title: The Optics of Human Skin

abstract: An integrated review of the transfer of optical radiation into human skin is presented, aimed at developing useful models for photomedicine. The component chromophores of epidermis and stratum corneum in general determine the attenuation of radiation in these layers, moreso than does optical scattering. Epidermal thickness and melanization are important factors for UV wavelengths less than 300nm, whereas the attenuation of UVA (320–400nm) and visible radiation is primarily via melanin. The selective penetration of all optical wavelengths into psoriatic skin can be maximized by application of clear lipophilic liquids, which decrease regular reflectance by a refractive-index matching mechanism. Sensitivity to wavelengths less than 320nm can be enhanced by prolonged aqueous bathing, which extracts urocanic acid and other diffusible epidermal chromophores. Optical properties of the dermis are modelled using the Kubelka-Munk approach, and calculations of scattering and absorption coefficients are presented. This simple approach allows estimates of the penetration of radiation in vivo using noninvasive measurements of cutaneous spectral remittance (diffuse reflectance). Although the blood chromophores Hb, HbO 2 , and bilirubin determine dermal absorption of wavelengths longer than 320nm, scattering by collagen fibers largely determines the depths to which these wavelengths penetrate the dermis, and profoundly modifies skin colors. An optical "window" exists between 600 and 1300nm, which offers the possibility of treating large tissue volumes with certain long-wavelength photosensitizers. Moreover, whenever photosensitized action spectra extend across the near UV and/or visible spectrum, judicious choice of wavelength allows some selection of the tissue layers directly affected.


title: Audio-visual emotion recognition: A dynamic, multimodal approach

abstract: Designing systems able to interact with students in a natural manner is a complex and far from solved problem. A key aspect of natural interaction is the ability to understand and appropriately respond to human emotions. This paper details our response to the continuous Audio/Visual Emotion Challenge (AVEC'12) whose goal is to predict four affective signals describing human emotions. The proposed method uses Fourier spectra to extract multi-scale dynamic descriptions of signals characterizing face appearance, head movements and voice. We perform a kernel regression with very few representative samples selected via a supervised weighted-distance-based clustering, that leads to a high generalization power. We also propose a particularly fast regressor-level fusion framework to merge systems based on different modalities. Experiments have proven the efficiency of each key point of the proposed method and our results on challenge data were the highest among 10 international research teams.


title: Real-time occlusion culling for models with large occluders

abstract: Real-Time Occlusion Culling for Models with Large Occluders SATYAN COORG SETH TELLER Computer Graphics Group MIT Laboratory for Computer Science Efficiently identifying polygons that are visible from a dynamic synthetic viewpoint is an important problem in computer graphics. Typically, visibility determination is performed using the z-buffer algorithm. As this algorithm must examine every triangle in the input scene, z-buffering can consume a significant fraction of graphics processing, especially on architectures that have a low performance or software z-buffer. One way to avoid needlessly processing invisible portions of the scene is to use an occlusion culling algorithm to discard invisible polygons early in the graphics pipeline. In this paper, we exploit the presence of large occluders in urban and architectural models to design a real-time occlusion culling algorithm. Our algorithm has the following features: it is conservative, i.e., it overestimates the set of visible polygons; it exploits spatial coherence by using a hierarchical data structure; and it exploits temporal coherence by reusing visibility information computed for previous viewpoints. The new algorithm significantly accelerates rendering of several complex test models. CR


title: Cannabinoid-related Agents in the Treatment of Anxiety Disorders: Current Knowledge and Future Perspectives

abstract: Rich evidence has shown that cannabis products exert a broad gamut of effects on emotional regulation. The main psychoactive ingredient of hemp, Δ9-tetrahydrocannabinol (THC), and its synthetic cannabinoid analogs have been reported to either attenuate or exacerbate anxiety and fear-related behaviors in humans and experimental animals. The heterogeneity of cannabis-induced psychological outcomes reflects a complex network of molecular interactions between the key neurobiological substrates of anxiety and fear and the endogenous cannabinoid system, mainly consisting of the arachidonic acid derivatives anandamide and 2-arachidonoylglycerol (2-AG) and two receptors, respectively termed CB1 and CB2. The high degree of interindividual variability in the responses to cannabis is contributed by a wide spectrum of factors, including genetic and environmental determinants, as well as differences in the relative concentrations of THC and other alkaloids (such as cannabidiol) within the plant itself. The present article reviews the currently available knowledge on the herbal, synthetic and endogenous cannabinoids with respect to the modulation of anxiety responses, and highlights the challenges that should be overcome to harness the therapeutic potential of some of these compounds, all the while limiting the side effects associated with cannabis consumption. In addition the article presents some promising patents on cannabinoid-related agents.


title: A Wideband Coplanar Waveguide Antenna Array With Series Feed

abstract: A novel wideband antenna array with coplanar waveguide series feed is proposed in this letter. By etching a symmetrical circular slot (SCS) structure on the bottom metallic layer, the impedance characteristic of the antenna array is greatly improved. Meanwhile, the gain of the antenna array at the lower band is improved by inserting another SCS in the center of the two array elements. The measured results show that the impedance bandwidth of the proposed antenna array is from 2.04 to 3.74 GHz (the relative bandwidth is 58.8%). Moreover, the maximum gain reaches 6.9 dBi.


title: Mining customer requirements from online reviews: A product improvement perspective

abstract: We propose a filtering model to predict helpfulness of reviews for product design.We provide a way to use the KANO model based on online reviews.We explore how to obtain insights from Big Data through knowledge-based view. Big data commerce has become an e-commerce trend. Learning how to extract valuable and real time insights from big data to drive smarter and more profitable business decisions is a main task of big data commerce. Using online reviews as an example, manufacturers have come to value how to select helpful online reviews and what can be learned from online reviews for new product development. In this research, we first proposed an automatic filtering model to predict the helpfulness of online reviews from the perspective of the product designer. The KANO method, which is based on the classical conjoint analysis model, is then innovatively applied to analyze online reviews to develop appropriate product improvement strategies. Moreover, an empirical case study using the new method is conducted with the data we acquired from JD.com, one of the largest electronic marketplaces in China. The case study indicates the effectiveness and robustness of the proposed approach. Our research suggests that the combination of big data and classical management models can bring success for big data commerce.


title: ImageJ2: ImageJ for the next generation of scientific image data

abstract: ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.


title: Compression by induction of hierarchical grammars

abstract: The paper describes a technique that constructs models of symbol sequences in the form of small, human-readable, hierarchical grammars. The grammars are both semantically plausible and compact. The technique can induce structure from a variety of different kinds of sequence, and examples are given of models derived from English text, C source code and a sequence of terminal control codes. It explains the grammatical induction technique, demonstrates its application to three very different sequences, evaluates its compression performance, and concludes by briefly discussing its use as a method for knowledge acquisition. >


title: Separable spectro-temporal Gabor filter bank features: Reducing the complexity of robust features for automatic speech recognition.

abstract: To test if simultaneous spectral and temporal processing is required to extract robust features for automatic speech recognition (ASR), the robust spectro-temporal two-dimensional-Gabor filter bank (GBFB) front-end from Schädler, Meyer, and Kollmeier [J. Acoust. Soc. Am. 131, 4134-4151 (2012)] was de-composed into a spectral one-dimensional-Gabor filter bank and a temporal one-dimensional-Gabor filter bank. A feature set that is extracted with these separate spectral and temporal modulation filter banks was introduced, the separate Gabor filter bank (SGBFB) features, and evaluated on the CHiME (Computational Hearing in Multisource Environments) keywords-in-noise recognition task. From the perspective of robust ASR, the results showed that spectral and temporal processing can be performed independently and are not required to interact with each other. Using SGBFB features permitted the signal-to-noise ratio (SNR) to be lowered by 1.2 dB while still performing as well as the GBFB-based reference system, which corresponds to a relative improvement of the word error rate by 12.8%. Additionally, the real time factor of the spectro-temporal processing could be reduced by more than an order of magnitude. Compared to human listeners, the SNR needed to be 13 dB higher when using Mel-frequency cepstral coefficient features, 11 dB higher when using GBFB features, and 9 dB higher when using SGBFB features to achieve the same recognition performance.


Knowledge transfer in learning to recognize visual object classes


title: Quality of life in individuals with spina bifida: a research update.

abstract: Quality of life (QOL) is an important concept for individuals with chronic health conditions. Measuring and supporting QOL in children, adolescents, and adults with spina bifida (SB) may be especially unique given the broad range of complex health and rehabilitative challenges they encounter. This article provides a research update on (a) conceptual and methodological considerations in measuring QOL and a related construct, health-related quality of life (HRQOL); (b) the state of the science in QOL and HRQOL for individuals with SB; and (c) gaps in the evidence and implications for future research. A synthesis of the literature was limited by the use of 17 different QOL and HRQOL instruments to measure these constructs and notable weaknesses in the most frequently used condition-specific HRQOL instrument. However, a tentative pattern of lower scores on the physical domain of HRQOL measures in individuals with SB in comparison to peers emerged, while discrepancies in emotional, social, and school domains varied by study. Recommendations for future research include using a lifespan approach to expand QOL studies to older adults with SB and focusing on intervention research to prevent or treat decreased HRQOL.


title: Head pose and gaze direction tracking for detecting a drowsy driver

abstract: This paper proposes a system that uses gaze direction tracking and head pose estimation to detect drowsiness of a driver. Head pose is estimated by calculating optic flow of the facial features, which are acquired with a corner detection algorithm. Analysis of the driver's head behavior leads to three moving components: nodding, shaking, and tilting. To track the gaze direction of the driver, we trace the center point of the pupil using CDF analysis and estimate the frequency of eye-movement.


title: "Time is the bottleneck": a qualitative study exploring why learners drop out of MOOCs

abstract: Why do over 90% of the learners in Massive Open Online Courses (MOOCs) never finish the course? There is a need for further studies focusing on the learners' experiences of participating in MOOCs and factors that influence the decision to complete or drop out of the course. To deepen our understanding of why learners complete or drop out of MOOCs, we report on a qualitative case study based on in-depth interviews with 34 learners with different degrees of course completion for two MOOCs. A qualitative analysis of the interviews led to the identification of four main factors influencing dropout: (1) the learner's perception of the course content, (2) the learner's perception of the course design, (3) the learner's social situation and characteristics, and (4) the learner's ability to find and manage time effectively. How the learners conceptualized a MOOC had a strong impact on how they engaged with the contents. We discuss the implications of our results for MOOC practice in terms of time, openness and accessibility and provide recommendations for future research.


title: Growth hormone is permissive for skeletal adaptation to mechanical loading

abstract: The Lewis dwarf (DW) rat was used as a model to test the hypothesis that growth hormone (GH) is permissive for new bone formation induced by mechanical loading in vivo. Adult female Lewis DW rats aged 6.2 +/- 0.1 months (187 +/- 18 g) were allocated to four vehicle groups (DW), four GH treatment groups at 32.5 mug/100 g body mass (DWGH1), and four GH treatment groups at 65 mug/100 g (DWGH2). Saline vehicle or GH was injected intraperitoneally (ip) at 6:30 p.m. and 6:30 a.m. before mechanical loading of tibias at 7:30 a.m. A single period of 300 cycles of four-point bending was applied to right tibias at 2.0 Hz, and magnitudes of 24, 29, 38, or 48N were applied. Separate strain gauge analyses in 5 DW rats validated the selection of loading magnitudes. After loading, double-label histomorphometry was used to assess bone formation at the periosteal surface (Ps.S) and endocortical surface (Ec.S) of tibias. Comparing left (unloaded) tibias among groups, GH treatment had no effect on bone formation. Bone formation in tibias in DW rats was insensitive to mechanical loading. At the Ec.S, mechanically induced lamellar bone formation increased in the DWGH2 group loaded at 48N (p < 0.05), and no significant increases in bone formation were observed among other groups. The percentage of tibias expressing woven bone formation (Wo.B) at the Ps.S was significantly greater in the DWGH groups compared with controls (p < 0.05). We concluded that GH influences loading-related bone formation in a permissive manner and modulates the responsiveness of bone tissue to mechanical stimuli by changing thresholds for bone formation.


title: A Categorization Scheme for Semantic Web Search Engines

abstract: Semantic web search engines are evolving and many prototype systems and some implementation have been devel- oped. However, there are some different views on what a semantic search engine should do. In this paper, a categorization scheme for semantic web search engines are introduced and elaborated. For each category, its components are described according to a proposed general architecture and various approaches employed in these components are discussed. We also propose some factors to evaluate systems in each category.


title: Answer-Set Programming Encodings for Argumentation Frameworks

abstract: Answer-set programming (ASP) has emerged as a declarative programming paradigm where problems are encoded as logic programs, such that the so-called answer sets of theses programs represent the solutions of the encoded problem. The efficiency of the latest ASP solvers reached a state that makes them applicable for problems of practical importance. Consequently, problems from many different areas, including diagnosis, data integration, and graph theory, have been successfully tackled via ASP. In this work, we present such ASP-encodings for problems associated to abstract argumentation frameworks (AFs) and generalisations thereof. Our encodings are formulated as fixed queries, such that the input is the only part depending on the actual AF to process. We illustrate the functioning of this approach, which is underlying a new argumentation system called ASPARTIX in detail and show its adequacy in terms of computational complexity.


title: Camera pose estimation using particle filters

abstract: In this paper we propose a pose estimation algorithm based on Particle filtering which uses LED sightings gathered from wireless sensor nodes (WSN) to estimate the pose of the camera. The LEDs act as (visual) markers for our pose estimation algorithm. We also compare the performance of our pose estimation algorithm against two reference algorithms — (i) Extended Kalman filtering (EKF) and (ii) Discrete Linear Transform (DLT) based approaches. The performance of all the three algorithms are evaluated for different camera frame rates, varying level of measurement noise and for different marker distribution. Our results (small-scale experimental and room-level simulation studies) show that the particle filtering algorithm gives an accuracy of a few millimetres in position and a few degrees in orientation.


title: Distinction Bias: Misprediction and Mischoice Due to Joint Evaluation

abstract: This research identifies a new source of failure to make accurate affective predictions or to make experientially optimal choices. When people make predictions or choices, they are often in the joint evaluation (JE) mode; when people actually experience an event, they are often in the single evaluation (SE) mode. The "utility function" of an attribute can vary systematically between SE and JE. When people in JE make predictions or choices for events to be experienced in SE, they often resort to their JE preferences rather than their SE preferences and overpredict the difference that different values of an attribute (e.g., different salaries) will make to their happiness in SE. This overprediction is referred to as the distinction bias. The present research also specifies when the distinction bias occurs and when it does not. This research contributes to literatures on experienced utility, affective forecasting, and happiness.


title: Vulvovaginitis in the Prepubertal Child

abstract: Vulvovaginitis is an inflammation of the vulva and vaginal tissues. Signs and symptoms include irritation and erythema of the vulva, vaginal discharge, soreness, itching, dysuria, excoriation of the area, and bleeding. It is one of the more common gynecological problems among prepubertal girls. In this review, we address methods of evaluation and physical findings in the prepubescent child. We will describe the causes of vulvovaginitis, which include physical, chemical, or infectious agents, and will discuss diagnostic testing and review the most common bacterial pathogens isolated from symptomatic prepubertal girls. This article will also review management strategies of improved hygiene and sitz baths and the indications for antibiotics.


title: The Mozart Effect: An Artifact of Preference

abstract: The “Mozart effect” reported by Rauscher, Shaw, and Ky (1993, 1995) indicates that spatial-temporal abilities are enhanced after listening to music composed by Mozart. We replicated and extended the effect in Experiment 1: Performance on a spatial-temporal task was better after participants listened to a piece composed by Mozart or by Schubert than after they sat in silence. In Experiment 2, the advantage for the music condition disappeared when the control condition consisted of a narrated story instead of silence. Rather, performance was a function of listeners' preference (music or story), with better performance following the preferred condition.


title: The Hydra Parallel Programming System

abstract: The Hydra Parallel Programming System, a new parallel language extension to Java, and its supporting software are described. It is a fairly simple yet powerful language designed to address a number of areas that have not received much attention. One of these areas is the recompilation of parallel programs at runtime to allow a parallel program to adapt to the architecture it is executing on. The first version of this software system focuses on smaller Symmetric Multiprocessing and compatible architectures which are becoming more common. This particular class of machines has a great need for more options in the area of parallel programming among the vastly popular Java language programmers. Hydra programs will run as sequential Java on machines that do not have the parallel support or do not have an implemented Hydra runtime system without requirement of any modifications to the program. This paper describes the language, compares it with other languages (specifically with JOMP, an OpenMP implementation for Java), presents a brief discussion on compiling and executing Hydra programs, presents some sample benchmarks and their performance on three platforms, and concludes with a discussion of issues and future directions for Hydra. Copyright © 2007 John Wiley & Sons, Ltd.


title: C-RAN simulator: A tool for evaluating 5G cloud-based networks system-level performance

abstract: Cloud radio-access network (C-RAN) has the most promising network architecture to support the new fifth generation (5G) mobile network. The C-RAN witnesses a revolutionary base station functionality split, where it is composed of simplified remote radio heads (RRH) and several baseband units (BBUs) pools in network backbone. The C-RAN assists in employing several functionalities more efficiently. It has privileges over the existing radio-access networks (RANs) in case of global resource management, interference management, and radio-access technology (RAT) selection to guarantee higher system throughput and lower energy consumption. In this paper, we demonstrate a C-RAN system-level simulator that performs centralized user scheduling, edge-user joint transmission (JT), and global per-antenna carrier aggregation (CA). In addition, we demonstrate the capability of our system-level simulator to investigate the bottleneck effect of the fronthaul-link capacity as the system expands or C-RAN coordinated facilities increase. Our analysis shows throughput enhancement compared to the traditional RAN; however, with high fronthaul links utilization rates.


title: Power Line Communication with Tree Based Interleaver in Iterative IDMA Systems

abstract: The power distribution networks are primary employed, in Power line communication (PLC) system, for transferring data during communication process. For achieving even higher data rate in multiple user environments, researchers have proposed various multiple access schemes for power line communication systems including CDMA and OFDM mechanisms. This paper includes the performance analysis of power line channel with recently proposed multiple access scheme popularly known as Interleave-Division Multiple-Access (IDMA) scheme utilizing unequal power allocation with random and tree based interleaving mechanisms. During the analysis, the simulations have been performed in MATLAB environment. The simulation results demonstrate suitability of IDMA mechanism for powerline communication in comparison to other available mechanisms. Also, tree based interleaver is observed with similar BER performance to that of random interleaver with reduced complexity and bandwidths requirements.


ABSTRACT A Sketching Interface for Procedural City Generation


title: Efficient frame-sequential label propagation for video object segmentation

abstract: In this work, we present an approach for segmenting objects in videos taken in complex scenes. It propagates initial object label through the entire video by a frame-sequential manner where the initial label is usually given by the user. The proposed method has several contributions which make the propagation much more robust and accurate than other methods. First, a novel supervised motion estimation algorithm is employed between each pair of neighboring frames, by which a predicted shape model can be warped in order to segment the similar color around object boundary. Second, unlike previous methods with fixed modeling range, we design a novel range-adaptive appearance model to handle the tough problem of occlusion. Last, the paper gives a reasonable framework based on GraphCut algorithm for obtaining the final label of the object by combining the clues from both appearance and motion. In the experiments, the proposed approach is evaluated qualitatively and quantitatively with some recent methods to show it achieves state-of-art results on multiple videos from benchmark data sets.


HeartChat : Heart Rate Augmented Mobile Messaging to Support Empathy and Awareness


title: GO-Docker: A Batch Scheduling System with Docker Containers

abstract: Multi user open source batch scheduling software based on Docker containers with custom scheduler and executor plugin mechanisms.


Skip-Gram - Zipf + Uniform = Vector Additivity


title: Fabrication and characterization of zirconium hydroxide-carboxymethyl cellulose sodium/plasticized Trichosanthes Kirilowii starch nanocomposites

abstract: Abstract Carboxymethyl cellulose sodium (CMC) was used as the template to prepare zirconium hydroxide nanoparticles. The resulted Zr-CMC was composed of about 29.4 wt% CMC and amorphous zirconium hydroxide. Zirconium hydroxide nanoparticles in size of 20–50 nm were encapsulated by CMC, which could improve the stability of zirconium hydroxide in water. Trichosanthes kirilowii ( TK ) is a traditional Chinese medicine, which is rich in starch. TK starch is attempted to be plasticized with glycerol to obtain glycerol-plasticized starch (GPS) in the casting process. And Zr-CMC as the filler was loaded in GPS matrix. Since Zr-CMC could form the good interaction with GPS matrix, Zr-CMC filler could be dispersed well in GPS matrix; thereby, improve the mechanical properties and water barrier of GPS matrix.


title: Induction Heating Technology and Its Applications: Past Developments, Current Technology, and Future Challenges

abstract: Induction heating (IH) technology is nowadays the heating technology of choice in many industrial, domestic, and medical applications due to its advantages regarding efficiency, fast heating, safety, cleanness, and accurate control. Advances in key technologies, i.e., power electronics, control techniques, and magnetic component design, have allowed the development of highly reliable and cost-effective systems, making this technology readily available and ubiquitous. This paper reviews IH technology summarizing the main milestones in its development and analyzing the current state of art of IH systems in industrial, domestic, and medical applications, paying special attention to the key enabling technologies involved. Finally, an overview of future research trends and challenges is given, highlighting the promising future of IH technology.


title: Saliency-guided object proposal for refined salient region detection

abstract: Automatic detection of visually salient regions across images is useful in many applications. Traditional methods predict saliency values of pixels in a bottom-up fashion and use low-level features. Recent researches demonstrate that high-level information are also important for salient region detection. In this paper, we propose a novel approach of integrating object-level information and bottom-up saliency model. By using saliency-guided object proposals, we implicitly remove the noisy salient regions and produce a refined saliency map. We experimentally show that our approach boosted the performance of bottom-up saliency models and performs favorably against the state-of-the-art methods.


Discovering Blind Spots of Predictive Models: Representations and Policies for Guided Exploration.


title: Notice of RetractionPsychometric theory applied to questionnaire-based appraisals on software process assessments: An initial report

abstract: Adopting a software process model for organizations requires them to begin with an appraisal of their current processes. However, the current existing automated tools for process assessments can not verify the authenticity of answers and are therefore limited in their reliability level depending only in the employees' responses. This paper presents our research in psychometric theory applied to questionnaire-based appraisals to determine feasibility of combining them together to develop a reliable assessment tool to provide more reliable evidences about organizations' current maturity/capability level. We have found a lot of work can be done to improve appraisals questionnaires with psychometric theory and we show here areas that can be improved and first steps in how it can be done.


A SURVEY ON IRIS RECOGNITION FOR AUTHENTICATION


title: Modeling changing dependency structure in multivariate time series

abstract: We show how to apply the efficient Bayesian changepoint detection techniques of Fearnhead in the multivariate setting. We model the joint density of vector-valued observations using undirected Gaussian graphical models, whose structure we estimate. We show how we can exactly compute the MAP segmentation, as well as how to draw perfect samples from the posterior over segmentations, simultaneously accounting for uncertainty about the number and location of changepoints, as well as uncertainty about the covariance structure. We illustrate the technique by applying it to financial data and to bee tracking data.


title: A Classification of Internet Retail Stores

abstract: This article describes an empirical method for classifying Internet retail sites for electronic commerce. The technique used 35 observable Internet retail site attributes and features of online stores as the raw data to classify the online stores into meaningful groups. This paper presents a classification of online retail stores based upon an August 1996 convenience sample of 137 Internet retail stores. Descriptive statistics for 44 variables provide a snapshot of key attributes and features of online stores. Subsequent cluster and factor analysis identified five distinct Web catalog interface categories: superstores, promotional stores, plain sales stores, one-page stores, and product listings. Online stores differ primarily on the three dimensions: size, service offerings, and interface quality. A preponderance of the stores in the study had limited product selection, few service features, and poor interfaces. This categorization provides a better understanding of the strategies pursued in Internet-based marketing and will be helpful for Internet retail store designers as well as for researchers to structure and target further analyses in this domain.


Gaze-based Detection of Mind Wandering during Lecture Viewing


University of Groningen Effects on the fluid interface fluctuations due to the interaction potential form Palasantzas, Georgios; Backx, Godelieve


title: Data Quality for the Information Age

abstract: From the Publisher: ::: Written by the inventor of many modern techniques for data quality, this comprehensive book provides business leaders, process owners, and information professionals with the background and methods necessary to set up a data quality program, make and sustain order of magnitude improvements, and create a unique and important business advantage.


title: A Comparison of Features for Synthetic Speech Detection

abstract: The performance of biometric systems based on automatic speaker recognition technology is severely degraded due to spoofing attacks with synthetic speech generated using diff erent voice conversion (VC) and speech synthesis (SS) techniques. Various countermeasures are proposed to detect this type of attack, and in this context, choosing an appropriate feature extraction technique for capturing relevant information from speech is an important issue. This paper presents a concise experimental review of different features for synthetic speech detection task. A wide variety of features considered in this stud y include previously investigated features as well as some other potentially useful features for characterizing real and sy nthetic speech. The experiments are conducted on recently released ASVspoof 2015 corpus containing speech data from a large number of VC and SS technique. Comparative results using two different classifiers indicate that features representing spectral information in high-frequency region, dynamic information of speech, and detailed information related to subband characteristics are considerably more useful in detecting synthetic sp eech. Index Terms: anti-spoofing, ASVspoof 2015, feature extraction, countermeasures


title: Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior

abstract: Information systems IS cannot be effective unless they are used. However, people sometimes do not use systems that could potentially increase their performance. This study compares two models that predict an individual's intention to use an IS: the technology acceptance model TAM and the theory of planned behavior TPB. The comparison was designed to be as fair as possible, not favoring one model over the other. Both TAM and TPB predicted intention to use an IS quite well, with TAM having a slight empirical advantage. TAM is easier to apply, but only supplies very general information on users' opinions about a system. TPB provides more specific information that can better guide development.


title: Surgical management of cloacal malformations: a review of 339 patients

abstract: Abstract Background/purpose The aim of this study was to describe lessons learned from the authors’ series of patients with cloaca and convey the improved understanding and surgical treatment of the condition’s wide spectrum of complexity. Methods The medical records of 339 patients with cloaca operated on by the authors were retrospectively reviewed. Results A total of 265 patients underwent primary operations, and 74 were secondary. All patients were approached posterior sagittally; 111 of them also required a laparotomy. The average length of the common channel was 4.7 cm for patients that required a laparotomy and 2.3 cm for those that did not. Vaginal reconstruction involved a vaginal pull-through in 196 patients, a vaginal flap in 38, vaginal switch in 30, and vaginal replacement in 75 (36 with rectum, 31 with ileum, and 8 with colon). One hundred twenty-two patients underwent a total urogenital mobilization. Complications included vaginal stricture or atresia in 17, urethral strictures in 6, and urethro-vaginal fistula in 19, all of which occurred before the introduction of the total urogenital mobilization. A total of 54% of all evaluated patients were continent of urine and 24% remain dry with intermittent catheterization through their native urethra and 22% through a Mitrofanoff-type of conduit. Seventy-eight percent of the patients with a common channel longer than 3 cm require intermittent catheterization compared with 28% when their common channel was shorter than 3 cm. Sixty percent of all cases have voluntary bowel movements (28% of them never soiled, and 72% soiled occasionally). Forty percent are fecally incontinent but remain clean when subjected to a bowel management program. Forty-eight patients born at other institutions with hydrocolpos were not treated correctly during the neonatal period. The surgeons failed to drain the dilated vaginas, which interfered with the drainage of the ureters and provoked urinary tract infections, pyocolpos, and/or vaginal perforation. In 24 patients, the colostomy was created too distally, and it interfered with the pull-through. Twenty-three patients suffered from colostomy prolapse. All of these patients required a colostomy, revision before the main repair. Thirty-six patients underwent reoperation because they had a persistent urogenital sinus after an operation done at another institution, and 38 patients underwent reoperation because they suffered from atresia or stenosis of the vagina or urethra. The series was divided into 2 distinct groups of patients: group A were those with a common channel shorter than 3 cm (62%) and group B had a common channel longer than 3 cm (38%). Conclusions The separation of these groups has important therapeutic and prognostic implications. Group A patients can be repaired posterior sagittally with a reproducible, relatively short operation. Because they represent the majority of patients, we believe that most well-trained pediatric surgeons can repair these type of malformations, and the prognosis is good. Group B patients (those with a common channel longer than 3 cm), usually require a laparotomy and have a much higher incidence of associated urologic problems. The surgeons who repair these malformations require special training in urology, and the operations are prolonged, technically demanding, and the functional results are not as good as in group A. It is extremely important to establish an accurate neonatal diagnosis, drain the hydrocolpos when present, and create an adequate, totally diverting colostomy, leaving enough distal colon available for the pull-through and fixing the colon to avoid prolapse. A correct diagnosis will allow the surgeon to repair the entire defect and avoid a persistent urogenital sinus. Cloacas comprise a spectrum of defects requiring a complex array of surgical decisions. The length of the common channel is an important determinant of the potential for urinary control, and predicts the extent of surgical repair.


title: A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing

abstract: Cloud Computing is emerging today as a commercial infrastructure that eliminates the need for maintaining expensive computing hardware. Through the use of virtualization, clouds promise to address with the same shared set of physical resources a large user base with different needs. Thus, clouds promise to be for scientists an alternative to clusters, grids, and supercomputers. However, virtualization may induce significant performance penalties for the demanding scientific computing workloads. In this work we present an evaluation of the usefulness of the current cloud computing services for scientific computing. We analyze the performance of the Amazon EC2 platform using micro-benchmarks and kernels. While clouds are still changing, our results indicate that the current cloud services need an order of magnitude in performance improvement to be useful to the scientific community.


title: Robust single-particle tracking in live-cell time-lapse sequences

abstract: Single-particle tracking methods allow detailed analysis of protein movement in cells, but existing tracking algorithms have substantial limitations, particularly at high particle densities. A new software tool overcomes some of these limitations and is used to track CD36 receptors and assay the lifetime of clathrin-coated pits.


title: Comparing the T and $\pi$ Equivalent Circuits for the Calculation of Transformer Inrush Currents

abstract: The most commonly used equivalent circuit for transformers is the traditional (Steinmetz) T-equivalent proposed toward the end of the 19th century. This model has two leakage impedance branches and one magnetizing branch. The T model properly represents the terminal behavior of the transformer for most low-frequency operating conditions. Another model derived from the principle of duality between magnetic and electric circuits exists, the π equivalent circuit, which has two magnetizing branches and one leakage branch. This paper shows that while the two equivalent circuits provide the same accuracy in steady state, better accuracy for the calculation of inrush currents is obtained with the π -equivalent circuit. Laboratory tests performed on three transformers with different characteristics demonstrate that inrush current simulations with the T equivalent circuit can have errors up to 73%, while the π equivalent estimates the measurements in every case within a few percent.


title: SewerSnort: A Drifting Sensor for In-situ Sewer Gas Monitoring

abstract: Biochemical activities in sewer pipes generate various volatile substances that lead to several serious problems such as malodor complaints and lawsuits, concrete and metal corrosion, increased operational costs, and health risks. Frequent inspections are critical to maintain sewer health, yet are extremely expensive given the extent of the sewer system and the "unfriendliness" of the environment. In this paper we propose SewerSnort, a low cost, unmanned, fully automated in-sewer gas monitoring system. A sensor float is introduced at the upstream station and drifts to the end pumping station, collecting location tagged gas measurements. The retrieved SewerSnort provides an accurate gas exposure profile to be used for preventive maintenance and/or repair. The key innovations of SewerSnort are the fully automated, end-to-end monitoring solution and the low energy self localizing strategy. From the implementation standpoint, the key enablers are the float mechanical design that fits the sewer constraints and the embedded sensor design that matches the float form factor and complies with the tight energy constraints. Experiments based on a dry land emulator demonstrate the feasibility of the SewerSnort concept, in particular, the localization technique and the embedded sensor design.


title: The Hydra . PowerGraph System Building Digital Archives with Directed and Typed Hypergraphs

abstract: Directed hypergraphs are known from graph theory [11] and are well understood within their own domain [7, 8, 9, 22, 23]. This paper provides an overview on the expressiveness of directed and typed hypergraphs as a modeling paradigm not only for the content of digital libraries and archives but a variety of applications. Furthermore, hypergraphs are sufficiently expressive to provide an implementation logic for conceptual models like CIDOC/CRM [18] in the context of museum-related systems and digital archives.


title: On the Effective Use of Pretraining for Natural Language Inference

abstract: Neural networks have excelled at many NLP tasks, but there remain open questions about the performance of pretrained distributed word representations and their interaction with weight initialization and other hyperparameters. We address these questions empirically using attention-based sequence-to-sequence models for natural language inference (NLI). Specifically, we compare three types of embeddings: random, pretrained (GloVe, word2vec), and retrofitted (pretrained plus WordNet information). We show that pretrained embeddings outperform both random and retrofitted ones in a large NLI corpus. Further experiments on more controlled data sets shed light on the contexts for which retrofitted embeddings can be useful. We also explore two principled approaches to initializing the rest of the model parameters, Gaussian and orthogonal, showing that the latter yields gains of up to 2.9% in the NLI task.


title: Tri-band microstrip antenna with L-shaped slots for Bluetooth/WLAN/WiMAX applications

abstract: This paper develops a compact tri-band microstrip patch antenna for multiband applications. The multiband functionality is yielded by utilizing four monolith L-shaped slots etched within the rectangular radiating patch. The proposed antenna is basically a rectangular microstrip patch and a partial ground plane fed by a 50 Ω microstrip line. The proposed antenna has a simple structure with overall size of 20×20×1.6 mm3. Extensive simulations are accomplished using 3D full-wave FEM-based EM simulator. The results evaluate good matching between the EM simulations and the measurements. The simulated radiation patterns at the desired resonant frequencies exhibits nearly Omni-directional. The proposed antenna can be used for Bluetooth, WLAN and WiMAX wireless communications applications at 2.4 GHz, 5.5 GHz and 3.5 GHz, respectively.


title: Looking for ELMo's Friends: Sentence-Level Pretraining Beyond Language Modeling

abstract: Work on the problem of contextualized word representation---the development of reusable neural network components for sentence understanding---has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo. This paper contributes the first large-scale systematic study comparing different pretraining tasks in this context, both as complements to language modeling and as potential alternatives. The primary results of the study support the use of language modeling as a pretraining task and set a new state of the art among comparable models using multitask learning with language models. However, a closer look at these results reveals worryingly strong baselines and strikingly varied results across target tasks, suggesting that the widely-used paradigm of pretraining and freezing sentence encoders may not be an ideal platform for further work.


title: A Differentially Fed Wideband Circularly Polarized Antenna

abstract: This study proposes a differentially fed circularly polarized (CP) antenna with common-mode (CM) suppression and a broad axial-ratio (AR) bandwidth for unidirectional radiation. First, the proposed antenna applies two crossed dipoles and two annular delay-line phase shifters to achieve circular polarization. Second, parasitic patches are used to broaden the AR bandwidth. Third, the crossed dipoles of the proposed antenna can be effectively excited under differential-mode operation, but not under CM operation; therefore, the antenna shows good CM suppression. The simulated results indicate that the proposed antenna has a 3 dB AR bandwidth of 31% (0.64 GHz, 1.74–2.38 GHz) at a CP center frequency of 2.06 GHz, and a wide –10 dB impedance bandwidth of 60.5% (1.30 GHz, 1.50–2.80 GHz) centered at 2.15 GHz. Besides, the CP antenna achieves stable radiation patterns and stable half-power beamwidths of ${\text{69}}^\circ \,\pm \,{\text{2}}^\circ $ at the xoz plane and ${\text{68}}^\circ \,\pm \,{\text{2}}^\circ $ at the yoz plane. A prototype is fabricated and tested. The measured results are in a good agreement with the simulated results.


title: Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach

abstract: Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHession, to conduct relation extractor learning using annotations from heterogeneous information source, e.g., knowledge base and domain heuristics. These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance. Identifying context information as the backbone of both relation extraction and true label discovery, we adopt embedding techniques to learn the distributed representations of context, which bridges all components with mutual enhancement in an iterative fashion. Extensive experimental results demonstrate the superiority of REHession over the state-of-the-art.


title: A Digital Security System with Door Lock System Using RFID Technology

abstract: RFID, Radio Frequency Identification is an inexpensive technology, can be implemented for several applications such as security, asset tracking, people tracking, inventory detection, access control applications. The main objective of this paper is to design and implement a digital security system which can deploy in secured zone where only authentic person can be entered. We implemented a security system containing door locking system using passive type of RFID which can activate, authenticate, and validate the user and unlock the door in real time for secure access. The advantage of using passive RFID is that it functions without a battery and passive tags are lighter and are less expensive than the active tags. A centralized system manages the controlling, transaction and operation task. The door locking system functions in real time as the door open quickly when user put their tag in contact of reader. The system also creates a log containing check-in and check-out of each user along with basic information of user. General Terms Ubiquitous computing, Security


Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms


title: The Aesthetic Unit Principle of Facial Aging

abstract: Importance In youth, facial aesthetic units flow together without perceptible division. The face appears as a single dynamic structure with a smooth contour and very little if any shadowing between different anatomical regions. As one ages, facial aesthetic units slowly become distinct. This process may be a consequence of differences in skin thickness, composition of subcutaneous tissue, contour of the facial skeleton, and location of facial ligaments. Although the impact of aesthetic unit separation is clinically apparent, its fundamental role in perceived facial aging has not yet been defined empirically. Objectives To evaluate and define the effect of aesthetic unit separation on facial aging and to empirically validate the rationale for the blending of aesthetic units as a principle for facial rejuvenation. Design, Setting, and Participants We prepared the photographs of 7 women for experimental evaluation of the presence or absence of facial aesthetic unit separation. Photographic stimuli were then presented to 24 naive observers in a blinded paired comparison. For each stimulus pair, observers were asked to select the facial photograph that they considered to be more youthful in appearance. Each stimulus was compared with all others. Main Outcomes and Measures We calculated a preference score for the total number of times any photograph was chosen to be more youthful compared with all others. Paired t tests were used to compare the preference scores between the facial stimuli with and without aesthetic unit separation. Results We generated 4032 responses for analysis. Photographs without facial aesthetic unit separation were consistently judged to be more youthful than their aged original or modified counterparts, with mean preference scores of 0.66 and 0.33, respectively ( P ≤ .047). When we selected the paired stimulus that directly compared one photograph with aesthetic unit separation with another with blended aesthetic units (2015 pairs), observers indicated that the photograph with the blended aesthetic unit was younger 95% of the time. Within-rater reliability was found to be very good ( r = 0.88). Conclusions and Relevance Our data support the hypothesis that facial aesthetic unit separation influences perceived facial youthfulness among photographs of women. The presence of facial aesthetic unit separation results in a less youthful appearance. Based on these empirical data, the concept of facial aesthetic unit separation appears to play a significant role in perceived facial aging. Level of Evidence NA.


Occupational Health Problems and Issues of Sleep Deprivation among Inbound Call Center Agents using Structural Equation Modelling


title: A Compact balanced symmetric discone antenna with optimized ultrawideband omnidirectional impulse radiation behavior

abstract: A symmetric compact discone antenna for the 3GHz to 20GHz frequency range is presented and characterized. The radiation pattern exhibits excellent frequency independence thanks to a careful theoretical and practical analysis of the radiated spherical modes. The time domain impulse response of the antenna is short and shows very low angular dependence. Moreover, the antenna in equipped with an improved feeding mechanism based on two separate discone antennas. Overall, the results show significantly improved impulse responses and input matching compared to a standard biconical antenna. Hence, the new antenna is well suited for advanced channel characterization, reference measurements, and system verifications.


title: Cinematic Lissajous figures: their production and applicability in experiments on reversible perspective.

abstract: In our previous studies of the reversibility function in Lissajous figures we have presented the stimuli directly from a cathode-ray tube.' The presentation of the figures in this manner necessarily involved certain difficulties some of which were quite accurately foreseen by Weber who first suggested the use of Lissajous figures in studies of reversibility.2 Probably the most exacting of these difficulties concerned the maintenance of stimulus constancy throughout experimentation. Day to day calibrations of the frequency output in the oscillators and of the line voltage in the oscilloscope were necessary to insure that the size of the figure, its brilliance and speed of rotation remained constant. Indeed, even within the time allowed for a single test-trial lasting from 1-2 min., continuous correction for drift in the oscillators and in the oscilloscope had to be made lest changes, particularly in the speed of rotation and in the position of the image on the screen, altered the stimulus-situation. Our earlier experiments proceeded under continuous and rigorous control because at no time could it be taken for granted that any single characteristic of the stimulus was constant.


title: Simple magic: Synthesis and in-memory Mapping of logic execution for memristor-aided logic

abstract: This paper presents a novel approach for designing and implementing in-memory logic operations. The uniqueness of this work is the development of SIMPLE, a framework that optimizes the execution of an arbitrary logic function, while considering all the constraints involved in performing it within a memristive memory. SIMPLE automatically generates a defined sequence of atomic memristor-aided logic NOR operations, whose implementation can be facilitated efficiently within the memory. Motivated to overcome the memory-CPU bottleneck, this approach designs an optimal solution in terms of performance by exploiting the parallelism of the memristor-aided logic NOR gates. SIMPLE achieves performance speedups of 1.94x compared to a previous work and 1.48x compared to a naive optimization based on standard synthesis tools.


title: I can't believe it's not causal! scalable causal consistency with no slowdown cascades

abstract: We describe the design, implementation, and evaluation of Occult (Observable Causal Consistency Using Lossy Timestamps), the first scalable, geo-replicated data store that provides causal consistency to its clients without exposing the system to the possibility of slowdown cascades, a key obstacle to the deployment of causal consistency at scale. Occult supports read/write transactions under PC-PSI, a variant of Parallel Snapshot Isolation that contributes to Occult's immunity to slowdown cascades by weakening how PSI replicates transactions committed at the same replica. While PSI insists that they all be totally ordered, PC-PSI simply requires total order Per Client session. Nonetheless, Occult guarantees that all transactions read from a causally consistent snapshot of the datastore without requiring any coordination in how transactions are asynchronously replicated.


title: Efficient Multicast Video Streaming for IPTV Service over WLAN Using CC-FEC

abstract: With rapidly growing IPTV market and IPTV standardization activity, multicasting starts to gain a lot of attention again. The use of multicasting conserves the bandwidth of a network and this becomes especially important with wireless channels with limited throughput. But, multicasting over wireless channel is facing many challenges. In this paper, we discuss the challenges for the multicast streaming to provide IPTV service over WLAN and present system architecture to model the wireless IPTV service environment. And we apply a novel CC-FEC (Cross- correlated Forwarding Error Correction) scheme to the model and analyze the efficiency of the scheme.


title: Efficacy of nucleopolyhedrovirus and azadirachtin on Spodoptera litura Fabricius (Lepidoptera: Noctuidae)

abstract: Spodoptera litura Fabricius (Lepidoptera: Noctuidae) is a destructive pest of subtropical and tropical agriculture, and has the potential to be a serious pest of forage crops in Asia. The effects of azadirachtin (AZA) and nucleopolyhedrovirus (NPV) on feeding and mortality of S. litura were evaluated in the laboratory. The nutritional indices were significantly affected. Gross dietary utilization (efficiency of conversion of ingested and digested food) of S. litura was also decreased after treatment with AZA and NPV. Food consumption, efficiency of conversion of ingested and digested food, relative growth rate, and consumption index values declined significantly, but at the same time an increase in approximate digestibility was also observed. Maximum larval weight (fifth instar) loss (68%) occurred, when AZA and NPV were combined. When consumed together (AZA and NPV) larvae died significantly faster compared with larvae that consumed NPV or AZA. Dose and larval instar dependent growth retardation was observed after treatment with NPV and AZA. Upon combination of AZA with NPV, nutritional indices decreased twofold even at reduced concentration of both.


title: An analog GFSK modulator in 0.35-/spl mu/m CMOS

abstract: An analog Gaussian frequency shift keying (GFSK) modulator designed in 0.35-/spl mu/m CMOS consumes 600 /spl mu/A from a 3-V supply and realizes an analog implementation of the FM differential equation. The modulator operates at baseband and is intended for use in a direct-conversion Bluetooth transmitter. It achieves a frequency deviation of 160 kHz with better than /spl plusmn/3% accuracy. The modulator implements an amplitude control loop to achieve a well-defined output swing. The total output harmonic distortion is less than 1%.


title: Review: Intrusion detection by machine learning: A review

abstract: The popularity of using Internet contains some risks of network attacks. Intrusion detection is one major research problem in network security, whose aim is to identify unusual access or attacks to secure internal networks. In literature, intrusion detection systems have been approached by various machine learning techniques. However, there is no a review paper to examine and understand the current status of using machine learning techniques to solve the intrusion detection problems. This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers. Related studies are compared by their classifier design, datasets used, and other experimental setups. Current achievements and limitations in developing intrusion detection systems by machine learning are present and discussed. A number of future research directions are also provided.


title: Regulating private power in health : case review

abstract: On 28 July 2010, Ebersohn AJ of the North Gauteng High Court reviewed and set aside regulations purportedly made in terms of section 90(1) (u) and (v) of the National Health Act 61 of 2003 (NHA). As a result of this judgment, the Regulations Relating to the Obtainment of Information and the Process of Determination and Publication of the Reference Price List (the Regulations) and all related acts - including the determination and publication of the annual national health reference price list (NHRPL) - are now invalid.


title: Transformation of the NAMUR MTP to OPC UA to allow plug and produce for modular process automation

abstract: Modularization is considered as one enabler for flexible and highly reconfigurable process plants. These characteristics are needed to overcome deficiencies regarding market volatility and shorter product innovation cycles. Current standardization activities aim at the specification of Module Type Package (MTP) files as a semantic description of modules for fast and efficient integration into process control systems. To increase the plug and produce character of the approach, online discovery for modules has to be enabled. OPC UA is considered as a suitable industrial communication technology for the communication between modules and process control systems. Therefore, this paper presents an approach for the representation of MTPs in OPC UA in order to enable the online discovery of process modules.


title: Enterprise Application Integration: An Overview

abstract: The concepts and architecture of enterprise application integration (EAI) were overviewed. The key technologies, the state of the art, and the open problems were addressed in the perspectives of application integration, data integration and business process management. It was pointed out that the technologies of EAI should be standardized based on the some technologies such as Web services. Integrating legacy systems, building global data schema, modeling & describing business processes are main research directions in the future.


SQuAD Reading Comprehension


title: Real-Time Brazilian License Plate Detection and Recognition Using Deep Convolutional Neural Networks

abstract: Automatic License Plate Recognition (ALPR) is an important task with many applications in Intelligent Transportation and Surveillance systems. As in other computer vision tasks, Deep Learning (DL) methods have been recently applied in the context of ALPR, focusing on country-specific plates, such as American or European, Chinese, Indian and Korean. However, either they are not a complete DL-ALPR pipeline, or they are commercial and utilize private datasets and lack detailed information. In this work, we proposed an end-to-end DL-ALPR system for Brazilian license plates based on state-of-the-art Convolutional Neural Network architectures. Using a publicly available dataset with Brazilian plates, the system was able to correctly detect and recognize all seven characters of a license plate in 63.18% of the test set, and 97.39% when considering at least five correct characters (partial match). Considering the segmentation and recognition of each character individually, we are able to segment 99% of the characters, and correctly recognize 93% of them.


title: The uncanny valley: Existence and explanations.

abstract: More than 40 years ago, Japanese roboticist Masahiro Mori (1970/2005) proposed the “uncanny valley” hypothesis, which predicted a nonlinear relation between robots’ perceived human likeness and the...


Reducing the Leakage in Practical Order-Revealing Encryption.


title: MODULAR NEURAL NETWORKS: A SURVEY

abstract: Modular Neural Networks (MNNs) is a rapidly growing field in artificial Neural Networks (NNs) research. This paper surveys the different motivations for creating MNNs: biological, psychological, hardware, and computational. Then, the general stages of MNN design are outlined and surveyed as well, viz., task decomposition techniques, learning schemes and multi-module decision-making strategies. Advantages and disadvantages of the surveyed methods are pointed out, and an assessment with respect to practical potential is provided. Finally, some general recommendations for future designs are presented.


title: FATHI ET AL.: SELF TRAINING AND ACTIVE LEARNING FOR VIDEO SEGMENTATION 1 Combining Self Training and Active Learning for Video Segmentation

abstract: This work addresses the problem of segmenting an object of interest out of a video. We show that video object segmentation can be naturally cast as a semi-supervised learning problem and be efficiently solved using harmonic functions. We propose an incremental self-training approach by iteratively labeling the least uncertain frame and updating similarity metrics. Our self-training video segmentation produces superior results both qualitatively and quantitatively. Moreover, usage of harmonic functions naturally supports interactive segmentation. We suggest active learning methods for providing guidance to user on what to annotate in order to improve labeling efficiency. We present experimental results using a ground truth data set and a quantitative comparison to a representative object segmentation system.


title: Investigation of spark gap discharge in a regime of very high repetitition rate

abstract: It is our interest to design and develop a high power pulser system employing microplasma discharge in a spark gap system. The type of discharge generates short electric pulses capable of both the characteristics of high repetition rate and fast rise time. Such a switch pulser has wide applications in the industry. An efficient electrical discharge in the spark gap is very important for the switching operation. The regime of operation utilizes the residual plasma in the inter pulse recovery of the spark gap to increase the repetition rate. Therefore, the mechanisms of the discharge in a controlled manner, such as the plasma discharge by the arc channel, the dielectric recovery process as well as residual plasma in the post arc period are of great importance to ensure the high repetition rate. The aforementioned mechanisms are established through optimization of the circuit element parameters such as the electrode geometry, the gap distance, the gas pressure, the gas type as well as the applied voltage and current ratings.


title: Multi-Layer Ensembling Techniques for Multilingual Intent Classification

abstract: In this paper we determine how multi-layer ensembling improves performance on multilingual intent classification. We develop a novel multi-layer ensembling approach that ensembles both different model initializations and different model architectures. We also introduce a new banking domain dataset and compare results against the standard ATIS dataset and the Chinese SMP2017 dataset to determine ensembling performance in multilingual and multi-domain contexts. We run ensemble experiments across all three datasets, and conclude that ensembling provides significant performance increases, and that multi-layer ensembling is a no-risk way to improve performance on intent classification. We also find that a diverse ensemble of simple models can reach perform comparable to much more sophisticated state-of-the-art models. Our best F 1 scores on ATIS, Banking, and SMP are 97.54%, 91.79%, and 93.55% respectively, which compare well with the state-of-the-art on ATIS and best submission to the SMP2017 competition. The total ensembling performance increases we achieve are 0.23%, 1.96%, and 4.04% F 1 respectively.


title: The neuropsychopharmacology of fronto-executive function: monoaminergic modulation.

abstract: We review the modulatory effects of the catecholamine neurotransmitters noradrenaline and dopamine on prefrontal cortical function. The effects of pharmacologic manipulations of these systems, sometimes in comparison with the indoleamine serotonin (5-HT), on performance on a variety of tasks that tap working memory, attentional-set formation and shifting, reversal learning, and response inhibition are compared in rodents, nonhuman primates, and humans using, in a behavioral context, several techniques ranging from microiontophoresis and single-cell electrophysiological recording to pharmacologic functional magnetic resonance imaging. Dissociable effects of drugs and neurotoxins affecting these monoamine systems suggest new ways of conceptualizing state-dependent fronto-executive functions, with implications for understanding the molecular genetic basis of mental illness and its treatment.


title: An empirical evaluation of information metrics for low-rate and high-rate DDoS attack detection

abstract: Abstract Distributed Denial of Service (DDoS) attacks represent a major threat to uninterrupted and efficient Internet service. In this paper, we empirically evaluate several major information metrics, namely, Hartley entropy, Shannon entropy, Renyi’s entropy, generalized entropy, Kullback–Leibler divergence and generalized information distance measure in their ability to detect both low-rate and high-rate DDoS attacks. These metrics can be used to describe characteristics of network traffic data and an appropriate metric facilitates building an effective model to detect both low-rate and high-rate DDoS attacks. We use MIT Lincoln Laboratory, CAIDA and TUIDS DDoS datasets to illustrate the efficiency and effectiveness of each metric for DDoS detection.


title: CodeRepair: PHY-layer partial packet recovery without the pain

abstract: Prior studies show that repairing partially corrupted packets, instead of retransmitting them in their entirety, holds potential in improving the performance of 802.11 networks. However, the efficiency of existing packet recovery approaches is severely limited by various overhead associated to redundant transmission and repeated channel contention. In this paper, we propose CodeRepair, a practical coding-based protocol that recovers partially corrupted 802.11 packets without these pains. The design of CodeRepair is based on two novel ideas. First, CodeRepair pushes the limit of 802.11 PHY to piggyback parities in the padded bits of OFDM, obviating the need of transmitting extra information for error correction. Second, CodeRepair corrects errors at the PHY layer, which is significantly more efficient than traditional link-layer approaches. This is due to the fact that a single coded bit usually affects the decoding of a group of data bits in 802.11 convolutional code. As a result, CodeRepair can salvage a partially corrupted packet by correcting a small number of erroneous coded bits using the padded parities. To reduce computational cost of error recovery, CodeRepair employs single parity code for correcting coded bit errors. We propose several techniques to augment the error correcting capability of single parity code without compromising its computation efficiency. Our evaluation shows that CodeRepair recovers an average of 34% partially corrupted packets, and improves the end-to-end link goodput by 59% on lossy 802.11 links.


title: GridGraph: large-scale graph processing on a single machine using 2-level hierarchical partitioning

abstract: In this paper, we present GridGraph, a system for processing large-scale graphs on a single machine. Grid-Graph breaks graphs into 1D-partitioned vertex chunks and 2D-partitioned edge blocks using a first fine-grained level partitioning in preprocessing. A second coarse-grained level partitioning is applied in runtime. Through a novel dual sliding windows method, GridGraph can stream the edges and apply on-the-fly vertex updates, thus reduce the I/O amount required for computation. The partitioning of edges also enable selective scheduling so that some of the blocks can be skipped to reduce unnecessary I/O. This is very effective when the active vertex set shrinks with convergence. ::: ::: Our evaluation results show that GridGraph scales seamlessly with memory capacity and disk bandwidth, and outperforms state-of-the-art out-of-core systems, including GraphChi and X-Stream. Furthermore, we show that the performance of GridGraph is even competitive with distributed systems, and it also provides significant cost efficiency in cloud environment.


title: Multi-View Video Summarization Using Bipartite Matching Constrained Optimum-Path Forest Clustering

abstract: The task of multi-view video summarization is to efficiently represent the most significant information from a set of videos captured for a certain period of time by multiple cameras. The problem is highly challenging because of the huge size of the data, presence of many unimportant frames with low activity, inter-view dependencies, and significant variations in illumination. In this paper, we propose a graph-theoretic solution to the above problems. Semantic feature in form of visual bag of words and visual features like color, texture, and shape are used to model shot representative frames after temporal segmentation . Gaussian entropy is then applied to filter out frames with low activity. Inter-view dependencies are captured via bipartite graph matching. Finally, the optimum-path forest algorithm is applied for the clustering purpose. Subjective as well as objective evaluations clearly indicate the effectiveness of the proposed approach.


title: Application of the stochastic mixing model to hyperspectral resolution enhancement

abstract: A maximum a posteriori (MAP) estimation method is described for enhancing the spatial resolution of a hyperspectral image using a higher resolution coincident panchromatic image. The approach makes use of a stochastic mixing model (SMM) of the underlying spectral scene content to develop a cost function that simultaneously optimizes the estimated hyperspectral scene relative to the observed hyperspectral and panchromatic imagery, as well as the local statistics of the spectral mixing model. The incorporation of the stochastic mixing model is found to be the key ingredient for reconstructing subpixel spectral information in that it provides the necessary constraints that lead to a well-conditioned linear system of equations for the high-resolution hyperspectral image estimate. Here, the mathematical formulation of the proposed MAP method is described. Also, enhancement results using various hyperspectral image datasets are provided. In general, it is found that the MAP/SMM method is able to reconstruct subpixel information in several principal components of the high-resolution hyperspectral image estimate, while the enhancement for conventional methods, like those based on least squares estimation, is limited primarily to the first principal component (i.e., the intensity component).


title: MUSIC-based algorithm for range-azimuth FMCW radar data processing without estimating number of targets

abstract: This paper presents an algorithm which based on 2D MUSIC technique and designed for the simultaneous estimation of range and azimuth target location in frequency modulate continuous wave (FMCW) radar systems. Forward-backward spatial smoothing technique is used to improve performance of the algorithm in fully or partially correlated situations. We show that 2D MUSIC spectrum can be estimate by using fast Fourier transform (FFT) without any information about number of targets that provide low computation complexity. The performance of the here-proposed algorithm is compared to the conventional digital beamforming technique (DBF). Simulation and experimental results are presented.


title: AGARSoC: Automated test and coverage-model generation for verification of accelerator-rich SoCs

abstract: SoC design trends show increasing integration of special-purpose, third-party hardware blocks to accelerate diverse types of computation. These accelerator blocks interact with each other in unexpected ways when integrated into a complex, accelerator-rich SoC. In this work we propose a novel solution that guides verification engineers to the high-priority accelerator interaction scenarios during RTL verification. We observe that interaction scenarios frequently exercised by software for the SoC, which is typically developed alongside the RTL, should be the highest priority targets for verification. To this end we analyze the behavior of software executed on high-level simulation models to identify commonly occurring accelerator interaction scenarios. We encapsulate scenarios observed from diverse software executions into an abstract representation that can then be used to extract coverage models and generate test programs. Our experiments show that our solution is able to identify frequently exercised scenarios, extract coverage models, and generate compact, high-quality tests for two completely different SoC designs.


title: Unusual enzymes involved in five pathways of glutamate fermentation

abstract: Anaerobic bacteria from the orders Clostridiales and Fusobacteriales are able to ferment glutamate by at least five different pathways, most of which contain enzymes with radicals in their catalytic pathways. The first two pathways proceed to ammonia, acetate and pyruvate via the coenzyme B12-dependent glutamate mutase, which catalyses the re-arrangement of the linear carbon skeleton to that of the branched-chain amino acid (2S,3S)-3-methylaspartate. Pyruvate then disproportionates either to CO2 and butyrate or to CO2, acetate and propionate. In the third pathway, glutamate again is converted to ammonia, CO2, acetate and butyrate. The key intermediate is (R)-2-hydroxyglutaryl-CoA, which is dehydrated to glutaconyl-CoA, followed by decarboxylation to crotonyl-CoA. The unusual dehydratase, containing an iron-sulfur cluster, is activated by an ATP-dependent one-electron reduction. The remaining two pathways require more then one organism for the complete catabolism of glutamate to short chain fatty acids. Decarboxylation of glutamate leads to 4-aminobutyrate, which is fermented by a second organism via the fourth pathway to acetate and butyrate, again mediated by an unusual dehydratase which catalyses the reversible dehydration of 4-hydroxybutyryl-CoA to crotonyl-CoA. The fifth pathway is the only one without decarboxylation, since the gamma-carboxylate of glutamate is reduced to the amino group of delta-aminovalerate, which then is fermented to acetate, propionate and valerate. The pathway involves the oxidative dehydration of 5-hydroxyvaleryl-CoA to 2,4-pentadienoyl-CoA followed by reduction to 3-pentenoyl-CoA and isomerisation to 2-pentenoyl-CoA.


title: A taxonomy for software testing projects

abstract: This article establishes taxonomy for software testing projects, allowing the development team or testing personnel to identify the tests to which the project must be subjected for validation. The taxonomy is focused on identifying software projects according to their technology. To establish the taxonomy, a development method comprised of 5 phases was applied. The developed taxonomy is comprised of 9 categories and 27 subcategories and was validated by a group of information technology (IT) managers and professionals in the field of IT through the use of a survey. The results obtained from the survey are subjected to the Mann-Whitney U test, which indicates that the taxonomy is validated. The taxonomy can be implemented in development organizations with or without a testing team that provides a classification for technology projects.


title: Ambience extraction and synthesis from stereo signals for multi-channel audio up-mix

abstract: In this paper we propose a frequency-domain technique to identify and extract the ambience information in stereo audio signals. The method is based on the computation of an inter-channel coherence index and a non-linear mapping function that allow us to determine time-frequency regions that consist mostly of ambience components in the two-channel signal. Ambience signals are then synthesized and used to feed the surround channels of a multichannel playback system. Simulation results demonstrate the effectiveness of the technique in extracting ambience information and up-mix tests on real audio reveal the various advantages and disadvantages of the system compared to previous up-mix strategies.


title: How Important is Weight Symmetry in Backpropagation?

abstract: Gradient backpropagation (BP) requires symmetric feedforward and feedback connections -- the same weights must be used for forward and backward passes. This "weight transport problem" (Grossberg 1987) is thought to be one of the main reasons to doubt BP's biologically plausibility. Using 15 different classification datasets, we systematically investigate to what extent BP really depends on weight symmetry. In a study that turned out to be surprisingly similar in spirit to Lillicrap et al.'s demonstration (Lillicrap et al. 2014) but orthogonal in its results, our experiments indicate that: (1) the magnitudes of feedback weights do not matter to performance (2) the signs of feedback weights do matter -- the more concordant signs between feedforward and their corresponding feedback connections, the better (3) with feedback weights having random magnitudes and 100% concordant signs, we were able to achieve the same or even better performance than SGD. (4) some normalizations/stabilizations are indispensable for such asymmetric BP to work, namely Batch Normalization (BN) (Ioffe and Szegedy 2015) and/or a "Batch Manhattan" (BM) update rule.


title: Practical partial evaluation for high-performance dynamic language runtimes

abstract: Most high-performance dynamic language virtual machines duplicate language semantics in the interpreter, compiler, and runtime system. This violates the principle to not repeat yourself. In contrast, we define languages solely by writing an interpreter. The interpreter performs specializations, e.g., augments the interpreted program with type information and profiling information. Compiled code is derived automatically using partial evaluation while incorporating these specializations. This makes partial evaluation practical in the context of dynamic languages: It reduces the size of the compiled code while still compiling all parts of an operation that are relevant for a particular program. When a speculation fails, execution transfers back to the interpreter, the program re-specializes in the interpreter, and later partial evaluation again transforms the new state of the interpreter to compiled code. We evaluate our approach by comparing our implementations of JavaScript, Ruby, and R with best-in-class specialized production implementations. Our general-purpose compilation system is competitive with production systems even when they have been heavily optimized for the one language they support. For our set of benchmarks, our speedup relative to the V8 JavaScript VM is 0.83x, relative to JRuby is 3.8x, and relative to GNU R is 5x.


title: Road-boundary detection and tracking using ladar sensing

abstract: Road-boundary detection is an integral and important function in advanced driver-assistance systems and autonomous vehicle navigation systems. A prominent feature of roads in urban, semi-urban, and similar environments, such as in theme parks, campus sites, industrial estates, science parks, and the like, is curbs on either side defining the road's boundary. Although vision is the most common and popular sensing modality used by researchers and automotive manufacturers for road-lane detection, it can pose formidable challenges in detecting road curbs under poor illumination, bad weather, and complex driving environments. This paper proposes a novel method based on extended Kalman filtering for fast detection and tracking of road curbs using successive range/bearing readings obtained from a scanning two-dimensional ladar measurement system. As compared with millimeter wave radar methods reported in the literature, the proposed technique is simpler and computationally more efficient. This is the first of its kind reported in the literature. Qualitative experimental results are presented from the application of the technique to a campus site environment to demonstrate the viability, effectiveness, and robustness.


title: A combined corner and edge detector

abstract: The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work. For example, we desire to obtain an understanding of natural scenes, containing roads, buildings, trees, bushes, etc., as typified by the two frames from a sequence illustrated in Figure 1. The solution to this problem that we are pursuing is to use a computer vision system based upon motion analysis of a monocular image sequence from a mobile camera. By extraction and tracking of image features, representations of the 3D analogues of these features can be constructed.


title: Radiant floor cooling coupled with dehumidification systems in residential buildings: A simulation-based analysis

abstract: Abstract The development of radiant cooling has stimulated an interest in new systems based on coupling ventilation with radiant cooling. However, radiant cooling systems may cause condensation to form on an active surface under warm and humid conditions during the cooling season. This phenomenon occurs when surface temperature falls below dew point. To prevent condensation, air humidity needs to be reduced with a dehumidification device or a mechanical ventilation system. There are two main options to achieve this. The first is to use dehumidification devices that reduce humidity, but are not coupled with ventilation, i.e. devices that handle room air and leave air change to infiltrations. The second is to combine a mechanical ventilation system with dehumidifying finned coils. This study analyzes the floor radiant cooling of a typical residential apartment within a multi-storey building in three Italian climate zones by means of a detailed simulation tool. Five systems were compared in terms of both indoor thermal comfort and energy consumption: radiant cooling without dehumidification; radiant cooling with a soft dehumidification device; radiant cooling with a dehumidification device which also supplies sensible cooling; radiant cooling coupled with fan coils; and radiant cooling with a mechanical ventilation system which dehumidifies and cools.


Control and its role in the process of ensuring knowledge continuity


title: Panorama image interpolation for real-time walkthrough

abstract: We propose a method to generate new views of a scene by capturing a few panorama images in real space and interpolating captured images. We describe a procedure for interpolating panoramas captured at four corners of a rectangle area without geometry, and present experimental results including walkthrough in real time. Our image-based method enables walking through space much more easily than using 3D modeling and rendering.


title: Harmonic Sum-based Method for Heart Rate Estimation using PPG Signals Affected with Motion Artifacts

abstract: Wearable photoplethysmography has recently become a common technology in heart rate (HR) monitoring. General observation is that the motion artifacts change the statistics of the acquired PPG signal. Consequently, estimation of HR from such a corrupted PPG signal is challenging. However, if an accelerometer is also used to acquire the acceleration signal simultaneously, it can provide helpful information that can be used to reduce the motion artifacts in the PPG signal. By dint of repetitive movements of the subjects hands while running, the accelerometer signal is found to be quasi-periodic. Over short-time intervals, it can be modeled by a finite harmonic sum (HSUM). Using the HSUM model, we obtain an estimate of the instantaneous fundamental frequency of the accelerometer signal. Since the PPG signal is a composite of the heart rate information (that is also quasi-periodic) and the motion artifact, we fit a joint HSUM model to the PPG signal. One of the harmonic sums corresponds to the heart-beat component in PPG and the other models the motion artifact. However, the fundamental frequency of the motion artifact has already been determined from the accelerometer signal. Subsequently, the HR is estimated from the joint HSUM model. The mean absolute error in HR estimates was 0.7359 beats per minute (BPM) with a standard deviation of 0.8328 BPM for 2015 IEEE Signal Processing cup data. The ground-truth HR was obtained from the simultaneously acquired ECG for validating the accuracy of the proposed method. The proposed method is compared with four methods that were recently developed and evaluated on the same dataset.


title: Analysis of user comments: an approach for software requirements evolution

abstract: User feedback is imperative in improving software quality. In this paper, we explore the rich set of user feedback available for third party mobile applications as a way to extract new/changed requirements for next versions. A potential problem using this data is its volume and the time commitment involved in extracting new/changed requirements. Our goal is to alleviate part of the process through automatic topic extraction. We process user comments to extract the main topics mentioned as well as some sentences representative of those topics. This information can be useful for requirements engineers to revise the requirements for next releases. Our approach relies on adapting information retrieval techniques including topic modeling and evaluating them on different publicly available data sets. Results show that the automatically extracted topics match the manually extracted ones, while also significantly decreasing the manual effort.


title: Highway State Gating for Recurrent Highway Networks: improving information flow through time

abstract: Recurrent Neural Networks (RNNs) play a major role in the field of sequential learning, and have outperformed traditional algorithms on many benchmarks. Training deep RNNs still remains a challenge, and most of the state-of-the-art models are structured with a transition depth of 2-4 layers. Recurrent Highway Networks (RHNs) were introduced in order to tackle this issue. These have achieved state-of-the-art performance on a few benchmarks using a depth of 10 layers. However, the performance of this architecture suffers from a bottleneck, and ceases to improve when an attempt is made to add more layers. In this work, we analyze the causes for this, and postulate that the main source is the way that the information flows through time. We introduce a novel and simple variation for the RHN cell, called Highway State Gating (HSG), which allows adding more layers, while continuing to improve performance. By using a gating mechanism for the state, we allow the net to"choose"whether to pass information directly through time, or to gate it. This mechanism also allows the gradient to back-propagate directly through time and, therefore, results in a slightly faster convergence. We use the Penn Treebank (PTB) dataset as a platform for empirical proof of concept. Empirical results show that the improvement due to Highway State Gating is for all depths, and as the depth increases, the improvement also increases.


title: Injection-Locked Wideband FM Demodulation at IF

abstract: A low-power FM demodulator operating across a 2–10-GHz IF bandwidth for its application in wideband heterodyne receivers is presented. A four-stage ring oscillator locks to one-fourth of the input FM, thereby reducing the energy required for wideband demodulation. The measurements show that the oscillator is capable of locking to at least a modulating frequency of 400 MHz, and further testing is limited by the FM source. Linear demodulation of the quadrature-phased outputs is realized using a low-power folded CMOS mixer, even as the fractional bandwidth of the input FM approaches unity. The inductorless 65-nm CMOS prototype occupies 0.17 mm2 and dissipates 3.2 mW from 1.2 V at the quiescent point. The measured SNR sensitivity is 8 dB and the demodulator bit-error rate is 0.1% at 10 Mb/s for a 45-mVpp input signal at IF.


title: 24"×18" Fan-out panel level packing

abstract: Fan-out Wafer Level Packaging (FOWLP) is one of the latest packaging trends in microelectronics. Mold embedding for this technology is currently done on wafer level up to 12"/300 mm size. For higher productivity and therewith lower costs larger mold embedding form factors are forecasted for the near future. Following the wafer level approach then the next step will be a reconfigured wafer diameter of 450 mm. An alternative option would be leaving the wafer shape and moving to panel sizes leading to Fan-out Panel Level Packaging (FOPLP). Sizes for the panel could range up to 18"×24" or even larger. For reconfigured mold embedding, compression mold processes are used in combination with liquid or granular compound. As an alternative process, lamination can be considered where also sheet compounds can be used. Using maskless laser direct imaging technologies (LDI) instead of photolithography has a high potential for further cost reduction with intrinsic process advantages. The LDI cost advantage is backed by LDI availability for large panel sizes, also including 450 mm wafer form factors. Already today PCB technologies offer the potential for large area panel packaging up to 24"×18"/610 × 457 mm ::: 2 ::: and can be applied to form a redistribution layer [RDL] for large area reconfigured wafers or panels, replacing thin film redistribution. For PCB based RDLs a resin coated copper sheet (RCC) is laminated on the reconfigured wafer or panel, respectively. Micro vias are drilled through the RCC layer to the die pads and electrically connected by Cu plating. Final process step is the etching of Cu lines using LDI techniques for maskless patterning. State of the art equipment and materials allow the manufacturing of structures down to 20 μm lines and spaces with a clear development trend to 10 μm lines and spaces and hence getting close to photolithography thin film structure sizes. Based on the technology described above the Fan-out Panel Level Packaging approach will be demonstrated on full 24"×18"/610×457 mm ::: 2 ::: format including large area embedding and redistribution. Related technology challenges as die shift, warpage, panel handling or yield will be discussed in detail. Using maskless LDI technology real die positions can be automatically adapted to the redistribution and hence less accurate die placement can be compensated and higher die shift could be tolerated which is a big advantage when moving towards large area with acceptable yield. In summary this paper describes the technological path from wafer level embedding to 24"×18" fan-out panel level packaging technology in combination with low cost PCB based RDL processes. The technology described offers a cost effective packaging solution for various scenarios e.g. as packages for handheld consumer applications or bio-medical applications as sensor integration into microfluidics.


title: StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity

abstract: Deep neural networks are a branch in machine learning that has seen a meteoric rise in popularity due to its powerful abilities to represent and model high-level abstractions in highly complex data. One area in deep neural networks that are ripe for exploration is neural connectivity formation. A pivotal study on the brain tissue of rats found that synaptic formation for specific functional connectivity in neocortical neural microcircuits can be surprisingly well modeled and predicted as a random formation. Motivated by this intriguing finding, we introduce the concept of StochasticNet where deep neural networks are formed via stochastic connectivity between neurons. As a result, any type of deep neural networks can be formed as a StochasticNet by allowing the neuron connectivity to be stochastic. Stochastic synaptic formations in a deep neural network architecture can allow for efficient utilization of neurons for performing specific tasks. To evaluate the feasibility of such a deep neural network architecture, we train a StochasticNet using four different image datasets (CIFAR-10, MNIST, SVHN, and STL-10). Experimental results show that a StochasticNet using less than half the number of neural connections as a conventional deep neural network achieves comparable accuracy and reduces overfitting on the CIFAR-10, MNIST, and SVHN data sets. Interestingly, StochasticNet with less than half the number of neural connections, achieved a higher accuracy (relative improvement in test error rate of $\sim 6$ % compared to ConvNet) on the STL-10 data set than a conventional deep neural network. Finally, the StochasticNets have faster operational speeds while achieving better or similar accuracy performances.