title: Comparison of Kettlebell Swings and Treadmill Running at Equivalent Rating of Perceived Exertion Values

abstract: The purpose of this study was to compare metabolic demand of a kettlebell (KB) swing routine with treadmill (TM) running at equivalent rating of perceived exertion (RPE). Thirteen subjects (11 male, 2 female, age = 21.4 ± 2.1 years, weight = 73.0 ± 9.2 kg) completed a 10-minute KB swing routine consisting of 35-second swing intervals followed by 25-second rest intervals. Men used a 16-kg KB, and women used an 8-kg KB. After 48 hours of rest, the subjects completed a 10-minute TM run at equivalent RPEs as measured during the swing workout. Metabolic data were monitored each minute during each exercise using an automated cart, with the final 7 minutes used for analysis. The RPE and heart rate (HR) recorded at minutes 5, 7, 9, and 10 increased by 2�3 and 7�9%, respectively, for each exercise, producing a significantly increasing pattern but no significant difference between exercises. Average HR and RPE were not significantly different between KB and TM and averaged 90 and 89%, respectively, of age-predicted HRmax. Oxygen consumption, METS, pulmonary ventilation, and calorie expenditure were significantly higher for TM (25�39%) than for KB. Respiratory exchange ratio (TM = 0.94 ± 0.04, KB = 0.95 ± 0.05) and respiratory rate (TM = 38 ± 7, KB = 36 ± 4 b·min-1) were not significantly different between the exercises at any time point. During TM and KB exercises matched for RPE, the subjects are likely to have higher oxygen consumption, work at a higher MET level, and burn more kilocalories per minute during TM running than during KB swings. However, according to the American College of Sports Medicine standards, this KB drill could provide sufficient exercise stress to produce gains in aerobic capacity.


title: Execution time estimation for workflow scheduling

abstract: Estimation of the execution time is an important part of the workflow scheduling problem. The aim of this paper is to highlight common problems in estimating the workflow execution time and propose a solution that takes into account the complexity and the randomness of the workflow components and their runtime. The solution proposed in this paper addresses the problems at different levels from task to workflow, including the error measurement and the theory behind the estimation algorithm. The proposed estimation algorithm can be integrated easily into a wide class of schedulers as a separate module. We use a dual stochastic representation, characteristic / distribution functions, in order to combine tasks' estimates into the overall workflow makespan. Additionally, we propose the workflow reductions - the operations on a workflow graph that do not decrease the accuracy of the estimates, but simplify the graph structure, hence increasing the performance of the algorithm.


title: A statistical measure for map consistency in SLAM

abstract: Map consistency is an important requirement for applications in which mobile robots need to effectively perform autonomous navigation tasks. While recent SLAM techniques provide an increased robustness even in the context of bad initializations or data association outliers, the question of how to determine whether or not the resulting map is consistent is still an open problem. In this paper, we introduce a novel measure for map consistency. We compute this measure by taking into account the discrepancies in the sensor data and leverage it to address two important problems in SLAM. First, we derive a statistical test for assessing whether a map is consistent or not. Second, we employ it to automatically set the free parameter of dynamic covariance scaling, a robust SLAM back-end. We present an evaluation of our approach on over 50 maps sourced from 16 publicly available datasets and illustrate its capability for the inconsistency detection and the tuning of the parameter of the back-end.


Multi-Entity Bayesian Networks Without Multi-Tears


title: Using RDMA efficiently for key-value services

abstract: This paper describes the design and implementation of HERD, a key-value system designed to make the best use of an RDMA network. Unlike prior RDMA-based key-value systems, HERD focuses its design on reducing network round trips while using efficient RDMA primitives; the result is substantially lower latency, and throughput that saturates modern, commodity RDMA hardware. ::: HERD has two unconventional decisions: First, it does not use RDMA reads, despite the allure of operations that bypass the remote CPU entirely. Second, it uses a mix of RDMA and messaging verbs, despite the conventional wisdom that the messaging primitives are slow. A HERD client writes its request into the server's memory; the server computes the reply. This design uses a single round trip for all requests and supports up to 26 million key-value operations per second with 5μs average latency. Notably, for small key-value items, our full system throughput is similar to native RDMA read throughput and is over 2X higher than recent RDMA-based key-value systems. We believe that HERD further serves as an effective template for the construction of RDMA-based datacenter services.


title: Data Driven Ontology Evaluation

abstract: The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the ‘fit’ between an ontology and a domain of knowledge. We consider a number of methods for measuring this ‘fit’ and propose a measure to evaluate structural fit, and a probabilistic approach to identifying the best ontology.


title: Abstract An Ontology Framework for Semantic Business Process Management

abstract: A core challenge in Business Process Management is the continuous, bi-directional translation between (1) a business requirements view on the process space of an enterprise and (2) the actual process space of this enterprise, constituted by the multiplicity of IT systems, resources, and human labor. Semantic Business Process Management (SBPM) [HeLD'05] 1 is a novel approach of increasing the level of automation in the translation between these two spheres, and is currently driven by major players from the ERP, BPM, and Semantic Web Services domain, namely SAP 2 . One core paradigm of SPBM is to represent the two spheres and their parts using ontology languages and to employ machine reasoning for the automated or semi-automated translation. In this paper, we (1) outline the representational requirements of SBPM, (2) propose a set of ontologies and formalisms, and (3) define the scope of these ontologies by giving competency questions, which is a common technique in the ontology engineering process.


title: Estimation of vehicle's vertical and lateral tire forces considering road angle and road irregularity

abstract: Vehicle dynamics is an essential topic in development of safety driving systems. These complex and integrated control units require precise information about vehicle dynamics, especially, tire/road contact forces. Nevertheless, it is lacking an effective and low-cost sensor to measure them directly. Therefore, this study presents a new method to estimate these parameters by using observer technologies and low-cost sensors which are available on the passenger cars in real environment. In the authors previous work, observers have been designed to estimate the vehicle tire/road contact forces and sideslip angles. However, the previous study just considered the situation of the vehicles running on a level road. In the authors recent study, vehicle mathematical models are reconstructed to suit banked road and inclined road. Then, Kalman Filter is used to improve the estimation of vehicle dynamics. Finally, the estimator is tested both on simulation CALLAS and on the experimental vehicle DYNA.


title: Conformal shielding investigation for SiP modules

abstract: This paper proposes a complete testing process for conformal shielding techniques. The testing process is systemic and cheap for developing conformal shielding techniques. A 2-D experiment setup and an exact solution are presented to evaluate the properties of metals and metallization techniques. A test vehicle is designed for providing broad band source and low unintended noise. A experiment setup using giga-hertz transverse electromagnetic (GTEM) cell is proposed for providing low noise floor and high sensitivity for measuring small radiation. The test vehicle coated by 0.04 μm sputtering SuS and 1 μm sputtering copper is manufactured and measured to evaluate shielding effectiveness of conformal shielding.


title: Imagining Replications: Graphical Prediction & Discrete Visualizations Improve Recall & Estimation of Effect Uncertainty

abstract: People often have erroneous intuitions about the results of uncertain processes, such as scientific experiments. Many uncertainty visualizations assume considerable statistical knowledge, but have been shown to prompt erroneous conclusions even when users possess this knowledge. Active learning approaches been shown to improve statistical reasoning, but are rarely applied in visualizing uncertainty in scientific reports. We present a controlled study to evaluate the impact of an interactive, graphical uncertainty prediction technique for communicating uncertainty in experiment results. Using our technique, users sketch their prediction of the uncertainty in experimental effects prior to viewing the true sampling distribution from an experiment. We find that having a user graphically predict the possible effects from experiment replications is an effective way to improve one's ability to make predictions about replications of new experiments. Additionally, visualizing uncertainty as a set of discrete outcomes, as opposed to a continuous probability distribution, can improve recall of a sampling distribution from a single experiment. Our work has implications for various applications where it is important to elicit peoples' estimates of probability distributions and to communicate uncertainty effectively.


title: Integrated Model of Cluster Supply Chain and Logistics Park

abstract: The organizational succession in cluster supply chain is not only a primary upgrading way of industrial cluster, but also a basis of integrating industrial cluster and Logistics Park. This paper analyzes how to integrate organizational succession in cluster supply chain and Logistics Park according to the logistics features of organizational succession in cluster supply chain, and puts forward three integrated models. Meanwhile, through the case study of IT industry in Suzhou, the logistics evolving regulation of organizational succession in cluster supply chain is explored, and some relevant conclusions and suggestions are drawn at last.


title: Multi-Tenant Slicing for Spectrum Management on the Road to 5G

abstract: The explosive data traffic demand in the context of the 5G revolution has stressed the need for network capacity increase. As the network densification has almost reached its limits, mobile network operators are motivated to share their network infrastructure and the available resources through dynamic spectrum management. Although some initial efforts have been made in this direction by concluding sharing agreements at a coarse granularity (i.e., months or years), the 5G developments require fine timescale agreements, mainly enabled by network slicing. In this article, taking into account the radical changes foreseen for next generation networks, we provide a thorough discussion of the challenges that network slicing brings in the different network parts, while introducing a new entity capable of managing the end-to-end slicing in a coherent manner. In addition, according to the paradigm shift of operators sharing their resources in a common centralized pool, we design a cooperative game to study the potential cooperation aspects among the participants. The experimental results highlight the performance and financial gains achievable by operators through multi-tenant slicing, providing them with the necessary incentives for network upgrade toward 5G.


title: Classification of breast cancer histology images using Convolutional Neural Networks

abstract: Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.


title: Vibrational spectra and phonon dispersion analysis of a single-walled zigzag carbon nanotube: A first principles study

abstract: This paper reports a vibrational spectroscopic study on a zigzag single-walled carbon nanotube (SWCNT) using the first-principles method based on density functional theory (DFT). The most suitable ...


title: Spatial and temporal dispersion in high bandwidth underwater laser communication links

abstract: A resurgence is occurring in the area of underwater laser communications. While acoustic systems are currently the more mature technology, they are ultimately band-limited to sub-MHz type data rates due to the frequency dependent absorption of acoustic energies in water. Advances in fiber optic and free space links have shown promise for optical links to provide data rates in excess of a gigabit per second. It is not surprising then, that laser links are being considered for Naval applications involving high bandwidth communications undersea. A major challenge in implementing optical links underwater arises from the spatial dispersion of photons due to scattering. Spatial spreading of the optical beam reduces the photon density at the receiver position. As such, optical links are only expected to be of greatest utility in links <100 m. Nonetheless, it appears that end users may accept limited link range in exchange for the gain in information bandwidth that optical links may provide. Additionally, researchers continue to study how spatial spreading affects the time encoded portion of the transmitted optical signal. Temporal dispersion arising from multiple scattering events may result in inter-symbol interference (ISI), further limiting link range and/or capacity. Researchers at NAVAIR in Patuxent River MD are currently investigating both the spatial and temporal effects of scattering on a laser link in turbid underwater environments. These links utilize an intensity modulated beam to implement coherent digital modulation schemes such as PSK and QAM. Through both modeling and experiment, the underwater channel is characterized both spatially and temporally. Results are providing insight to system requirements of link range, pointing accuracy, photo-receiver requirements, modulation frequency, and optimal modulation format.


title: A Secure Data Deduplication Scheme for Cloud Storage

abstract: As more corporate and private users outsource their data to cloud storage providers, recent data breach incidents make end-to-end encryption an increasingly prominent requirement. Unfortunately, semantically secure encryption schemes render various cost-effective storage optimization techniques, such as data deduplication, ineffective. We present a novel idea that differentiates data according to their popularity. Based on this idea, we design an encryption scheme that guarantees semantic security for unpopular data and provides weaker security and better storage and bandwidth benefits for popular data. This way, data deduplication can be effective for popular data, whilst semantically secure encryption protects unpopular content. We show that our scheme is secure under the Symmetric External Decisional Diffie-Hellman Assumption in the random oracle model.


title: The impact of branching heuristics in propositional satisfiability algorithms

abstract: This paper studies the practical impact of the branching heuristics used in Propositional Satisfiability (SAT) algorithms, when applied to solving real-world instances of SAT. In addition, different SAT algorithms are experimentally evaluated. The main conclusion of this study is that even though branching heuristics are crucial for solving SAT, other aspects of the organization of SAT algorithms are also essential. Moreover, we provide empirical evidence that for practical instances of SAT, the search pruning techniques included in the most competitive SAT algorithms may be of more fundamental significance than branching heuristics.


title: An automated system to detect and recognize vehicle license plates of Bangladesh

abstract: Bangladesh is a country in South Asia that uses Retro Reflective license plates. The plate has two lines with words, letters, and digits. An automated system to detect and recognize these plates is presented in this paper. The system is divided into four parts: plate detection, extraction, character segmentation and recognition. At first, the input image is enhanced using CLAHE and a matched filter specially designed for license plates with two lines is applied. Then tilt correction using Radon transformation, binarization and cleaning are performed. For character segmentation, mean intensity based horizontal and vertical projection is used. In recognition, we have used two different Convolutional Neural Network (CNN) to classify digits and letters. Tesseract OCR is used for district names. We have developed a dataset of over 400 images of different vehicles (e.g., private car, bus, truck etc.) taken at different times of day (including nights). The plates in this dataset are in different angles, including blurry, worn-out and muddy ones. On this dataset, the proposed system achieved a success rate of 96.8% in detection, 89.5% in extraction, 98.6% in segmentation and 98.0% in character recognition.


title: Implicit and explicit preferences for physical attractiveness in a romantic partner: A double dissociation in predictive validity.

abstract: Five studies develop and examine the predictive validity of an implicit measure of the preference for physical attractiveness in a romantic partner. Three hypotheses were generally supported. First, 2 variants of the go/no-go association task revealed that participants, on average, demonstrate an implicit preference (i.e., a positive spontaneous affective reaction) for physical attractiveness in a romantic partner. Second, these implicit measures were not redundant with a traditional explicit measure: The correlation between these constructs was .00 on average, and the implicit measures revealed no reliable sex differences, unlike the explicit measure. Third, explicit and implicit measures exhibited a double dissociation in predictive validity. Specifically, explicit preferences predicted the extent to which attractiveness was associated with participants’ romantic interest in opposite-sex photographs but not their romantic interest in real-life opposite-sex speed-daters or confederates. Implicit preferences showed the opposite pattern. This research extends prior work on implicit processes in romantic relationships and offers the first demonstration that any measure of a preference for a particular characteristic in a romantic partner (an implicit measure of physical attractiveness, in this case) predicts individuals’ evaluation of live potential romantic partners.


Ramesh Sawant: Story of an Entrepreneur’s Journey without the Map1


title: Homo floresiensis: Microcephalic, pygmoid, Australopithecus, or Homo?

abstract: Abstract The remarkable partial adult skeleton (LB1) excavated from Liang Bua cave on the island of Flores, Indonesia, has been attributed to a new species, Homo floresiensis, based upon a unique mosaic of primitive and derived features compared to any other hominin. The announcement precipitated widespread interest, and attention quickly focused on its possible affinities. LB1 is a small-bodied hominin with an endocranial volume of 380–410 cm3, a stature of 1 m, and an approximate geological age of 18,000 years. The describers [Brown, P., Sutikna, T., Morwood, M.J., Soejono, R.P., Jatmiko, Wayhu Saptomo, E., Awe Due, R., 2004. A new small-bodied hominin from the Late Pleistocene of Flores, Indonesia. Nature 431, 1055–1061] originally proposed that H. floresiensis was the end product of a long period of isolation of H. erectus or early Homo on a small island, a process known as insular dwarfism. More recently Morwood, Brown, and colleagues [Morwood, M.J., Brown, P., Jatmiko, Sutikna, T., Wahyu Saptomo, E., Westaway, K.E., Awe Due, R., Roberts, R.G., Maeda, T., Wasisto, S., Djubiantono, T., 2005. Further evidence for small-bodied hominins from the Late Pleistocene of Flores, Indonesia. Nature 437, 1012–1017] reviewed this assessment in light of new material from the site and concluded that H. floresiensis is not likely to be descended from H. erectus, with the genealogy of the species remaining uncertain. Other interpretations, namely that LB1 is a pygmy or afflicted with microcephaly, have also been put forward. We explore the affinities of LB1 using cranial and postcranial metric and non-metric analyses. LB1 is compared to early Homo, two microcephalic humans, a ‘pygmoid’ excavated from another cave on Flores, H. sapiens (including African pygmies and Andaman Islanders), Australopithecus, and Paranthropus. Based on these comparisons, we conclude that it is unlikely that LB1 is a microcephalic human, and it cannot be attributed to any known species. Its attribution to a new species, Homo floresiensis, is supported.


title: Evaluating the Effectiveness of Keyword Search

abstract: The prevalence of free text search in web search engines has inspired recent interest in keyword search on relational databases. Whereas relational queries formally specify matching tuples, keyword queries are imprecise expressions of the user’s information need. The correctness of search results depends on the user’s subjective assessment. As a result, the empirical evaluation of a keyword retrieval system’s effectiveness is essential. In this paper, we examine the evolving practices and resources for effectiveness evaluation of keyword searches on relational databases. We compare practices with the longer-standing full-text evaluation methodologies in information retrieval. In the light of this comparison, we make some suggestions for the future development of the art in evaluating keyword search effectiveness.


title: On the dimensionality of organizational justice: A construct validation of a measure.

abstract: This study explores the dimensionality of organizational justice and provides evidence of construct validity for a new justice measure. Items for this measure were generated by strictly following the seminal works in the justice literature. The measure was then validated in 2 separate studies. Study 1 occurred in a university setting, and Study 2 occurred in a field setting using employees in an automobile parts manufacturing company. Confirmatory factor analyses supported a 4-factor structure to the measure, with distributive, procedural, interpersonal, and informational justice as distinct dimensions. This solution fit the data significantly better than a 2- or 3-factor solution using larger interactional or procedural dimensions. Structural equation modeling also demonstrated predictive validity for the justice dimensions on important outcomes, including leader evaluation, rule compliance, commitment, and helping behavior. Individuals are the subject of decisions virtually every day of their organizational lives. Some of these decisions deal with the salaries individuals make, some deal with the projects they perform, and some deal with the social settings in which they function. These decisions have both economic and socioemotional consequences, many of which form the foundation for why individuals work in organizations in the first place (Cropanzano & Schminke, 2001). The importance of those consequences causes individuals to judge the decision making they experience with a very critical eye. Thus, one of the first questions they ask in the wake of decisions is "Was that fair?" The notion of fairness, or justice, has become an increasingly visible construct in the social sciences over the last 3 decades. Initially, researchers focused on the justice of decision outcomes, termed distributive justice (Adams, 1965; Deutsch, 1975; Homans,


title: Nutrigenomics: goals and strategies

abstract: Nutrigenomics is the application of high-throughput genomics tools in nutrition research. Applied wisely, it will promote an increased understanding of how nutrition influences metabolic pathways and homeostatic control, how this regulation is disturbed in the early phase of a diet-related disease and to what extent individual sensitizing genotypes contribute to such diseases. Ultimately, nutrigenomics will allow effective dietary-intervention strategies to recover normal homeostasis and to prevent diet-related diseases.


fBGD: Learning Embeddings From Positive Unlabeled Data with BGD


title: CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping

abstract: The Reference-based Super-resolution (RefSR) super-resolves a low-resolution (LR) image given an external high-resolution (HR) reference image, where the reference image and LR image share similar viewpoint but with significant resolution gap x8. Existing RefSR methods work in a cascaded way such as patch matching followed by synthesis pipeline with two independently defined objective functions, leading to the inter-patch misalignment, grid effect and inefficient optimization. To resolve these issues, we present CrossNet, an end-to-end and fully-convolutional deep neural network using cross-scale warping. Our network contains image encoders, cross-scale warping layers, and fusion decoder: the encoder serves to extract multi-scale features from both the LR and the reference images; the cross-scale warping layers spatially aligns the reference feature map with the LR feature map; the decoder finally aggregates feature maps from both domains to synthesize the HR output. Using cross-scale warping, our network is able to perform spatial alignment at pixel-level in an end-to-end fashion, which improves the existing schemes both in precision (around 2dB-4dB) and efficiency (more than 100 times faster).


title: Diagnosing Error in Temporal Action Detectors

abstract: Despite the recent progress in video understanding and the continuous rate of improvement in temporal action localization throughout the years, it is still unclear how far (or close?) we are to solving the problem. To this end, we introduce a new diagnostic tool to analyze the performance of temporal action detectors in videos and compare different methods beyond a single scalar metric. We exemplify the use of our tool by analyzing the performance of the top rewarded entries in the latest ActivityNet action localization challenge. Our analysis shows that the most impactful areas to work on are: strategies to better handle temporal context around the instances, improving the robustness w.r.t. the instance absolute and relative size, and strategies to reduce the localization errors. Moreover, our experimental analysis finds the lack of agreement among annotator is not a major roadblock to attain progress in the field. Our diagnostic tool is publicly available to keep fueling the minds of other researchers with additional insights about their algorithms.


title: A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval

abstract: In this study, we investigate learning-to- ::: rank and query refinement approaches for ::: information retrieval in the pharmacogenomic domain. The goal is to improve the ::: information retrieval process of biomedical curators, who manually build knowledge bases for personalized medicine. We ::: study how to exploit the relationships be- ::: tween genes, variants, drugs, diseases and ::: outcomes as features for document ranking and query refinement. ::: For a supervised approach, we are faced with a ::: small amount of annotated data and a large ::: amount of unannotated data. Therefore, ::: we explore ways to use a neural document ::: auto-encoder in a semi-supervised approach. We show that a combination of established algorithms, feature-engineering ::: and a neural auto-encoder model yield ::: promising results in this setting.


title: A lead free joining technology for high temperature interconnects using Transient Liquid Phase Soldering (TLPS)

abstract: This Paper reports an emerging lead-free joining technology for high temperature application, which can be used for operating temperatures above 200 °C. It is called: “Transient Liquid Phase Soldering (TLPS)”. The TLPS paste used contains a tin-copper powder mixture and is almost completely transformed into Cu ::: 6 ::: Sn ::: 5 ::: and Cu ::: 3 ::: Sn intermetallic phases after soldering. Due to the reaction between the liquid tin and the copper powder a skeleton of intermetallic phases are formed immediately during soldering and prevents the paste from collapsing so that a lot of voids remain in the solder line. The challenge for this investigation was to understand the mechanism of the skeleton formation, describe them in detail and find possibilities to avoid the skeleton formation. In this paper a new TLPS paste and two processes are described as a means to manufacture an almost void-less joint. Furthermore, a model was developed that describes the TLPS process in detail. The activation of the TLPS joint is crucial and will be described. Temperature cycling results, failure mechanisms and conclusions to increase the lifetime as well as reliability of such TLPS interconnects will be presented in this paper.


3D Object Structure Recovery via Semi-supervised Learning on Videos


Logistic Regression


title: Association between the CTLA-4 +49A/G polymorphism and Graves’ disease: A meta-analysis

abstract: The +49A/G polymorphism of the cytotoxic T-lymphocyte-associated antigen-4 gene (CTLA-4) has been associated with Graves’ disease (GD). However, results have been inconsistent. The aim of this study was to quantitatively summarize the evidence for CTLA-4 +49A/G polymorphism and GD. Electronic search of PubMed was conducted to select studies. Case-control studies containing available genotype frequencies of CTLA-4 +49 were chosen, and Odds ratio (OR) with 95% confidence interval (CI) was used to assess the strength of this association. Forty-two case-control studies including 8,288 cases and 9,372 controls were identified. Three studies were eliminated from the total 42 studies due to a p-value <0.05 (p-value for Hardy-Weinberg equilibrium in control group) in these studies which induced significant publication bias. The overall results suggested that the variant genotypes were highly associated (p<0.01) with GD risk in all genetic models (additive model: OR, 1.443; 95% CI, 1.319–1.578; p<0.001; recessive model: OR, 1.589; 95% CI, 1.396–1.808; p<0.001; dominant model: OR, 1.621; 95% CI, 1.430–1.837; p<0.001). Similarly, in the subgroup analyses for ethnicity (Caucasian, Asian), the results were positive. This meta-analysis suggests that the CTLA-4 +49A/G polymorphism is highly associated (p<0.01) with increased risk of GD, especially in Caucasians and Asians. To validate this association, further studies with larger participants worldwide are needed to examine associations between this polymorphism and GD.


title: A DWT Based Approach for Image Steganography

abstract: In this paper we propose a new steganography technique which embeds the secret messages in frequency domain. According to different users' demands on the embedding capa c- ity and image quality, the proposed algorithm is divided into two modes and 5 cases. Unlike the space domain approaches, secret messages are embedded in the high frequency coefficients re- sulted from Discrete Wavelet Transform. Coefficients in the low frequency sub-band are pre- served unaltered to improve the image quality. Some basic mathematical operations are per- formed on the secret messages before embedding. These operations and a well-designed map- ping Table keep the messages away from stealing, destroying from unintended users on the inter- net and hence provide satisfactory security.


title: How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies

abstract: Using deep neural nets as function approximator for reinforcement learning tasks have recently been shown to be very powerful for solving problems approaching real-world complexity. Using these results as a benchmark, we discuss the role that the discount factor may play in the quality of the learning process of a deep Q-network (DQN). When the discount factor progressively increases up to its final value, we empirically show that it is possible to significantly reduce the number of learning steps. When used in conjunction with a varying learning rate, we empirically show that it outperforms original DQN on several experiments. We relate this phenomenon with the instabilities of neural networks when they are used in an approximate Dynamic Programming setting. We also describe the possibility to fall within a local optimum during the learning process, thus connecting our discussion with the exploration/exploitation dilemma.


title: Representation learning for geospatial areas using large-scale mobility data from smart card

abstract: With the deployment of modern infrastructures for public transit, several studies have analyzed the transition patterns of people by using smart card data and have characterized the areas. In this paper, we propose a novel embedding method to obtain a vector representation of a geospatial area using transition patterns of people from the large-scale data of their smart cards. We extend a network embedding by taking into account geographical constraints on people transitioning in the real world. We conducted an experiment using smart card data in a large network of railroads in Kansai areas in Japan. We obtained a vector representation of each railroad station using the proposed embedding method. The results show that the proposed method performs better than the existing network embedding methods in the task of multi-label classification for purposes of going to a railroad station. Our proposed method can contribute to predicting people flow by discovering underlying representations of geospatial areas from mobility data.


title: Introduction to the special issue on agent-based computational economics

abstract: A brief overview of agent-based computational economics (ACE) is given, followed by a synopsis of the articles included in this special issue on ACE and in a companion special issue on ACE scheduled to appear in the Journal of Economic Dynamics and Control.


title: Code coverage and test suite effectiveness: Empirical study with real bugs in large systems

abstract: During software maintenance, testing is a crucial activity to ensure the quality of program code as it evolves over time. With the increasing size and complexity of software, adequate software testing has become increasingly important. Code coverage is often used as a yardstick to gauge the comprehensiveness of test cases and the adequacy of testing. A test suite quality is often measured by the number of bugs it can find (aka. kill). Previous studies have analysed the quality of a test suite by its ability to kill mutants, i.e., artificially seeded faults. However, mutants do not necessarily represent real bugs. Moreover, many studies use small programs which increases the threat of the applicability of the results on large real-world systems. In this paper, we analyse two large software systems to measure the relationship of code coverage and its effectiveness in killing real bugs from the software systems. We use Randoop, a random test generation tool to generate test suites with varying levels of coverage and run them to analyse if the test suites can kill each of the real bugs or not. In this preliminary study, we have performed an experiment on 67 and 92 real bugs from Apache HTTPClient and Mozilla Rhino, respectively. Our experiment finds that there is indeed statistically significant correlation between code coverage and bug kill effectiveness. The strengths of the correlation, however, differ for the two software systems. For HTTPClient, the correlation is moderate for both statement and branch coverage. For Rhino, the correlation is strong for both statement and branch coverage.


title: Further Characterization of Staphylococcal Gamma-Hemolysin

abstract: It was confirmed that staphylococcal gamma-hemolysin is composed of two separate proteins (gamma-lysin components I and II) which act synergistically. The molecular weights of the two components, determined by gel filtration, are 29,000 and 26,000, respectively, and their isoelectric points, determined by isoelectric focusing, are at pH 9.8 and 9.9. Both components are susceptible to the action of Pronase and subtilisin. A wide range of lipids, some in minute amounts, are capable of inhibiting the hemolytic activity of gamma-lysin.


title: Implementation and Stability Study of Dynamic Droop in Islanded Microgrids

abstract: This paper presents a dynamic droop load sharing scheme based on the available generation capacity of the distributed generation (DG) units. Since conventional droop schemes share loads proportional to units’ ratings, they suffer from the inability to maintain an efficient operating point when their input renewable power varies, without imposing their new operating point on other connected DGs in the microgrid. This problem is mainly due to the insensitivity of the droop scheme to the varying nature of the renewable resources used, including wind and solar photovoltaic (PV). A control method is proposed for PV systems; however, it is applicable for all types of droop-controlled renewable DG. A stability analysis of the proposed scheme on DG units is also presented to identify theoretical and practical limits. The proposed scheme identifies the dc operating zone of the inverter-based source as irradiance level changes and conditions the droop parameters appropriately for an efficient load sharing based on available generation, while the rating of each unit is also taken into account. The proposed scheme provides energy saving, since energy demand from a local auxiliary generator is reduced. The proposed method is validated using MATLAB/SIMULINK simulations.


title: Reasoning about programs in continuation-passing style.

abstract: Plotkin's λ-value calculus is sound but incomplete for reasoning about βeegr;-transformations on programs in continuation-passing style (CPS). To find a complete extension, we define a new, compactifying CPS transformation and an “inverse”mapping, un -CPS, both of which are interesting in their own right. Using the new CPS transformation, we can determine the precise language of CPS terms closed under β7eegr;-transformations. Using the un -CPS transformation, we can derive a set of axioms such that every equation between source programs is provable if and only if βe can prove the corresponding equation between CPS programs. The extended calculus is equivalent to an untyped variant of Moggi's computational λ-calculus.


title: Monitoring pollution: Applying IoT to create a smart environment

abstract: Air quality has been negatively affected by industrial activities, which have caused imbalances in nature. The issue of air pollution has become a big concern for many people, especially those living in industrial areas. Air pollution levels can be measured using smart sensors. Additionally, Internet of Things (IoT) technology can be integrated to remotely detect pollution without any human interaction. The data gathered by such a system can be transmitted instantly to a web-based application to facilitate monitoring real time data and allow immediate risk management. In this paper, we describe an entire Internet of Things (IoT) system that monitors air pollution by collecting real-time data in specific locations. This data is analyzed and measured against a predetermined threshold. The collected data is sent to the concerned official organization to notify them in case of any violation so that they can take the necessary measures. Furthermore, if the value of the measured pollutants exceeds the threshold, an alarm system is triggered taking several actions to warn the surrounding people.


title: A feather-weight virtual machine for windows applications

abstract: Many fault-tolerant and intrusion-tolerant systems require the ability to execute unsafe programs in a realistic environment without leaving permanent damages. Virtual machine technology meets this requirement perfectly because it provides an execution environment that is both realistic and isolated. In this paper, we introduce an OS level virtual machine architecture for Windows applications called Feather-weight Virtual Machine (FVM), under which virtual machines share as many resources of the host machine as possible while still isolated from one another and from the host machine. The key technique behind FVM is namespace virtualization, which isolates virtual machines by renaming resources at the OS system call interface. Through a copy-on-write scheme, FVM allows multiple virtual machines to physically share resources but logically isolate their resources from each other. A main technical challenge in FVM is how to achieve strong isolation among different virtual machines and the host machine, due to numerous namespaces and interprocess communication mechanisms on Windows. Experimental results demonstrate that FVM is more flexible and scalable, requires less system resource, incurs lower start-up and run-time performance overhead than existing hardware-level virtual machine technologies, and thus makes a compelling building block for security and fault-tolerant applications.


title: Transductive Zero-shot Recognition via shared model space learning

abstract: Zero-shot Recognition (ZSR) is to learn recognition models for novel classes without labeled data. It is a challenging task and has drawn considerable attention in recent years. The basic idea is to transfer knowledge from seen classes via the shared attributes. This paper focus on the transductive ZSR, i.e., we have unlabeled data for novel classes. Instead of learning models for seen and novel classes separately as in existing works, we put forward a novel joint learning approach which learns the shared model space (SMS) for models such that the knowledge can be effectively transferred between classes using the attributes. An effective algorithm is proposed for optimization. We conduct comprehensive experiments on three benchmark datasets for ZSR. The results demonstrates that the proposed SMS can significantly outperform the state-of-the-art related approaches which validates its efficacy for the ZSR task.


title: An Integrated Framework for Service Quality, Customer Value, Satisfaction: Evidence from China's Telecommunication Industry

abstract: Service quality, customer satisfaction and customer value have become the priority of both manufacturers and service provider in the increasingly intensified competition for customers in today's customer-centered era. However, findings regarding service quality, customer satisfaction and customer value are rather divergent and related studies are fragmented, especially for the complicated interrelationships among them. Thus, less is known about the relative impacts of quality-related factors on customer value and customer satisfaction up to now and the moderating role of customer value in the relationship between service quality and customer satisfaction has been neglected. Further, it is very difficult to find related studies, supported by evidence, that focus on service quality, customer satisfaction and customer value, and their influences on customer behavior intentions in the telecommunication industry. In this paper, much attention is paid to the measurement model of service quality in China's mobile communication market based on the well-known SERVQUAL model, but with reasonable modification on the basis of focus group discussions and expert opinions to reflect the specific industry attributes and the special culture of China. By taking a disaggregated approach, the key drivers of service quality, customer value and customer satisfaction are first identified and the impact of customer perceived sacrifice on customer value is emphasized. Then attention is given to the systematic study of the dynamic relationships among them, especially the moderating effect of customer value on the relationship between service quality and customer satisfaction, which is followed by the examination of their influences on behavior intentions of customers. Results are based on the development of structural equation models using Partial Least Square technique.


Intelligent OCR Processing


title: Conditional Deep Learning for energy-efficient and enhanced pattern recognition

abstract: Deep learning neural networks have emerged as one of the most powerful classification tools for vision related applications. However, the computational and energy requirements associated with such deep nets can be quite high, and hence their energy-efficient implementation is of great interest. Although traditionally the entire network is utilized for the recognition of all inputs, we observe that the classification difficulty varies widely across inputs in real-world datasets; only a small fraction of inputs require the full computational effort of a network, while a large majority can be classified correctly with very low effort. In this paper, we propose Conditional Deep Learning (CDL) where the convolutional layer features are used to identify the variability in the difficulty of input instances and conditionally activate the deeper layers of the network. We achieve this by cascading a linear network of output neurons for each convolutional layer and monitoring the output of the linear network to decide whether classification can be terminated at the current stage or not. The proposed methodology thus enables the network to dynamically adjust the computational effort depending upon the difficulty of the input data while maintaining competitive classification accuracy. We evaluate our approach on the MNIST dataset. Our experiments demonstrate that our proposed CDL yields 1.91× reduction in average number of operations per input, which translates to 1.84× improvement in energy. In addition, our results show an improvement in classification accuracy from 97.5% to 98.9% as compared to the original network.


Video-based Framework for Safer and Smarter Computer Aided Surgery


title: Light Trapping in Ultrathin CIGS Solar Cells with Nanostructured Back Mirrors

abstract: Novel architectures for light trapping in ultrathin Cu(In,Ga)Se $_2$ (CIGS) solar cells are proposed and numerically investigated. They are composed of a flat CIGS layer with nanostructured back mirrors made of highly reflective metals. Multi-resonant absorption is obtained for two different patterns of nanostructured mirrors. It leads to a dramatic increase in the short-circuit current predicted for solar cells with very thin CIGS layers. We analyze the resonance phenomena and the density of photogenerated carriers in the absorber. We discuss the impact of the material used for the buffer layer (CdS and ZnS) and the back mirror (Mo, Cu, Au, and Ag). We investigate various CIGS thicknesses from 100 to 500 nm, and we compare our numerical results with experimental data taken from the literature. We predict a short-circuit current of $J_{\text{sc}}$ = 33.6 mA/cm $^2$ for a realistic solar cell made of a 200-nm-thick CIGS absorber with a copper nanostructured mirror. It opens a way toward ultrathin CIGS solar cells with potential conversion efficiencies up to 20%.


title: User profile preserving social network embedding

abstract: This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPPSNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPPSNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.


title: Pocket reflectometry

abstract: We present a simple, fast solution for reflectance acquisition using tools that fit into a pocket. Our method captures video of a flat target surface from a fixed video camera lit by a hand-held, moving, linear light source. After processing, we obtain an SVBRDF. ::: We introduce a BRDF chart, analogous to a color "checker" chart, which arranges a set of known-BRDF reference tiles over a small card. A sequence of light responses from the chart tiles as well as from points on the target is captured and matched to reconstruct the target's appearance. ::: We develop a new algorithm for BRDF reconstruction which works directly on these LDR responses, without knowing the light or camera position, or acquiring HDR lighting. It compensates for spatial variation caused by the local (finite distance) camera and light position by warping responses over time to align them to a specular reference. After alignment, we find an optimal linear combination of the Lambertian and purely specular reference responses to match each target point's response. The same weights are then applied to the corresponding (known) reference BRDFs to reconstruct the target point's BRDF. We extend the basic algorithm to also recover varying surface normals by adding two spherical caps for diffuse and specular references to the BRDF chart. ::: We demonstrate convincing results obtained after less than 30 seconds of data capture, using commercial mobile phone cameras in a casual environment.


title: Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

abstract: PURPOSE ::: We propose a single network trained by pixel-to-label deep learning to address the general issue of automatic multiple organ segmentation in three-dimensional (3D) computed tomography (CT) images. Our method can be described as a voxel-wise multiple-class classification scheme for automatically assigning labels to each pixel/voxel in a 2D/3D CT image. ::: ::: ::: METHODS ::: We simplify the segmentation algorithms of anatomical structures (including multiple organs) in a CT image (generally in 3D) to a majority voting scheme over the semantic segmentation of multiple 2D slices drawn from different viewpoints with redundancy. The proposed method inherits the spirit of fully convolutional networks (FCNs) that consist of "convolution" and "deconvolution" layers for 2D semantic image segmentation, and expands the core structure with 3D-2D-3D transformations to adapt to 3D CT image segmentation. All parameters in the proposed network are trained pixel-to-label from a small number of CT cases with human annotations as the ground truth. The proposed network naturally fulfills the requirements of multiple organ segmentations in CT cases of different sizes that cover arbitrary scan regions without any adjustment. ::: ::: ::: RESULTS ::: The proposed network was trained and validated using the simultaneous segmentation of 19 anatomical structures in the human torso, including 17 major organs and two special regions (lumen and content inside of stomach). Some of these structures have never been reported in previous research on CT segmentation. A database consisting of 240 (95% for training and 5% for testing) 3D CT scans, together with their manually annotated ground-truth segmentations, was used in our experiments. The results show that the 19 structures of interest were segmented with acceptable accuracy (88.1% and 87.9% voxels in the training and testing datasets, respectively, were labeled correctly) against the ground truth. ::: ::: ::: CONCLUSIONS ::: We propose a single network based on pixel-to-label deep learning to address the challenging issue of anatomical structure segmentation in 3D CT cases. The novelty of this work is the policy of deep learning of the different 2D sectional appearances of 3D anatomical structures for CT cases and the majority voting of the 3D segmentation results from multiple crossed 2D sections to achieve availability and reliability with better efficiency, generality, and flexibility than conventional segmentation methods, which must be guided by human expertise.


title: Use of cost-effectiveness analysis to compare the efficiency of study identification methods in systematic reviews

abstract: Meta-research studies investigating methods, systems, and processes designed to improve the efficiency of systematic review workflows can contribute to building an evidence base that can help to increase value and reduce waste in research. This study demonstrates the use of an economic evaluation framework to compare the costs and effects of four variant approaches to identifying eligible studies for consideration in systematic reviews. A cost-effectiveness analysis was conducted using a basic decision-analytic model, to compare the relative efficiency of ‘safety first’, ‘double screening’, ‘single screening’ and ‘single screening with text mining’ approaches in the title-abstract screening stage of a ‘case study’ systematic review about undergraduate medical education in UK general practice settings. Incremental cost-effectiveness ratios (ICERs) were calculated as the ‘incremental cost per citation ‘saved’ from inappropriate exclusion’ from the review. Resource use and effect parameters were estimated based on retrospective analysis of ‘review process’ meta-data curated alongside the ‘case study’ review, in conjunction with retrospective simulation studies to model the integrated use of text mining. Unit cost parameters were estimated based on the ‘case study’ review’s project budget. A base case analysis was conducted, with deterministic sensitivity analyses to investigate the impact of variations in values of key parameters. Use of ‘single screening with text mining’ would have resulted in title-abstract screening workload reductions (base case analysis) of >60 % compared with other approaches. Across modelled scenarios, the ‘safety first’ approach was, consistently, equally effective and less costly than conventional ‘double screening’. Compared with ‘single screening with text mining’, estimated ICERs for the two non-dominated approaches (base case analyses) ranged from £1975 (‘single screening’ without a ‘provisionally included’ code) to £4427 (‘safety first’ with a ‘provisionally included’ code) per citation ‘saved’. Patterns of results were consistent between base case and sensitivity analyses. Alternatives to the conventional ‘double screening’ approach, integrating text mining, warrant further consideration as potentially more efficient approaches to identifying eligible studies for systematic reviews. Comparable economic evaluations conducted using other systematic review datasets are needed to determine the generalisability of these findings and to build an evidence base to inform guidance for review authors.


title: Joint Trajectory and Communication Design for UAV-Enabled Multiple Access

abstract: Unmanned aerial vehicles (UAVs) have attracted significant interest recently in wireless communication due to their high maneuverability, flexible deployment, and low cost. This paper studies a UAV-enabled wireless network where the UAV is employed as an aerial mobile base station (BS) to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users by jointly optimizing the multiuser communication scheduling and UAV trajectory over a finite horizon. The formulated problem is shown to be a mixed integer non-convex optimization problem that is difficult to solve in general. We thus propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization techniques, which is guaranteed to converge to at least a locally optimal solution. To achieve fast convergence and stable throughput, we further propose a low-complexity initialization scheme for the UAV trajectory design based on the simple circular trajectory. Extensive simulation results are provided which show significant throughput gains of the proposed design as compared to other benchmark schemes.


title: Instantaneous Reactive Power in Systems with Stochastic Electric Power Processes

abstract: The paper is dedicated to the estimation of instantaneous reactive power in a DC traction power system. Currently, the integral and frequency methods are mostly used to define reactive power and do not give high accuracy in the calculations of power balance or cannot describe the essence of physical processes. The problem is complicated in DC electric transport systems due to random impulse character of voltages and currents. As a comparison, the calculation of reactive power according to various approaches was performed for the DE1 electric locomotive. It is suggested that the instantaneous reactive power should be used for the analysis of electromagnetic exchange processes in DC power systems. The paper shows the definition and main formulas for defining the instantaneous reactive power taking into consideration the random character of voltage and current. Numerical calculations together with statistical analysis were performed for two variants of experimentally recorded voltages and currents: the first one - on the bus-bar of traction substation, which supplies the section with the Pendolino trains in Poland and the second one - on the current collector of the VL8 electric locomotive in Ukraine. They confirm the validity of the aforementioned method.


title: Induction Heating Appliances: Toward More Flexible Cooking Surfaces

abstract: Efficient energy management in residential areas is a key issue in modern energy systems. In this scenario, induction heating (IH) becomes an alternative to classical heating technologies because of its advantages such as efficiency, quickness, safety, and accurate power control. In this article, the design of modern flexible cooking surfaces featuring IH technology is presented. The main advantages and technical challenges are given, and the design of the inductor system and the power electronic converter is detailed. The feasibility of the proposed system is verified through a laboratory prototype.


title: Self-efficacy: An essential motive to learn

abstract: During the past two decades, self-efficacy has emerged as a highly effective predictor of students' motivation and learning. As a performance-based measure of perceived capability, self-efficacy differs conceptually and psychometrically from related motivational constructs, such as outcome expectations, self-concept, or locus of control. Researchers have succeeded in verifying its discriminant validity as well as convergent validity in predicting common motivational outcomes, such as students' activity choices, effort, persistence, and emotional reactions. Self-efficacy beliefs have been found to be sensitive to subtle changes in students' performance context, to interact with self-regulated learning processes, and to mediate students' academic achievement.


title: Speeding up Tabular Reinforcement Learning Using State-Action Similarities

abstract: One of the most prominent approaches for speeding up reinforcement learning is injecting human prior knowledge into the learning agent. This paper proposes a novel method to speed up temporal difference learning by using state-action similarities. These hand-coded similarities are tested in three well-studied domains of varying complexity, demonstrating our approach's benefits.


title: A weighted sparse coding framework for saliency detection

abstract: There is an emerging interest on using high-dimensional datasets beyond 2D images in saliency detection. Examples include 3D data based on stereo matching and Kinect sensors and more recently 4D light field data. However, these techniques adopt very different solution frameworks, in both type of features and procedures on using them. In this paper, we present a unified saliency detection framework for handling heterogenous types of input data. Our approach builds dictionaries using data-specific features. Specifically, we first select a group of potential foreground superpixels to build a primitive saliency dictionary. We then prune the outliers in the dictionary and test on the remaining superpixels to iteratively refine the dictionary. Comprehensive experiments show that our approach universally outperforms the state-of-the-art solution on all 2D, 3D and 4D data.


title: A Survey of Mobile Cloud Computing Application Models

abstract: Smart phones are now capable of supporting a wide range of applications, many of which demand an ever increasing computational power. This poses a challenge because smart phones are resource-constrained devices with limited computation power, memory, storage, and energy. Fortunately, the cloud computing technology offers virtually unlimited dynamic resources for computation, storage, and service provision. Therefore, researchers envision extending cloud computing services to mobile devices to overcome the smartphones constraints. The challenge in doing so is that the traditional smartphone application models do not support the development of applications that can incorporate cloud computing features and requires specialized mobile cloud application models. This article presents mobile cloud architecture, offloading decision affecting entities, application models classification, the latest mobile cloud application models, their critical analysis and future research directions.


title: Clinical application of a modular ankle robot for stroke rehabilitation.

abstract: Background ::: Advances in our understanding of neuroplasticity and motor learning post-stroke are now being leveraged with the use of robotics technology to enhance physical rehabilitation strategies. Major advances have been made with upper extremity robotics, which have been tested for efficacy in multi-site trials across the subacute and chronic phases of stroke. In contrast, use of lower extremity robotics to promote locomotor re-learning has been more recent and presents unique challenges by virtue of the complex multi-segmental mechanics of gait.


title: Root Rot of Miniature Roses Caused by Pythium helicoides

abstract: Miniature roses growing in an ebb-and-flow watering system developed dieback during the summer growing season of 1996 in Gifu Prefecture. The main diagnostic symptoms were chlorosis of leaf followed by blight, and a brown, water-soaked root rot followed by dieback. Pythium isolates were recovered from the rotted root. The isolates form proliferous ellipsoidal papillate sporangia, spherical smooth oogonia, elongate antheridia, and aplerotic oospores. The optimum temperature for hyphal growth was 35°C with a growth rate of 34 mm/24 hr. Optimum temperature of zoospore formation (25-30°C) was lower than that of mycelial growth, and zoospores were produced even at 10°C. The isolates were identified as P. helicoides on the basis of these characteristics. In pathogenicity tests disease severity was highest at the highest tested temperature (35°C) at which the disease naturally occurred in summer. Four days after inoculation, the leaves turned yellow and the roots had a water-soaked rot, followed by leaf blight and root dieback after 7 days. The disease transmission test showed that diseased plants were found throughout the bench after 10 days.


title: The Use of Hyaluronidase in Cosmetic Dermatology: A Review of the Literature

abstract: Background: Hyaluronidase can be employed to manage a variety of complications associated with cosmetic hyaluronic acid (HA) filler injection. However, the indications and treatment protocol for hyaluronidase use have not been well established.


title: Data Approximation Using Biarcs

abstract: . An algorithm for data approximation with biarcs is presented. The method uses a specific formulation of biarcs appropriate for parametric curves in Bezier or NURBS formulation. A base curve is applied to obtain tangents and anchor points for the individual arcs joining in G 1 continuity. Data sampled from circular arcs or straight line segments is represented precisely by one biarc. The method is most useful in numerical control to drive the cutter along straight line or circular paths.


title: Database resources of the National Center for Biotechnology Information

abstract: The National Center for Biotechnology Information(NCBI) is a big system of bioinfor-matics. It provides data analysis and retrieval and resources that operate on the data in GenBank and a variety of other biological data made available through NCBI's Web site. NCBI data retrieval resources include Entrez, PubMed, LocusLink and the Taxonomy Browser. Data analysis resources include BLAST, Electronic PCR, OrfFinder, etc. It is a very important biological resources for us to do research.


title: The Detection of Series Arc Fault in Photovoltaic Systems Based on the Arc Current Entropy

abstract: This paper presents a series arc-fault detection algorithm for photovoltaic (PV) systems, which relies on the quantum probability model theory. The algorithm determines the presence of the arc by calculating the modified Tsallis entropy of the PV panel current. Based on the calculated entropy, the algorithm is able to differentiate an arc state (when the current variations are chaotic) from a no-arc state (when the current variations are ordered). The proposed algorithm enables arc-fault detection on a plug-and-play principle, requiring no prior information about the PV system in which it operates. The operation of the algorithm was first simulated on the prerecorded data from the system with the PV panel simulator and the commercial PV inverter. A laboratory prototype of the detector was then built and tested in a real 1.6-kW grid-connected PV system with different PV inverter, without any additional parameter adjustments. Both the sensitivity and the robustness of the detector were confirmed. The tests have shown that both the sustained series arcs and the small sparking series arcs were successfully detected, and there was no false detection due to the MPP tracker operation, PV current step change, or inverter turn on transient.


title: Comparing Textual and Block Interfaces in a Novice Programming Environment

abstract: Visual, block-based programming environments present an alternative way of teaching programming to novices and have proven successful in classrooms and informal learning settings. However, few studies have been able to attribute this success to specific features of the environment. In this study, we isolate the most fundamental feature of these environments, the block interface, and compare it directly to its textual counterpart. We present analysis from a study of two groups of novice programmers, one assigned to each interface, as they completed a simple programming activity. We found that while the interface did not seem to affect users' attitudes or perceived difficulty, students using the block interface spent less time off task and completed more of the activity's goals in less time.


title: Central effects of stress hormones in health and disease: understanding the protective and damaging effects of stress and stress mediators

abstract: Stress begins in the brain and affects the brain, as well as the rest of the body. Acute stress responses promote adaptation and survival via responses of neural, cardiovascular, autonomic, immune and metabolic systems. Chronic stress can promote and exacerbate pathophysiology through the same systems that are dysregulated. The burden of chronic stress and accompanying changes in personal behaviors (smoking, eating too much, drinking, poor quality sleep; otherwise referred to as “lifestyle”) is called allostatic overload. Brain regions such as hippocampus, prefrontal cortex and amygdala respond to acute and chronic stress and show changes in morphology and chemistry that are largely reversible if the chronic stress lasts for weeks. However, it is not clear whether prolonged stress for many months or years may have irreversible effects on the brain. The adaptive plasticity of chronic stress involves many mediators, including glucocorticoids, excitatory amino acids, endogenous factors such as brain neurotrophic factor (BDNF), polysialated neural cell adhesion molecule (PSA-NCAM) and tissue plasminogen activator (tPA). The role of this stress-induced remodeling of neural circuitry is discussed in relation to psychiatric illnesses, as well as chronic stress and the concept of top-down regulation of cognitive, autonomic and neuroendocrine function. This concept leads to a different way of regarding more holistic manipulations, such as physical activity and social support as an important complement to pharmaceutical therapy in treatment of the common phenomenon of being “stressed out”. Policies of government and the private sector play an important role in this top-down view of minimizing the burden of chronic stress and related lifestyle (i.e. allostatic overload).


title: Maintainability defects detection and correction: a multi-objective approach

abstract: Software defects often lead to bugs, runtime errors and software maintenance difficulties. They should be systematically prevented, found, removed or fixed all along the software lifecycle. However, detecting and fixing these defects is still, to some extent, a difficult, time-consuming and manual process. In this paper, we propose a two-step automated approach to detect and then to correct various types of maintainability defects in source code. Using Genetic Programming, our approach allows automatic generation of rules to detect defects, thus relieving the designer from a fastidious manual rule definition task. Then, we correct the detected defects while minimizing the correction effort. A correction solution is defined as the combination of refactoring operations that should maximize as much as possible the number of corrected defects with minimal code modification effort. We use the Non-dominated Sorting Genetic Algorithm (NSGA-II) to find the best compromise. For six open source projects, we succeeded in detecting the majority of known defects, and the proposed corrections fixed most of them with minimal effort.


title: Entropy-Based Term Weighting Schemes for Text Categorization in VSM

abstract: Term weighting schemes have been widely used in information retrieval and text categorization models. In this paper, we first investigate into the limitations of several state-of-the-art term weighting schemes in the context of text categorization tasks. Considering that category-specific terms are more useful to discriminate different categories, and these terms tend to have smaller entropy with respect to these categories, we then explore the relationship between a term's discriminating power and its entropy with respect to a set of categories. To this end, we propose two entropy-based term weighting schemes (i.e., tf.dc and tf.bdc) which measure the discriminating power of a term based on its global distributional concentration in the categories of a corpus. To demonstrate the effectiveness of the proposed term weighting schemes, we compare them with seven state-of-the-art schemes on a long-text corpus and a short-text corpus respectively. Our experimental results show that the proposed schemes outperform the state-of-the-art schemes in text categorization tasks with KNN and SVM.


title: A haptic wristwatch for eyes-free interactions

abstract: We present a haptic wristwatch prototype that makes it possible to acquire information from a companion mobile device through simple eyes-free gestures. The wristwatch we have built uses a custom-made piezoelectric actuator combined with sensors to create a natural, inconspicuous, gesture-based interface. Feedback is returned to the user in the form of haptic stimuli that are delivered to the wrist. We evaluated the capabilities and limitations of our prototype through two user experiments. One experiment verified that the apparatus could be used as a tactile notification mechanism. The other experiment assessed the feasibility of using a cover-and-hold gesture on the wristwatch to obtain numerical data tactually. Results from the numerosity experiment and feedback from participants prompted us to redesign the cover-and-hold gesture to provide users with additional control over the interaction. We qualitatively evaluated the redesigned interaction by handing the prototype to users so that they could use it in a realistic work environment. Taken together, results from the experiments and the validation process indicate that a wrist accessory can be effectively used to perform discreet, closed-loop, eyes-free interactions with a mobile device.


title: Essential Bacillus subtilis genes

abstract: To estimate the minimal gene set required to sustain bacterial life in nutritious conditions, we carried out a systematic inactivation of Bacillus subtilis genes. Among approximately 4,100 genes of the organism, only 192 were shown to be indispensable by this or previous work. Another 79 genes were predicted to be essential. The vast majority of essential genes were categorized in relatively few domains of cell metabolism, with about half involved in information processing, one-fifth involved in the synthesis of cell envelope and the determination of cell shape and division, and one-tenth related to cell energetics. Only 4% of essential genes encode unknown functions. Most essential genes are present throughout a wide range of Bacteria, and almost 70% can also be found in Archaea and Eucarya. However, essential genes related to cell envelope, shape, division, and respiration tend to be lost from bacteria with small genomes. Unexpectedly, most genes involved in the Embden-Meyerhof-Parnas pathway are essential. Identification of unknown and unexpected essential genes opens research avenues to better understanding of processes that sustain bacterial life.


title: UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

abstract: UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.


title: Experience of adjunctive cannabis use for chronic non-cancer pain: Findings from the Pain and Opioids IN Treatment (POINT) study

abstract: Abstract Background There is increasing debate about cannabis use for medical purposes, including for symptomatic treatment of chronic pain. We investigated patterns and correlates of cannabis use in a large community sample of people who had been prescribed opioids for chronic non-cancer pain. Methods The POINT study included 1514 people in Australia who had been prescribed pharmaceutical opioids for chronic non-cancer pain. Data on cannabis use, ICD-10 cannabis use disorder and cannabis use for pain were collected. We explored associations between demographic, pain and other patient characteristics and cannabis use for pain. Results One in six (16%) had used cannabis for pain relief, 6% in the previous month. A quarter reported that they would use it for pain relief if they had access. Those using cannabis for pain on average were younger, reported greater pain severity, greater interference from and poorer coping with pain, and more days out of role in the past year. They had been prescribed opioids for longer, were on higher opioid doses, and were more likely to be non-adherent with their opioid use. Those using cannabis for pain had higher pain interference after controlling for reported pain severity. Almost half (43%) of the sample had ever used cannabis for recreational purposes, and 12% of the entire cohort met criteria for an ICD-10 cannabis use disorder. Conclusions Cannabis use for pain relief purposes appears common among people living with chronic non-cancer pain, and users report greater pain relief in combination with opioids than when opioids are used alone.


title: Data Stream Clustering Algorithms: A Review

abstract: Data stream mining has become a research area of some interest in recent years. The key challenge in data stream mining is extracting valuable knowledge in real time from a massive, continuous, dynamic data stream in only a single scan. Clustering is an efficient tool to overcome this problem. Data stream clustering can be applied in various fields such as financial transactions, telephone records, sensor network monitoring, telecommunications, website analysis, weather monitoring, and e-business. Data stream clustering presents some challenges; it needs to be done in a short time frame with limited memory using a single-scan process. Moreover, because data stream outliers are hidden, clustering algorithms must be able to detect outliers and noise. In addition, the algorithms have to handle concept drift and detect arbitrary shaped clusters. Several algorithms have been proposed to overcome these challenges. This paper presents a review of five types of data stream clustering approaches: partitioning, hierarchical, density-based, grid-based and model-based. The different data stream clustering algorithms in the literature by considering their respective advantages and disadvantages are discussed .


title: Restful Web Services

abstract: "Every developer working with the Web needs to read this book." -- David Heinemeier Hansson, creator of the Rails framework "RESTful Web Services finally provides a practical roadmap for constructing services that embrace the Web, instead of trying to route around it." -- Adam Trachtenberg, PHP author and EBay Web Services Evangelist You've built web sites that can be used by humans. But can you also build web sites that are usable by machines? That's where the future lies, and that's what RESTful Web Services shows you how to do. The World Wide Web is the most popular distributed application in history, and Web services and mashups have turned it into a powerful distributed computing platform. But today's web service technologies have lost sight of the simplicity that made the Web successful. They don't work like the Web, and they're missing out on its advantages. This book puts the "Web" back into web services. It shows how you can connect to the programmable web with the technologies you already use every day. The key is REST, the architectural style that drives the Web. This book: Emphasizes the power of basic Web technologies -- the HTTP application protocol, the URI naming standard, and the XML markup languageIntroduces the Resource-Oriented Architecture (ROA), a common-sense set of rules for designing RESTful web servicesShows how a RESTful design is simpler, more versatile, and more scalable than a design based on Remote Procedure Calls (RPC)Includes real-world examples of RESTful web services, like Amazon's Simple Storage Service and the Atom Publishing ProtocolDiscusses web service clients for popular programming languagesShows how to implement RESTful services in three popular frameworks -- Ruby on Rails, Restlet (for Java), and Django (for Python)Focuses on practical issues: how to design and implement RESTful web services and clients This is the first book that applies the REST design philosophy to real web services. It sets down the best practices you need to make your design a success, and the techniques you need to turn your design into working code. You can harness the power of the Web for programmable applications: you just have to work with the Web instead of against it. This book shows you how.


title: Optimal Adaptive Cruise Control with Guaranteed String Stability

abstract: A two-level Adaptive Cruise Control (ACC) synthesis method is presented in this paper. At the upper level, desired vehicle acceleration is computed based on vehicle range and range rate measurement. At the lower (servo) level, an adaptive control algorithm is designed to ensure the vehicle follows the upper level acceleration command accurately. It is shown that the servo-level dynamics can be included in the overall design and string stability can be guaranteed. In other words, the proposed control design produces minimum negative impact on surrounding vehicles. The performance of the proposed ACC algorithm is examined by using a microscopic simulation program – ACCSIM created at the University of Michigan. The architecture and basic functions of ACCSIM are described in this paper. Simulation results under different ACC penetration rate and actuator/engine bandwidth are reported.


title: Critical Success Factors to Improve the Game Development Process from a Developers Perspective

abstract: The growth of the software game development industry is enormous and is gaining importance day by day. This growth imposes severe pressure and a number of issues and challenges on the game development community. Game development is a complex process, and one important game development choice is to consider the developer perspective to produce good quality software games by improving the game development process. The objective of this study is to provide a better understanding of the developers dimension as a factor in software game success. It focuses mainly on an empirical investigation of the effect of key developer factors on the software game development process and eventually on the quality of the resulting game. A quantitative survey was developed and conducted to identify key developer factors for an enhanced game development process. For this study, the developed survey was used to test the research model and hypotheses. The results provide evidence that game development organizations must deal with multiple key factors to remain competitive and to handle high pressure in the software game industry. The main contribution of this paper is to investigate empirically the influence of key developer factors on the game development process.


title: CAPTAIN: Comprehensive Composition Assistance for Photo Taking

abstract: Many people are interested in taking astonishing photos and sharing with others. Emerging hightech hardware and software facilitate ubiquitousness and functionality of digital photography. Because composition matters in photography, researchers have leveraged some common composition techniques to assess the aesthetic quality of photos computationally. However, composition techniques developed by professionals are far more diverse than well-documented techniques can cover. We leverage the vast underexplored innovations in photography for computational composition assistance. We propose a comprehensive framework, named CAPTAIN (Composition Assistance for Photo Taking), containing integrated deep-learned semantic detectors, sub-genre categorization, artistic pose clustering, personalized aesthetics-based image retrieval, and style set matching. The framework is backed by a large dataset crawled from a photo-sharing Website with mostly photography enthusiasts and professionals. The work proposes a sequence of steps that have not been explored in the past by researchers. The work addresses personal preferences for composition through presenting a ranked-list of photographs to the user based on user-specified weights in the similarity measure. The matching algorithm recognizes the best shot among a sequence of shots with respect to the user's preferred style set. We have conducted a number of experiments on the newly proposed components and reported findings. A user study demonstrates that the work is useful to those taking photos.


title: Behavior Recognition of a Person in a Daily Video Using Joint Position Information

abstract: In this paper, we propose a method to recognize people's specific behaviors in images acquired in everyday life. The poses that occur when taking a specific action are represented using the joint coordinates of the body. To reduce the influence of the perspective effect on the locations in an image, the coordinates of joints for each person were normalized and the pose was classified using SVM with the normalized coordinates. Experimental results on video images of CCTV camera on ordinary alleys showed that the proposed method relatively recognized a specific behavior.


title: Deep-learning the Latent Space of Light Transport

abstract: We suggest a method to directly deep-learn light transport, i. e., the mapping from a 3D geometry-illumination-material configuration to a shaded 2D image. While many previous learning methods have ...


abstract: Nowadays many accidents occur on roads due to increase in traffic or due to rash driving on highways. In many situations, the family members or the ambulance authorities are not informed in time. This results in a delay of medical aid to the victim, which may at times prove to be fatal. The main objective of our paper ‘Vehicle Tracking and Accident Notification System’ is to avoid such mishaps. The vehicle tracking system provides security to all vehicles by tracking them continuously whereas accident notification rescues people who have met with an accident by detecting accidents with the help of various sensors. Both the systems, provide improved security to all vehicles and prevent death due to untimely treatment by the implementation of Global Positioning System (GPS), General Packet Radio Service (GPRS), Global System for Mobile (GSM) technologies.


title: Bridging Network Embedding and Graph Summarization

abstract: An important reason behind the prevalence of node representation learning is their superiority in downstream machine learning tasks on graphs. However, storing the vector-based node representation of massive real-world graphs often requires space that is orders of magnitude larger. To alleviate this issue, we introduce the problem of latent network summarization that is complementary to the problem of network embedding, and propose a general framework called Multi-LENS. Instead of deriving dense node-wise representations, the goal of latent network summarization is to summarize the structural properties of the graph in a latent space with dimensionality that is independent of the nodes or edges in the input graph. The size-independent graph summary given by Multi-LENS consists of (i) a set of relational aggregators with their compositions (relational functions) that captures structural features of multi-order node-centric subgraphs, and (ii) the low-rank approximations to matrices that incorporate captured structural features. In addition, Multi-LENS is able to derive node embeddings on the fly from this latent summary due to its inductive properties. Multi-LENS bridges advantages brought by both embeddings and graph summarization, and applies to graphs with or without directionality, weights, attributes or labels. Extensive experiments on both synthetic and real-world graphs show that Multi-LENS achieves 2 - 89% improvement in AUC for link prediction, while requiring less than 79x space compared to existing representation learning approaches. We also show the effectiveness of Multi-LENS summaries in anomaly and event detection on two real-world graphs.


title: Neural Networks Meet Physical Networks: Distributed Inference Between Edge Devices and the Cloud

abstract: We believe that most future video uploaded over the network will be consumed by machines for sensing tasks such as automated surveillance and mapping rather than for human consumption. Today's systems typically collect raw data from distributed sensors, such as drones, with the computer vision logic implemented in the cloud using deep neural networks (DNNs). They use standard video encoding techniques, send it over the network, and then decompress it at the cloud before using the vision DNN. In other words, data encoding and distribution is decoupled from the sensing goal. This is bandwidth inefficient because video encoding schemes, such as MPEG4, might send data tailored for human perception but irrelevant for the overall sensing goal. We argue that data collection and distribution mechanisms should be co-designed with the eventual sensing objective. Specifically, we propose a distributed DNN architecture that learns end-to-end how to represent the raw sensor data and send it over the network such that it meets the eventual sensing task's needs. Such a design naturally adapts to varying network bandwidths between the sensors and the cloud, as well as automatically sends task-appropriate data features.


title: Anomaly Detection using Autoencoders in High Performance Computing Systems

abstract: Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states).We propose a novel approach for anomaly detection in HighPerformance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with).We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).


title: Mining group stock portfolio by using grouping genetic algorithms

abstract: In this paper, a grouping genetic algorithm based approach is proposed for dividing stocks into groups and mining a set of stock portfolios, namely group stock portfolio. Each chromosome consists of three parts. Grouping and stock parts are used to indicate how to divide stocks into groups. Stock portfolio part is used to represent the purchased stocks and their purchased units. The fitness of each chromosome is evaluated by the group balance and the portfolio satisfaction. The group balance is utilized to make the groups represented by the chromosome have as similar number of stocks as possible. The portfolio satisfaction is used to evaluate the goodness of profits and satisfaction of investor's requests of all possible portfolio combinations that can generate from a chromosome. Experiments on a real data were also made to show the effectiveness of the proposed approach.


Stability of Haptic Displays


title: Time and Thyme Are not Homophones: The Effect of Lemma Frequency on Word Durations in Spontaneous Speech

abstract: Frequent words tend to shorten. But do homophone pairs, such as time and thyme , shorten equally if one member of the pair is frequent? This study reports an analysis of roughly 90,000 tokens of homophones in the Switchboard corpus of American English telephone conversations, in which it was found that high-frequency words like time are significantly shorter than their low-frequency homophones like thyme . The effect of lemma frequency persisted when local speaking rate, predictability from neighboring words, position relative to pauses, syntactic category, and orthographic regularity were brought under statistical control. These findings have theoretical implications for the locus of frequency information in linguistic competence and in models of language production, and for the role of articulatory routinization in shortening.


title: Embodying Emotions: What Emotion Theorists Can Learn from Simulations of Emotions

abstract: Cognitively-oriented theories have dominated the recent history of the study of emotion. However, critics of this perspective suggest the role of the body in the experience of emotion is largely ignored by cognitive theorists. As an alternative to the cognitive perspective, critics are increasingly pointing to William James' theory, which emphasized somatic aspects of emotions. This emerging emphasis on the embodiment of emotions is shared by those in the field of AI attempting to model human emotions. Behavior-based agents in AI are attempts to model the role the body might play in the experiencing of emotions. Progress in creating such behavior-based models that function in their environments has been slow, suggesting some potential problems with Jamesian alternatives to cognitive perspectives of emotions. Heidegger's and Merleau-Ponty's conceptions of embodiment are suggested as alternatives to James' and as means for addressing the shortcomings of the cognitive perspective.


title: A review of homomorphic encryption and software tools for encrypted statistical machine learning

abstract: Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first decrypt. We review these homomorphic encryption schemes in a manner accessible to statisticians and machine learners, focusing on pertinent limitations inherent in the current state of the art. These limitations restrict the kind of statistics and machine learning algorithms which can be implemented and we review those which have been successfully applied in the literature. Finally, we document a high performance R package implementing a recent homomorphic scheme in a general framework.


Multimodal Stacked Denoising Autoencoders


title: Recovering Semantic Traceability Links between APIs and Security Vulnerabilities: An Ontological Modeling Approach

abstract: Over the last decade, a globalization of the software industry took place, which facilitated the sharing and reuse of code across existing project boundaries. At the same time, such global reuse also introduces new challenges to the software engineering community, with not only components but also their problems and vulnerabilities being now shared. For example, vulnerabilities found in APIs no longer affect only individual projects but instead might spread across projects and even global software ecosystem borders. Tracing these vulnerabilities at a global scale becomes an inherently difficult task since many of the existing resources required for such analysis still rely on proprietary knowledge representation. In this research, we introduce an ontology-based knowledge modeling approach that can eliminate such information silos. More specifically, we focus on linking security knowledge with other software knowledge to improve traceability and trust in software products (APIs). Our approach takes advantage of the Semantic Web and its reasoning services, to trace and assess the impact of security vulnerabilities across project boundaries. We present a case study, to illustrate the applicability and flexibility of our ontological modeling approach by tracing vulnerabilities across project and resource boundaries.


title: Universal mobility-field curves for electrons and holes in MOS inversion layers

abstract: The mobility of carriers in a silicon surface inversion layer is one of the most important parameters required to accurately model and predict MOSFET device and circuit performance. It has been found that electron mobility follows a universal curve when plotted as a function of an effective normal field regardless of substrate bias, substrate doping (≤ 1017 cm−3) and nominal process variations [1]. Although accurate modeling of p-channel MOS devices has become important due to the prevalence of CMOS technology, the existence of a universal hole mobility-field relationship has not been demonstrated. Furthermore, the effect on mobility of low-temperature and rapid high-temperature processing, which are commonly used in modern VLSI technology to control impurity diffusion, is unknown.