emergence and evolution of cultures? These questions have been approached from different vantage points in different disciplines. Here we present a view that is currently developing out of the converging work of developmental psychologists, evolutionary psychologists and cognitive anthropologists.


title: Tensor decompositions for feature extraction and classification of high dimensional datasets

abstract: Feature extraction and selection are key factors in model reduction, classification and pattern recognition problems. This is especially important for input data with large dimensions such as brain recording or multiview images, where appropriate feature extraction is a prerequisite to classification. To ensure that the reduced dataset contains maximum information about input data we propose algorithms for feature extraction and classification. This is achieved based on orthogonal or nonnegative tensor (multi-array) decompositions, and higher order (multilinear) discriminant analysis (HODA), whereby input data are considered as tensors instead of more conventional vector or matrix representations. The developed algorithms are verified on benchmark datasets, using constraints imposed on tensors and/or factor matrices such as orthogonality and nonnegativity.


Recommender System for Literature Review and Writing


title: Understanding user behavior in online social networks: a survey

abstract: Currently, online social networks such as Facebook, Twitter, Google+, LinkedIn, and Foursquare have become extremely popular all over the world and play a significant role in people?s daily lives. People access OSNs using both traditional desktop PCs and new emerging mobile devices. With more than one billion users worldwide, OSNs are a new venue of innovation with many challenging research problems. In this survey, we aim to give a comprehensive review of state-of-the-art research related to user behavior in OSNs from several perspectives. First, we discuss social connectivity and interaction among users. Also, we investigate traffic activity from a network perspective. Moreover, as mobile devices become a commodity, we pay attention to the characteristics of social behaviors in mobile environments. Last but not least, we review malicious behaviors of OSN users, and discuss several solutions to detect misbehaving users. Our survey serves the important roles of both providing a systematic exploration of existing research highlights and triggering various potentially significant research in these topics.


title: Hierarchical dynamic power management using model-free reinforcement learning

abstract: Model-free reinforcement learning (RL) has become a promising technique for designing a robust dynamic power management (DPM) framework that can cope with variations and uncertainties that emanate from hardware and application characteristics. Moreover, the potentially significant benefit of performing application-level scheduling as part of the system-level power management should be harnessed. This paper presents an architecture for hierarchical DPM in an embedded system composed of a processor chip and connected I/O devices (which are called system components.) The goal is to facilitate saving in the system component power consumption, which tends to dominate the total power consumption. The proposed (online) adaptive DPM technique consists of two layers: an RL-based component-level local power manager (LPM) and a system-level global power manager (GPM). The LPM performs component power and latency optimization. It employs temporal difference learning on semi-Markov decision process (SMDP) for model-free RL, and it is specifically optimized for an environment in which multiple (heterogeneous) types of applications can run in the embedded system. The GPM interacts with the CPU scheduler to perform effective application-level scheduling, thereby, enabling the LPM to do even more component power optimizations. In this hierarchical DPM framework, power and latency tradeoffs of each type of application can be precisely controlled based on a user-defined parameter. Experiments show that the amount of average power saving is up to 31.1% compared to existing approaches.


title: What Is New in Rome IV

abstract: Functional gastrointestinal disorders (FGIDs) are diagnosed and classified using the Rome criteria; the criteria may change over time as new scientific data emerge. The Rome IV was released in May 2016. The aim is to review the main changes in Rome IV. FGIDs are now called disorders of gut-brain interaction (DGBI). Rome IV has a multicultural rather than a Western-culture focus. There are new chapters including multicultural, age-gender-women's health, intestinal microenvironment, biopsychosocial, and centrally mediated disorders. New disorders have been included although not truly FGIDs, but fit the new definition of DGBI including opioid-induced gastrointestinal hyperalgesia , opioid-induced constipation , and cannabinoid hyperemesis . Also, new FGIDs based on available evidence including reflux hypersensitivity and centrally mediated abdominal pain syndrome . Using a normative survey to determine the frequency of normal bowel symptoms in the general population changes in the time frame for diagnosis were introduced. For irritable bowel syndrome (IBS) only pain is required and discomfort was eliminated because it is non-specific, having different meanings in different languages. Pain is now related to bowel movements rather than just improving with bowel movements (ie, can get worse with bowel movement). Functional bowel disorders (functional diarrhea , functional constipation , IBS with predominant diarrhea [IBS-D], IBS with predominant constipation [IBS-C ], and IBS with mixed bowel habits ) are considered to be on a continuum rather than as independent entities. Clinical applications such as diagnostic algorithms and the Multidimensional Clinical Profile have been updated. The new Rome IV iteration is evidence-based, multicultural oriented and with clinical applications. As new evidence become available, future updates are expected.


title: Cramer-Rao Lower Bound and Information Geometry

abstract: This article focuses on an important piece of work of the world renowned Indian statistician, Calyampudi Radhakrishna Rao. In 1945, C. R. Rao (25 years old then) published a pathbreaking paper [43], which had a profound impact on subsequent statistical research. Roughly speaking, Rao obtained a lower bound to the variance of an estimator. The importance of this work can be gauged, for instance, by the fact that it has been reprinted in the volume Breakthroughs in Statistics: Foundations and Basic Theory [32]. There have been two major impacts of this work: ::: ::: ::: First, it answers a fundamental question statisticians have always been interested in, namely, how good can a statistical estimator be? Is there a fundamental limit when estimating statistical parameters? ::: ::: ::: Second, it opens up a novel paradigm by introducing differential geometric modeling ideas to the field of Statistics. In recent years, this contribution has led to the birth of a flourishing field of Information Geometry [6].


title: Information theory in molecular biology

abstract: This article introduces the physics of information in the context of molecular biology and genomics. Entropy and information, the two central concepts of Shannon’s theory of information and communication, are often confused with each other but play transparent roles when applied to statistical ensembles (i.e., identically prepared sets) of symbolic sequences. Such an approach can distinguish between entropy and information in genes, predict the secondary structure of ribozymes, and detect the covariation between residues in folded proteins. We also review applications to molecular sequence and structure analysis, and introduce new tools in the characterization of resistance mutations, and in drug design.  2004 Elsevier B.V. All rights reserved.


title: Active exposure control for robust visual odometry in HDR environments

abstract: We propose an active exposure control method to improve the robustness of visual odometry in HDR (high dynamic range) environments. Our method evaluates the proper exposure time by maximizing a robust gradient-based image quality metric. The optimization is achieved by exploiting the photometric response function of the camera. Our exposure control method is evaluated in different real world environments and outperforms the built-in auto-exposure function of the camera. To validate the benefit of our approach, we adapt a state-of-the-art visual odometry pipeline (SVO) to work with varying exposure time and demonstrate improved performance using our exposure control method in challenging HDR environments.


title: Memory-centric system interconnect design with Hybrid Memory Cubes

abstract: Memory bandwidth has been one of the most critical system performance bottlenecks. As a result, the HMC (Hybrid Memory Cube) has recently been proposed to improve DRAM bandwidth as well as energy efficiency. In this paper, we explore different system interconnect designs with HMCs. We show that processor-centric network architectures cannot fully utilize processor bandwidth across different traffic patterns. Thus, we propose a memory-centric network in which all processor channels are connected to HMCs and not to any other processors as all communication between processors goes through intermediate HMCs. Since there are multiple HMCs per processor, we propose a distributor-based network to reduce the network diameter and achieve lower latency while properly distributing the bandwidth across different routers and providing path diversity. Memory-centric networks lead to some challenges including higher processor-to-processor latency and the need to properly exploit the path diversity. We propose a pass-through microarchitecture, which, in combination with the proper intra-HMC organization, reduces the zero-load latency while exploiting adaptive (and non-minimal) routing to load-balance across different channels. Our results show that memory-centric networks can efficiently utilize processor bandwidth for different traffic patterns and achieve higher performance by providing higher memory bandwidth and lower latency.