title: Task Scheduling for Highly Concurrent Analytical and Transactional Main-Memory Workloads

abstract: Task scheduling typically employs a worker thread per hardware context to process a dynamically changing set of tasks. It is an appealing solution to exploit modern multi-core processors, as it eases parallelization and avoids unnecessary context switches and their associated costs. Naively bundling DBMS operations into tasks, however, can result in sub-optimal usage of CPU resources: highly contending transactional workloads involve blocking tasks. Moreover, analytical queries assume they can use all available resources while issuing tasks, resulting in an excessive number of tasks and an unnecessary associated scheduling overhead. In this paper, we show how to overcome these problems and exploit the performance benefits of task scheduling for main-memory DBMS. Firstly, we use application knowledge about blocking tasks to dynamically adapt the number of workers and aid the OS scheduler to saturate CPU resources. In addition, we show that analytical queries should issue a low number of tasks in cases of high concurrency, to avoid excessive synchronization, communication and scheduling costs. To achieve that, we maintain a concurrency hint, reflecting recent CPU availability, that partitionable analytical operations can use as a limit while adjusting their task granularity. We integrate our scheduler into a commercial main-memory column-store, and show that it improves the performance of mixed workloads, by up to 12.5% for analytical queries and 370% for transactional queries.


title: Wearable 3D lung ventilation monitoring system with multi frequency electrical impedance tomography

abstract: The wearable 3D lung ventilation monitoring system is proposed with electrical impedance tomography (EIT). It is implemented on the hub-module and impedance acquisition belt. Thanks to the active electrode SoC in the impedance acquisition belt, the proposed system provides wide (94dB) and accurate (1.4mΩ) impedance acquisition. Since the optimal frequency of stimulation current varies from individual, the selecting optimal stimulation frequency scheme is proposed for multi-frequency EIT. After the measured impedance data is transferred to hub-SoC in the Hub board, it is preprocessed and transferred to an external imaging device using Bluetooth module. As a result, the proposed system can display EIT images which are reconstructed with 90% of accuracy and 10 frames/s of real-time 3D lung images. It has only 357g weight and operates at least 6 hours with 3.7V 200mAh battery.


title: Design of a miniature 6-axis force/torque sensor for robotic applications

abstract: A 6-axis force/torque (F/T) information is important to improve the performance of robotic applications. This paper presents a novel design of 6-axis F/T sensor that provides much simple configuration and easy fabrication/assembly processes. To use the space of the sensor efficiently, a new capacitance sensing cell is designed to detect a normal and a shear forces at the same time. The configuration of the sensor is developed, consisting of only three parts: a sensing printed circuit board, a deformable part, and a base part.


On Scalability and Performance of Permissioned Blockchain Systems


title: Low-cost AUV based on Arduino open source microcontroller board for oceanographic research applications in a collaborative long term deployment missions and suitable for combining with an USV as autonomous automatic recharging platform

abstract: The challenge of extending the autonomy in AUV deployments is one of the most important issues in oceanographic research today. The possibility of maintaining a team of AUV under deployment in a defined area of interest for a long period could provide an additional source of information [8]. All this data in combination with the measures provided by buoys and sea gliders used for slow motion and long range operations will be very valuable. A group of low cost AUV's in alternative automatic switching system navigation-charging operation, could allow a kind of continuous surveying operation. This work is the continuation of the ideas that some of the authors previously presented in the AUV 2010 conference at MBARI [8]. At this conference was proposed the great interest for researching oceanic processes on two areas near Cartagena, Spain: cape Tinoso and the Mar Menor a shallow coastal lagoon. Both areas require a different research structure configuration because of their opposite characteristics. The Mar Menor is a shallow salty lagoon 20 miles long with 7 m of maximum depth and particular features. This lagoon seems to present a sort of oceanic behavior and can be compared with the major oceans but a minor scale. The second area considered is cape Tinoso, a very deep area in the Mediterranean Sea where the presence of a self-break provides an interesting potential for the research of the effect of upwelling currents.


title: A stem cell-specific silencer in the primer-binding site of a retrovirus.

abstract: Retrovirus expression in embryonal carcinoma (EC) cells is blocked at a postintegration stage of the viral life cycle, in part because of the inadequate function of the viral long terminal repeat promoter in this cell type. However, selection for retrovirus expression in EC cells has identified mutations in Moloney murine leukemia virus (M-MuLV) located in the tRNA primer-binding site (PBS) region which relieve the EC cell-specific repression. We have found that exchanging the M-MuLV proline PBS for a glutamine one in a recombinant virus permits expression in EC cells. By using the recombinant virus as a backbone, the EC cell-specific repressor-binding site (RBS) element has been mapped to M-MuLV nucleotides 147 to 174. The RBS does not require precise positioning downstream of the M-MuLV promoter and can function in either orientation and in an intron, indicating that the regulatory effect is probably at the DNA, rather than RNA, level. We also show that the RBS element can repress heterologous promoters from an upstream position. Our results indicate that the RBS acts as a silencer that its inhibitory effect is mediated by a trans-acting factor, and that the mechanism of action is probably at the level of transcription. Through in vitro binding assays we have identified a binding factor which specifically recognizes the wild-type RBS sequence (binding factor A). The binding characteristics of factor A suggest that it is a stem cell repressor which acts at the M-MuLV RBS. Our DNA-binding assays also have identified a unique binding factor (binding factor Hp) which specifically recognizes a hemimethylated form of the wild-type RBS. This factor may play a role in methylation mediated control of retrovirus expression in EC cells.


title: Linear Scan Register Allocation in the Context of SSA Form and Register Constraints

abstract: Linear scan register allocation is an efficient alternative to the widely used graph coloring approach. We show how this algorithm can be applied to register-constrained architectures like the Intel x86. Our allocator relies on static single assignment form, which simplifies data flow analysis and tends to produce short live intervals. It makes use of lifetime holes and instruction weights to improve the quality of the allocation. Our measurements confirm that linear scan is several times faster than graph coloring for medium-sized to large programs.


title: Design and analysis of single patch, 2×1 and 4×1 microstrip antenna arrays

abstract: Considering the changing trends in communication system, the requirement of a low cost, minimal weight and a low profile antenna is much needed. Therefore, fulfilling the above-mentioned requirements microstrip patch antennas are designed. The objective of this paper is to design a single patch, 2×1 and 4×1 patch antenna array and also analyze their advantages and disadvantages depending upon the results obtained. Antenna parameters such as radiation pattern, antenna gain, directivity and power obtained are studied. The work involves designing of single patch, 2×1 and 4×1 patch arrayat resonant frequency of 2.4GHz. The results are obtained using Advance Design System (ADS).


title: Examining the Factors Influencing Participants ’ Knowledge Sharing Behavior in Virtual Learning Communities

abstract: Increasing organizations and educational institutions have implemented virtual learning communities to encourage knowledge sharing. However, this task can not be accomplished simply by grouping people together and telling them “sharing your knowledge will make you learn better”. This research attempts to examine the factors influencing knowledge sharing from the perspective of human behavior. Theory of Planned Behavior is integrated with social network ties and empirical findings from virtual learning community literature to develop the research model. The current research model comprises eight hypotheses to explore questions of whether social network ties, learners’ attitude toward knowledge sharing, learners’ beliefs of their capabilities in performing online knowledge sharing, and subjective norms relate to knowledge sharing intention, which leads to actual behavior in a virtual learning environment. This study empirically validates the hypothesized relationships using a field survey of college students and MBA students enrolling courses conducted in a virtual learning community. Attitude, subjective norm, Web-specific self-efficacy and social network ties are shown to be good predictors of knowledge sharing intention which, in turn, is significantly associated with knowledge sharing behavior. Knowledge creation self-efficacy does not significantly impact knowledge sharing intention.


title: A Radial Displacement Self-sensing Method for Bearingless Switched Reluctance Motors

abstract: Since traditional displacement sensors were sensitive to temperature and electromagnetic noise,and tended to cause size,cost,and complexity increment,a kind of practical radial displacement self-test method was put forward in this paper.This method is based on mutual inductance between torque winding and radial force winding,which is a nonlinear function of radial displacement.Detailed steps were as follows: firstly,injected high frequency testing current into the torque-winding;secondly,filtered the mutual inductance voltage on radial force winding caused by this test current;thirdly,demodulated the measured signal and then processed with a low-pass filter to separate the direct current portion;and lastly,rotor’s radial displacement could be detected after simple mathematical treatment.With this method,no special radial displacement sensor is required anymore,and the reliability of whole system can also be improved.Simulation and dSPACE platform experimental results confirmed this simple and convenient method.


title: Illumination-Aware Age Progression

abstract: We present an approach that takes a single photograph of a child as input and automatically produces a series of age-progressed outputs between 1 and 80 years of age, accounting for pose, expression, and illumination. Leveraging thousands of photos of children and adults at many ages from the Internet, we first show how to compute average image subspaces that are pixel-to-pixel aligned and model variable lighting. These averages depict a prototype man and woman aging from 0 to 80, under any desired illumination, and capture the differences in shape and texture between ages. Applying these differences to a new photo yields an age progressed result. Contributions include relightable age subspaces, a novel technique for subspace-to-subspace alignment, and the most extensive evaluation of age progression techniques in the literature.


Towards a Blockchain Ontology


title: Fake finger detection by skin distortion analysis

abstract: Attacking fingerprint-based biometric systems by presenting fake fingers at the sensor could be a serious threat for unattended applications. This work introduces a new approach for discriminating fake fingers from real ones, based on the analysis of skin distortion. The user is required to move the finger while pressing it against the scanner surface, thus deliberately exaggerating the skin distortion. Novel techniques for extracting, encoding and comparing skin distortion information are formally defined and systematically evaluated over a test set of real and fake fingers. The proposed approach is privacy friendly and does not require additional expensive hardware besides a fingerprint scanner capable of capturing and delivering frames at proper rate. The experimental results indicate the new approach to be a very promising technique for making fingerprint recognition systems more robust against fake-finger-based spoofing attempts


title: How deep is knowledge tracing?

abstract: In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to predicting students' performance as they work through a series of exercises---termed deep knowledge tracing or DKT---has demonstrated a stunning performance advantage over the mainstay of the field, Bayesian knowledge tracing or BKT. In this article, we attempt to understand the basis for DKT's advantage by considering the sources of statistical regularity in the data that DKT can leverage but which BKT cannot. We hypothesize four forms of regularity that BKT fails to exploit: recency effects, the contextualized trial sequence, inter-skill similarity, and individual variation in ability. We demonstrate that when BKT is extended to allow it more flexibility in modeling statistical regularities---using extensions previously proposed in the literature---BKT achieves a level of performance indistinguishable from that of DKT. We argue that while DKT is a powerful, useful, general-purpose framework for modeling student learning, its gains do not come from the discovery of novel representations---the fundamental advantage of deep learning. To answer the question posed in our title, knowledge tracing may be a domain that does not require `depth'; shallow models like BKT can perform just as well and offer us greater interpretability and explanatory power.


title: Control of Micro Mirrors for High Precision Performance

abstract: Microelectromechanical Systems (MEMS) technology has already led to advances in optical imaging, scanning, communications and adaptive applications. Many of these efforts have been approached without the use of feedback control techniques that are common in macro-scale operations to ensure repeatable and precise performance. This paper examines control techniques and related issues of precision performance as applied to a one-degree-of-freedom electrostatic MEMS micro mirror.


title: Anisotropic Diffusion Descriptors

abstract: Spectral methods have recently gained popularity in many domains of computer graphics and geometry processing, especially shape processing, computation of shape descriptors, distances, and correspondence. Spectral geometric structures are intrinsic and thus invariant to isometric deformations, are efficiently computed, and can be constructed on shapes in different representations. A notable drawback of these constructions, however, is that they are isotropic, i.e., insensitive to direction. In this paper, we show how to construct direction-sensitive spectral feature descriptors using anisotropic diffusion on meshes and point clouds. The core of our construction are directed local kernels acting similarly to steerable filters, which are learned in a task-specific manner. Remarkably, while being intrinsic, our descriptors allow to disambiguate reflection symmetries. We show the application of anisotropic descriptors for problems of shape correspondence on meshes and point clouds, achieving results significantly better than state-of-the-art methods.


title: A systematic review of standing and treadmill desks in the workplace

abstract: Abstract Objectives Standing and treadmill desks are intended to reduce the amount of time spent sitting in today's otherwise sedentary office. Proponents of these desks suggest that health benefits may be acquired as standing desk use discourages long periods of sitting, which has been identified as an independent health risk factor. Our objectives were thus to analyze the evidence for standing and treadmill desk use in relation to physiological (chronic disease prevention and management) and psychological (worker productivity, well-being) outcomes. Methods A computer-assisted systematic search of Medline, PubMed, PsycINFO, SPORTDiscus, CINAHL, CENTRAL, and EMBASE databases was employed to identify all relevant articles related to standing and treadmill desk use. Results Treadmill desks led to the greatest improvement in physiological outcomes including postprandial glucose, HDL cholesterol, and anthropometrics, while standing desk use was associated with few physiological changes. Standing and treadmill desks both showed mixed results for improving psychological well-being with little impact on work performance. Discussion Standing and treadmill desks show some utility for breaking up sitting time and potentially improving select components of health. At present; however, there exist substantial evidence gaps to comprehensively evaluate the utility of each type of desk to enhance health benefits by reducing sedentary time.


title: Sampling of Attributed Networks from Hierarchical Generative Models

abstract: Network sampling is a widely used procedure in social network analysis where a random network is sampled from a generative network model (GNM). Recently proposed GNMs, allow generation of networks with more realistic structural characteristics than earlier ones. This facilitates tasks such as hypothesis testing and sensitivity analysis. However, sampling of networks with correlated vertex attributes remains a challenging problem. While the recent work of \cite{Pfeiffer:14} has provided a promising approach for attributed-network sampling, the approach was developed for use with relatively simple GNMs and does not work well with more complex hierarchical GNMs (which can model the range of characteristics and variation observed in real world networks more accurately). In contrast to simple GNMs where the probability mass is spread throughout the space of edges more evenly, hierarchical GNMs concentrate the mass to smaller regions of the space to reflect dependencies among edges in the network---this produces more realistic network characteristics, but also makes it more difficult to identify candidate networks from the sampling space. In this paper, we propose a novel sampling method, CSAG, to sample from hierarchical GNMs and generate networks with correlated attributes. CSAG constrains every step of the sampling process to consider the structure of the GNM---in order to bias the search to regions of the space with higher likelihood. We implemented CSAG using mixed Kronecker Product Graph Models and evaluated our approach on three real-world datasets. The results show that CSAG jointly models the correlation and structure of the networks better than the state of the art. Specifically, CSAG maintains the variability of the underlying GNM while providing a ≥ 5X reduction in attribute correlation error.


title: How opinions are received by online communities: A case study on Amazon.com helpfulness votes

abstract: There are many on-line settings in which users publicly express opinions. A number of these offer mechanisms for other users to evaluate these opinions; a canonical example is Amazon.com, where reviews come with annotations like "26 of 32 people found the following review helpful." Opinion evaluation appears in many off-line settings as well, including market research and political campaigns. Reasoning about the evaluation of an opinion is fundamentally different from reasoning about the opinion itself: rather than asking, "What did Y think of X?", we are asking, "What did Z think of Y's opinion of X?" Here we develop a framework for analyzing and modeling opinion evaluation, using a large-scale collection of Amazon book reviews as a dataset. We find that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product. As part of our approach, we develop novel methods that take advantage of the phenomenon of review "plagiarism" to control for the effects of text in opinion evaluation, and we provide a simple and natural mathematical model consistent with our findings. Our analysis also allows us to distinguish among the predictions of competing theories from sociology and social psychology, and to discover unexpected differences in the collective opinion-evaluation behavior of user populations from ifferent countries.


title: An Intelligent Secure and Privacy-Preserving Parking Scheme Through Vehicular Communications

abstract: There are always frustrations for drivers in finding parking spaces and being protected from auto theft. In this paper, to minimize the drivers' hassle and inconvenience, we propose a new intelligent secure privacy-preserving parking scheme through vehicular communications. The proposed scheme is characterized by employing parking lot RSUs to surveil and manage the whole parking lot and is enabled by communication between vehicles and the RSUs. Once vehicles that are equipped with wireless communication devices, which are also known as onboard units, enter the parking lot, the RSUs communicate with them and provide the drivers with real-time parking navigation service, secure intelligent antitheft protection, and friendly parking information dissemination. In addition, the drivers' privacy is not violated. Performance analysis through extensive simulations demonstrates the efficiency and practicality of the proposed scheme.


title: Theory building from cases: Opportunities and challenges

abstract: This article discusses the research strategy of theory building from cases, particularly multiple cases. Such a strategy involves using one or more cases to create theoretical constructs, propositions, and/or midrange theory from case-based, empirical evidence. Replication logic means that each case serves as a distinct experiment that stands on its own merits as an analytic unit. The frequent use of case studies as a research strategy has given rise to some challenges that can be mitigated by the use of very precise wording and thoughtful research design.


title: Autonomous Take-Off and Flight of a Tethered Aircraft for Airborne Wind Energy

abstract: A control design approach to achieve fully autonomous take-off and flight maneuvers with a tethered aircraft is presented and demonstrated in real-world flight tests with a small-scale prototype. A ground station equipped with a controlled winch and a linear motion system accelerates the aircraft to take-off speed and controls the tether reeling in order to limit the pulling force. This setup corresponds to airborne wind energy systems with ground-based energy generation and rigid aircrafts. A simple model of the aircraft's dynamics is introduced and its parameters are identified from experimental data. A model-based, hierarchical feedback controller is then designed, whose aim is to manipulate the elevator, aileron and propeller inputs in order to stabilize the aircraft during the take-off and to achieve figure-of-eight flight patterns parallel to the ground. The controller operates in a fully decoupled mode with respect to the ground station. Parameter tuning and stability/robustness aspect are discussed, too. The experimental results indicate that the controller is able to achieve satisfactory performance and robustness, notwithstanding its simplicity, and confirm that the considered take-off approach is technically viable and solves the issue of launching this kind of airborne wind energy systems in a compact space and at low additional cost.


title: FEM analysis of one element prosthesis on dental implant

abstract: In this paper, a complete biomechanical system made up of bone, implant and ceramic crown under axial loading is investigated by Finite Element Method (FEM). The system response evaluation has a major role in detecting the critical zones where possible damage, failure or rupture may occur jeopardizing the implant stability. Very accurate 3D geometrical model is used to calculate the stress, strain and displacement state in implant and surrounding bone. This analysis is completely concordant with clinical studies.


title: Facial Soft Tissue Depths in Craniofacial Identification (Part I): An Analytical Review of the Published Adult Data*

abstract: With the ever increasing production of average soft tissue depth studies, data are becoming increasingly complex, less standardized, and more unwieldy. So far, no overarching review has been attempted to determine: the validity of continued data collection; the usefulness of the existing data subcategorizations; or if a synthesis is possible to produce a manageable soft tissue depth library. While a principal components analysis would provide the best foundation for such an assessment, this type of investigation is not currently possible because of a lack of easily accessible raw data (first, many studies are narrow; second, raw data are infrequently published and/or stored and are not always shared by some authors). This paper provides an alternate means of investigation using an hierarchical approach to review and compare the effects of single variables on published mean values for adults whilst acknowledging measurement errors and within-group variation. The results revealed: (i) no clear secular trends at frequently investigated landmarks; (ii) wide variation in soft tissue depth measures between different measurement techniques irrespective of whether living persons or cadavers were considered; (iii) no clear clustering of non-Caucasoid data far from the Caucasoid means; and (iv) minor differences between males and females. Consequently, the data were pooled across studies using weighted means and standard deviations to cancel out random and opposing study-specific errors, and to produce a single soft tissue depth table with increased sample sizes (e.g., 6786 individuals at pogonion).


title: Trajectory planning and tracking of ball and plate system using hierarchical fuzzy control scheme

abstract: Ball and plate system is the extension of traditional ball and beam system. In this paper, a trajectory planning and tracking problem of ball and plate system is put forward to proof-test diverse control schemes. Firstly, we derive the mathematical model of the ball and plate system in detail. Then a hierarchical fuzzy control scheme is proposed to deal with this problem. Our scheme is composed of three levels. The lowest level is a TS type fuzzy tacking controller; the middle level is a fuzzy supervision controller that takes actions in extreme situations; and the top level is a fuzzy planning controller that determines the desired trajectory. In order to optimize the ball's moving trajectory, the genetic algorithm is introduced to adjust the output membership functions of the fuzzy planning controller. Simulation results have shown that the hierarchical fuzzy control scheme can control the ball from a point to another without hitting the obstacles and in the least time.


title: Semi-Supervised Self-Training of Object Detection Models

abstract: The construction of appearance-based object detection systems is time-consuming and difficult because a large number of training examples must be collected and manually labeled in order to capture variations in object appearance. Semi-supervised training is a means for reducing the effort needed to prepare the training set by training the model with a small number of fully labeled examples and an additional set of unlabeled or weakly labeled examples. In this work we present a semi-supervised approach to training object detection systems based on self-training. We implement our approach as a wrapper around the training process of an existing object detector and present empirical results. The key contributions of this empirical study is to demonstrate that a model trained in this manner can achieve results comparable to a model trained in the traditional manner using a much larger set of fully labeled data, and that a training data selection metric that is defined independently of the detector greatly outperforms a selection metric based on the detection confidence generated by the detector.


title: Cluster based Outlier Detection

abstract: detection is a fundamental issue in data mining, specifically it has been used to detect and remove anomalous objects from data.mining. The proposed approach to detect outlier includes three methods which are clustering, pruning and computing outlier score. For clustering k-means algorithm is used which partition the dataset into given number of clusters. In pruning, based on some distance measure, points which are closed to centroid of each cluster are pruned. For the unpruned points, local distance based outlier factor (LDOF) measure is calculated. A measure called LDOF, tells how much a point is deviating from its neighbors. The high LDOF value of a point indicates that the point is deviating more from its neighbors and probably it may be an outlier. General Terms Outlier, cluster, pruning, outlier score, k nearest neighbor KeywordsLDOF


title: A Machine Learning Approach for Performance Prediction and Scheduling on Heterogeneous CPUs

abstract: As heterogeneous systems become more ubiquitous, computer architects will need to develop novel CPU scheduling techniques capable of exploiting the diversity of computational resources. Accurately estimating the performance of applications on different heterogeneous resources can provide a significant advantage to heterogeneous schedulers seeking to improve system performance. Recent advances in machine learning techniques including artificial neural network models have led to the development of powerful and practical prediction models for a variety of fields. As of yet, however, no significant leaps have been taken towards employing machine learning for heterogeneous scheduling in order to maximize system throughput.In this paper we propose a unique throughput maximizing heterogeneous CPU scheduling model that uses machine learning to predict the performance of multiple threads on diverse system resources at the scheduling quantum granularity. We demonstrate how lightweight artificial neural networks (ANNs) can provide highly accurate performance predictions for a diverse set of applications thereby helping to improve heterogeneous scheduling efficiency. We show that online training is capable of increasing prediction accuracy but deepening the complexity of the ANNs can result in diminishing returns. Notably, our approach yields 25% to 31% throughput improvements over conventional heterogeneous schedulers for CPU and memory intensive applications.


title: A comparative evaluation of outlier detection algorithms: experiments and analyses

abstract: Abstract We survey unsupervised machine learning algorithms in the context of outlier detection. This task challenges state-of-the-art methods from a variety of research fields to applications including fraud detection, intrusion detection, medical diagnoses and data cleaning. The selected methods are benchmarked on publicly available datasets and novel industrial datasets. Each method is then submitted to extensive scalability, memory consumption and robustness tests in order to build a full overview of the algorithms’ characteristics.


title: An ERP study of syntactic processing in English and nonsense sentences

abstract: The timecourse of the interaction between syntactic and semantic information during sentence processing in monolingual native English speakers was investigated using event-related potentials (ERPs). To examine the effects of semantic information on syntactic processing, the results for normal English sentences were compared to those for semantically impoverished nonsense (Jabberwocky) sentences. Within each sentence type condition, half of the sentences contained a syntactic violation. Violations elicited a larger amplitude N1 and more negative ERPs around 200 ms after the onset of the critical word relative to the grammatical condition. Although these effects were observed in both sentence types, they were anteriorly distributed for English sentences only. Moreover, the P600 elicited by the syntactic violation was attenuated in processing Jabberwocky as compared to English sentences. These results suggest that semantic and syntactic information are integrated during the earlier stages of syntactic processing indexed by the anterior negativities, and that these interactions continue in the later stages of processing indexed by the P600.


title: A Tutorial on Battery Simulation- Matching Power Source to Electronic System

abstract: We use an electrical circuit model to simulate the performance of a bat- tery as it powers the operation of a digital circuit. For a hypothetical elec- tronic system containing 70 million gates implemented in 45nm CMOS tech- nology the problem ofnding a suitable battery is analyzed. The proposed three part solution consists of (1) circuit simulation to determine critical path delay and average current as functions of supply voltage, (2) battery simulation to determine its efficiency and lifetime (time between recharges) at various current loads and tond suitable batteries for the electronic sys- tem, and (3) derivation of operational modes (supply voltages and clock frequencies) for maximum performance and minimum energy, respectively.


title: CARSKit: A Java-Based Context-Aware Recommendation Engine

abstract: Recommender system has been demonstrated as one of the most useful tools to assist users' decision makings. Several recommendation algorithms have been developed and implemented by both commercial and open-source recommendation libraries. Context-aware recommender system (CARS) emerged as a novel research direction during the past decade and many contextual recommendation algorithms have been proposed. Unfortunately, no recommendation engines start to embed those algorithms in their kits, due to the special characteristics of the data format and processing methods in the domain of CARS. This paper introduces an open-source Java-based context-aware recommendation engine named as CARSKit which is recognized as the 1st open source recommendation library specifically designed for CARS. It implements the state-of-the-art context-aware recommendation algorithms, and we will showcase the ease with which CARSKit allows recommenders to be configured and evaluated in this demo.


title: Performance of Tri-Band Multi-Polarized Array Antenna for 5G Mobile Base Station Adopting Polarization and Directivity Control

abstract: In this paper, the performance of tri-band multi-polarized adaptive array antenna for 5G mobile base station is introduced adopting the polarization and directivity control. First, the design of multi-band circularly polarized antenna element at 28/38/48 GHz is presented. The measured results show that the designed antenna element has a reflection coefficient of less than −20 dB in the assigned frequency bands of 28/38/48 GHz with a realized antenna gain of 7.82 dB, 8.39 dB, and 7.73 dB, respectively. Based on the designed antenna element, a 32-element are distributed in an octagonal prism configuration is presented as possible candidates for the future 5G mmWave cellular networks. The antenna array performance is studied in terms of gain, radiation efficiency, return loss, and axial ratio for different scenarios, in addition, the metrics of total scan pattern and coverage efficiency are investigated. To optimize the antenna parameters and synthesizing multi-beam patterns, a modified version of hybrid gravitational search algorithm and particle swarm optimization algorithm is introduced. This enhancement has improved the global search ability and accelerates the convergence capability. The proposed array antenna achieved good S11 < −20 dB with high isolation between the array elements. Besides, an acceptable realized gain of 16.85 dB on average with a radiation efficiency of more than 82 % is obtained in the entire operation bands.


title: Randomized Quasi-Newton Updates are Linearly Convergent Matrix Inversion Algorithms

abstract: We develop and analyze a broad family of stochastic/randomized algorithms for inverting a matrix. We also develop specialized variants maintaining symmetry or positive definiteness of the iterates. All methods in the family converge globally and linearly (i.e., the error decays exponentially), with explicit rates. In special cases, we obtain stochastic block variants of several quasi-Newton updates, including bad Broyden (BB), good Broyden (GB), Powell-symmetric-Broyden (PSB), Davidon-Fletcher-Powell (DFP) and Broyden-Fletcher-Goldfarb-Shanno (BFGS). Ours are the first stochastic versions of these updates shown to converge to an inverse of a fixed matrix. Through a dual viewpoint we uncover a fundamental link between quasi-Newton updates and approximate inverse preconditioning. Further, we develop an adaptive variant of randomized block BFGS, where we modify the distribution underlying the stochasticity of the method throughout the iterative process to achieve faster convergence. By inverting several matrices from varied applications, we demonstrate that AdaRBFGS is highly competitive when compared to the well established Newton-Schulz and minimal residual methods. In particular, on large-scale problems our method outperforms the standard methods by orders of magnitude. Development of efficient methods for estimating the inverse of very large matrices is a much needed tool for preconditioning and variable metric optimization methods in the advent of the big data era.


title: Teaching self-driving cars to dream: A deeply integrated, innovative approach for solving the autonomous vehicle validation problem

abstract: Validating autonomous vehicles is a tough problem that, if not solved in a timely manner, might hinder the release of autonomous vehicles. Standard software testing techniques might not be sufficient to validate such complex systems, which is why, in this paper, we present a novel approach to solve this problem. We introduce a new validation space concept that is based on a network of spatiotemporal state lattices to generate the motion of dynamic traffic participants. This space is sparse and fractured in the beginning but becomes more and more integrated and dense as the vehicles experience new situations. Therefore we propose new testing techniques for efficiency as well as an overall workflow that requires a new safety module to be embedded into the automated vehicle's architecture.


title: Inter-turn short circuit fault analysis of PWM inverter fed three-phase induction motor using Finite Element Method

abstract: This paper deals with the inter-turn short circuit fault analysis of Pulse Width Modulated (PWM) inverter fed three-phase Induction Motor (IM) using Finite Element Method (FEM). The short circuit in the stator winding of a 3-phase IM start with an inter-turn fault and if left undetected it progresses to a phase-phase fault or phase-ground fault. In main fed IM a popular technique known as Motor Current Signature Analysis (MCSA) is used to detect the inter-turn fault. But if the machine is fed from PWM inverter MCSA fails, due to high frequency inverter switching, the current spectrum will be rich in noise causing the fault detection difficult. An electromagnetic field analysis of inverter fed IM is carried out with 25% and 50% of stator winding inter-turn short circuit fault severity using FEM. The simulation is carried out on a 2.2kW IM using Ansys Maxwell Finite Element Analysis (FEA) tool. Comparisons are made on the various electromagnetic field parameters like flux lines distribution, flux density, radial air gap flux density between a healthy and faulty (25% & 50% severity) IM.


title: MobiClique: middleware for mobile social networking

abstract: We consider a mobile ad hoc network setting where Bluetooth enabled mobile devices communicate directly with other devices as they meet opportunistically. We design and implement a novel mobile social networking middleware named MobiClique. MobiClique forms and exploits ad hoc social networks to disseminate content using a store-carry-forward technique. Our approach distinguishes itself from other mobile social software by removing the need for a central server to conduct exchanges, by leveraging existing social networks to bootstrap the system, and by taking advantage of the social network overlay to disseminate content. We also propose an open API to encourage third-party application development. We discuss the system architecture and three example applications. We show experimentally that MobiClique successfully builds and maintains an ad hoc social network leveraging contact opportunities between friends and people sharing interest(s) for content exchanges. Our experience also provides insight into some of the key challenges and short-comings that researchers face when designing and deploying similar systems.


title: Comparing Person- and Process-centric Strategies for Obtaining Quality Data on Amazon Mechanical Turk

abstract: In the past half-decade, Amazon Mechanical Turk has radically changed the way many scholars do research. The availability of a massive, distributed, anonymous crowd of individuals willing to perform general human-intelligence micro-tasks for micro-payments is a valuable resource for researchers and practitioners. This paper addresses the challenges of obtaining quality annotations for subjective judgment oriented tasks of varying difficulty. We design and conduct a large, controlled experiment (N=68,000) to measure the efficacy of selected strategies for obtaining high quality data annotations from non-experts. Our results point to the advantages of person-oriented strategies over process-oriented strategies. Specifically, we find that screening workers for requisite cognitive aptitudes and providing training in qualitative coding techniques is quite effective, significantly outperforming control and baseline conditions. Interestingly, such strategies can improve coder annotation accuracy above and beyond common benchmark strategies such as Bayesian Truth Serum (BTS).


title: Molecular Docking on FPGA and GPU Platforms

abstract: Molecular docking is an important problem of bioinformatics aiming at the prediction of binding poses of molecules. Auto Dock is a popular, open-source docking software applying a computationally expensive but parallelizable algorithm. This paper introduces an FPGA-based and a GPU-based implementation of Auto Dock and shows how the original algorithm can be effectively accelerated on the two different platforms. According to test runs, both implementations achieve significant speedups over Auto Dock running on a single CPU core and on a quad-core system. Comparison of the two implementations proves that many-core graphics processing units can be a real alternative to FPGAs in the field of high performance computing.


title: Towards WiFi mobility without fast handover

abstract: WiFi is emerging as the preferred connectivity solution for mobile clients because of its low power consumption and high capacity. Dense urban WiFi deployments cover entire central areas, but the one thing missing is a seamless mobile experience. Mobility in WiFi has traditionally pursued fast handover, but we argue that this is the wrong goal to begin with. Instead, we propose that mobile clients should connect to all the access points they see, and split traffic over them with the newly deployed MPTCP protocol. We let a mobile connect to multiple APs on the same channel, or on different channels, and show via detailed experiments and simulation that this solution greatly enhances capacity and reliability of TCP connections straight away for certain flavors of WiFi a/b/g. We also find there are situations where connecting to multiple APs severely decreases throughput, and propose a series of client-side changes that make this solution robust across a wide range of scenarios.


title: Performance Measurement on Scale-Up and Scale-Out Hadoop with Remote and Local File Systems

abstract: MapReduce is a popular computing model for parallel data processing on large-scale datasets, which can vary from gigabytes to terabytes and petabytes. Though Hadoop MapReduce normally uses Hadoop Distributed File System (HDFS) local file system, it can be configured to use a remote file system. Then, an interesting question is raised: for a given application, which is the best running platform among the different combinations of scale-up and scale-out Hadoop with remote and local file systems. However, there has been no previous research on how different types of applications (e.g., CPU-intensive, data-intensive) with different characteristics (e.g., input data size) can benefit from the different platforms. Thus, in this paper, we conduct a comprehensive performance measurement of different applications on scale-up and scaleout clusters configured with HDFS and a remote file system (i.e., OFS), respectively. We identify and study how different job characteristics (e.g., input data size, the number of file reads/writes, and the amount of computations) affect the performance of different applications on the different platforms. This study is expected to provide a guidance for users to choose the best platform to run different applications with different characteristics in the environment that provides both remote and local storage, such as HPC cluster.


title: A HYBRID HEURISTIC FOR THE MINIMUM WEIGHT VERTEX COVER PROBLEM

abstract: Given an undirected graph with weights associated with its vertices, the minimum weight vertex cover problem seeks a subset of vertices with minimum sum of weights such that each edge of the graph has at least one endpoint belonging to the subset. In this paper, we propose a hybrid approach, combining a steady-state genetic algorithm and a greedy heuristic, for the minimum weight vertex cover problem. The genetic algorithm generates vertex cover, which is then reduced to minimal weight vertex cover by the heuristic. We have evaluated the performance of our algorithm on several test problems of varying sizes. Computational results show the effectiveness of our approach in solving the minimum weight vertex cover problem.


title: Learning Time-Based Presence Probabilities

abstract: Many potential pervasive computing applications could use predictions of when a person will be at a certain place. Using a survey and GPS data from 34 participants in 11 households, we develop and test algorithms for predicting when a person will be at home or away. We show that our participants' self-reported home/away schedules are not very accurate, and we introduce a probabilistic home/away schedule computed from observed GPS data. The computation includes smoothing and a soft schedule template. We show how the probabilistic schedule outperforms both the self-reported schedule and an algorithm based on driving time. We also show how to combine our algorithm with the best part of the drive time algorithm for a slight boost in performance.


title: Comparing VR and non-VR driving simulations: An experimental user study

abstract: Up to now, most driving simulators use either small monitors or large immersive projection setups like 2D/3D screens or a CAVE. The recent improvements of VR-HMDs led to an increased application in driving simulation. However, the influence and comparability of various VR and non-VR displays has been hardly investigated. We present results of a user study investigating the different influence of non-VR (2D, stereoscopic 3D) and VR (HMD) on physiological responses, simulation sickness, and driving performance within a single driving simulator. In the study, 94 participants performed the Lane Change Task. Results indicate that a VR-HMD leads to similar data as stereoscopic 3D or 2D screens. We observed no significant difference regarding physiological responses or lane change performance. However, we measured significantly increased simulator sickness in the VR-HMD condition compared to stereoscopic 3D.


title: Support Spinor Machine

abstract: We generalize a support vector machine to a support spinor machine by using the mathematical structure of wedge product over vector machine in order to extend field from vector field to spinor field. The separated hyperplane is extended to Kolmogorov space in time series data which allow us to extend a structure of support vector machine to a support tensor machine and a support tensor machine moduli space. Our performance test on support spinor machine is done over one class classification of end point in physiology state of time series data after empirical mode analysis and compared with support vector machine test. We implement algorithm of support spinor machine by using Holo-Hilbert amplitude modulation for fully nonlinear and nonstationary time series data analysis.


title: Practical fast searching in strings

abstract: SUMMARY The problem of searching through text to find a specified substring is considered in a practical setting. It is discovered that a method developed by Boyer and Moore can outperform even special-purpose search instructions that may be built into the, computer hardware. For very short substrings however, these special purpose instructions are fastest-provided that they are used in an optimal way.


title: Implementing a Flipped Classroom: An Instructional Module

abstract: Flipped classrooms are shifting the way teachers provide instruction by inverting traditional teaching methods to engage students in the learning process. Using technology, lectures are moved out of the classroom and delivered online as a means to free up class time for interaction and collaboration. In order to effectively implement a flipped classroom, teachers must possess a set of requisite technical skills, conceptual knowledge and pedagogical expertise. Through this study, a web-based instructional module was developed to provide this information to prospective teachers interested in implementing a flipped classroom. Results indicated that the module was effective in delivering an overview of the required material, but could have benefitted from the inclusion of added examples of working implementations to raise the confidence level of the participants. Added support through a learning community, either in-person or online, would help to provide guidance through initiation and expand on the shared experiences of the individuals.


title: Searching optimal movements in multi-player games with imperfect information

abstract: This paper proposes a search method of optimal movements in multi-player games with imperfect information in order to implement a Mahjong player exceeding human top players. The proposed method uses tabu search. It does not use the record of a game. Tabu search is applied to the states of discarding tiles and using the tile that other players discarded. The experiments were carried out in order to evaluate the proposed method. In the experiment of evaluating the rate of concordance of the usage of tiles, the maximum rate of concordance reached to 83%. This means effective winning hands could be found in the initial states. In the experiment of playing a game with benchmark players, it is shown that the proposed method is better than benchmark players. From these results, the possibility of the effective search of optimal solution by using tabu search was indicated.


title: Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning

abstract: A major challenge for video captioning is to combine audio and visual cues. Existing multi-modal fusion methods have shown encouraging results in video understanding. However, the temporal structures of multiple modalities at different granularities are rarely explored, and how to selectively fuse the multi-modal representations at different levels of details remains uncharted. In this paper, we propose a novel hierarchically aligned cross-modal attention (HACA) framework to learn and selectively fuse both global and local temporal dynamics of different modalities. Furthermore, for the first time, we validate the superior performance of the deep audio features on the video captioning task. Finally, our HACA model significantly outperforms the previous best systems and achieves new state-of-the-art results on the widely used MSR-VTT dataset.


title: Estimating Rule Quality for Knowledge Base Completion with the Relationship between Coverage Assumption

abstract: Currently, there are many large, automatically constructed knowledge bases (KBs). One interesting task is learning from a KB to generate new knowledge either in the form of inferred facts or rules that define regularities. One challenge for learning is that KBs are necessarily open world: we cannot assume anything about the truth values of facts not included in the KB. From a learning perspective, this means we lack negative examples. To address this problem, we propose a novel score function for evaluating the quality of a first-order definite clause learned from a KB. Our metric attempts to include information about the facts not in the KB when evaluating the quality of a potential rule. Empirically, we find that our metric results in more precise predictions than previous approaches.


title: Design of a Dual-Band High-Gain Antenna Array for WLAN and WiMAX Base Station

abstract: A dual-band $3 \times 2$ antenna array with high gain is presented in this letter. A dual-band folded dipole antenna is utilized as the antenna array element. A broadband symmetric feed network is designed to maintain each array element with the identical current magnitude and phase. The antenna array has a low profile of 12 mm (about $0.1{\lambda _{\rm L}}$ ; ${\lambda _{\rm L}}$ is the wavelength in free space at the lowest operating frequency). Dual bands $2.3\!\sim\! 2.56$ and $3\!\sim\! 4.2~\hbox{GHz}$ for return loss (RL) being higher than 10 dB can be obtained in simulation, while two 10-dB RL bands $2.31\!\sim\! 2.51$ and $3.1\!\sim\! 4.15~\hbox{GHz}$ in measurement cover the wireless local area network (WLAN, $2.4\!\sim\! 2.48~\hbox{GHz}$ ) and Worldwide Interoperability for Microwave Access (WiMAX, $3.3\!\sim\! 3.8~\hbox{GHz}$ ). The measured results also show that this planar antenna array has a high gain $14.3\!\sim \!15.1~\hbox{dBi}\;(2.31\!\sim\! 2.51~\hbox{GHz})$ , $16.2\!\sim\! 17.3~\hbox{dBi}$ ( $3.1\!\sim\! 4.15~\hbox{GHz}$ ) in $ + { z}$ -direction and a low cross-polarization level $ .


Graph Classification with 2 D Convolutional Neural Networks


title: Communication Steps for Parallel Query Processing

abstract: We study the problem of computing conjunctive queries over large databases on parallel architectures without shared storage. Using the structure of such a query q and the skew in the data, we study tradeoffs between the number of processors, the number of rounds of communication, and the per-processor load—the number of bits each processor can send or can receive in a single round—that are required to compute q. Since each processor must store its received bits, the load is at most the number of bits of storage per processor. When the data are free of skew, we obtain essentially tight upper and lower bounds for one round algorithms, and we show how the bounds degrade when there is skew in the data. In the case of skewed data, we show how to improve the algorithms when approximate degrees of the (necessarily small number of) heavy-hitter elements are available, obtaining essentially optimal algorithms for queries such as skewed simple joins and skewed triangle join queries. For queries that we identify as treelike, we also prove nearly matching upper and lower bounds for multi-round algorithms for a natural class of skew-free databases. One consequence of these latter lower bounds is that for any v > 0, using p processors to compute the connected components of a graph, or to output the path, if any, between a specified pair of vertices of a graph with m edges and per-processor load that is O(m/p1−v) requires Ω(logp) rounds of communication. Our upper bounds are given by simple structured algorithms using MapReduce. Our one-round lower bounds are proved in a very general model, which we call the Massively Parallel Communication (MPC) model, that allows processors to communicate arbitrary bits. Our multi-round lower bounds apply in a restricted version of the MPC model in which processors in subsequent rounds after the first communication round are only allowed to send tuples.


title: Interpreting the Public Sentiment Variations on Twitter

abstract: Millions of users share their opinions on Twitter, making it a valuable platform for tracking and analyzing public sentiment. Such tracking and analysis can provide critical information for decision making in various domains. Therefore it has attracted attention in both academia and industry. Previous research mainly focused on modeling and tracking public sentiment. In this work, we move one step further to interpret sentiment variations. We observed that emerging topics (named foreground topics ) within the sentiment variation periods are highly related to the genuine reasons behind the variations. Based on this observation, we propose a Latent Dirichlet Allocation (LDA) based model, Foreground and Background LDA (FB-LDA), to distill foreground topics and filter out longstanding background topics . These foreground topics can give potential interpretations of the sentiment variations. To further enhance the readability of the mined reasons, we select the most representative tweets for foreground topics and develop another generative model called Reason Candidate and Background LDA (RCB-LDA) to rank them with respect to their “popularity” within the variation period. Experimental results show that our methods can effectively find foreground topics and rank reason candidates. The proposed models can also be applied to other tasks such as finding topic differences between two sets of documents.


The Curse of Mobile Marketing: A Mixed Methods Study on Individuals’ Switch to Mobile Ad Blockers


title: Acting Thoughts: Towards a Mobile Robotic Service Assistant for Users with Limited Communication Skills

abstract: As autonomous service robots become more affordable and thus available also for the general public, there is a growing need for user friendly interfaces to control the robotic system. Currently available control modalities typically expect users to be able to express their desire through either touch, speech or gesture commands. While this requirement is fulfilled for the majority of users, paralyzed users may not be able to use such systems. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The brain-computer interface (BCI) system is composed of several interacting components, i.e., non-invasive neuronal signal recording and decoding, high-level task planning, motion and manipulation planning as well as environment perception. In various experiments, we demonstrate its applicability and robustness in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results demonstrate, our system is capable of adapting to frequent changes in the environment and reliably completing given tasks within a reasonable amount of time. Combined with high-level planning and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.


title: Two-dimensional Disjunctively Constrained Knapsack Problem: Heuristic and exact approaches

abstract: Abstract This work deals with the 0–1 knapsack problem in its two-dimensional version considering a conflict graph, where each edge in this graph represents a pair of items that must not be packed together. This problem arises as a subproblem of the bin packing problem and in supply chain scenarios. We propose some integer programming formulations that are solved with a branch-and-cut algorithm. The formulation is based on location-allocation variables mixing the one- and two-dimensional versions of this problem. When a candidate solution is found, a feasibility test is performed with a constraint programming algorithm, which verifies if it satisfies the two-dimensional packing constraints. Moreover, bounds and valid cuts are also investigated. A heuristic that generates iteratively a solution and has components of Tabu search and Simulated Annealing approaches is proposed. The results are extended to consider complete shipment of items, where subsets of items all have to be loaded or left out completely. This constraint is applied in many real-life packing problems, such as packing parts of machinery, or when delivering cargo to different customers. Experiments on several instances derived from the literature indicate the competitiveness of our algorithms, which solved 99% of the instances to optimality requiring short computational time.


title: Investigating Value Co-Creation in Innovation of IT-enabled Services: An Empirical Study of Mobile Data Services

abstract: Service innovation is increasingly being recognized as an important strategy for firms to sustain competitive advantage. The interconnectivity and interactivity afforded through information technologies (IT) has transformed how new services are created, delivered, and experienced. With the newness of the phenomenon and the lack of understanding, information systems (IS) research examining the influence of innovativeness of IT-enabled service on its performance has been limited. Further, customers are considered as important value co-creators and increasingly included in organizational innovation efforts. However, it is unclear how customer participation affects the performance of new IT-enabled services. Also, the interaction effect of service innovativeness and customer participation on new service performance remains unexplored. Motivated thus, this paper aims to address the knowledge gap by drawing on the value co-creation perspective and service innovation literatures. We construct a model to explain the effects of service innovativeness and customer participation on new IT-enabled service performance. A preliminary test of the model was performed using initial data on mobile data service (MDS) applications obtained from the Androidzoom platform. The results reveal that both service innovativeness and customer participation positively impact new IT-enabled service performance in terms of the number of downloads of the applications. Also, customer participation is found to positively moderate the relationship between service innovativeness and new IT-enabled service performance. The potential contributions and plans for further testing and enhancing the model are described.


title: A privacy-compliant fingerprint recognition system based on homomorphic encryption and Fingercode templates

abstract: The privacy protection of the biometric data is an important research topic, especially in the case of distributed biometric systems. In this scenario, it is very important to guarantee that biometric data cannot be steeled by anyone, and that the biometric clients are unable to gather any information different from the single user verification/identification. In a biometrie system with high level of privacy compliance, also the server that processes the biometrie matching should not learn anything on the database and it should be impossible for the server to exploit the resulting matching values in order to extract any knowledge about the user presence or behavior. Within this conceptual framework, in this paper we propose a novel complete demonstrator based on a distributed biometrie system that is capable to protect the privacy of the individuals by exploiting cryptosystems. The implemented system computes the matching task in the encrypted domain by exploiting homomorphic encryption and using Fingercode templates. The paper describes the design methodology of the demonstrator and the obtained results. The demonstrator has been fully implemented and tested in real applicative conditions. Experimental results show that this method is feasible in the cases where the privacy of the data is more important than the accuracy of the system and the obtained computational time is satisfactory.


title: Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach

abstract: Background: Sepsis is one of the leading causes of mortality in hospitalized patients. Despite this fact, a reliable means of predicting sepsis onset remains elusive. Early and accurate sepsis onset predictions could allow more aggressive and targeted therapy while maintaining antimicrobial stewardship. Existing detection methods suffer from low performance and often require time-consuming laboratory test results. Objective: To study and validate a sepsis prediction method, InSight, for the new Sepsis-3 definitions in retrospective data, make predictions using a minimal set of variables from within the electronic health record data, compare the performance of this approach with existing scoring systems, and investigate the effects of data sparsity on InSight performance. Methods: We apply InSight, a machine learning classification system that uses multivariable combinations of easily obtained patient data (vitals, peripheral capillary oxygen saturation, Glasgow Coma Score, and age), to predict sepsis using the retrospective Multiparameter Intelligent Monitoring in Intensive Care (MIMIC)-III dataset, restricted to intensive care unit (ICU) patients aged 15 years or more. Following the Sepsis-3 definitions of the sepsis syndrome, we compare the classification performance of InSight versus quick sequential organ failure assessment (qSOFA), modified early warning score (MEWS), systemic inflammatory response syndrome (SIRS), simplified acute physiology score (SAPS) II, and sequential organ failure assessment (SOFA) to determine whether or not patients will become septic at a fixed period of time before onset. We also test the robustness of the InSight system to random deletion of individual input observations. Results: In a test dataset with 11.3% sepsis prevalence, InSight produced superior classification performance compared with the alternative scores as measured by area under the receiver operating characteristic curves (AUROC) and area under precision-recall curves (APR). In detection of sepsis onset, InSight attains AUROC = 0.880 (SD 0.006) at onset time and APR = 0.595 (SD 0.016), both of which are superior to the performance attained by SIRS (AUROC: 0.609; APR: 0.160), qSOFA (AUROC: 0.772; APR: 0.277), and MEWS (AUROC: 0.803; APR: 0.327) computed concurrently, as well as SAPS II (AUROC: 0.700; APR: 0.225) and SOFA (AUROC: 0.725; APR: 0.284) computed at admission (P<.001 for all comparisons). Similar results are observed for 1-4 hours preceding sepsis onset. In experiments where approximately 60% of input data are deleted at random, InSight attains an AUROC of 0.781 (SD 0.013) and APR of 0.401 (SD 0.015) at sepsis onset time. Even with 60% of data missing, InSight remains superior to the corresponding SIRS scores (AUROC and APR, P<.001), qSOFA scores (P=.0095; P<.001) and superior to SOFA and SAPS II computed at admission (AUROC and APR, P<.001), where all of these comparison scores (except InSight) are computed without data deletion. Conclusions: Despite using little more than vitals, InSight is an effective tool for predicting sepsis onset and performs well even with randomly missing data. [JMIR Med Inform 2016;4(3):e28]


title: Smoking Cessation in Clinical Practice: How to Get Patients to Stop

abstract: Tobacco use is an etiologic agent for many diseases treated by cardiac, vascular, and thoracic surgeons and contributes to increased perioperative complications and long-term risk. Smoking cessation may be challenging for patients and can be frustrating for clinicians. Lack of familiarity and pessimistic views toward cessation methods lead to underuse by physicians. Evidence-supported measures that increase chances of cessation include direct physician advice, approved pharmacotherapy, structured counseling, and a follow-up plan. Approved pharmacotherapy consists of varenicline, bupropion, or nicotine replacement therapy in the form of long-acting patches and short-acting forms of nicotine such as gum, lozenges, prescription nasal spray, or prescription inhaler. Direct physician advice is critical and strengthened when combined with more in-depth counseling from a specialist who may have more expertise and time. Integrating assessment and referral to counseling services into a clinical workflow can deliver resources in an efficient manner with the goal of providing the best available resources to all patients.


title: Different uses of Bronfenbrenner’s ecological theory in public mental health research: what is their value for guiding public mental health policy and practice?

abstract: Bronfenbrenner’s ecological theory is appealing as a conceptual tool for guiding public mental health interventions. However, his theory underwent significant changes since its first inception duri ...


title: Gate-Induced Drain Leakage Reduction in Cylindrical Dual-Metal Hetero-Dielectric Gate All Around MOSFET

abstract: In this paper, an analytical model of dual-metal hetero-dielectric (DM-HD) cylindrical gate all around (GAA) MOSFET has been proposed to address and solve a substantial issue of gate-induced drain leakage (GIDL) current in order to improve the device reliability, band-to-band tunneling (BTBT), and OFF state leakages. The structure is based upon asymmetric gate oxide structure by combining silicon dioxide (SiO2) gate dielectric at source side and vacuum dielectric at drain side, which significantly reduces BTBT and OFF-state gate leakages, thereby making it suitable for low-power applications. It is examined that GIDL i.e., an OFF-state leakage phenomenon, is reduced in DM-HD GAA MOSFET by lowering the BTBT. This can be done by reducing OFF-state leakages due to: 1) increase in tunneling width and 2) increased barrier height from source to channel. The results show that the OFF-state leakage current in DM-HD GAA MOSFET reduces to an order of $10^{-\textsf {13}}$ over $10^{-\textsf {9}}$ A as in the case of conventional GAA MOSFET. The results so obtained are compared with those of cylindrical dual-metal GAA (DM-GAA) MOSFET and GAA MOSFET to analyze its performance. OFF-state leakages at higher temperatures have also been analyzed.


title: Person re-identification with fusion of hand-crafted and deep pose-based body region features

abstract: Person re-identification (re-ID) aims to accurately re- trieve a person from a large-scale database of images cap- tured across multiple cameras. Existing works learn deep representations using a large training subset of unique per- sons. However, identifying unseen persons is critical for a good re-ID algorithm. Moreover, the misalignment be- tween person crops to detection errors or pose variations leads to poor feature matching. In this work, we present a fusion of handcrafted features and deep feature representa- tion learned using multiple body parts to complement the global body features that achieves high performance on un- seen test images. Pose information is used to detect body regions that are passed through Convolutional Neural Net- works (CNN) to guide feature learning. Finally, a metric learning step enables robust distance matching on a dis- criminative subspace. Experimental results on 4 popular re-ID benchmark datasets namely VIPer, DukeMTMC-reID, Market-1501 and CUHK03 show that the proposed method achieves state-of-the-art performance in image-based per- son re-identification.


title: Variational Methods for Normal Integration

abstract: The need for an efficient method of integration of a dense normal field is inspired by several computer vision tasks, such as shape-from-shading, photometric stereo, deflectometry, etc. Inspired by edge-preserving methods from image processing, we study in this paper several variational approaches for normal integration, with a focus on non-rectangular domains, free boundary and depth discontinuities. We first introduce a new discretization for quadratic integration, which is designed to ensure both fast recovery and the ability to handle non-rectangular domains with a free boundary. Yet, with this solver, discontinuous surfaces can be handled only if the scene is first segmented into pieces without discontinuity. Hence, we then discuss several discontinuity-preserving strategies. Those inspired, respectively, by the Mumford-Shah segmentation method and by anisotropic diffusion, are shown to be the most effective for recovering discontinuities.


title: Automated Generation of Music Playlists: Survey and Experiments

abstract: Most of the time when we listen to music on the radio or on our portable devices, the order in which the tracks are played is governed by so-called playlists. These playlists are basically sequences of tracks that traditionally are designed manually and whose organization is based on some underlying logic or theme. With the digitalization of music and the availability of various types of additional track-related information on the Web, new opportunities have emerged on how to automate the playlist creation process. Correspondingly, a number of proposals for automated playlist generation have been made in the literature during the past decade. These approaches vary both with respect to which kind of data they rely on and which types of algorithms they use. In this article, we review the literature on automated playlist generation and categorize the existing approaches. Furthermore, we discuss the evaluation designs that are used today in research to assess the quality of the generated playlists. Finally, we report the results of a comparative evaluation of typical playlist generation schemes based on historical data. Our results show that track and artist popularity can play a dominant role and that additional measures are required to better characterize and compare the quality of automatically generated playlists.


title: Lovelet: a heartwarming communication tool for intimate people by constantly conveying situation data

abstract: WHAT IS REQUIRED TO CONVEY AFFECTION Today, various personal communication tools have been widely used and people can mutually communicate at anytime and anywhere. However, it is still difficult to convey delicate feelings, e.g., affection, by using such tools. A possible reason of this difficulty is lack of real-time awareness on mutual situation, timely action and non-verbal information. For instance, when out for a walk in the snow, you might say “mind your step, it’s slippery” to your dear partner and reach out for him/her. Such timely verbal and non-verbal actions convey your delicate feelings to the partner. On the phone, you can partially convey such feelings by voice, tone of voice, intonation and so forth. However, it is impossible to always piece out the situation of the partner by the phone, e-mail and so on, and hence it is also very difficult to timely react to the partner’s situation.


title: In-Ear EEG Biometrics for Feasible and Readily Collectable Real-World Person Authentication

abstract: The use of electroencephalogram (EEG) as a biometrics modality has been investigated for about a decade; however, its feasibility in real-world applications is not yet conclusively established, mainly due to the issues with collectability and reproducibility. To this end, we propose a readily deployable EEG biometrics system based on a “one-fits-all” viscoelastic generic in-ear EEG sensor (collectability), which does not require skilled assistance or cumbersome preparation. Unlike most existing studies, we consider data recorded over multiple recording days and for multiple subjects (reproducibility) while, for rigour, the training and test segments are not taken from the same recording days. A robust approach is considered based on the resting state with eyes closed paradigm, the use of both parametric (autoregressive model) and non-parametric (spectral) features, and supported by simple and fast cosine distance, linear discriminant analysis, and support vector machine classifiers. Both the verification and identification forensics scenarios are considered and the achieved results are on par with the studies based on impractical on-scalp recordings. Comprehensive analysis over a number of subjects, setups, and analysis features demonstrates the feasibility of the proposed ear-EEG biometrics, and its potential in resolving the critical collectability, robustness, and reproducibility issues associated with current EEG biometrics.


Urine Drug Screening : Practical Guide for Clinicians REVIEW


title: SDNetkit: A testbed for experimenting SDN in multi-domain networks

abstract: Mininet is the de-facto standard simulation environment for experimenting with SDN enabled networks based on the OpenFlow protocol. Although Mininet is powerful and not resource hungry, it has a strong limitation: it is not possible to use it for networks in which both OpenFlow and standard distributed routing protocols (e.g. Open Short Path First, OSPF) simultaneously run. In this paper we present SDNetkit, an enhanced release of the widely used Netkit network emulator that overcomes the limitation imposed by Mininet. We improved Netkit by adding all needed software to run OpenFlow based networks (e.g. OpenVSwitch and the Ryu framework). We show two use cases in which OpenFlow and standard protocols coexist. In particular, we address interoperability problems by presenting one use case in which OpenFlow nodes interact with standard ones (e.g. OSPF routers) in multi-domain networks, as well as one use case in which the OpenFlow protocol and OSPF run on the same machine, discussing some problems related to specific configurations. We believe that having the possibility to experiment SDN also in presence of interoperability scenarios results in opening to new research perspectives.


title: Synthetic Sampling for Multi-Class Malignancy Prediction

abstract: We explore several oversampling techniques for an imbalanced multi-label classification problem, a setting often encountered when developing models for Computer-Aided Diagnosis (CADx) systems. While most CADx systems aim to optimize classifiers for overall accuracy without considering the relative distribution of each class, we look into using synthetic sampling to increase per-class performance when predicting the degree of malignancy. Using low-level image features and a random forest classifier, we show that using synthetic oversampling techniques increases the sensitivity of the minority classes by an average of 7.22% points, with as much as a 19.88% point increase in sensitivity for a particular minority class. Furthermore, the analysis of low-level image feature distributions for the synthetic nodules reveals that these nodules can provide insights on how to preprocess image data for better classification performance or how to supplement the original datasets when more data acquisition is feasible.


title: Influence of mecanum wheels construction on accuracy of the omnidirectional platform navigation (on exanple of KUKA youBot robot)

abstract: The object of the study is an omnidirectional platform of the mobile robot KUKA youBot, its platform is equipped with two pairs of omnidirectional mecanum-wheels. A kinematic analysis of the system is carried out taking into account number of wheel rollers and its real geometry. The accuracy of the algorithm for calculating the coordinates of the mobile platform using odometric information that does not take into account the construction of the wheels is studied. An algorithm is proposed to improve the accuracy of odometric navigation.


IEEE 802.11ax: On Time Synchronization in Asynchronous OFDM Uplink Multi-User MIMO Physical Layer


title: On the Complexity of Linear Prediction: Risk Bounds

abstract: This work characterizes the generalization ability of algorithms whose predictions are linear in the input vector. To this end, we provide sharp bounds for Rademacher and Gaussian complexities of (constrained) linear classes, which directly lead to a number of generalization bounds. This derivation provides simplified proofs of a number of corollaries including: risk bounds for linear prediction (including settings where the weight vectors are constrained by either L2 or L1 constraints), margin bounds (including both L2 and L1 margins, along with more general notions based on relative entropy), a proof of the PAC-Bayes theorem, and upper bounds on L2 covering numbers (with Lp norm constraints and relative entropy constraints). In addition to providing a unified analysis, the results herein provide some of the sharpest risk and margin bounds. Interestingly, our results show that the uniform convergence rates of empirical risk minimization algorithms tightly match the regret bounds of online learning algorithms for linear prediction, up to a constant factor of 2.


title: A Pan-Cancer Proteogenomic Atlas of PI3K/AKT/mTOR Pathway Alterations

abstract: Summary Molecular alterations involving the PI3K/AKT/mTOR pathway (including mutation, copy number, protein, or RNA) were examined across 11,219 human cancers representing 32 major types. Within specific mutated genes, frequency, mutation hotspot residues, in silico predictions, and functional assays were all informative in distinguishing the subset of genetic variants more likely to have functional relevance. Multiple oncogenic pathways including PI3K/AKT/mTOR converged on similar sets of downstream transcriptional targets. In addition to mutation, structural variations and partial copy losses involving PTEN and STK11 showed evidence for having functional relevance. A substantial fraction of cancers showed high mTOR pathway activity without an associated canonical genetic or genomic alteration, including cancers harboring IDH1 or VHL mutations, suggesting multiple mechanisms for pathway activation.


Adjustable Autonomy for Human-Centered Autonomous Systems on Mars


title: Hierarchically Clustered Representation Learning

abstract: The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which often involves cohesive clustering with a focus on instance relations. To overcome the limitations of flat clustering, we introduce hierarchically-clustered representation learning (HCRL), which simultaneously optimizes representation learning and hierarchical clustering in the embedding space. Compared with a few prior works, HCRL firstly attempts to consider a generation of deep embeddings from every component of the hierarchy, not just leaf components. In addition to obtaining hierarchically clustered embeddings, we can reconstruct data by the various abstraction levels, infer the intrinsic hierarchical structure, and learn the level-proportion features. We conducted evaluations with image and text domains, and our quantitative analyses showed competent likelihoods and the best accuracies compared with the baselines.


title: High-Quality Self-Supervised Deep Image Denoising

abstract: We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a "blind spot" in the receptive field, we address two of its shortcomings: inefficient training and somewhat disappointing final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise.


title: Spring-Mass Walking With ATRIAS in 3D: Robust Gait Control Spanning Zero to 4.3 KPH on a Heavily Underactuated Bipedal Robot

abstract: We present a reduced-order approach for dynamic and efficient bipedal control, culminating in 3D balancing and walking with ATRIAS, a heavily underactuated bipedal robot. These results are a development toward solving a number of enduring challenges in bipedal locomotion: achieving robust 3D gaits at various speeds and transitioning between them, all while minimally draining on-board energy supplies. Our reduced-order control methodology works by extracting and exploiting general dynamical behaviors from the spring-mass model of bipedal walking. When implemented on a robot with spring-mass passive dynamics, e.g. ATRIAS, this controller is sufficiently robust to balance while subjected to pushes, kicks, and successive dodge-ball strikes. The controller further allowed smooth transitions between stepping in place and walking at a variety of speeds (up to 1.2 m/s). The resulting gait dynamics also match qualitatively to the reduced-order model, and additionally, measurements of human walking. We argue that the presented locomotion performance is compelling evidence of the effectiveness of the presented approach; both the control concepts and the practice of building robots with passive dynamics to accommodate them.Copyright © 2015 by ASME


Towards Unified Biomedical Modeling with Subgraph Mining and Factorization Algorithms


title: Implementation model for end hosts to achieve power-efficient data transmission

abstract: With the wide proliferation of high-performance Information and Communication Technology (ICT) devices, power consumption in the networking field has become a paramount issue. This paper focuses on data transmission between two end hosts and proposes a novel implementation model from the viewpoint of power efficiency during data transmission. Through inter-component coordination and traffic-driven control of power-efficient components, significant power saving is anticipated without any influence on the data transmission throughput. Experimental results verify the feasibility of the implementation model, and show that by using this implementation model, more power can be saved than in traditional approaches, which are based solely on individual components.


title: Forensic analysis of encrypted instant messaging applications on Android

abstract: Smartphone market is growing day by day and according to Statista, as of 2017, 68.4% of the U.S. population uses smartphones. Similarly, the amount of information stored on these mobile devices is tremendous and ranging from personal details, contacts, applications data, to exchange of texts and media. This information can become a significant evidence during a digital forensics investigation and thereafter in courts. As Android is one of the leading smartphone operating systems worldwide, it is important to have the knowledge of Android forensics. Moreover, chat messaging between the users becoming the most prominent communication medium particularly among the youth. The exponential increase in the interception of chat messages on mobile devices led to implementation of end to end encryption. This is mainly due to the concerns raised on privacy and security of user data on smartphones. In this paper we analyze widely used encrypted Instant Messaging (IM) applications namely WeChat, Telegram, Viber and Whatsapp. We also show how these applications store data in the Android file system. In addition we also discuss forensic implications of the IM applications that are utilizing encryption. Analysis of artifacts collected from these applications is performed using the Android Debugging Bridge (ADB) tool and some other open source tools. Moreover, we also present the challenges faced during the collection of the forensically important artifacts.


title: K-nearest-neighbor consistency in data clustering: incorporating local information into global optimization

abstract: Nearest neighbor consistency is a central concept in statistical pattern recognition, especially the kNN classification methods and its strong theoretical foundation. In this paper, we extend this concept to data clustering, requiring that for any data point in a cluster, its k-nearest neighbors and mutual nearest neighbors should also be in the same cluster. We study properties of the cluster k-nearest neighbor consistency and propose kNN and kMN consistency enforcing and improving algorithms. Extensive experiments on internet newsgroup datasets using the K-means clustering algorithm with kNN consistency enhancement show that kNN / kMN consistency can be improved significantly (about 100% for 1MN and 1NN consistencies) while the clustering accuracy is improved simultaneously. This indicates the local consistency information helps the global cluster objective function optimization.


title: Fault-Tolerant Fuzzy Gain-Scheduled PID for a Quadrotor Helicopter Testbed in the Presence of Actuator Faults

abstract: Abstract In the current study, an adaptive PID controller is proposed for fault-tolerant control of a quadrotor helicopter system in the presence of actuator faults. A fuzzy inference scheme is used to tune in real-time the controller gains. Tracking errors and change in tracking errors are used in this fuzzy scheduler to make the system act faster and more effectively in the event of fault occurrence. Two fault scenarios are investigated: the loss of control effectiveness in all actuators and the loss of control effectiveness in one single actuator. The proposed adaptive PID controller is compared with the conventional one through an experimental application to a quadrotor helicopter testbed at the Department of Mechanical and Industrial Engineering of Concordia University. The obtained results show the effectiveness of the proposed method.


Chapter 2 Type Theory and Lexical Decomposition


Learning from Data: Concepts, Theory, and Methods


title: Įįjih and Request Formation in Upper Tanana: Evidence from Narrative Texts

abstract: Formation and use of direct positive and negative requests in Upper Tanana (Dene) are investigated through qualitative analysis of narrative texts. Choice of request form depends on speakers’ evaluation of their entitlement, as well as of the contingencies involved in granting the request. Negative requests are relatively infrequent because they are easily construed as criticism of the hearer’s knowledge of įįjih , the moral underpinnings of Upper Tanana society, and are thus usually avoided unless the addressee is someone of whom knowledge of įįjih cannot be expected.


title: Model-Based Development of Leaping in a Hexapod Robot

abstract: We report on the model-based development of leaping behavior in a RHex-style hexapod robot. A three-legged model is proposed to analyze the dynamic behavior of leaping, and this serves as a guide for implementing the behavior on the empirical robot. The model has a rigid body and three massless and compliant legs, which have rolling contact with the ground for better modeling the leg behavior of the empirical robot. By investigating the model's behavior, a two-step leaping maneuver is developed. The first step is utilized for adjusting the body pitch, synchronizing the phases of all six legs, and speeding up the body's forward velocity. This provides adequate initial conditions for the second step leaping, which creates a long-distance flight and adequate landing for follow-up running. In addition, we also report on the strategy of stride length regulation. With implementation of the range sensor, the robot can regulate its stride in order to reach a specific and desired position for leaping. The gait transition and initiation of leaping is fully autonomous. The behavioral development is implemented in the RHex-style robot and is evaluated experimentally.


title: Towards understanding IT security professionals and their tools

abstract: We report preliminary results of our ongoing field study of IT professionals who are involved in security management. We interviewed a dozen practitioners from five organizations to understand their workplace and tools. We analyzed the interviews using a variation of Grounded Theory and predesigned themes. Our results suggest that the job of IT security management is distributed across multiple employees, often affiliated with different organizational units or groups within a unit and responsible for different aspects of it. The workplace of our participants can be characterized by their responsibilities, goals, tasks, and skills. Three skills stand out as significant in the IT security management workplace: inferential analysis, pattern recognition, and bricolage.


title: The role of the right inferior frontal gyrus: inhibition and attentional control

abstract: There is growing interest regarding the role of the right inferior frontal gyrus (RIFG) during a particular form of executive control referred to as response inhibition. However, tasks used to examine neural activity at the point of response inhibition have rarely controlled for the potentially confounding effects of attentional demand. In particular, it is unclear whether the RIFG is specifically involved in inhibitory control, or is involved more generally in the detection of salient or task relevant cues. The current fMRI study sought to clarify the role of the RIFG in executive control by holding the stimulus conditions of one of the most popular response inhibition tasks–the Stop Signal Task–constant, whilst varying the response that was required on reception of the stop signal cue. Our results reveal that the RIFG is recruited when important cues are detected, regardless of whether that detection is followed by the inhibition of a motor response, the generation of a motor response, or no external response at all.


title: Punctuation prediction using a bidirectional recurrent neural network with part-of-speech tagging

abstract: Most automatic speech recognition (ASR) systems are incapable of generating punctuation, making it difficult to read the transcribed output and less appropriate for tasks such as dictation. This paper introduces a procedure to automatically insert punctuation into unpunctuated sentences by using a bidirectional recurrent neural network with attention mechanism and Part-of-Speech (POS) Tags. Using the WikiText Long Term Dependency Language Modelling Dataset and handling 11 different punctuation symbols, the model managed to achieve a punctuation error rate of 31.4% and an F1 score of 78.5%. When the system was trained on consecutive sentences and a smaller dataset using the Europarl v7 corpus, the model still managed to achieve a punctuation error rate of 48.1% and an F1 score of 64.7%. In both cases, our proposed system outperforms previous state-of-the-art systems trained on the same datasets, showing the advantage of using POS tags information and an encoderdecoder network.


title: Evolution of silicon power devices and challenges to material limit

abstract: The author first briefly reviews recent success of MOS gate power devices. The main objective is to predict, for the first time, the silicon limit characteristics of IGBTs for its on-resistance and SOA. The author also proposes ideal gate drive in order to realize the ultimate limit of high speed switching of MOS gate power devices. The results lead to new FOM, characterizing the high speed switching capability of various power devices


title: Implementation Aspects of Channel Estimation for 3GPP LTE Terminals

abstract: In this paper, hardware implementation aspects of the channel estimator in 3GPP LTE terminals are investigated. A channel estimation ASIC, which handles the real-time channel estimation, is presented. Compared to traditional correlator-based channel estimators, the channel estimator presented boosts the throughput at feasible silicon cost by adopting a recently proposed estimation method named Approximate Linear Minimum Mean Square Error (ALMMSE). In this paper, both the architecture and VLSI implementation of the estimator are elaborated. Implemented using a 65nm CMOS process, the channel estimator supports the full 20MHz bandwidth of 3GPP LTE and consumes only 49 kgates.


title: Self-Contained Photoacid Generator Triggered by Photocyclization of Triangle Terarylene Backbone.

abstract: We herein propose a new type of efficient neutral photoacid generator. A photoinduced 6π-electrocyclization reaction of photochromic triangle terarylenes triggers subsequent release of a Brønsted acid, which took place from the photocyclized form. A H-atom and its conjugate base were introduced at both sides of a 6π-system to form the self-contained photoacid generator. UV irradiation to the 6π-system produces a cyclohexa-1,3-diene part with a H-atom and a conjugate base on the sp(3) C-atoms at 5- and 6-positions, respectively, which spontaneously release an acid molecule quantitatively forming a polyaromatic compound. A net quantum yield of photoacid generation as high as 0.52 under ambient conditions and a photoinitiated cationic polymerization of an epoxy monomer are demonstrated.


Bio-Medical Waste Management : An Overview of Various Technologies