title: 2003): Correlating Automated and Human Assessments of Machine Translation Quality

abstract: We describe a large-scale investigation of the correlation between human judgments of machine translation quality and the automated metrics that are increasingly used to drive progress in the field. We compare the results of 124 human evaluations of machine translated sentences to the scores generated by two automatic evaluation metrics (BLEU and NIST). When datasets are held constant or file size is sufficiently large, BLEU and NIST scores closely parallel human judgments. Surprisingly, this was true even though these scores were calculated using just one human reference. We suggest that when human evaluators are forced to make decisions without sufficient context or domain expertise, they fall back on strategies that are not unlike determining n-gram precision.


title: A tunable LTE main antenna solution for metallic cover mobile phone

abstract: It has become a popular trend in using metallic housing for mobile phone. The metallic housing often posts a great challenge towards the design of internal antenna which is used commonly in mobile devices. As such, it has become a common practice that most will integrate the metallic housing as part of the antenna for the mobile phone. This paper presents an antenna solution for mobile phone with a metallic back cover. The antenna solution is for the Main Antenna operating at several frequency bands in the range of 700–960MHz, 1710–2170MHz, 2300–2400MHz as well as 2500–2690MHz, which cover the GSM850, GSM900, DCS1800, PCS1900, WCDMA2100, and various LTE Bands in the 4G cellular network. The antenna solution presented in this paper, demonstrates the use of tunable component, integrating with the metallic housing to provide flexible tuning to cover the various frequency spectrums in the 2G, 3G and 4G cellular network.


title: Sales and Marketing Integration: A Proposed Framework

abstract: In this paper, we identify sales and marketing activities, and common impediments to their integration. We then discuss the concept of sales-marketing integration, and distinguish it from related concepts such as involvement and communication. Following this, we discuss approaches businesses can use to improve sales-marketing integration as well as their potential costs and drawbacks. The paper concludes with a set of propositions identifying the conditions under which sales-marketing integration has the greatest impact on firm performance


title: A survey paper on big data analytics

abstract: In recent years, the internet application and communication have seen a lot of development and reputation in the field of Information Technology. These internet applications and communication are continually generating the large size, different variety and with some genuine difficult multifaceted structure data called big data. As a consequence, we are now in the era of massive automatic data collection, systematically obtaining many measurements, not knowing which one will be relevant to the phenomenon of interest. For example, E-commerce transactions include activities such as online buying, selling or investing. Thus they generate the data which are high in dimensional and complex in structure. The traditional data storage techniques are not adequate to store and analyses those huge volume of data. Many researchers are doing their research in dimensionality reduction of the big data for effective and better analytics report and data visualization. Hence, the aim of the survey paper is to provide the overview of the big data analytics, issues, challenges and various technologies related with Big Data.


title: Inhibition of acid secretion in guinea pigs by tricyclic antidepressants: comparison with ranitidine and omeprazole.

abstract: The antisecretory properties of imipramine on gastric secretion in guinea pig in comparison with other antisecretory agents was determined. In awake guinea pigs s.c. infusion of histamine (30 micrograms/kg/hr) increased acid and fluid secretion by 3- to 4-fold. When acid output peaked, a bolus administration of the tricyclic anti-depressant imipramine inhibited acid and fluid secretion. Imipramine and other agents, such as ranitidine and omeprazole, inhibited gastric secretion in a dose-dependent fashion. The most potent was the H2-antagonist ranitidine (IC50, 0.2-0.3 mumol/kg), followed by the gastric H-K-adenosine triphosphatase inhibitor, omeprazole (IC50, 0.5-0.6 mumol/kg). Imipramine (IC50 1-2 mumol/kg) was the least potent of the inhibitors. Both ranitidine and omeprazole could abolish acid secretion, but maximal inhibition with imipramine was 60% of initial. Promethazine (25 mumol/kg), an H1 antagonist, and atropine (12 mumol/kg), a muscarinic antagonist, inhibited gastric secretion by 40 to 50%. Imipramine and atropine also inhibited basal acid secretion. In dispersed gastric cells comparison between imipramine and omeprazole showed that imipramine was about 5-fold more potent than omeprazole in blocking histamine or dibutyryl cyclic AMP stimulation of aminopyrine accumulation. Imipramine probably acts as a protonophore by increasing the rate of proton-gradient dissipation rather than by interfering with the hydrogen-pump system because, in gastric membranes, imipramine was 20-fold less potent than omeprazole in inhibiting the gastric H-K-adenosine triphosphatase activity. These results suggest that imipramine administered s.c. in guinea pigs is a potent antisecretory drug. Its action may be due to a combination of anticholinergic and antihistamine H2 activities.


title: Plagiarism Detection Using Stopword n-Grams

abstract: In this paper a novel method for detecting plagiarized passages in document collections is presented. In contrast to previous work in this field that uses content terms to represent documents, the proposed method is based on a small list of stopwords (i.e., very frequent words). We show that stopword n-grams reveal important information for plagiarism detection since they are able to capture syntactic similarities between suspicious and original documents and they can be used to detect the exact plagiarized passage boundaries. Experimental results on a publicly available corpus demonstrate that the performance of the proposed approach is competitive when compared with the best reported results. More importantly, it achieves significantly better results when dealing with difficult plagiarism cases where the plagiarized passages are highly modified and most of the words or phrases have been replaced with synonyms. © 2011 Wiley Periodicals, Inc.


title: A Model for Android and iOS Applications Risk Calculation: CVSS Analysis and Enhancement Using Case-Control Studies

abstract: Various researchers have shown that the Common Vulnerability Scoring System (CVSS) has many drawbacks and may not provide a precise view of the risks related to software vulnerabilities. However, many threat intelligence platforms and industry-wide standards are relying on CVSS score to evaluate cyber security compliance. This paper suggests several improvements to the calculation of Impact and Exploitability sub-scores within the CVSS, improve its accuracy and help threat intelligence analysts to focus on the key risks associated with their assets. We will apply our suggested improvements against risks associated with several Android and iOS applications and discuss achieved improvements and advantages of our modelling, such as the importance and the impact of time on the overall CVSS score calculation.


title: A multichannel convolutional neural network for cross-language dialog state tracking

abstract: The fifth Dialog State Tracking Challenge (DSTC5) introduces a new cross-language dialog state tracking scenario, where the participants are asked to build their trackers based on the English training corpus, while evaluating them with the unlabeled Chinese corpus. Although the computer-generated translations for both English and Chinese corpus are provided in the dataset, these translations contain errors and careless use of them can easily hurt the performance of the built trackers. To address this problem, we propose a multichannel Convolutional Neural Networks (CNN) architecture, in which we treat English and Chinese language as different input channels of one single CNN model. In the evaluation of DSTC5, we found that such multichannel architecture can effectively improve the robustness against translation errors. Additionally, our method for DSTC5 is purely machine learning based and requires no prior knowledge about the target language. We consider this a desirable property for building a tracker in the cross-language context, as not every developer will be familiar with both languages.


title: Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

abstract: Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. Despite their benefits for achieving high data rate communications, their potential advantages for tracking of the location and rotation angle of the user terminals are not well investigated. A joint heuristic beam selection and user position and orientation tracking approach is proposed. First, the user location is tracked in the uplink by joint beam selection together with time-of-arrival (TOA) and angle-of-arrival (AOA) tracking at the base station (BS). Then, the user rotation angle is obtained using the location information by joint beam selection and tracking at the mobile station (MS). The beam selection, TOA and AOA tracking, at the BS and MS are performed during the data transmission phase. Numerical results demonstrate that the proposed method performs close to the estimated position and rotation angle in the training phase with reduced complexity and reduced number of required pilots for the estimation.


title: IKEv2 Protocol Fuzzing Test on Simulated ASA

abstract: With the development of the Internet of Things, more and more private information is exposed online. Protecting the security of the users' information has become more and more important. Routers and firewalls are the important components of the core network whose security cannot be ignored. And we focus our research on Cisco ASA because Cisco network equipment is the most widely deployed in the global Internet and has an absolute advantage in technology and market share. This paper describes how to perform security testing on Cisco ASA without hardware devices. What's more, we have built a vulnerability analysis environment for the simulated ASA and proposed a fuzzing test method for IKEv2 based on Scapy. And we have found some bugs in ASA IKEv2 with it.


title: A System Framework for Sharing Economy

abstract: The continuous growth of the sharing economy sector and the emergence of several famous brands like Uber and Airbnb raise many social and academic issues that need to be researched. So far, most of research are focused on the economic models or market strategies about sharing economy, few research works have been done from the perspective of information technologies. However, information system offers the essential platform to realize goods and services access and sharing. This paper aims to analyze the characteristics of sharing economy information systems, and propose a system framework, which could be considered a reference model to analyze or build new applications in this sector. In this paper, different collaboration models are firstly identified, then the success factors enabled by information technologies are introduced. Finally, a hierarchical system framework and its components are introduced, which can be considered as a reference model when researchers and engineers study or build sharing economy systems.


Generative Adversarial Guided Learning for Domain Adaptation


title: Ontologies for Software Project Management: A Review

abstract: Software Project Management is a knowledge intensive process that can benefit substantially from ontology development and ontology engineering. Ontology development could facilitate or improve substantially the software development process through the improvement of knowledge management, the increase of software and artefacts reusability, and the establishment of internal consistency within project management processes of various phases of software life cycle. A large number of ontologies have been developed attempting to address various software engineering aspects, such as requirements engineering, components reuse, domain modelling, etc. In this paper, we present a systematic literature review focusing on software project management ontologies. The literature review, among other, has identified lack of standardization in terminology and concepts, lack of systematic domain modelling and use of ontologies mainly in prototype ontology systems that address rather limited aspects of software project management processes.


title: The world's major religions' points of viewon end-of-life decisionsin the intensive care unit

abstract: Objective ::: Recent research has shown that the religious affiliation of both physicians and patients markedly influences end-of-life decisions in the intensive care unit in the Western world. The world's major religions' standings on withholding and withdrawing of therapy, on hastening of the death process when providing pain relief (double effect) and on euthanasia are described. This review also discusses whether nutrition should be provided to patients in a permanent vegetative state, and the issues of brain death and organ donation.


title: Large-scale energy-efficient graph traversal: a path to efficient data-intensive supercomputing

abstract: Graph traversal is a widely used algorithm in a variety of fields, including social networks, business analytics, and high-performance computing among others. There has been a push for HPC machines to be rated not just in Petaflops, but also in "GigaTEPS" (billions of traversed edges per second), and the Graph500 benchmark has been established for this purpose. Graph traversal on single nodes has been well studied and optimized on modern CPU architectures. However, current cluster implementations suffer from high latency data communication with large volumes of transfers across nodes, leading to inefficiency in performance and energy consumption. In this work, we show that we can overcome these constraints using a combination of efficient low-overhead data compression techniques to reduce transfer volumes along with latency-hiding techniques. Using an optimized single node graph traversal algorithm [1], our novel cluster optimizations result in over 6.6X performance improvements over state-of-the-art data transfer techniques, and almost an order of magnitude in energy savings. Our resulting implementation of the Graph500 benchmark achieves 115 GigaTEPS on a 320-node/5120 core Intel® Endeavor cluster with E5-2700 Sandybridge nodes, which matches the second ranked result in the most recent November 2011 Graph500 list [2] with about 5.6X fewer nodes. Our cluster optimizations only have a 1.8X overhead in overall performance from the performance of the optimized single-node implementation, and allows for near-linear scaling with number of nodes. Our algorithm on 1024 nodes on an Intel® Xeon® X5670 Westmere processor (with lower per-node performance) for a large multi-Terabyte graph attained 195 GigaTEPS in performance, proving the high scalability of our algorithm. Our per-node performance is the highest in the top 10 of the Nov 2011 Graph500 list.


title: Case Study on a Software Communications Architecture Component for Hardware Acceleration

abstract: In this paper we introduce an architecture for hardware acceleration of Software Communication Architecture (SCA) components. Intercommunication, as well as responsiveness and power dissipation, is essential for the components of SCA. Thus, we studied the SCA to explore the hotspot function and optimize its overall performance to not only operate efficiently but also in an energy-efficient way. We spotted the amplifier function takes a significant amount of the execution time which involves single-precision floating-point computation. A case study on the implementation of the amplifier component on the Virtex5 Field-Programmable Gate Array (FPGA) platform is presented. Consequently, we make a comparison of the hardware accelerated amplifier implementation employing a floating-point unit (FPU) engine over pure software implementation with no FPU support. We obtained a speedup of up to 2x faster while minimizing the energy consumption by up to 100% with a power consumption increase of 0.68%, achieving our goal.


title: Design of two-stage fully-integrated CMOS power amplifier for K-band applications

abstract: This paper presents a K-band power amplifier integrated circuit using Samsung 65 nm CMOS process. The power amplifier adopts two-stage configuration for high power gain. The input, output, and inter-stage transformers are integrated. By neutralizing gate-drain capacitance using cross-coupled capacitors, the power gain and stability were improved. Its chip size is 0.78 × 0.62 mm2. The implemented two-stage power amplifier showed a power gain of 19.6 dB, a saturated output power of 13.5 dBm, and an efficiency of 7.19 % with a supply voltage of 1.1 V at the frequency band of 24 GHz.


title: Directive array based pattern reconfigurable antenna

abstract: In this paper, a high-gain pattern reconfigurable antenna system is presented. It consists of one driven monopole antenna at the centre, which is surrounded by six parasitic elements. Each parasitic element made up of metallic strip, loaded with switch and backed by dielectric layer. Parasitic elements will behave like transparent and opaque surface to a vertically polarized incident wave for OFF and ON states of switch respectively. Therefore entire assembly becomes reconfigurable and beam steering is achieved. Proposed antenna offers a high and uniform gain of ∼ 8 dBi with beam steering along the entire azimuth plane. The simulation has been carried out for ISM band (fr=2.45 GHz) using CST Microwave studio software.


title: Abnormal event detection in crowded scenes using sparse representation

abstract: We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given a collection of normal training examples, e.g., an image sequence or a collection of local spatio-temporal patches, we propose the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. To condense the over-completed normal bases into a compact dictionary, a novel dictionary selection method with group sparsity constraint is designed, which can be solved by standard convex optimization. Observing that the group sparsity also implies a low rank structure, we reformulate the problem using matrix decomposition, which can handle large scale training samples by reducing the memory requirement at each iteration from O(k^2) to O(k) where k is the number of samples. We use the columnwise coordinate descent to solve the matrix decomposition represented formulation, which empirically leads to a similar solution to the group sparsity formulation. By designing different types of spatio-temporal basis, our method can detect both local and global abnormal events. Meanwhile, as it does not rely on object detection and tracking, it can be applied to crowded video scenes. By updating the dictionary incrementally, our method can be easily extended to online event detection. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our method.


title: Contemporary Research on Parenting The Case for Nature and Nurture

abstract: Current findings on parental influences provide more sophisticated and less deterministic explanations than did earlier theory and research on parenting. Contemporary research approaches include (a) behavior-genetic designs, augmented with direct measures of potential environmental influences; (b) studies distinguishing among children with different genetically influenced predispositions in terms of their responses to different environmental conditions; (c) experimental and quasi-experimental studies of change in children's behavior as a result of their exposure to parents' behavior, after controlling for children's initial characteristics; and (d) research on interactions between parenting and nonfamilial environmental influences and contexts, illustrating contemporary concern with influences beyond the parent-child dyad. These approaches indicate that parental influences on child development are neither as unambiguous as earlier researchers suggested nor as insubstantial as current critics claim.


Enzymes: Physical Principles


title: Business Process Verification- Finally a Reality!

abstract: Purpose – The purpose of this paper is to demonstrate that process verification has matured to a level where it can be used in practice. This paper reports on new verification techniques that can be used to assess the correctness of real‐life models.Design/methodology/approach – The proposed approach relies on using formal methods to determine the correctness of business processes with cancellation and OR‐joins. The paper also demonstrates how reduction rules can be used to improve the efficiency. These techniques are presented in the context of the workflow language yet another workflow language (YAWL) that provides direct support for 20 most frequently used patterns found today (including cancellation and OR‐joins). But the results also apply to other languages with these features (e.g. BPMN, EPCs, UML activity diagrams, etc.). An editor has been developed that provides diagnostic information based on the techniques presented in this paper.Findings – The paper proposes four properties for business proce...


title: Visually Debugging Restricted Boltzmann Machine Training with a 3D Example

abstract: Restricted Boltzmann Machines (RBMs) are being applied to a growing number of problems with great success. In the process of training an RBM one must pick a number of parameters, but often these parameters are brittle and produce poor performance when slightly o. Here we describe several useful visualizations to assist in choosing appropriate values for these parameters. We also demonstrate a successful application of an RBM to a unique domain: learning a representation of synthetic 3D shapes.


title: On the effects of dimensionality reduction on high dimensional similarity search

abstract: The dimensionality curse has profound effects on the effectiveness of high-dimensional similarity indexing from the performance perspective. One of the well known techniques for improving the indexing performance is the method of dimensionality reduction. In this technique, the data is transformed to a lower dimensional space by finding a new axis-system in which most of the data variance is preserved in a few dimensions. This reduction may also have a positive effect on the quality of similarity for certain data domains such as text. For other domains, it may lead to loss of information and degradation of search quality. Recent research indicates that the improvement for the text domain is caused by the re-enforcement of the semantic concepts in the data. In this paper, we provide an intuitive model of the effects of dimensionality reduction on arbitrary high dimensional problems. We provide an effective diagnosis of the causality behind the qualitative effects of dimensionality reduction on a given data set. The analysis suggests that these effects are very data dependent. Our analysis also indicates that currently accepted techniques of picking the reduction which results in the least loss of information are useful for maximizing precision and recall, but are not necessarily optimum from a qualitative perspective. We demonstrate that by making simple changes to the implementation details of dimensionality reduction techniques, we can considerably improve the quality of similarity search.


title: Novel Techniques for Fraud Detection in Mobile Telecommunication Networks

abstract: This paper discusses the status of research on detection of fraud undertaken as part of the European Commission-funded ACTS ASPeCT (Advanced Security for Personal Communications Technologies) project. A first task has been the identification of possible fraud scenarios and of typical fraud indicators which can be mapped to data in toll tickets. Currently, the project is exploring the detection of fraudulent behaviour based on a combination of absolute and differential usage. Three approaches are being investigated: a rule-based approach, and two approaches based on neural networks, where both supervised and unsupervised learning are considered. Special attention is being paid to the feasibility of the implementations.


title: Modeling and Rendering Escher-Like Impossible Scenes

abstract: Inspired by the drawings of “impossible” objects by artists such as M.C. Escher, we describe a mathematical theory which captures some of the underlying principles of their work. Using this theory, we show how impossible three-dimensional scenes may be modeled and rendered synthetically.


title: A Side-Channel Analysis Resistant Description of the AES S-Box

abstract: So far, efficient algorithmic countermeasures to secure the AES algorithm against (first-order) differential side-channel attacks have been very expensive to implement. In this article, we introduce a new masking countermeasure which is not only secure against first-order side-channel attacks, but which also leads to relatively small implementations compared to other masking schemes implemented in dedicated hardware. ::: ::: Our approach is based on shifting the computation of the finite field inversion in the AES S-box down to GF(4). In this field, the inversion is a linear operation and therefore it is easy to mask. ::: ::: Summarizing, the new masking scheme combines the concepts of multiplicative and additive masking in such a way that security against first-order side-channel attacks is maintained, and that small implementations in dedicated hardware can be achieved.


De-anonymizing Programmers via Code


title: Increasing Security Sensitivity With Social Proof: A Large-Scale Experimental Confirmation

abstract: One of the largest outstanding problems in computer security is the need for higher awareness and use of available security tools. One promising but largely unexplored approach is to use social proof: by showing people that their friends use security features, they may be more inclined to explore those features, too. To explore the efficacy of this approach, we showed 50,000 people who use Facebook one of 8 security announcements'7 variations of social proof and 1 non-social control-to increase the exploration and adoption of three security features: Login Notifications, Login Approvals, and Trusted Contacts. Our results indicated that simply showing people the number of their friends that used security features was most effective, and drove 37% more viewers to explore the promoted security features compared to the non-social announcement (thus, raising awareness). In turn, as social announcements drove more people to explore security features, more people who saw social announcements adopted those features, too. However, among those who explored the promoted features, there was no difference in the adoption rate of those who viewed a social versus a non-social announcement. In a follow up survey, we confirmed that the social announcements raised viewer's awareness of available security features.


title: Electrochemical Synthesis for Flowerlike and Fusiform Christmas-Tree-like Cerium Hexacyanoferrate(II)

abstract: A facile electrochemical approach was developed for the controllable synthesis of cerium hexacyanoferrate(II) (CeHCF(II)) flowerlike and fusiform Christmas-tree-like microparticles. The composition, morphology, and structure of the as-prepared CeHCF(II) products were characterized by the techniques such as powder X-ray diffraction (XRD), infrared spectroscopy (IR), thermogravimetry (TG), differential scanning calorimetry (DSC), X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray spectroscopy (EDS), and scanning electron microscopy (SEM). The formula of the prepared CeHCF(II) could be assigned to be KCe[Fe(CN)6]·4H2O. It was observed that the size and morphology of the CeHCF(II) microparticles were altered by electrodepositing potential and time, the molar ratio of Ce(NO3)3 and K3[Fe(CN)6], and the acidity of electrolyte. To the best of our knowledge, this morphologies of CeHCF(II) have not been reported before. The as-prepared CeHCF(II) microparticles showed good ultraviolet luminescent proper...


title: Åsgard Subsea Gas Compression—Technology Qualification Testing With High-Speed VSD and Very Long Step-Out Cable

abstract: The power system including Variable Speed Drives, transformers and long subsea power cables is one of the main building blocks in the development of subsea gas compression. This paper presents results from full scale technology qualification testing of a power system onshore where the Variable Speed Drive is controlling a 188 Hz, 7.8 MW gas compressor motor over a simulated distance of 47 km. The extensive testing shows that it is possible to control the compressor/motor speed from start-up throughout the whole load envelope without need for motor voltage and current feedback. By use of estimator-based control it is also possible to provide motor overcurrent protection. The paper shows how resonances in the long cable system can be handled by use of optimization of the generated voltage waveform instead of using Variable Speed Drive sine filter. The successful technology qualification shows applicability for subsea compressors with a rated frequency of up to 200 Hz, at least 8 MW shaft power and a step-out distance of at least 50 km.


title: An imbalanced data classification algorithm of improved autoencoder neural network

abstract: Imbalanced data classification problem has always been a hotspot in the field of machine learning research. Pointing to the overfitting and noise problems of oversampling algorithm when synthesizing new minority class samples, the current study proposed a stacked denoising autoencoder neural network (SDAE) algorithm based on cost-sensitive oversampling, combining the cost-sensitive learning with denoising autoencoder neural network. The proposed algorithm can not only oversample minority class sample through misclassification cost, but it can denoise and classify the sampled dataset. Experiment shows that, compared with the traditional stacked autoencoder neural network (SAE) and oversampling autoencoder neural network without denoising process (OS-SAE), the proposed algorithm improves the classification accuracy of minority class of imbalanced datasets.


title: Hyperparameter Optimization: A Spectral Approach

abstract: We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters. The algorithm --- an iterative application of compressed sensing techniques for orthogonal polynomials --- requires only uniform sampling of the hyperparameters and is thus easily parallelizable. ::: Experiments for training deep neural networks on Cifar-10 show that compared to state-of-the-art tools (e.g., Hyperband and Spearmint), our algorithm finds significantly improved solutions, in some cases better than what is attainable by hand-tuning. In terms of overall running time (i.e., time required to sample various settings of hyperparameters plus additional computation time), we are at least an order of magnitude faster than Hyperband and Bayesian Optimization. We also outperform Random Search 8x. ::: Additionally, our method comes with provable guarantees and yields the first improvements on the sample complexity of learning decision trees in over two decades. In particular, we obtain the first quasi-polynomial time algorithm for learning noisy decision trees with polynomial sample complexity.


title: Will students pass a competitive exam that they failed in their dreams?

abstract: Abstract We tested whether dreams can anticipate a stressful exam and how failure/success in dreams affect next-day performance. We collected information on students’ dreams during the night preceding the medical school entrance exam. Demographic, academic, sleep and dream characteristics were compared to the students’ grades on the exam. Of the 719 respondents to the questionnaire (of 2324 total students), 60.4% dreamt of the exam during the night preceding it. Problems with the exam appeared in 78% of dreams and primarily involved being late and forgetting answers. Reporting a dream about the exam on the pre-exam night was associated with better performance on the exam ( p = .01). The frequency of dreams concerning the exam during the first term predicted proportionally higher performance on the exam ( R = 0.1, p = .01). These results suggest that the negative anticipation of a stressful event in dreams is common and that this episodic simulation provides a cognitive gain.


title: Off-Isocenter Winston-Lutz Test for Stereotactic Radiosurgery/Stereotactic Body Radiotherapy

abstract: The ::: single-isocenter technique in linear accelerator-based stereotactic ::: radiosurgery/stereotactic body radiotherapy (SRS/SBRT) has been broadly used to ::: treat multiple lesions. However, quantitative study to verify that the ::: mechanical field center coincides with the radiation field center when both are ::: off from the isocenter has never been performed. We developed an innovative ::: method to measure this accuracy, called the off-isocenter Winston-Lutz test, ::: and here we provided a practical clinical guideline to implement this ::: technique. We used ImagePro V.6 to analyze images of a Winston-Lutz phantom ::: obtained using a Varian 21EX linear accelerator with an electronic portal ::: imaging device, set up as for single-isocenter SRS/SBRT for multiple lesions. ::: We investigated asymmetry field centers that were 3 cm and 5 cm away from the ::: isocenter, as well as performing the standard Winston-Lutz test. We used a ::: special beam configuration to acquire images while avoiding collision, and we ::: investigated both jaw and multileaf collimation. For the jaw collimator ::: setting, at 3 cm off-isocenter, the mechanical field deviated from the ::: radiation field by about 2.5 mm; at 5 cm, the deviation was above 3 mm, up to ::: 4.27 mm. For the multileaf collimator setting, at 3 cm off- isocenter, the ::: deviation was below 1 mm; at 5 cm, the deviation was above 1 mm, up to 1.72 mm, ::: which was 72% higher than the tolerance threshold. These results indicated that ::: the further the asymmetry field center is from the machine isocenter, the ::: larger the deviation of the mechanical field from the radiation field, and the ::: distance between the center of the asymmetry field and the isocenter should not ::: exceed 3 cm in our clinic. We recommend that every clinic that uses linear ::: accelerator, multileaf collimator-based SRS/SBRT perform the off-isocenter ::: Winston-Lutz test in addition to the standard Winston-Lutz test and use their ::: own deviation data to create planning guideline.


title: Bridge risk investigation diagnostic grouped exploratory (BRIDGE) bot

abstract: BRIDGE bot is a 158 g, 10.7 × 8.9 × 6.5 cm3, magnetic-wheeled robot designed to traverse and inspect steel bridges. Utilizing custom magnetic wheels, the robot is able to securely adhere to the bridge in any orientation. The body platform features flexible, multi-material legs that enable a variety of plane transitions as well as robot shape manipulation. The robot is equipped with a Cortex-M0 processor, inertial sensors, and a modular wireless radio. A camera is included to provide images for detection and evaluation of identified problems. The robot has been demonstrated moving through plane transitions from 45° to 340° as well as over obstacles up to 9.5 mm in height. Preliminary use of sensor feedback to improve plane transitions has also been demonstrated.


title: Expanding Rural Cellular Networks with Virtual Coverage

abstract: The cellular system is the world's largest network, providing service to over five billion people. Operators of these networks face fundamental trade-offs in coverage, capacity and operating power. These trade-offs, when coupled with the reality of infrastructure in poorer areas, mean that upwards of a billion people lack access to this fundamental service. Limited power infrastructure, in particular, hampers the economic viability of wide-area rural coverage. ::: ::: In this work, we present an alternative system for implementing large-scale rural cellular networks. Rather than providing constant coverage, we instead provide virtual coverage: coverage that is only present when requested. Virtual coverage powers the network on-demand, which reduces overall power draw, lowers the cost of rural connectivity, and enables new markets. ::: ::: We built a prototype cellular system utilizing virtual coverage by modifying a GSM base station and a set of Motorola phones to support making and receiving calls under virtual coverage. To support the billions of already-deployed devices, we also implemented a small radio capable of adding backwards-compatible support for virtual coverage to existing GSM handsets. We demonstrate a maximum of 84% power and cost savings from using virtual coverage. We also evaluated virtual coverage by simulating the potential power savings on real-world cellular networks in two representative developing counties: one in sub-Saharan Africa and one in South Asia. Simulating power use based on realworld call records obtained from local mobile operators, we find our system saves 21-34% of power draw at night, and 7-21% during the day. We expect even more savings in areas currently off the grid. These results demonstrate the feasibility of implementing such a system, particularly in areas with solar or otherwise-intermittent power sources.


title: Linear prediction models with graph regularization for web-page categorization

abstract: We present a risk minimization formulation for learning from both text and graph structures which is motivated by the problem of collective inference for hypertext document categorization. The method is based on graph regularization formulated as a well-formed convex optimization problem. We present numerical algorithms for our formulation, and show that such combination of local text features and link information can lead to improved predictive accuracy.


title: An effective defensive node against jamming attacks in sensor networks

abstract: Summary Jamming is defined as the act of intentionally directing electromagnetic energy towards a communication system to disrupt or prevent signal transmission. In the context of wireless sensor networks (WSNs), jamming is the type of attack which interferes with the radio frequencies used by sensor nodes and may be viewed as a special case of denial of service (DoS) attacks. Herein, we outline the possible jamming attacks a WSN may encounter. The main contribution of this paper is the outline of the design specifications of a prototype node ‘Ares’ that effectively defends jamming attacks. Our focus is on frequency hopping spread spectrum (FHSS) and direct sequence spread spectrum (DSSS), two of the most effective countermeasures against jamming. The main novel aspect of Ares is that it uses a hybrid FHSS–DSSS approach to defend jamming attacks. We suggest the use of a specific FHSS technique in 5 GHz band with 51 frequency channels wherein the channel sequence is generated using a key (which derives from a secret word), known only to the sink and the sensor nodes, as a seed. Each channel uses DSSS modulation with 16-bit pseudo noise (PN) code. The PN code derives from the same key used for FHSS channel generation. The pre-eminence of our method against alternative anti-jamming techniques is demonstrated through extensive simulation tests. Copyright © 2008 John Wiley & Sons, Ltd.


title: On the Equivalence Between SLNR and MMSE Precoding Schemes with Single-Antenna Receivers

abstract: This letter considers transmit precoding schemes based on the maximum signal-to-leakage-and-noise ratio (SLNR) in multiuser MIMO systems with single-antenna receivers. The closed-form solution of SLNR design is generally given in the form of the eigenvector associated to the maximum eigenvalue. In this letter, analytic expressions of SLNR-based solutions and resulting SLNR are derived for a generic power allocation (GPA). The solution is shown to be a function of user-allocated power and an arbitrary phase shift. Under equal power allocation (EPA), the SLNR precoding scheme is shown to be equivalent to the minimum mean square error (MMSE) precoding scheme. Several useful implications in terms of the possible extension of existing algorithms and performance analysis are also discussed.


title: KVM, Xen and Docker: A performance analysis for ARM based NFV and cloud computing

abstract: Virtualization is a mature technology which has shown to provide computing resource and cost optimization while enabling consolidation, isolation and hardware abstraction through the concept of virtual machine. Recently, by sharing the operating system resources and simplifying the deployment of applications, containers are getting a more and more popular alternative to virtualization for specific use cases. As a result, today these two technologies are competing to provide virtual instances for cloud computing, Network Functions Virtualization (NFV), High Performance Computing (HPC), avionic and automotive platforms. In this paper, the performance of the most important open source hypervisor (KVM and Xen) and container (Docker) solutions are compared on the ARM architecture, which is rapidly emerging in the server world. The extensive system and Input/Output (I/O) performance measurements included in this paper show a slightly better performance for containers in CPU bound workloads and request/response networking; conversely, thanks to their caching mechanisms, hypervisors perform better in most disk I/O operations and TCP streaming benchmark.


title: A systematic test case selection methodology for product lines: results and insights from an industrial case study

abstract: In the context of product lines, test case selection aims at obtaining a set of relevant test cases for a product from the entire set of test cases available for a product line. While working on a research-based innovation project on automated testing of product lines of Video Conferencing Systems (VCSs) developed by Cisco, we felt the need to devise a cost-effective way of selecting relevant test cases for a product. To fulfill such need, we propose a systematic and automated test selection methodology using: 1) Feature Model for Testing (FM_T) to capture commonalities and variabilities of a product line; 2) Component Family Model for Testing (CFM_T) to model the structure of test case repository; 3) A tool to automatically build restrictions from CFM_T to FM_T and traces from CFM_T to the actual test cases. Using our methodology, a test engineer is only required to select relevant features through FM_T at a higher level of abstraction for a product and the corresponding test cases will be obtained automatically. We evaluate our methodology by applying it to a VCS product line called Saturn with seven commercial products and the results show that our methodology can significantly reduce cost measured as test selection time and at the same time achieves higher effectiveness (feature coverage, feature pairwise coverage and fault detection) as compared with the current manual process. Moreover, we conduct a questionnaire-based study to solicit the views of test engineers who are involved in developing FM_T and CFM_T. The results show that test engineers are positive about adapting our methodology in their current practice. Finally, we present a set of lessons learnt while applying product line engineering at Cisco for test case selection.


title: PRIMARY MANDIBULAR RECONSTRUCTION USING THE AO RECONSTRUCTION PLATE

abstract: Primary reconstruction of segmental defects of the mandible following cancer resection remains a major problem. In 1976, a stainless steel plate was developed by AO/ASIF (Arbeitsgemeinschaft für Osteosynthesefragen/Association for the Study of Internal Fixation) for mandibular reconstruction. This plate completely stabilizes both mandibular stumps without the need for intermaxillary or external fixation. In addition, when bone grafting is performed, the design of this plate promotes revascularization by allowing maximal contact between grafted cancellous bone and surrounding soft tissue. Since April 1985, we have used this reconstruction plate in six patients undergoing composite resection. The technique did not compromise cancer resection, excessively prolong operating time, or alter postoperative management. All six patients have retained the AO reconstruction plate and have been reconstructed successfully with achievement of satisfactory function and cosmesis.


title: Types of Cognition and its Implications for future High-Level Cognitive Machines

abstract: This work summarizes part of current knowledge on High-level Cognitive process and its relation with biological hardware. Thus, it is possible to identify some paradoxes which could impact the development of future technologies and artificial intelligence: we may make a High-level Cognitive Machine, sacrificing the principal attribute of a machine, its accuracy.


title: Opium poppy: blueprint for an alkaloid factory

abstract: Opium poppy (Papaver somniferum) produces a large number of benzylisoquinoline alkaloids, including the narcotic analgesics morphine and codeine, and has emerged as one of the most versatile model systems to study alkaloid metabolism in plants. As summarized in this review, we have taken a holistic strategy—involving biochemical, cellular, molecular genetic, genomic, and metabolomic approaches—to draft a blueprint of the fundamental biological platforms required for an opium poppy cell to function as an alkaloid factory. The capacity to synthesize and store alkaloids requires the cooperation of three phloem cell types—companion cells, sieve elements, and laticifers—in the plant, but also occurs in dedifferentiated cell cultures. We have assembled an opium poppy expressed sequence tag (EST) database based on the attempted sequencing of more than 30,000 cDNAs from elicitor-treated cell culture, stem, and root libraries. Approximately 23,000 of the elicitor-induced cell culture and stem ESTs are represented on a DNA microarray, which has been used to examine changes in transcript profile in cultured cells in response to elicitor treatment, and in plants with different alkaloid profiles. Fourier transform-ion cyclotron resonance mass spectrometry and proton nuclear magnetic resonance mass spectroscopy are being used to detect corresponding differences in metabolite profiles. Several new genes involved in the biosynthesis and regulation of alkaloid pathways in opium poppy have been identified using genomic tools. A biological blueprint for alkaloid production coupled with the emergence of reliable transformation protocols has created an unprecedented opportunity to alter the chemical profile of the world’s most valuable medicinal plant.


title: A time-aware spatio-textual recommender system

abstract: We present two novel unified models to provide review and POI recommendations.We consider simultaneously the spatial, the textual, and the temporal factor.We measure the temporal influence in each time interval for different range distances.Results indicate that the combination of the three dimensions exhibit higher accuracy. Location-Based Social Networks (LBSNs) allow users to post ratings and reviews and to notify friends of these posts. Several models have been proposed for Point-of-Interest (POI) recommendation that use explicit (i.e.ratings, comments) or implicit (i.e.statistical scores, views, and user influence) information. However the models so far fail to capture sufficiently user preferences as they change spatially and temporally. We argue that time is a crucial factor because user check-in behavior might be periodic and time dependent, e.g. check-in near work in the mornings and check-in close to home in the evenings. In this paper, we present two novel unified models that provide review and POI recommendations and consider simultaneously the spatial, textual and temporal factors. In particular, the first model provides review recommendations by incorporating into the same unified framework the spatial influence of the users reviews and the textual influence of the reviews. The second model provides POI recommendations by combining the spatial influence of the users check-in history and the social influence of the users reviews into another unified framework. Furthermore, for both models we consider the temporal dimension and measure the impact of time on various time intervals. We evaluate the performance of our models against 10 other methods in terms of precision and recall. The results indicate that our models outperform the other methods.


title: Estimation of planar curves, surfaces, and nonplanar space curves defined by implicit equations with applications to edge and range image segmentation

abstract: The author addresses the problem of parametric representation and estimation of complex planar curves in 2-D surfaces in 3-D, and nonplanar space curves in 3-D. Curves and surfaces can be defined either parametrically or implicitly, with the latter representation used here. A planar curve is the set of zeros of a smooth function of two variables x-y, a surface is the set of zeros of a smooth function of three variables x-y-z, and a space curve is the intersection of two surfaces, which are the set of zeros of two linearly independent smooth functions of three variables x-y-z For example, the surface of a complex object in 3-D can be represented as a subset of a single implicit surface, with similar results for planar and space curves. It is shown how this unified representation can be used for object recognition, object position estimation, and segmentation of objects into meaningful subobjects, that is, the detection of 'interest regions' that are more complex than high curvature regions and, hence, more useful as features for object recognition. >


title: Deconstructing and reconstructing theory of mind

abstract: Usage of the term ‘theory of mind’ (ToM) has exploded across fields ranging from developmental psychology to social neuroscience and psychiatry research. However, its meaning is often vague and inconsistent, its biological bases are a subject of debate, and the methods used to study it are highly heterogeneous. Most crucially, its original definition does not permit easy downward translation to more basic processes such as those studied by behavioral neuroscience, leaving the interpretation of neuroimaging results opaque. We argue for a reformulation of ToM through a systematic two-stage approach, beginning with a deconstruction of the construct into a comprehensive set of basic component processes, followed by a complementary reconstruction from which a scientifically tractable concept of ToM can be recovered.


title: Modified Histogram based contrast enhancement using Homomorphic Filtering for medical images

abstract: In medical image processing, low contrast image analysis is a challenging problem. Low contrast digital images reduce the ability of observer in analyzing the image. Histogram based techniques are used to enhance contrast of all type of medical images. They are mainly used for all type of medical images such as for Mias-mammogram images, these methods are used to find exact locations of cancerous regions and for low-dose CT images, these methods are used to intensify tiny anatomies like vessels, lungs nodules, airways and pulmonary fissures. The most effective method used for contrast enhancement is Histogram Equalization (HE). Here we propose a new method named “Modified Histogram Based Contrast Enhancement using Homomorphic Filtering” (MH-FIL) for medical images. This method uses two step processing, in first step global contrast of image is enhanced using histogram modification followed by histogram equalization and then in second step homomorphic filtering is used for image sharpening, this filtering if followed by image normalization. To evaluate the effectiveness of our method we choose two widely used metrics Absolute Mean Brightness Error (AMBE) and Entropy. Based on results of these two metrics this algorithm is proved as a flexible and effective way for medical image enhancement and can be used as a pre-processing step for medical image understanding and analysis.


title: ThemeRiver: Visualizing thematic changes in large document collections

abstract: The ThemeRiver visualization depicts thematic variations over time within a large collection of documents. The thematic changes are shown in the context of a time-line and corresponding external events. The focus on temporal thematic change within a context framework allows a user to discern patterns that suggest relationships or trends. For example, the sudden change of thematic strength following an external event may indicate a causal relationship. Such patterns are not readily accessible in other visualizations of the data. We use a river metaphor to convey several key notions. The document collection's time-line, selected thematic content and thematic strength are indicated by the river's directed flow, composition and changing width, respectively. The directed flow from left to right is interpreted as movement through time and the horizontal distance between two points on the river defines a time interval. At any point in time, the vertical distance, or width, of the river indicates the collective strength of the selected themes. Colored "currents" flowing within the river represent individual themes. A current's vertical width narrows or broadens to indicate decreases or increases in the strength of the individual theme.


title: Using a neural network in the software testing process

abstract: Software testing forms an integral part of the software development life cycle. Since the objective of testing is to ensure the conformity of an application to its specification, a test “oracle” is needed to determine whether a given test case exposes a fault or not. Using an automated oracle to support the activities of human testers can reduce the actual cost of the testing process and the related maintenance costs. In this paper, we present a new concept of using an artificial neural network as an automated oracle for a tested software system. A neural network is trained by the backpropagation algorithm on a set of test cases applied to the original version of the system. The network training is based on the “black-box” approach, since only inputs and outputs of the system are presented to the algorithm. The trained network can be used as an artificial oracle for evaluating the correctness of the output produced by new and possibly faulty versions of the software. We present experimental results of using a two-layer neural network to detect faults within mutated code of a small credit approval application. The results appear to be promising for a wide range of injected faults. ? 2002 John Wiley & Sons, Inc.


title: Overcoming Challenges of Semantic Question Answering in the Semantic Web

abstract: Semantic Question Answering (SQA) removes two major access requirements to the Semantic Web: the mastery of a formal query language like SPARQL and knowledge of a specific vocabulary. Because of the complexity of natural language, SQA presents difficult challenges and many research opportunities. Instead of a shared effort, however, many essential components are redeveloped, which is an inefficient use of researcher's time and resources. This survey analyzes 62 different SQA systems, which are systematically and manually selected using predefined inclusion and exclusion criteria, leading to 70 selected publications out of 1960 candidates. We identify common challenges, structure solutions, and provide recommendations for future systems. This work is based on publications from the end of 2010 to July 2015 and is also compared to older but similar surveys.


title: Sentiment analysis system adaptation for multilingual processing

abstract: We study different strategies to classify sentiment from tweets, using supervised learning with hybrid features.We experiment with English and Spanish data and compare against benchmark competitions.We employ machine-translated data from other languages for training.We show that the use of multilingual data improves the sentiment classification accuracy. Nowadays opinion mining systems play a strategic role in different areas such as Marketing, Decision Support Systems or Policy Support. Since the arrival of the Web 2.0, more and more textual documents containing information that express opinions or comments in different languages are available. Given the proven importance of such documents, the use of effective multilingual opinion mining systems has become of high importance to different fields. This paper presents the experiments carried out with the objective to develop a multilingual sentiment analysis system. We present initial evaluations of methods and resources performed in two international evaluation campaigns for English and for Spanish. After our participation in both competitions, additional experiments were carried out with the aim of improving the performance of both Spanish and English systems by using multilingual machine-translated data. Based on our evaluations, we show that the use of hybrid features and multilingual, machine-translated data (even from other languages) can help to better distinguish relevant features for sentiment classification and thus increase the precision of sentiment analysis systems.


title: Handwritten Tamil Character Recognition using Moments

abstract: Optical Character Recognition systems have been effectively developed for recognizing the printed characters of many non-Indian languages like English, Chinese. At early stages few research works were carried out for recognizing the handwritten characters and now varies efforts are on the way for the development of efficient systems for recognizing the Indian languages, especially for Tamil, a south Indian language widely used in Tamilnadu, Pudhucherry, Singapore, Srilanka. In this paper, an OCR system is developed for the recognition of basic characters in handwritten Tamil language, which can handle different font sizes and font types. Zernike moments and Legendre Polynomial which have been used in pattern recognition are used in this system to extract the features of handwritten Tamil characters. A comparative study is performed in this paper. Neural classifiers have been used for the classification of Tamil characters.


title: A Chatbot for Automatic Processing of Learner Concerns in an Online Learning Platform

abstract: In this article, we present a chatbot model that can automatically respond to learners’ concerns on an online training platform. The proposed chatbot model is based on an adaptation of the similarity of Dice to understand the concerns of learners. The first phase of this approach allows selecting the pre-established concerns that the teacher has in a knowledge base which are closest to those posed by the learner. The second phase consists of selecting among these k most appropriate concerns based on a measure of similarity built on the concept of domain keywords. The experimentation of the prototype of this chatbot makes it possible to find the adequate answers. In the case, where the question refers to a question from the teacher, the learner is asked if the question identified is the one he was referring to. If he answers in the affirmative, the instructions associated with his request are sent to him. If not, the learner’s concern is sent to the human tutor. The hybridization of this chatbot with the human agent comes to enrich the initial knowledge base of the chatbot. The results obtained with the concept based on the keywords of the domain are encouraging. The learner’s comprehension rate is above 50% when applying the concept of domain keywords while the measure of Dice is below 50%.


title: Exploring residents’ experiences of mealtimes in care homes: A qualitative interview study

abstract: Many interventions aim to alleviate well-documented problems of malnutrition in residential care homes and improve residents’ health and wellbeing. Despite some positive findings, little is known about how and why mealtime interventions might be effective, and in particular, what effects residents’ experiences of mealtimes have on health outcomes. The aim of this study was to gain an insight into these experiences and explore some of the issues that may impact on residents’ enjoyment of meals, and resulting health and wellbeing. Semi-structured interviews were conducted with eleven residents from four care homes in the South West UK. Thematic analysis was used to derive content and meaning from transcribed interviews. Interviews were supplemented by researcher observations of mealtimes. The dining experience was a focal point for participants’ broader experiences of residing in a care home. Three themes pertaining to residents’ experiences were identified: (1) Emotional and psychological connections with other residents; (2) managing competing interests with limited resources; and (3) familiarity and routine. Mealtimes are a mainstay of life in a care home through which residents’ experiences are characterised, exemplified and magnified. Understanding how residents interact with one another, accommodating their preferences and encouraging autonomy may enhance their mealtime experiences. It may also help to ease the transition from independent-living to life in care, which can be particularly stressful for some residents, and improve health and wellbeing over the long-term.


title: On the Guard Period Design in 5G TDD Wide Area

abstract: Achieving the trade-off between coverage, ultra-low latency and capacity is a major challenge for a 5th Generation (5G) wide area concept, especially when operating in Time Division Duplex (TDD) mode. In this paper, we focus on the design of the Guard Period (GP) which is needed to accommodate the transition between downlink (DL) and uplink (UL) transmissions in a 5G TDD wide area system operating at below 6 GHz carrier frequencies. The necessity of serving cell edge users with long propagation delays while coping with latency constraints may lead to a large GP overhead, thus reducing the resource utilization in the cell. We propose a distance-dependent scheduling technique which is meant at reducing the GP overhead while ensuring sufficient coverage. Numerical analysis shows the benefits of the proposed techniques in terms of available radio resources, especially for very large cells operating with short frames.


title: Building a distributed key-value store with FPGA-based microservers

abstract: Energy efficiency is one of the major challenges in datacenters, and a promising way to tackle it are microservers. These scaled down machines with smaller CPUs, less peripherals and tighter integration improve energy efficiency, but often at the expense of lower performance. In this work we explore the tailoring of standard software components to specialized hardware as a way to get the energy efficiency of microservers without compromising performance.


title: STAR: SLA-aware Autonomic Management of Cloud Resources

abstract: Cloud computing has recently emerged as an important service to manage applications efficiently over the Internet. Various cloud providers offer pay per use cloud services that requires Quality of Service (QoS) management to efficiently monitor and measure the delivered services through Internet of Things (IoT) and thus needs to follow Service Level Agreements (SLAs). However, providing dedicated cloud services that ensure user's dynamic QoS requirements by avoiding SLA violations is a big challenge in cloud computing. As dynamism, heterogeneity and complexity of cloud environment is increasing rapidly, it makes cloud systems insecure and unmanageable. To overcome these problems, cloud systems require self-management of services. Therefore, there is a need to develop a resource management technique that automatically manages QoS requirements of cloud users thus helping the cloud providers in achieving the SLAs and avoiding SLA violations. In this paper, we present SLA-aware autonomic resource management technique called STAR which mainly focuses on reducing SLA violation rate for the efficient delivery of cloud services. The performance of the proposed technique has been evaluated through cloud environment. The experimental results demonstrate that STAR is efficient in reducing SLA violation rate and in optimizing other QoS parameters which effect efficient cloud service delivery.


Exhaled breath metabolomics as a non – invasive diagnostic tool for ARDS European Respiratory Journal 2014


title: Exploiting Mobility for Energy Efficient Data Collection in Wireless Sensor Networks

abstract: We analyze an architecture based on mobility to address the problem of energy efficient data collection in a sensor network. Our approach exploits mobile nodes present in the sensor field as forwarding agents. As a mobile node moves in close proximity to sensors, data is transferred to the mobile node for later depositing at the destination. We present an analytical model to understand the key performance metrics such as data transfer, latency to the destination, and power. Parameters for our model include: sensor buffer size, data generation rate, radio characteristics, and mobility patterns of mobile nodes. Through simulation we verify our model and show that our approach can provide substantial savings in energy as compared to the traditional ad-hoc network approach.


title: Voluntary motion support by an upper limb support system based on bioelectrical signals for heavy overhead tasks

abstract: We have developed a wearable upper limb support system (ULSS) for support during heavy overhead tasks. The purpose of this study is to develop the voluntary motion support algorithm for the ULSS, and to confirm the effectiveness of the ULSS with the developed algorithm through dynamic evaluation experiments. The algorithm estimates the motor intention of the wearer based on a bioelectrical signal (BES). The ULSS measures the BES via electrodes attached onto the triceps brachii, deltoid, and clavicle. The BES changes in synchronization with the motion of the wearer's upper limbs. The algorithm changes a control phase by comparing the BES and threshold values. The algorithm achieves voluntary motion support for dynamic tasks by changing support torques of the ULSS in synchronization with the control phase. Five healthy adult males moved heavy loads vertically overhead in the evaluation experiments. In a random instruction experiment, the volunteers moved in synchronization with random instructions, and we confirmed that the control phase changes in synchronization with the random instructions. In a motion support experiment, we confirmed that the average number of the vertical motion with the ULSS increased 2.3 times compared to the average number without the ULSS. As a result, the ULSS with the algorithm supports the motion voluntarily, and it has a positive effect on the support. In conclusion, we could develop the novel voluntary motion support algorithm of the ULSS.


title: On existence and stability of equilibria of DC Microgrid with constant power loads

abstract: The problem of existence and stability of equilibria of DC microgirds with constant power loads methods is addressed in this paper. Constant power loads (CPLs) often cause instability due to its negative impedance characteristics. What is the worse, CPLs may cause that the system admits no equilibria for its nonlinearity. The purpose of paper can be summarized as: A) designing a controller to overcome CPLs instability; B) obtaining the sufficient conditions to guarantee the existence of the equilibria; C) Under the conditions of equilibria existing, obtaining the sufficient conditions of local stability. For the first objective, the method based on virtual resistance and virtual inductance is proposed. For the second objective, we transform the problem of solvability of quadratic equations into the existence of the fixed point of a mapping. Thus, the analytical sufficient conditions is obtained based on Tarski fixed point theorem. Moreover, a numerical method is also provided to reduce conservatism. For the third objective, the small-signal model is established to predict the system qualitative behavior around equilibria. The stability conditions are obtained by using quadratic eigenvalue problem (GEP) theorem. These explicit conditions are obtained as a function of system parameters and it lead to build reliable microgrid.


title: Revisiting "Collaboration" Under Conditions of "Mobility"

abstract: With the modern day imperative of individuals being perennially on the move in order to keep pace with their work and business lives, the notion of "mobile collaboration" has come to assume a great deal of importance, both among scholars and practitioners. While the literature continues to extol the promises and virtues of mobility, there have been few illustrations on what collaboration means under conditions of mobility, and how it actually occurs. This paper takes a preliminary step toward clarifying the notion of mobile collaboration and developing a set of integrative themes capturing its essence


Iterative filter with finite measurements for suddenly maneuvering targets


title: Bandit Convex Optimization: Towards Tight Bounds

abstract: Bandit Convex Optimization (BCO) is a fundamental framework for decision making under uncertainty, which generalizes many problems from the realm of online and statistical learning. While the special case of linear cost functions is well understood, a gap on the attainable regret for BCO with nonlinear losses remains an important open question. In this paper we take a step towards understanding the best attainable regret bounds for BCO: we give an efficient and near-optimal regret algorithm for BCO with strongly-convex and smooth loss functions. In contrast to previous works on BCO that use time invariant exploration schemes, our method employs an exploration scheme that shrinks with time.


title: Optimal Adaptive Waveform Design for Cognitive MIMO Radar

abstract: This paper addresses the problem of adaptive waveform design for estimation of parameters of linear systems. This problem arises in several applications such as radar, sonar, or tomography. In the proposed technique, the transmit/input signal waveform is optimally determined at each step based on the observations in the previous steps. The waveform is determined to minimize the Bayesian Cramer-Rao bound (BCRB) or the Reuven-Messer bound (RMB) for estimation of the unknown system parameters at each step. The algorithms are tested for spatial transmit waveform design in multiple-input multiple-output radar target angle estimation at very low signal-to-noise ratio. The proposed techniques allow to automatically focusing the transmit beam toward the target direction. The simulations show that the proposed adaptive waveform design methods achieve significantly higher rate of performance improvement as a function of the pulse index, compared to other signal transmission methods, in terms of estimation accuracy.


title: The parallel iterative closest point algorithm

abstract: This paper describes a parallel implementation developed to improve the time performance of the Iterative Closest Point Algorithm. Within each iteration, the correspondence calculations are distributed among the processor resources. At the end of each iteration, the results of the correspondence determination are communicated back to a central processor and the current transformation is calculated A number of additional techniques were developed that served to improve upon this basic scheme. Calculating the partial sums within each distributed resource made it unnecessary to transmit the correspondence values back to the central processor, which reduced the communication overhead, and improved time performance. Randomly distributing the points among the processor resources resulted in a better load balancing, which further improved time performance. We also found that thinning the image by randomly removing a certain percentage of the points did not improve the performance, when viewed as the progression of mse with time. The method was implemented and tested on a 22 node Beowulf class cluster. For a large image, linear performance improvements were obtained for up to 16 processors, while they held for up to 8 processors with a smaller image.


title: Action video game training for cognitive enhancement

abstract: Here we review the literature examining the perceptual, attentional, and cognitive benefits of playing one sub-type of video games known as ‘action video games,’ as well as the mechanistic underpinnings of these behavioral effects. We then outline evidence indicating the potential usefulness of these commercial off-the-shelf games for practical, real-world applications such as rehabilitation or the training of job-related skills. Finally, we discuss potential core characteristics of action video games that allow for wide learning generalization.


title: VSI: A Visual Saliency-Induced Index for Perceptual Image Quality Assessment

abstract: Perceptual image quality assessment (IQA) aims to use computational models to measure the image quality in consistent with subjective evaluations. Visual saliency (VS) has been widely studied by psychologists, neurobiologists, and computer scientists during the last decade to investigate, which areas of an image will attract the most attention of the human visual system. Intuitively, VS is closely related to IQA in that suprathreshold distortions can largely affect VS maps of images. With this consideration, we propose a simple but very effective full reference IQA method using VS. In our proposed IQA model, the role of VS is twofold. First, VS is used as a feature when computing the local quality map of the distorted image. Second, when pooling the quality score, VS is employed as a weighting function to reflect the importance of a local region. The proposed IQA index is called visual saliency-based index (VSI). Several prominent computational VS models have been investigated in the context of IQA and the best one is chosen for VSI. Extensive experiments performed on four largescale benchmark databases demonstrate that the proposed IQA index VSI works better in terms of the prediction accuracy than all state-of-the-art IQA indices we can find while maintaining a moderate computational complexity. The MATLAB source code of VSI and the evaluation results are publicly available online at http://sse.tongji.edu.cn/linzhang/IQA/VSI/VSI.htm.


title: The Psychological and Spiritual Dimensions of Palliative Care: A Descriptive Systematic Review

abstract: ABSTRACT ::: ::: Objective: To study the relationship between the psychological and spiritual dimensions in adult palliative care. ::: ::: Methods: The data sources used were Medline, Web of Knowledge and Scopus, using the following key concepts: “palliative care” and “psychology” and “spirituality”. The search took place 5th December 2017, at 10:20 am, and no articles were excluded based on publication date. Out of a total of 1038 articles found, 65 met the pre-selected criteria and were included in the descriptive qualitative synthesis. ::: ::: Results: Some lack of definitional consensus of spirituality is still evident. Nevertheless, spiritual wellbeing was considered an important component of quality of life, closely related to physical and psychological distress symptoms, therefore highlighting the importance of psychological and psychiatric care in enhancing the wellbeing of palliative care patients. ::: ::: Conclusion: Understanding the interface of the psychological and spiritual dimensions of adult palliative care patients may contribute to a more efficient therapeutic practice. Therefore, training healthcare professionals to improve spiritual support and appropriately addressing these issues should be of great importance in palliative care settings, thus promoting an appropriate response to the patients’ needs and dying process.


title: A facial wearable robot with eyelid gating mechanism for supporting eye blink

abstract: Facial paralysis causes loss of control over facial muscles such as the orbicularis oculi which closes the eyelid. People with facial paralysis thus are not able to blink which could cause dry eye and/or permanent damage to the cornea. Also, during the convalescent phase of facial paralysis, there is possibility to get Synkinesis because of inappropriate rehabilitation. However, few devices for rehabilitation or tools for activity of daily life for them are available. We propose a novel approach for supporting eyelid gating movement with facial wearable robotic technology. This robot has novel eyelid gating mechanism made of silicon, and provides suitable support for eyelid gating movement using its deformation, triggered by wink detection on healthy side. We control the deformation designed to eyelid movement with simple rotation movement. We evaluated the proposed system with 2 healthy participants and verified the feasibility of eyelid gating support for people with facial paralysis.


title: Is There a Paradox of Aging: When the Negative Aging Stereotype Meets the Positivity Effect in Older Adults

abstract: Background/Study Context: Socioemotional selectivity theory (SST) states that the positivity effect is a result of older adults’ emotion regulation and that older adults derive more emotional satisfaction from prioritizing positive information processing. The authors explored whether the positivity effect appeared when the negative aging stereotype was activated in older adults and also whether the effect differed between mixed and unmixed valence conditions.Methods: Sixty younger (18–23 years of age) and 60 older (60–87 years of age) adults were randomly assigned to a control group and a priming group, in which the negative aging stereotype was activated. All the participants were asked to select 15 words that best described the elderly from a mixed-word list (positive and negative words were mixed together) and from an unmixed-word list (positive and negative words were separated).Results: Older adults in the control group selected more positive words, whereas among younger adults, selection did not dif...


title: Comparison of state-of-the-art high-speed high-power machines: Research study including a design example of a 50000 rpm induction machine

abstract: This paper presents and discusses the state of the art in high-speed electrical machines. It is connected to a project in which there is a challenge to design a high-speed turbo generator with a rated speed of 50000 rpm and a continuous power of 700 kW for usage in aircraft. For this reason, typical electrical machines are compared in order to examine their advantages and disadvantages in identifying the best topology and in fulfilling given requirements. Finally, FEM results provide an indication of the feasibility of fulfilling project requirements.


title: Systematic Approach Based on Best Practices to Develop Requirements Engineering (RE) Guideline in an Organization

abstract: Best practices in developing Requirements Engineering are not new. They are easily comprehended by experts familiar to Requirements Engineering. However, for IT Personnel that are new to this field, understanding the process may pose major challenges. Thus, a Requirements Engineering Best Practices Work Flow was developed as a tool to assist in the development of Requirements Engineering. The work flow consists of four phases: Exploratory, Development, Measurement and Experts panel review. Hopefully, this Requirements Engineering Best Practices Work Flow will be a practical and helpful tool to any IT Personnel in developing Requirements Engineering in the organization.


title: DEVELOPMENT OF A WHOLE-SEDIMENT TOXICITY TEST USING A BENTHIC MARINE MICROALGA

abstract: An acute whole-sediment toxicity test with a benthic marine microalga was developed and optimized using flow cytometry to distinguish algae (based on their chlorophyll a autofluorescence) from sediment particles. Of seven benthic marine algae screened, the diatom Entomoneis cf punctulata was most suitable because of its tolerance of a wide range of water and sediment physicochemical parameters, including salinity, pH, ammonia, and sulfide. A whole-sediment and water-only toxicity test based on inhibition of esterase activity in this species was developed. Enzyme activity rather than growth was used as the test endpoint, as nutrient release from sediments has previously been found to stimulate algal growth, potentially masking contaminant toxicity. The sensitivity of the bioassay to a range of metals (copper, zinc, cadmium, lead, arsenic, manganese) and phenol in water-only exposures was compared to the standard 72-h growth rate inhibition test. The esterase enzyme inhibition test was sensitive to copper, with a 3-h inhibitory concentration to cause a 50% (IC50) reduction in a fluorescein diacetate fluorescence value of 97 +/- 39 microg Cu/L. A concentration-dependent response was also observed in the presence of sediment particles (copper tailings), with and without dilution, using a control clean sediment. The primary route of exposure to copper was via pore water rather than by direct contact with tailings particles. This is the first whole-sediment bioassay developed with a benthic alga suitable for sediment quality assessment in marine/estuarine systems, and its advantages and limitations are discussed.


title: Surface defects detection for ceramic tiles using image processing and morphological techniques

abstract: ⎯ Quality control in ceramic tile manufacturing is hard, labor intensive and it is performed in a harsh industrial environment with noise, extreme temperature and humidity. It can be divided into color analysis, dimension verification, and surface defect detection, which is the main purpose of our work. Defects detection is still based on the judgment of human operators while most of the other manufacturing activities are automated so, our work is a quality control enhancement by integrating a visual control stage using image processing and morphological operation techniques before the packing operation to improve the homogeneity of batches received by final users. Keywords⎯ Quality control, Defects detection, Visual control, Image processing, Morphological operation.


title: Detecting and Tracking Disease Outbreaks by Mining Social Media Data

abstract: The emergence and ubiquity of online social networks have enriched web data with evolving interactions and communities both at mega-scale and in real-time. This data offers an unprecedented opportunity for studying the interaction between society and disease outbreaks. The challenge we describe in this data paper is how to extract and leverage epidemic outbreak insights from massive amounts of social media data and how this exercise can benefit medical professionals, patients, and policymakers alike. We attempt to prepare the research community for this challenge with four datasets. Publishing the four datasets will commoditize the data infrastructure to allow a higher and more efficient focal point for the research community.


title: Exploring Lexical, Syntactic, and Semantic Features for Chinese Textual Entailment in NTCIR RITE Evaluation Tasks

abstract: We computed linguistic information at the lexical, syntactic, and semantic levels for Recognizing Inference in Text (RITE) tasks for both traditional and simplified Chinese in NTCIR-9 and NTCIR-10. Techniques for syntactic parsing, named-entity recognition, and near synonym recognition were employed, and features like counts of common words, statement lengths, negation words, and antonyms were considered to judge the entailment relationships of two statements, while we explored both heuristics-based functions and machine-learning approaches. The reported systems showed robustness by simultaneously achieving second positions in the binary-classification subtasks for both simplified and traditional Chinese in NTCIR-10 RITE-2. We conducted more experiments with the test data of NTCIR-9 RITE, with good results. We also extended our work to search for better configurations of our classifiers and investigated contributions of individual features. This extended work showed interesting results and should encourage further discussion.


title: On contact-induced grammaticalization

abstract: Grammaticalization is based on universal strategies of conceptual transfer. Contact-induced language change on the other hand is an areally confined process resulting from specific historical events. What this suggests is that the two constitute quite divergent phenomena and, in fact, in the relevant literature they tend to be described as mutually exclusive processes. Accordingly, this literature abounds with discussions on whether some specific grammatical change is due to the former or the latter. The position taken in this paper is that the two are in no way mutually exclusive; rather, perhaps more often than not, they jointly conspire in triggering grammatical change.


title: Recovering Accurate 3D Human Pose in the Wild Using IMUs and a Moving Camera

abstract: In this work, we propose a method that combines a single hand-held camera and a set of Inertial Measurement Units (IMUs) attached at the body limbs to estimate accurate 3D poses in the wild. This poses many new challenges: the moving camera, heading drift, cluttered background, occlusions and many people visible in the video. We associate 2D pose detections in each image to the corresponding IMU-equipped persons by solving a novel graph based optimization problem that forces 3D to 2D coherency within a frame and across long range frames. Given associations, we jointly optimize the pose of a statistical body model, the camera pose and heading drift using a continuous optimization framework. We validated our method on the TotalCapture dataset, which provides video and IMU synchronized with ground truth. We obtain an accuracy of 26 mm, which makes it accurate enough to serve as a benchmark for image-based 3D pose estimation in the wild. Using our method, we recorded 3D Poses in the Wild (3DPW), a new dataset consisting of more than 51, 000 frames with accurate 3D pose in challenging sequences, including walking in the city, going up-stairs, having coffee or taking the bus. We make the reconstructed 3D poses, video, IMU and 3D models available for research purposes at http://virtualhumans.mpi-inf.mpg.de/3DPW.


title: Compact Convolutional Neural Networks for Multi-Class, Personalised, Closed-Loop EEG-BCI

abstract: For many people suffering from motor disabilities, assistive devices controlled with only brain activity are the only way to interact with their environment. Natural tasks often require different kinds of interactions, involving different controllers the user should be able to select in a self-paced way. We developed a Brain-Computer Interface (BCI) allowing users to switch between four control modes in a self-paced way in real-time. Since the system is devised to be used in domestic environments in a user-friendly way, we selected non-invasive electroencephalographic (EEG) signals and convolutional neural networks (CNNs), known for their ability to find the optimal features in classification tasks. We tested our system using the Cybathlon BCI computer game, which embodies all the challenges inherent to real-time control. Our preliminary results show that an efficient architecture (SmallNet), with only one convolutional layer, can classify 4 mental activities chosen by the user. The BCI system is run and validated online. It is kept up-to-date through the use of newly collected signals along playing, reaching an online accuracy of 47.6% where most approaches only report results obtained offline. We found that models trained with data collected online better predicted the behaviour of the system in real-time. This suggests that similar (CNN based) offline classifying methods found in the literature might experience a drop in performance when applied online. Compared to our previous decoder of physiological signals relying on blinks, we increased by a factor 2 the amount of states among which the user can transit, bringing the opportunity for finer control of specific subtasks composing natural grasping in a self-paced way. Our results are comparable to those shown at the Cybathlon's BCI Race but further improvements on accuracy are required.


title: Understanding dv/dt of 15 kV SiC N-IGBT and its control using active gate driver

abstract: The ultrahigh voltage (> 12 kV) SiC IGBTs are promising power semiconductor devices for medium voltage power conversion due to feasibility of simple two-level topologies, reduced component count and extremely high efficiency. However, the current devices generate high dv/dt during switching transitions because of the deep punch-through design. This paper investigates the behavior of dv/dt during the two-slope (different slopes before and after punch-through) turn-on and turn-off voltage transitions of these devices, by varying the device current, temperature and field-stop buffer layer design. It is shown that the dv/dt can be minimized by increasing the gate resistance, by taking the turn-on transition as reference. However, it is found that the increase in gate resistance has very weak impact on dv/dt above the punch-through voltage, and also resulting in significantly increased switching energy loss. It is shown that this problem can be addressed by using a two-stage active gate driver, where the gate current is appropriately controlled to limit the dv/dt over punch-through voltage and to minimize the switching energy loss under the punch-through voltage. Experimental results on 15 kV SiC N-IGBTs with field-stop buffer layer thickness of 2 μm and 5 μm are presented up to 11 kV with a detailed discussion of the results.


title: Lightweight Unsupervised Deep Loop Closure

abstract: Robust efficient loop closure detection is essential for large-scale real-time SLAM. In this paper, we propose a novel unsupervised deep neural network architecture of a feature embedding for visual loop closure that is both reliable and compact. Our model is built upon the autoencoder architecture, tailored specifically to the problem at hand. To train our network, we inflict random noise on our input data as the denoising autoencoder does, but, instead of applying random dropout, we warp images with randomized projective transformations to emulate natural viewpoint changes due to robot motion. Moreover, we utilize the geometric information and illumination invariance provided by histogram of oriented gradients (HOG), forcing the encoder to reconstruct a HOG descriptor instead of the original image. As a result, our trained model extracts features robust to extreme variations in appearance directly from raw images, without the need for labeled training data or environment-specific training. We perform extensive experiments on various challenging datasets, showing that the proposed deep loop-closure model consistently outperforms the state-of-the-art methods in terms of effectiveness and efficiency. Our model is fast and reliable enough to close loops in real time with no dimensionality reduction, and capable of replacing generic off-the-shelf networks in state-of-the-art ConvNet-based loop closure systems.


title: Artistic paper-cut of human portraits

abstract: This paper presents a method to render artistic paper-cut of human portraits. Rendering paper-cut images from photographs can be considered as an inhomogeneous image binarization problem, to which ideal solutions should reproduce vivid image details with sparse cuts. Especially for portrait paper-cut, good artworks should capture impressive facial features. To achieve this goal, our approach integrates bottom-up and top-down cues to better determine the binary values. In the bottom-up phase, facial components are localized on the input photograph, and their draft binary versions are proposed. In the top-down phase, we use pre-collected representative paper-cut templates, with which we synthesize the final paper-cut image by matching them with the bottom-up proposals. Experimental results show that our approach can produce visually satisfactory results.


title: Learning Algorithms for the Classification Restricted Boltzmann Machine

abstract: Recent developments have demonstrated the capacity of restricted Boltzmann machines (RBM) to be powerful generative models, able to extract useful features from input data or construct deep artificial neural networks. In such settings, the RBM only yields a preprocessing or an initialization for some other model, instead of acting as a complete supervised model in its own right. In this paper, we argue that RBMs can provide a self-contained framework for developing competitive classifiers. We study the Classification RBM (ClassRBM), a variant on the RBM adapted to the classification setting. We study different strategies for training the ClassRBM and show that competitive classification performances can be reached when appropriately combining discriminative and generative training objectives. Since training according to the generative objective requires the computation of a generally intractable gradient, we also compare different approaches to estimating this gradient and address the issue of obtaining such a gradient for problems with very high dimensional inputs. Finally, we describe how to adapt the ClassRBM to two special cases of classification problems, namely semi-supervised and multitask learning.


title: The effects of aromatherapy in relieving symptoms related to job stress among nurses.

abstract: Workplace-related stress has become today's most serious occupational hazard. Aromatherapy is a simple, convenient and non-invasive method of stress relief. There is little research regarding the efficacy of aromatherapy by means of inhaling essential oil in reducing workplace stress-related symptoms among nurses. Therefore, this study was to examine the effectiveness of lavender oil inhalation in reducing job stress-related symptoms among nurses. The 53 nurses in the experimental group pinned small bottles containing 3% lavender oil on the clothes of their right chests, whereas 57 participants in the control group pinned bottles with no lavender oil. Aromatherapy was shown to be effective in the reduction of the number of stress symptoms for 3 or 4 days. The stress symptoms of the experimental group decreased from 6.1 to 2.8 after aromatherapy was carried out (P = 0.126, 0.159, 0.035 and 0.026). This represented a significant decrease in stress, whereas the stress symptoms in the control group increased from 5.6 to 5.8. Hospital staff managers are still encouraged to include aromatherapy concepts and techniques in the continuing education of nursing staff. Concurrently, future research should focus on the possible side effects of aromatherapy to assure safety.


title: Blurring the border between real and virtual parking environments

abstract: Modern multi-level indoor parking environments promise to alleviate the parking problems in modern cities but they are oftentimes stressful for human drivers. Increasing automation of the parking process has the potential for significant gains in efficiency, safety and comfort but requires highly accurate sensing and monitoring of the environment. Another challenge is the appropriate visualization of large amounts of sensor data from disparate sources, in an intuitively understandable way. We address these challenges with our platform VPIPE for realistic visualization of 3D parking environments, parking lots and sensor data of vehicles. As central building block for this platform, we propose a cost-effective camera-based parking lot monitoring system that uses a cascade of Random Forest and Artificial Neural Network classifiers. The achieved detection accuracy in our parking testbed is 94.98%.


title: Talking about the Moving Image: A Declarative Model for Image Schema Based Embodied Perception Grounding and Language Generation

abstract: We present a general theory and corresponding declarative model for the embodied grounding and natural language based analytical summarisation of dynamic visuo-spatial imagery. The declarative mode ...


title: Very High Frequency PWM Buck Converters Using Monolithic GaN Half-Bridge Power Stages With Integrated Gate Drivers

abstract: Integration is a key step in utilizing advances in GaN technologies and enabling efficient switched-mode power conversion at very high frequencies (VHF). This paper addresses design and implementation of monolithic GaN half-bridge power stages with integrated gate drivers optimized for pulsewidth-modulated (PWM) dc–dc converters operating at 100 MHz switching frequency. Three gate-driver circuit topologies are considered for integration with half-bridge power stages in a 0.15- $\mu$ m depletion-mode GaN-on-SiC process: an active pull-up driver, a bootstrapped driver, and a novel modified active pull-up driver. An analytical loss model is developed and used to optimize the monolithic GaN chips, which are then used to construct 20 V, 5 W, 100 MHz synchronous buck converter prototypes. With the bootstrapped and the modified pull-up gate-driver circuits, power stage efficiencies above 91% and total efficiencies close to 88% are demonstrated. The modified active pull-up driver, which offers 80% reduction in the driver area, is found to be the best-performing approach in the depletion-mode GaN process. These results demonstrate feasibility of high-efficiency VHF PWM dc–dc converters based on high levels of integration in GaN processes.