title: A low-power FSK/OOK transmitter for 915 MHz ISM band

abstract: A PLL-based, low-power, 915 MHz FSK/OOK transmitter is presented. The PLL uses gain-boosted LC VCO and hybrid injection-locked divider topologies to reduce power consumption with acceptable phase noise. In FSK mode, the transmitter consumes 367 µW from a 1.2 V supply with −18.6 dBm output power and achieves maximum 3 Mbps speed (122.3 pJ/bit). In OOK, power consumption is 314 µW with maximum 20 Mbps speed (15.7 pJ/bit). The transmitter occupies 0.29 mm2 of die area in a 130 nm CMOS process.


title: 0.25 μm SiGe BiCMOS 30GHz balanced vector modulator

abstract: Vector modulators are a key component in phased array antennas and communications systems. The paper describes a novel design methodology for a bi-directional, reflection-type balanced vector modulator using metal-oxide-semiconductor field-effect (MOS) transistors as active loads, which provides an improved constellation quality. The fabricated IC occupies 787 × 1325 μm2 and exhibits a minimum transmission loss of 9 dB and return losses better than 14 dB. As an application example, its use in a 16-QAM modulator is verified.


title: Expressive Wearable Sonification and Visualisation: Design and Evaluation of a Flexible Display

abstract: In this paper we examine a wearable sonification and visualisation display that uses physical analogue visualisation and digital sonification to convey feedback about the wearer's activity and environment. Intended to bridge a gap between art aesthetics, fashionable technologies and informative physical computing, the user experience evaluation reveals the wearers' responses and understanding of a novel medium for wearable expression. The study reveals useful insights for wearable device design in general and future iterations of this sonification and visualisation display.


title: Model Selection for Financial Distress Prediction by Aggregating TOPSIS and PROMETHEE Rankings

abstract: Many models have been explored for financial distress prediction, but no consistent conclusions have been drawn on which method shows the best behavior when different performance evaluation measures are employed. Accordingly, this paper proposes the integration of the ranking scores given by two popular multiple-criteria decision-making tools as an important step to help decision makers in selecting the model(s) properly. Selection of the most appropriate prediction method is here shaped as a multiple-criteria decision-making problem that involves a number of performance measures (criteria) and a set of techniques (alternatives). An empirical study is carried out to assess the performance of ten algorithms over six real-life bankruptcy and credit risk databases. The results reveal that the use of a unique performance measure often leads to contradictory conclusions, while the multiple-criteria decision-making techniques may yield a more reliable analysis. Besides, these allow the decision makers to weight the relevance of the individual performance metrics as a function of each particular problem.


title: The critical factors affecting E-Government adoption: A Conceptual Framework in Vietnam

abstract: lectronic government (e-government) has established as an effective mechanism for increasing government productivity and efficiency and a key enabler of citizencentric services. However, e-government implementation is surrounded by technological, governing and social issues, which have to be considered and treated carefully in order to facilitate this change. This research attempts to explore and investigate the key challenges that influence e-government implementation and the factors influencing citizen adoption in Vietnam. It develops a conceptual framework on the basis of existing experiences drawn from administrative reforms. Survey data from public employee will be used to test the proposed hypothesis and the model. Therefore, this research has identified factors that determine if the citizen will adopt E-government services and thereby aiding governments in accessing what is required to increase adoption. We will also highlight several research, practitioner and policy implications. Keyword: E-government, Vietnam, research model


Individual Robotic Arms Manipulator Control Employing Electromyographic Signals Acquired by Myo Armbands


title: Polycystic Kidney Disease in the Medaka (Oryzias latipes) pc Mutant Caused by a Mutation in the Gli-Similar3 (glis3) Gene

abstract: Polycystic kidney disease (PKD) is a common hereditary disease in humans. Recent studies have shown an increasing number of ciliary genes that are involved in the pathogenesis of PKD. In this study, the Gli-similar3 (glis3) gene was identified as the causal gene of the medaka pc mutant, a model of PKD. In the pc mutant, a transposon was found to be inserted into the fourth intron of the pc/glis3 gene, causing aberrant splicing of the pc/glis3 mRNA and thus a putatively truncated protein with a defective zinc finger domain. pc/glis3 mRNA is expressed in the epithelial cells of the renal tubules and ducts of the pronephros and mesonephros, and also in the pancreas. Antisense oligonucleotide-mediated knockdown of pc/glis3 resulted in cyst formation in the pronephric tubules of medaka fry. Although three other glis family members, glis1a, glis1b and glis2, were found in the medaka genome, none were expressed in the embryonic or larval kidney. In the pc mutant, the urine flow rate in the pronephros was significantly reduced, which was considered to be a direct cause of renal cyst formation. The cilia on the surface of the renal tubular epithelium were significantly shorter in the pc mutant than in wild-type, suggesting that shortened cilia resulted in a decrease in driving force and, in turn, a reduction in urine flow rate. Most importantly, EGFP-tagged pc/glis3 protein localized in primary cilia as well as in the nucleus when expressed in mouse renal epithelial cells, indicating a strong connection between pc/glis3 and ciliary function. Unlike human patients with GLIS3 mutations, the medaka pc mutant shows none of the symptoms of a pancreatic phenotype, such as impaired insulin expression and/or diabetes, suggesting that the pc mutant may be suitable for use as a kidney-specific model for human GLIS3 patients.


title: An Operation Sequence Model for Explainable Neural Machine Translation

abstract: We propose to achieve explainable neural machine translation (NMT) by changing the output representation to explain itself. We present a novel approach to NMT which generates the target sentence by monotonically walking through the source sentence. Word reordering is modeled by operations which allow setting markers in the target sentence and move a target-side write head between those markers. In contrast to many modern neural models, our system emits explicit word alignment information which is often crucial to practical machine translation as it improves explainability. Our technique can outperform a plain text system in terms of BLEU score under the recent Transformer architecture on Japanese-English and Portuguese-English, and is within 0.5 BLEU difference on Spanish-English.


title: Millimeter Wave Communication: A Comprehensive Survey

abstract: Millimeter wave (mmWave) communication has raised increasing attentions from both academia and industry due to its exceptional advantages. Compared with existing wireless communication techniques, such as WiFi and 4G, mmWave communications adopt much higher carrier frequencies and thus come with advantages including huge bandwidth, narrow beam, high transmission quality, and strong detection ability. These advantages can well address difficult situations caused by recent popular applications using wireless technologies. For example, mmWave communications can significantly alleviate the skyrocketing traffic demand of wireless communication from video streaming. Meanwhile, mmWave communications have several natural disadvantages, e.g., severe signal attenuation, easily blocked by obstacles, and small coverage, due to its short wavelengths. Hence, the major challenge is how to overcome its shortcomings while fully utilizing its advantages. In this paper, we present a taxonomy based on the layered model and give an extensive review on mmWave communications. Specially, we divide existing efforts into four categories that investigate: physical layer, medium access control (MAC) layer, network layer, and cross layer optimization, respectively. First, we present an overview of some technical details in physical layer. Second, we summarize available literature in MAC layer that pertains to protocols and scheduling schemes. Third, we make an in-depth survey of related research work in network layer, providing brain storming and methodology for enhancing the capacity and coverage of mmWave networks. Fourth, we analyze available research work related to cross layer allocation/optimization for mmWave communications. Fifth, we make a review of mmWave applications to illustrate how mmWave technology can be employed to satisfy other services. At the end of each section described above, we point out the inadequacy of existing work and identify the future work. Sixth, we present some available resources for mmWave communications, including related books about mmWave, commonly used mmWave frequencies, existing protocols based on mmWave, and experimental platforms. Finally, we have a simple summary and point out several promising future research directions.


title: 2D Barcode Detection using Images for Drone-assisted Inventory Management

abstract: Drone-assisted inventory management is attractive for companies with large warehouses and factories. Additionally, there is an interest in a novel method that automatically detects target barcodes using a IR-based camera, which enables efficient drone path planning and results in reducing power consumption. In this paper, we propose an efficient detection framework which determines the localizations of 2D barcodes. Many regional proposals of 2D barcodes are reduced to a few candidate regions according to the distance information between the drone and the target 2D barcode. Visual features of the selected candidate regions are extracted by LBP and HOG methods, respectively. To gain discriminant power for classification, SVM is used at the end of the procedure. The final detection region is determined by a weighted sum-based score fusion method. To validate the performance of the proposed method, we collect 2D barcode images under real-life warehouse conditions and obtain extensive experiment results.


title: DeepSense-Inception: Gait Identification from Inertial Sensors with Inception-like Architecture and Recurrent Network

abstract: Gait recognition has been considered as a new promising approach for biometric-based authentication. Gait signals are commonly obtained by collecting motion data from inertial sensors (accelerometers, gyroscopes) integrated in mobile and wearable devices. Motion data is subsequently transformed to a feature space for recognition procedure. One fashionable, effective way to extract features automatically is using conventional Convolutional Neural Networks (CNN) as feature extractors. In this paper, we propose DeepSense-Inception (DSI), a new method inspired from DeepSense, to recognize users from their gait features using Inception-like modules for better feature extraction than conventional CNN. Experiments for user identification on UCI Human Activity Recognition dataset demonstrate that our method not only achieves an accuracy of 99.9%, higher than that of DeepSense (99.7%), but also uses only 149K parameters, less than one third of the parameters in DeepSense (529K parameters). Thus, our method can be implemented more efficiently in limited resource systems.


Select-and-Evaluate: A Learning Framework for Large-Scale Knowledge Graph Search


title: An Introduction to Dew Computing: Definition, Concept and Implications

abstract: Since the end of the 1990s, the world has witnessed a tremendous growth in the area of information and communication technology (ICT), starting with grid computing, cloud computing (CC), and fog computing to recently introduced edge computing. Although, these technologies are still in very good shape, they do heavily rely on connectivity, i.e., Internet. To address this challenge, this paper proposes a novel dew-cloud architecture that brings the power of CC together with the dew computing (DC). Originally, the dew-cloud architecture is an extension of the existing client-server architecture, where two servers are placed at both ends of the communication link. With the help of a dew server, a user has more control and flexibility to access his/her personal data in the absence of an Internet connection. Primarily, the data are stored at the dew server as a local copy upon which instantiation of the Internet is synchronized with the master copy at the cloud side. Users can browse, read, write, or append data on the local dew site, which is a local Web form of an actual website. With the incorporation of the dew domain naming system and dew domain name redirection, mapping between different local dew sites has become possible. Novel services, such as infrastructure-as-a-dew, software-as-a-dew service, and software-as-a-dew product, are, hereby, introduced along with the DC. This paper presents the following as key contributions: 1) a precise and concrete definition of DC; 2) detailed and comprehensive discussions of its concept and working principle; 3) application potentials; and 4) technical challenges. The motto of this paper is to conceptualize the fact of empowerment of the ICT-user base with almost an Internet-free surfing experience in coming days.


title: Communication via extracellular vesicles enhances viral infection of a cosmopolitan alga

abstract: Communication between microorganisms in the marine environment has immense ecological impact by mediating trophic-level interactions and thus determining community structure 1 . Extracellular vesicles (EVs) are produced by bacteria 2,3 , archaea 4 , protists 5 and metazoans, and can mediate pathogenicity 6 or act as vectors for intercellular communication. However, little is known about the involvement of EVs in microbial interactions in the marine environment 7 . Here we investigated the signalling role of EVs produced during interactions between the cosmopolitan alga Emiliania huxleyi and its specific virus (EhV, Phycodnaviridae) 8 , which leads to the demise of these large-scale oceanic blooms 9,10 . We found that EVs are highly produced during viral infection or when bystander cells are exposed to infochemicals derived from infected cells. These vesicles have a unique lipid composition that differs from that of viruses and their infected host cells, and their cargo is composed of specific small RNAs that are predicted to target sphingolipid metabolism and cell-cycle pathways. EVs can be internalized by E. huxleyi cells, which consequently leads to a faster viral infection dynamic. EVs can also prolong EhV half-life in the extracellular milieu. We propose that EVs are exploited by viruses to sustain efficient infectivity and propagation across E. huxleyi blooms. As these algal blooms have an immense impact on the cycling of carbon and other nutrients 11,12 , this mode of cell-cell communication may influence the fate of the blooms and, consequently, the composition and flow of nutrients in marine microbial food webs.


title: Detecting Anomalies in Activities of Daily Living of Elderly Residents via Energy Disaggregation and Cox Processes

abstract: Monitoring the health of the elderly living independently in their own homes is a key issue in building sustainable healthcare models which support a country's ageing population. Existing approaches have typically proposed remotely monitoring the behaviour of a household's occupants through the use of additional sensors. However the costs and privacy concerns of such sensors have significantly limited their potential for widespread adoption. In contrast, in this paper we propose an approach which detects Activities of Daily Living, which we use as a proxy for the health of the household residents. Our approach detects appliance usage from existing smart meter data, from which the unique daily routines of the household occupants are learned automatically via a log Gaussian Cox process. We evaluate our approach using two real-world data sets, and show it is able to detect over 80% of kettle uses while generating less than 10% false positives. Furthermore, our approach allows earlier interventions in households with a consistent routine and fewer false alarms in the remaining households, relative to a fixed-time intervention benchmark.


title: Evaluation of high-temperature exposure of photovoltaic modules

abstract: Photovoltaic (PV) modules operate at temperatures above ambient owing to the thermal energy of sunlight. The operating temperature primarily depends on the ambient temperature, incident sunlight, mounting configuration, packaging configuration, and wind speed. In this paper, the cumulative thermal degradation is modeled to follow Arrhenius behavior. The data are analyzed to determine the constant temperature that would give average aging equivalent to the variable temperatures observed in the field. These equivalent temperatures are calculated for various locations using six configurations, providing a technical basis for defining accelerated thermal-endurance and -degradation testing. This data may also be useful as a starting point for studies of the combined effects of elevated temperature and other factors such as UV, moisture, and mechanical stress. Copyright © 2011 John Wiley & Sons, Ltd.


title: Optimal Design of a Highly Integrated Brushless Rotor Power Supply for Electrically-Excited Generators in Vehicle-Related Applications

abstract: As part of the ongoing NGC-project (Next Generation Car) related to the research of new technologies for future road vehicles at the DLR, this paper is dedicated to the substitution of slip rings through an inductive power transfer system for field winding supply in wound rotor synchronous generators. Through function integrated lightweight design, due to the attachment of the rotary transformer and the co-rotating rectifier on an existing fan and the usage of standardized components for the rotational transformer, a volumetric optimized mounting as well as cost-effective system can be realized. The design of the system, regarding an optimal integration into the constraint space includes a variety of different challenges, which are subject of this work. For design purpose, an analytical model of the inductive link as well as the compensation circuitry is presented. Thereafter, a model for the rough estimation of the thermal behavior of the system inside the cooling environment was set up. A subsequent multi-criteria optimization procedure has been applied and the solution was used for a prototypical implementation of the brushless rotor supply into a given generator.


Deep Voice 2 : Multi-Speaker Neural Text-to-Speech


title: Reclamation of acid pickling waste: A facile route for preparation of single-phase Fe3O4 nanoparticle

abstract: Abstract Using an alternative method of dropwise addition of iron salt in NaOH aqueous solution, nanocrystalline Fe 3 O 4 materials were prepared from acid pickling waste as a starting material with ultrasonic enhancement and polyethylene glycol as a dispersant, as proved by XRD, TEM, TG–DSC and ICP-MS. The results showed that the Fe 3 O 4 material was a well-crystallized magnetite with an average size of about 25 nm and purity 99.15%. Magnetic measurement revealed the nanocrystals were stronger superparamagnetic with a saturation magnetization of 82.1 emu/g.


title: Prevalence, Severity, and Unmet Need for Treatment of Mental Disorders in the World Health Organization World Mental Health Surveys

abstract: Context Little is known about the extent or severity of untreated mental disorders, especially in less-developed countries. Objective To estimate prevalence, severity, and treatment of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) mental disorders in 14 countries (6 less developed, 8 developed) in the World Health Organization (WHO) World Mental Health (WMH) Survey Initiative. Design, Setting, and Participants Face-to-face household surveys of 60463 community adults conducted from 2001-2003 in 14 countries in the Americas, Europe, the Middle East, Africa, and Asia. Main Outcome Measures The DSM-IV disorders, severity, and treatment were assessed with the WMH version of the WHO Composite International Diagnostic Interview (WMH-CIDI), a fully structured, lay-administered psychiatric diagnostic interview. Results The prevalence of having any WMH-CIDI/DSM-IV disorder in the prior year varied widely, from 4.3% in Shanghai to 26.4% in the United States, with an interquartile range (IQR) of 9.1%-16.9%. Between 33.1% (Colombia) and 80.9% (Nigeria) of 12-month cases were mild (IQR, 40.2%-53.3%). Serious disorders were associated with substantial role disability. Although disorder severity was correlated with probability of treatment in almost all countries, 35.5%.to 50.3% of serious cases in developed countries and 76.3% to 85.4% in less-developed countries received no treatment in the 12 months before the interview. Due to the high prevalence of mild and subthreshold cases, the number of those who received treatment far exceeds the number of untreated serious cases in every country. Conclusions Reallocation of treatment resources could substantially decrease the problem of unmet need for treatment of mental disorders among serious cases. Structural barriers exist to this reallocation. Careful consideration needs to be given to the value of treating some mild cases,. especially those at risk for progressing to more serious disorders.


title: Brain – computer interfacing in disorders of consciousness

abstract: Background: Recent neuroimaging research has strikingly demonstrated the existence of covert awareness in some patients with disorders of consciousness (DoC). These findings have highlighted the potential for the development of simple brain–computer interfaces (BCI) as a diagnosis in behaviourally unresponsive patients.Objectives: This study here reviews current EEG-based BCIs that hold potential for assessing and eventually assisting patients with DoC. It highlights key areas for further development that might eventually make their application feasible in this challenging patient group.Methods: The major types of BCIs proposed in the literature are considered, namely those based on the P3 potential, sensorimotor rhythms, steady state oscillations and slow cortical potentials. In each case, a brief overview of the relevant literature is provided and then their relative merits for BCI applications in DoC are considered.Results: A range of BCI designs have been proposed and tested for enabling communication...


title: Recovery for overloaded mobile edge computing

abstract: Mobile edge computing (MEC) allows the use of its services with low latency, location awareness and mobility support to make up for the disadvantages of cloud computing. But an overloaded MEC system or any MEC system failure significantly degrades quality of experience (QoE) and negates the advantages of MEC. In this paper, we propose two different recovery schemes for overloaded or broken MEC. One recovery scheme is where an overloaded MEC offloads its work to available neighboring MECs within transfer range. The other recovery scheme is for situations when there is no available neighboring MEC within transfer range. The second scheme accesses user devices of a neighboring MEC adjacent to the overloaded MEC as ad-hoc relay nodes in order to bridge the transfer disconnect between two MECs. Numerical experiments demonstrated the performance and verified the possibility of the proposed schemes, providing insightful results for potential future research. Fail-safe mobile-edge computing system is proposed.The system uses multihop wireless network.The system allows multi destination data transfer for mobile users to relay.


title: Founders, foundations and early identities: football’s early growth in Barcelona

abstract: This essay considers football’s development in Barcelona in its first generation from 1898 through the 1910s and how it was shaped by the regional, national, religious and foreign identities of its participants and the activity itself. It starts with the foundations of F.C. Barcelona and F.C. Catala, the different stories about how this happened, and the role played by Protestant Hans Gamper in Catholic Barcelona. The second section focuses on the development of other clubs like Real Club Deportivo Espanol, early tournaments including the Copa Macaya and struggles at the end of the decade. The final portion addresses how some of these issues played out in the 1910s, when the sport began to expand beyond its almost exclusively middle-class origins to draw in a mass audience.


title: Stock market trading rule discovery using pattern recognition and technical analysis

abstract: This study examines the potential profit of bull flag technical trading rules using a template matching technique based on pattern recognition for the Nasdaq Composite Index (NASDAQ) and Taiwan Weighted Index (TWI). To minimize measurement error due to data snooping, this study performed a series of experiments to test the effectiveness of the proposed method. The empirical results indicated that all of the technical trading rules correctly predict the direction of changes in the NASDAQ and TWI. This finding may provide investors with important information on asset allocation. Moreover, better bull flag template price fit is associated with higher average return. The empirical results demonstrated that the average return of trading rules conditioned on bull flag significantly better than buying every day for the study period, especially for TWI.


title: Characterization of esophageal body and lower esophageal sphincter motor function in the very premature neonate.

abstract: OBJECTIVES ::: To characterize esophageal body and lower esophageal sphincter (LES) motor function in very premature infants. ::: ::: ::: STUDY DESIGN ::: Esophageal manometry was performed in 12 very premature infants of 26 to 33 weeks' postmenstrual age (PMA) (body weights of 610-1360 g). Esophageal motor patterns were recorded for 30 minutes with a perfused micromanometric sleeve assembly (outer diameter, 2.0 mm). ::: ::: ::: RESULTS ::: Esophageal pressure waves triggered by dry swallows were predominantly (84%) peristaltic in propagation sequence. All infants showed tonic LES contraction; the mean resting LES pressure (LESP) for individual infants ranged from 5.0 +/- 4.1 mm Hg to 20.0 +/- 4.8 mm Hg. In all infants the LES relaxed (duration, 5.8 +/- 3.0 seconds; nadir pressure, 1.8 +/- 2.6 mm Hg) in response to pharyngeal swallows. Transient LES relaxations (TLESRs) (duration, 21.7 +/- 8.7 seconds; nadir pressure, 0.1 +/- 1.8 mm Hg) occurred on average 2.6 +/- 1.6 times per study; 86% of these relaxations triggered esophageal body common cavity events known to be associated with gastroesophageal reflux. ::: ::: ::: CONCLUSIONS ::: Esophageal motor function is well developed in very premature infants. Our data also suggest that TLESR is the predominant mechanism of reflux in these babies.


title: Model of multidisciplinary simulation integration in helicopter rotor blade design process

abstract: The design of the helicopter rotor blade is subjected to several discipline influence during the design process that cause substantial changes in its structure properties and dynamical properties. Therefore, it is important to consider different discipline tool effects on the helicopter rotor blade design process. In this article, a model of multidisciplinary simulation integration (MSI) is established and used to solve the problem of process integration and resources share of different discipline tools in the rotor blade design process. First, the numerical process of rotor blade design is analysed and a flow framework model of two-level flows and a group of nodes is proposed to integrate different discipline tools in the rotor blade design process to act as an integration mechanism. Then, some key enabling technologies of MSI are discussed by analysing integration relations of different discipline tools. Integrated work model of different type of discipline tools is given to realise the proposed flow framework. In the end, the model of MSI is realised based on Visual C++ 6.0, OpenGL and SQL SERVER database. Examples show that the proposed flow framework and the established model are satisfied for improving design efficiency and sharing simulation resources in the rotor blade design process.


title: TigerGraph: A Native MPP Graph Database

abstract: We present TigerGraph, a graph database system built from the ground up to support massively parallel computation of queries and analytics. ::: TigerGraph's high-level query language, GSQL, is designed for compatibility with SQL, while simultaneously allowing NoSQL programmers to continue thinking in Bulk-Synchronous Processing (BSP) terms and reap the benefits of high-level specification. ::: GSQL is sufficiently high-level to allow declarative SQL-style programming, yet sufficiently expressive to concisely specify the sophisticated iterative algorithms required by modern graph analytics and traditionally coded in general-purpose programming languages like C++ and Java. ::: We report very strong scale-up and scale-out performance over a benchmark we published on GitHub for full reproducibility.


title: The balanced scorecard: a new challenge

abstract: Purpose – The purpose of this paper is to focus on one strategy known as “The Balanced Scorecard”, discussing the growing importance of balanced scorecard performance systems, exploring issues that organizations face in building and implementing scorecard systems, and sharing lessons learned from Australian organizations that have taken the balanced scorecard journey.Design/methodology/approach – The approach taken is the case study methodology to depict the real world examples of organisations that have confidence in the “Balanced scorecard performance system“ so that other organisations can follow suit.Findings – The paper concludes that the balanced scorecard approach may require some substantial changes in culture within the organization.. The balanced scorecard requires understanding, commitment and support from the very top of the business down. The balanced scorecard will evolve. As culture changes and develops to accept the new approach and members of the organisation mature within the new culture...


title: Semi Lobar Holoprosencephaly with Vertebral Segmentation Defects

abstract: Holoprosencephaly is the most common embryonic brain defect. Foetuses who survive during intrauterine life are born with varying grades of brain and facial deformities. Extra craniofacial manifestations are common. Vertebral segmentation defects are rarely seen with holoprosencephaly, mainly in association with holoprosencephaly diencephalic hamartoblastoma (HDH) association. A female infant was born at term by normal delivery. Birth head circumference was below the 3rd percentile. Antenatal scan had showed microcephaly as the only abnormality. Physical examination revealed microcephaly, ocular hypotelorism, left ear skin tag and short neck. MRI of the brain showed semilobar holoprosencephaly. Neck radiograph revealed gross vertebral segmentation defect involving cervical and upper thoracic vertebrae. She had initial feeding difficulties. She showed severe global developmental delay and had underlying central diabetes insipidus. Vertebral segmentation defect is rare in holoprosencephaly.


title: Low Q electrically small linear and elliptical polarized spherical dipole antennas

abstract: Electrically small antennas are generally presumed to exhibit high impedance mismatch (high VSWR), low efficiency, high quality factor (Q); and, therefore, narrow operating bandwidth. For an electric or magnetic dipole antenna, there is a fundamental lower bound for the quality factor that is determined as a function of the antenna's occupied physical volume. In this paper, the quality factor of a resonant, electrically small electric dipole is minimized by allowing the antenna geometry to utilize the occupied spherical volume to the greatest extent possible. A self-resonant, electrically small electric dipole antenna is presented that exhibits an impedance near 50 Ohms, an efficiency in excess of 95% and a quality factor that is within 1.5 times the fundamental lower bound at a value of ka less than 0.27. Through an arrangement of the antenna's wire geometry, the electrically small dipole's polarization is converted from linear to elliptical (with an axial ratio of 3 dB), resulting in a further reduction in the quality factor. The elliptically polarized, electrically small antenna exhibits an impedance near 50 Ohms, an efficiency in excess of 95% and it has an omnidirectional, figure-eight radiation pattern.


title: Tracking the Evolution of Communities in Dynamic Social Networks

abstract: Real-world social networks from a variety of domains can naturally be modelled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Recently, researchers have begun to consider the problem of tracking the evolution of groups of users in dynamic scenarios. Here we describe a model for tracking the progress of communities over time in a dynamic network, where each community is characterised by a series of significant evolutionary events. This model is used to motivate a community-matching strategy for efficiently identifying and tracking dynamic communities. Evaluations on synthetic graphs containing embedded events demonstrate that this strategy can successfully track communities over time in volatile networks. In addition, we describe experiments exploring the dynamic communities detected in a real mobile operator network containing millions of users.


title: Predicting The Next App That You Are Going To Use

abstract: Given the large number of installed apps and the limited screen size of mobile devices, it is often tedious for users to search for the app they want to use. Although some mobile OSs provide categorization schemes that enhance the visibility of useful apps among those installed, the emerging category of homescreen apps aims to take one step further by automatically organizing the installed apps in a more intelligent and personalized way. In this paper, we study how to improve homescreen apps' usage experience through a prediction mechanism that allows to show to users which app she is going to use in the immediate future. The prediction technique is based on a set of features representing the real-time spatiotemporal contexts sensed by the homescreen app. We model the prediction of the next app as a classification problem and propose an effective personalized method to solve it that takes full advantage of human-engineered features and automatically derived features. Furthermore, we study how to solve the two naturally associated cold-start problems: app cold-start and user cold-start. We conduct large-scale experiments on log data obtained from Yahoo Aviate, showing that our approach can accurately predict the next app that a person is going to use.


title: Delegation-based authentication and authorization for the IP-based Internet of Things

abstract: IP technology for resource-constrained devices en- ables transparent end-to-end connections between a vast variety of devices and services in the Internet of Things (IoT). To protect these connections, several variants of traditional IP security protocols have recently been proposed for standardization, most notably the DTLS protocol. In this paper, we identify significant resource requirements for the DTLS handshake when employing public-key cryptography for peer authentication and key agree- ment purposes. These overheads particularly hamper secure com- munication for memory-constrained devices. To alleviate these limitations, we propose a delegation architecture that offloads the expensive DTLS connection establishment to a delegation server. By handing over the established security context to the con- strained device, our delegation architecture significantly reduces the resource requirements of DTLS-protected communication for constrained devices. Additionally, our delegation architecture naturally provides authorization functionality when leveraging the central role of the delegation server in the initial connection establishment. Hence, in this paper, we present a comprehensive, yet compact solution for authentication, authorization, and secure data transmission in the IP-based IoT. The evaluation results show that compared to a public-key-based DTLS handshake our delegation architecture reduces the memory overhead by 64 %, computations by 97 %, network transmissions by 68 %.


title: DNA sequence compression using the Burrows-Wheeler Transform

abstract: We investigate off-line dictionary oriented approaches to DNA sequence compression, based on the Burrows-Wheeler Transform (BWT). The preponderance of short repeating patterns is an important phenomenon in biological sequences. Here, we propose off-line methods to compress DNA sequences that exploit the different repetition structures inherent in such sequences. Repetition analysis is performed based on the relationship between the BWT and important pattern matching data structures, such as the suffix tree and suffix array. We discuss how the proposed approach can be incorporated in the BWT compression pipeline.


title: Design of dual-band bandpass filters using cross-shaped resonator and spurline

abstract: This paper presents a microstrip dual-band bandpass filter (BPF) based on cross-shaped resonator and spurline. It is shown that spurlines added into input/output ports of a cross-shaped resonator generate an additional notch band. Using even and odd-mode analysis the proposed structure is realized and designed. The proposed bandpass filter has dual passband from 1.9 GHz to 2.4 GHz and 9.5 GHz to 11.5 GHz.


title: Probabilistic Weighted Automata

abstract: Nondeterministic weighted automata are finite automata with numerical weights on transitions. They define quantitative languages L that assign to each word w a real number L(w). The value of an infinite word w is computed as the maximal value of all runs over w, and the value of a run as the maximum, limsup, liminf, limit average, or discounted sum of the transition weights. We introduce probabilistic weighted automata, in which the transitions are chosen in a randomized (rather than nondeterministic) fashion. Under almost-sure semantics (resp. positive semantics), the value of a word w is the largest real v such that the runs over w have value at least v with probability 1 (resp. positive probability). ::: We study the classical questions of automata theory for probabilistic weighted automata: emptiness and universality, expressiveness, and closure under various operations on languages. For quantitative languages, emptiness and universality are defined as whether the value of some (resp. every) word exceeds a given threshold. We prove some of these questions to be decidable, and others undecidable. Regarding expressive power, we show that probabilities allow us to define a wide variety of new classes of quantitative languages, except for discounted-sum automata, where probabilistic choice is no more expressive than nondeterminism. Finally, we give an almost complete picture of the closure of various classes of probabilistic weighted automata for the following pointwise operations on quantitative languages: max, min, sum, and numerical complement.


title: Constructing Browser Fingerprint Tracking Chain Based on LSTM Model

abstract: Web attacks have increased rapidly in recent years. However, traditional methods are useless to track web attackers. Browser fingerprint, as a stateless tracking technique, can be used to solve this problem. Given browser fingerprint changes easily and frequently, it is easy to lose track. Therefore, we need to improve the stability of browser fingerprint by linking the new one to the previous chain. In this paper, we propose LSTM model to learn the potential relationship of browser fingerprint evolution. In addition, we adjust the input feature vector to time series and construct training set to train the model. The results show that our model can construct the tracking chain perfectly well with average ownership up to 99.3%.


title: A YY1-binding site is required for accurate human LINE-1 transcription initiation

abstract: The initial step in Long Interspersed Element-1 (LINE-1) retrotransposition requires transcription from an internal promoter located within its 5'-untranslated region (5'-UTR). Previous studies have identified a YY1 (Yin Yang 1)-binding site as an important sequence in LINE-1 transcription. Here, we demonstrate that mutations in the YY1-binding site have only minor effects on transcription activation of the full-length 5'-UTR and LINE-1 mobility in a single round cultured cell retrotransposition assay. Instead, these mutations disrupt proper initiation of transcription from the +1 site of the 5'-UTR. Thus, we propose that the YY1-binding site functions as a component of the LINE-1 core promoter to direct accurate transcription initiation. Indeed, this sequence may explain the evolutionary success of LINE-1 by enabling full-length retrotransposed copies to undergo autonomous retrotransposition in subsequent generations.


title: KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications

abstract: Service recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly, yielding the big data analysis problem for service recommender systems. Consequently, traditional service recommender systems often suffer from scalability and inefficiency problems when processing or analysing such large-scale data. Moreover, most of existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users' preferences, and therefore fails to meet users' personalized requirements. In this paper, we propose a Keyword-Aware Service Recommendation method, named KASR, to address the above challenges. It aims at presenting a personalized service recommendation list and recommending the most appropriate services to the users effectively. Specifically, keywords are used to indicate users' preferences, and a user-based Collaborative Filtering algorithm is adopted to generate appropriate recommendations. To improve its scalability and efficiency in big data environment, KASR is implemented on Hadoop, a widely-adopted distributed computing platform using the MapReduce parallel processing paradigm. Finally, extensive experiments are conducted on real-world data sets, and results demonstrate that KASR significantly improves the accuracy and scalability of service recommender systems over existing approaches.


title: DDD: A New Ensemble Approach for Dealing with Concept Drift

abstract: Online learning algorithms often have to operate in the presence of concept drifts. A recent study revealed that different diversity levels in an ensemble of learning machines are required in order to maintain high generalization on both old and new concepts. Inspired by this study and based on a further study of diversity with different strategies to deal with drifts, we propose a new online ensemble learning approach called Diversity for Dealing with Drifts (DDD). DDD maintains ensembles with different diversity levels and is able to attain better accuracy than other approaches. Furthermore, it is very robust, outperforming other drift handling approaches in terms of accuracy when there are false positive drift detections. In all the experimental comparisons we have carried out, DDD always performed at least as well as other drift handling approaches under various conditions, with very few exceptions.


title: Unsupervised Image to Sequence Translation with Canvas-Drawer Networks

abstract: Encoding images as a series of high-level constructs, such as brush strokes or discrete shapes, can often be key to both human and machine understanding. In many cases, however, data is only available in pixel form. We present a method for generating images directly in a high-level domain (e.g. brush strokes), without the need for real pairwise data. Specifically, we train a"canvas"network to imitate the mapping of high-level constructs to pixels, followed by a high-level"drawing"network which is optimized through this mapping towards solving a desired image recreation or translation task. We successfully discover sequential vector representations of symbols, large sketches, and 3D objects, utilizing only pixel data. We display applications of our method in image segmentation, and present several ablation studies comparing various configurations.


title: Software Metrics for Agile Software Development

abstract: Agile software development methods are nowadays wide spread and accepted. From the software measurement point-of-view not all metrics and methods from conventional lifecycle models can be used without adaptation. Distinct techniques in agile software development like refactoring needs new approaches and quality models in the area of software measurement. Therefore this paper describes a quality model, distinct metrics and their implementation into a measurement tool for quality management in agile software development.


title: Data Poisoning Attack against Unsupervised Node Embedding Methods

abstract: Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have attracted growing interests given their simplicity and effectiveness. However, although these methods have been proved effective in a variety of applications, none of the existing work has analyzed the robustness of them. This could be very risky if these methods are attacked by an adversarial party. In this paper, we take the task of link prediction as an example, which is one of the most fundamental problems for graph analysis, and introduce a data positioning attack to node embedding methods. We give a complete characterization of attacker's utilities and present efficient solutions to adversarial attacks for two popular node embedding methods: DeepWalk and LINE. We evaluate our proposed attack model on multiple real-world graphs. Experimental results show that our proposed model can significantly affect the results of link prediction by slightly changing the graph structures (e.g., adding or removing a few edges). We also show that our proposed model is very general and can be transferable across different embedding methods. Finally, we conduct a case study on a coauthor network to better understand our attack method.


title: Hierarchical Game-Theoretic Planning for Autonomous Vehicles

abstract: The actions of an autonomous vehicle on the road affect and are affected by those of other drivers, whether overtaking, negotiating a merge, or avoiding an accident. This mutual dependence, best captured by dynamic game theory, creates a strong coupling between the vehicle’s planning and its predictions of other drivers’ behavior, and constitutes an open problem with direct implications on the safety and viability of autonomous driving technology. Unfortunately, dynamic games are too computationally demanding to meet the real-time constraints of autonomous driving in its continuous state and action space. In this paper, we introduce a novel game-theoretic trajectory planning algorithm for autonomous driving, that enables real-time performance by hierarchically decomposing the underlying dynamic game into a long-horizon “strategic” game with simplified dynamics and full information structure, and a short-horizon “tactical” game with full dynamics and a simplified information structure. The value of the strategic game is used to guide the tactical planning, implicitly extending the planning horizon, pushing the local trajectory optimization closer to global solutions, and, most importantly, quantitatively accounting for the autonomous vehicle and the human driver’s ability and incentives to influence each other. In addition, our approach admits non-deterministic models of human decision-making, rather than relying on perfectly rational predictions. Our results showcase richer, safer, and more effective autonomous behavior in comparison to existing techniques.


title: Resilience and well-being of university nursing students in Hong Kong: a cross-sectional study

abstract: Background ::: University nursing students experience higher levels of academic stress than those of other disciplines. Academic stress leads to psychological distress and has detrimental effects on well-being. The ability to overcome such adversity and learn to be stronger from the experience is regarded as resilience. Resilience is found to have an impact on learning experience, academic performance, course completion and, in the longer term, professional practice. Resilience and positive coping strategies can resist stress and improve personal well-being. However, the relationship between resilience and well-being remains unexplored in nursing students, which are significant attributes to their academic success and future career persistence.


title: Auxiliary Image Regularization for Deep CNNs with Noisy Labels

abstract: Precisely-labeled data sets with sufficient amount of samples are very important for training deep convolutional neural networks (CNNs). However, many of the available real-world data sets contain erroneously labeled samples and those errors substantially hinder the learning of very accurate CNN models. In this work, we consider the problem of training a deep CNN model for image classification with mislabeled training samples - an issue that is common in real image data sets with tags supplied by amateur users. To solve this problem, we propose an auxiliary image regularization technique, optimized by the stochastic Alternating Direction Method of Multipliers (ADMM) algorithm, that automatically exploits the mutual context information among training images and encourages the model to select reliable images to robustify the learning process. Comprehensive experiments on benchmark data sets clearly demonstrate our proposed regularized CNN model is resistant to label noise in training data.


title: IKNN: Informative k-nearest neighbor pattern classification

abstract: The K-nearest neighbor (KNN) decision rule has been a ubiquitous classification tool with good scalability. Past experience has shown that the optimal choice of Kdepends upon the data, making it laborious to tune the parameter for different applications. We introduce a new metric that measures the informativeness of objects to be classified. When applied as a query-based distance metric to measure the closeness between objects, two novel KNN procedures, Locally Informative-KNN (LI-KNN) and Globally Informative-KNN (GI-KNN), are proposed. By selecting a subset of most informative objects from neighborhoods, our methods exhibit stability to the change of input parameters, number of neighbors(K) and informative points (I). Experiments on UCI benchmark data and diverse real-world data sets indicate that our approaches are application-independent and can generally outperform several popular KNN extensions, as well as SVM and Boosting methods.


title: Factors Influencing Impulse Buying During an Online Purchase Transaction

abstract: Using actual purchasing behavior by visitors to a High School Reunion web store, this study examines the factors that lead to an increased willingness by on-line consumers to purchase impulse items. Consistent with mental accounting and the psychophysics of prices, we find that people's purchase of the impulse item increases with the total amount spent on other items. We also find that linking a US $1 donation to the impulse item, thereby providing a reason to purchase, increases the frequency of the impulse purchase.


title: Control of the power in induction heating systems with L-LC resonant voltage source inverters

abstract: This paper is a synthesis of the control circuit in an induction heating system with three-phase fully-controlled rectifier, single-phase voltage source inverter and L-LC resonant load. The attention is mainly directed on the control of the power transmitted to the induction coil. It is highlighted that the analytical design of a proportional integral derivative or proportional integral controller is practically impossible when the direct control of the current through inductor is desired. The two possibilities of power control taken into consideration are the inverter output current control and the inverter input voltage control. The design of the current and voltage controllers of proportional integral derivative type was carried out successfully based on Modulus Optimum criterion in Kessler variant. The performances of the control system in both variants of the power control were tested and validated by simulation under the Matlab-Simulink environment by using real parameters of induction coils and heated pipes from a leading Romanian manufacturer.


title: A multilevel taxonomy and requirements for an optimal traffic-classification model

abstract: Identifying Internet traffic applications is essential for network security and management. The steady emergence of new Internet applications, together with the use of encryption and obfuscation techniques, ensures that traffic classification remains a hot research topic. Much research has been devoted to this topic by the research community in the last decade. However, an optimal traffic classification model has yet to be defined. Many techniques and formats have been described, with the current literature therefore lacking appropriate benchmarks expressed in a consistent terminology. Moreover, existing surveys are outdated and do not include many recent advances in the field. In this article, we present a systematic multilevel taxonomy that covers a broad range of existing and recently proposed methods, together with examples of vendor classification techniques. Our taxonomy assists in defining a consistent terminology. It could be useful in future benchmarking contexts by characterizing and comparing methods at three different levels. From this perspective, we describe key features and provide design hints for future classification models, while emphasizing the main requirements for promoting future research efforts. To motivate researchers and other interested parties, we collect and share data captured from real traffic, using two models to protect data privacy. Copyright © 2014 John Wiley & Sons, Ltd.


title: Real time lane detection and tracking system evaluated in a hardware-in-the-loop simulator

abstract: This paper presents a new real time lane detection and tracking system which succeeds in sharp curved roads. The Warp Perspective Mapping (WPM) method was used to change the video image perspective to top view. A two dimensional Gabor filter and vertical pixel density calculation method was used to find initial lane regions. Second derivative approach of Composite Bezier spline fitting method calculates the exact coordinates of the control points. Score function based on pixel matching was used for determining lanes in curved roads. A template lane matching method with ease of implementation was used for tracking the detected lanes. The proposed system was tested using offline and real time hardware-in-the-loop simulations. The detected and tracked lane information is used in a lane keeping control system, realized in a hardware-in-the-loop setting. The high fidelity, high order, realistic and nonlinear vehicle model in Carmaker HIL is used in hardware-in-the-loop testing. The lane keeping controller works as code in a dSpace microautobox general purpose electronic control unit which obtains the lane data from the vision system running on a laptop PC and Carmaker vehicle data from a dSpace compact simulator for calculating the required steering actions. The system performance was evaluated on different scenarios which contain straight and sharp curved roads, obstacle vehicles and different lane marking types. Average success rate is calculated as % 98.7.


title: 3D Virtual Tracing and Depth Perception Problem on Mobile AR

abstract: Mobile Augmented Reality (AR) is most commonly implemented using a camera and a flat screen. Such implementation removes binocular disparity from users' observation. To compensate, people use alternative depth cues (e.g. depth ordering). However, these cues may also get distorted in certain AR implementations, creating depth distortion which is problematic in situations where precise hand interaction within AR workspace is required such as when transcribing augmented instructions to physical objects (e.g. virtual tracing -- creating a physical sketch on a 2D or 3D object given a virtual image on a mobile device). In this paper we explore how depth distortion affects 3D virtual tracing by implementing a first of its kind 3D virtual tracing prototype and run an observational study. Drawing performance exceeded our expectations suggesting that the lack of visual depth cues, whilst holding the object in hand, is not as problematic as initially predicted. However, when placing the object on the stand and drawing with only one hand (the other is used for holding the phone) their performance drastically decreased.


title: Relative Response of Biological and Non-Biological Monitors in a Coastal Environment

abstract: A field study was carried out in the spring and summer of 2003 (2 months each), to assess the efficiency of alternative exposure modes of biological monitors – lichen biomass and tree-bark biomass – together with prospective, non-biological monitors – cellulose acetate and Chelex-100™ resin – versus conventional transplants of the same species. After exposure to the marine atmosphere of Sines (SW Portugal), saline elements (Cl, Na, K, Mg) contents were determined in all samples by instrumental neutron activation analysis (INAA). Manganese was used as the crustal reference for data normalisation. As a whole, the results pointed to a consistent elemental accumulation regardless of the season (spring, summer), and to some fair reproducibility of data. There were good correlations between Cl and Na in terms of both raw and normalised data, for either the biomonitors or the cellulose acetate. Elemental ratios generally agreed with the average relative composition of bulk seawater. The degree of association between Cl and Na appeared rather unaffected by the normalisation procedure, which suggests that (i) both elements most likely had an almost exclusive marine origin; and (ii) accounting for crustal inputs via Mn did not bias the results. Other sources than the sea-spray might contribute to the Mg levels, though, as only Mn-normalised data showed intrinsic (correlation) and extrinsic (ratio) consistency. The performance of the Chelex-100™ resin was rather disappointing when compared to the other monitors.


title: Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections

abstract: Recurrent Neural Networks (RNNs) have been successfully used in many applications. However, the problem of learning long-term dependencies in sequences using these networks is still a major challenge. Recent methods have been suggested to solve this problem by constraining the transition matrix to be unitary during training, which ensures that its norm is exactly equal to one. These methods either have limited expressiveness or scale poorly with the size of the network when compared with the simple RNN case, especially when using stochastic gradient descent (SGD) with a small mini-batch size. Our contributions are as follows. We first show that constraining the transition matrix to be unitary is a special case of an orthogonal constraint. Therefore, it may not be necessary to work with complex-valued matrices. Then we present a new parametrisation of the transition matrix which allows efficient training of an RNN while ensuring that the matrix is always orthogonal. Using our approach, one SGD step can, in the worst case, be performed in time complexity $\mathcal{O}(T n^2)$, where $T$ and $n$ are the length of the input sequence and the size of the hidden layer respectively. This time complexity is the same as that of the simple RNN. Finally, we test our new parametrisation on problems with long-term dependencies. Our results show that the orthogonal constraint on the transition matrix applied through our parametrisation gives similar benefits to the unitary constraint, without the time complexity limitations.


Paper recommendation using citation proximity in bibliographic coupling


title: Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics

abstract: Multivariate data sets including hundreds of variables are increasingly common in many application areas. Most multivariate visualization techniques are unable to display such data effectively, and a common approach is to employ dimensionality reduction prior to visualization. Most existing dimensionality reduction systems focus on preserving one or a few significant structures in data. For many analysis tasks, however, several types of structures can be of high significance and the importance of a certain structure compared to the importance of another is often task-dependent. This paper introduces a system for dimensionality reduction by combining user-defined quality metrics using weight functions to preserve as many important structures as possible. The system aims at effective visualization and exploration of structures within large multivariate data sets and provides enhancement of diverse structures by supplying a range of automatic variable orderings. Furthermore it enables a quality-guided reduction of variables through an interactive display facilitating investigation of trade-offs between loss of structure and the number of variables to keep. The generality and interactivity of the system is demonstrated through a case scenario.


title: Joint topology design and mapping of service function chains for efficient, scalable, and reliable network functions virtualization

abstract: NFV is promising to lower the network operator’s capital expenditure and operational expenditure by replacing proprietary hardware-based network equipment with software-based VNFs that can be consolidated into telecom clouds. In particular, NFV provides an efficient way to deploy network services using SFCs that consist of a set of VNFs interconnected by virtual links. A practical but theoretically challenging problem related to NFV management and orchestration is how to jointly optimize the topology design and mapping of multiple SFCs such that the TBC is minimized, which is called the JTDM problem. In this article, we propose a novel heuristic algorithm, Closed-Loop with Critical Mapping Feedback, to efficiently address the JTDM problem. While minimizing the TBC, we also propose scalable and reliable JTDM strategies that can significantly reduce the network reconfigurations and enhance the service reliability, respectively.


title: Ensuring data integrity using blockchain technology

abstract: Blockchain is a relatively new technology that has shown a lot of possibilities. It emerged in 2009 as a public ledger of all Bitcoin transactions. Blockchain technology is finding applications in wide range of areas: digital assets and stocks, smart contracts, record keeping, ID systems, cloud storage, ride sharing, etc. We investigate the blockchains' activity in terms of how to store, retrieve and share files in decentralized network.


title: Anaerobic and aerobic energy metabolism in the larvae (tetrathyridia) of Mesocestoides corti.

abstract: Abstract The tetrathyridia of Mesocestoides corti produce lactate, succinate, acetate, and CO 2 as major carbon-containing end products during in vitro incubation with glucose as the substrate. Differences in the rate of glucose consumption and lactate production under anaerobic or aerobic conditions were observed, but their significance could not be determined. However, succinate production was greatly decreased in the presence of oxygen. The relative activities and intracellular distribution of various enzymes involved in energy-supplying metabolism of the larvae appear to conform to the pathways observed in other parasitic helminths known to produce lactate, succinate, and volatile fatty acids as metabolic end products. Some common features found in this respect are the relatively low pyruvate kinase activity, the presence of a highly active cytoplasmic phosphoenolpyruvate carboxylase and the capability of mitochondrial membrane bound fumarate reductase to reduce fumarate by means of NADH. Although a stimulatory effect of fructose-1,6-diphosphate on the reaction velocity of pyruvate kinase occurred, the absolute activity of this enzyme is very low. Nearly all the enzymes required for Krebs cycle activity are available in the tetrathyridia. Under the assay conditions employed by us, only NAD-dependent isocitrate dehydrogenase could not be demonstrated. The small amounts of 14 CO 2 liberated from 6- 14 C-glucose suggest that the cycle in its classical form probably only functions at a very low rate. The incorporation of 14 C from labeled glucose into glycogen indicates the presence of enzymes capable of glycogenesis. The incorporation rate was found to be higher in the presence of oxygen than under anaerobic conditions. On account of the very low NAD-linked glycerol-3-phosphate dehydrogenase activity the glycerolphosphate cycle may be of minor importance for the tetrathyridia. As a result of these studies a scheme for the main carbohydrate dissimilating pathways in the tetrathyridia is proposed and the significance of oxygen with respect to energy-supplying metabolism is discussed.


title: Robust Cognitive Beamforming With Bounded Channel Uncertainties

abstract: This paper studies the robust beamforming design for a multi-antenna cognitive radio (CR) network, which transmits to multiple secondary users (SUs) and coexists with a primary network of multiple users. We aim to maximize the minimum of the received signal-to-interference-plus-noise ratios (SINRs) of the SUs, subject to the constraints of the total SU transmit power and the received interference power at the primary users (PUs) by optimizing the beamforming vectors at the SU transmitter based on imperfect channel state information (CSI). To model the uncertainty in CSI, we consider a bounded region for both cases of channel matrices and channel covariance matrices. As such, the optimization is done while satisfying the interference constraints for all possible CSI error realizations. We shall first derive equivalent conditions for the interference constraints and then convert the problems into the form of semi-definite programming (SDP) with the aid of rank relaxation, which leads to iterative algorithms for obtaining the robust optimal beamforming solution. Results demonstrate the achieved robustness and the performance gain over conventional approaches and that the proposed algorithms can obtain the exact robust optimal solution with high probability.


title: Culture of embryonic renal collecting duct epithelia in a gradient container

abstract: During organogenesis the ampullar epithelium of the renal collecting duct acts as an inducer which generates all of the nephron anlagen. As development proceeds, one part of the collecting duct cells in the ampullar tip retain their inducer capability, while others develop into the functional epithelium consisting of principal and intercalated (IC) cells. The events leading from the embryonic inducer to the mature tissue are unknown. We investigated the maturation of embryonic collecting duct epithelium derived from neonatal rabbit kidney under in vitro conditions. To prevent dedifferentiation the epithelia were cultured on kidney-specific support material within a tissue carrier. Apical and basal compartments of the epithelia were simulated in a gradient culture container. The two sides of the epithelium were each constantly perfused with a different medium. During the 14-day incubation the tissue was not subcultured. The development of collecting duct cell features was investigated with morphological and immunohistochemical methods. Both light and electron microscopy revealed morphologically intact epithelia following gradient culture. The polarized cells rested on a uniformly developed basement membrane. The continuous application of aldosterone during the culture modulated the development of collecting duct cell characteristics. Both basal and luminal administration of aldosterone initiated differentiation in the embryonic epithelia. Using the sodium (Na) channel blocker amiloride, it was demonstrated that Na channels are involved in the differentiation of the IC cell phenotype.


The Social Component of Information Systems—How Sociability Contributes to Technology Acceptance


title: Automated Finite-Element Analysis for Deformable Registration of Prostate Images

abstract: Two major factors preventing the routine clinical use of finite-element analysis for image registration are: 1) the substantial labor required to construct a finite-element model for an individual patient's anatomy and 2) the difficulty of determining an appropriate set of finite-element boundary conditions. This paper addresses these issues by presenting algorithms that automatically generate a high quality hexahedral finite-element mesh and automatically calculate boundary conditions for an imaged patient. Medial shape models called m-reps are used to facilitate these tasks and reduce the effort required to apply finite-element analysis to image registration. Encouraging results are presented for the registration of CT image pairs which exhibit deformation caused by pressure from an endorectal imaging probe and deformation due to swelling.


title: Brain–machine interfaces: past, present and future

abstract: Since the original demonstration that electrical activity generated by ensembles of cortical neurons can be employed directly to control a robotic manipulator, research on brain–machine interfaces (BMIs) has experienced an impressive growth. Today BMIs designed for both experimental and clinical studies can translate raw neuronal signals into motor commands that reproduce arm reaching and hand grasping movements in artificial actuators. Clearly, these developments hold promise for the restoration of limb mobility in paralyzed subjects. However, as we review here, before this goal can be reached several bottlenecks have to be passed. These include designing a fully implantable biocompatible recording device, further developing real-time computational algorithms, introducing a method for providing the brain with sensory feedback from the actuators, and designing and building artificial prostheses that can be controlled directly by brain-derived signals. By reaching these milestones, future BMIs will be able to drive and control revolutionary prostheses that feel and act like the human arm.


title: Effect of Temperature Variation on Gate Tunneling Currents in Nanoscale MOSFETs

abstract: In this paper, an analytical model for gate tunneling current has been deployed by solving Schrodinger equation using Wentzel-Kramer-Brillouin (WKB) approximation method for trapezoidal potential barrier. The gate tunneling current has been computed for direct tunneling from channel to gate as well as for tunneling from source drain extension (SDE) region to gate. Effect of temperature variation on gate tunneling current with SiO2 thickness of 4 nm to 1 nm range has been studied at various gate voltages. To study the effect of temperature on gate tunneling current, the related parameters have been modeled based on physics. Effect of variation of substrate doping concentration (Na) on gate tunneling current in n-MOSFET has also been studied. These studies have been used to bring out the design margins available in equivalent oxide thickness (EOT) and Na.


title: A Relay Based Routing Protocol for Wireless In-Body Sensor Networks

abstract: Efficient energy utilization of in-body sensors is one of the hot areas of current research. In this paper, we present a relay based routing protocol for in vivo wireless body area sensor networks. Firstly, we decrease the communication distance of in-body sensors to its minimum value. Secondly, we do not allow multi-hopping of data among in-body sensors. So, in our proposed protocol, relays along with one coordinator are deployed on the clothes of a patient. As placed on the patients clothes, relays could easily be recharged and replaced. In-body sensors send the sensed data to nearby relay, which forwards the data to the coordinator and finally to the end station. Moreover, the proposed protocol is provided with linear programming based mathematical models for network lifetime maximization and end-to-end-delay minimization. Simulation results show that our proposed protocol performs better than the selected routing protocols in terms of energy efficiency.


title: Mobile Development Process Spiral

abstract: Recent advances in mobile technology have led to ever-increasing demands for specialized software for these devices. Furthermore, the need for mobility has introduced unique constraints such as context, connectivity, size and data entry methods that impact on the apps' usability. These constraints can create usability errors that could increase risk levels especially in contexts like finance, health, or road navigation. The desire of Enterprises to embed their mobile apps' development into their business cycle increases the need to have a specialized software development process. Although many agile methods have been introduced over the last decade, none of methods has focused on reducing usability errors. This paper proposes a novel Mobile Development Process Spiral which is a Usability-Driven-Model. The process is designed to integrate usability into existing application development processes and recommends usability techniques for assessing mobile apps.


title: Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses

abstract: Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.


title: The ultimate hyper redundant robotic arm based on omnidirectional joints

abstract: Hyper-redundant robots have high potential in applications with complicated environment or where interaction between robots and human beings is needed. However, current hyper redundant robots are designed with limited agility because a joint in hyper redundant robots can only rotate around a fixed axis. In this paper, we propose an ultimate hyper redundant robotic arm design in which ever joint is omnidirectional, thus pushing the boundary of current hyper-redundant robots in terms of accessibility and agility. In the proposed design, each joint in the hyper redundant robot is an omnidirectional ball joint. The rigidity of each ball joint is controlled by shape memory alloys (SMA). The rotation of the ball joints is also controlled by shape memory alloys. In order to demonstrate the feasibility of the proposed ultimate hyper redundant robotic arm, a prototype has been made and illustrated in this paper.


title: Social Engineering Detection Using Neural Networks

abstract: Social Engineering (SE) is considered to be one of the most common problems facing information security today. Detecting social engineering is important because it attempts to secure organisations, consumers and systems from attempts to gain unauthorized access or to reveal some secrets by manipulating employees. The aim of this work is to introduce a new technique for detecting social engineering using neural networks. In this work we have used benchmark data and developed a new technique to extract features that can be used for neural network testing and training. Initial results are encouraging and indicate that machine learning can add an extra layer of security to protect individuals and organisations from social engineering attacks. Future work includes expanding the data set to include additional attack scenarios and benchmark data.


title: A multi-day activity-based inventory routing model with space–time–needs constraints

abstract: We extend activity routing problems to consider ‘needs’ satisfaction over multiple days using an inventory routing problem concept. The resulting inventory-based selective household activity routing problem allows activity type choice, duration choice, activity destination choice, departure time, and scheduling of activities, all within space–time–needs constraints. A Lagrangian relaxation-based algorithm is proposed to solve the model for multiple days. A computational study is conducted. The model can measure several effects: heterogeneity in impacts of travel time changes on different days; the use of dual price as a threshold when needs play a role in the utility maximisation objectives; the possibility for activity participation consolidation due to increased travel time; and non-uniform impacts of travel time increase on utilities across a heterogeneous population. Comparison of the algorithm's performance against a commercial package showed average objective values that differed by only 1.8% or less.


title: Problem Identification by Mining Trouble Tickets

abstract: IT systems of today's enterprises are continuously monitored and managed by a team of resolvers. Any problem in the system is reported in the form of trouble-tickets. A ticket contains various details of the observed problem. However, the knowledge of the actual problem is hidden in the ticket description along with other information. Knowledge of issues helps the service providers to better plan to improve cost and quality of operations. In this paper, we address the problem of extracting the issues from ticket descriptions. We discuss various challenges in issue extraction and present algorithms to handle different scenarios. We demonstrate the effectiveness of the proposed algorithms through two real-world case-studies.


title: Rehabilitation of grasping and forearm pronation/supination with the Haptic Knob

abstract: This paper investigates robot-assisted rehabilitation after stroke using the Haptic Knob, a 2 degree-of-freedom end-effector based robotic device to train grasping and wrist pronation/supination. Nine chronic stroke subjects trained over a period of 6 weeks, with 3 one-hour sessions of robot-assisted therapy per week, consisting of two exercises requiring active participation promoted by therapeutic games. Results of standard clinical assessments demonstrate the positive effects of robot-assisted therapy with the Haptic Knob. Subjects improved by a mean of 4.3 points in the Fugl-Meyer assessment scale, together with a decrease in hand impairments such as abnormal muscle tone frequently observed in stroke subjects. Significant improvements were also observed in motor function of the upper arm as a result of the robot-assisted therapy, suggesting homogeneous improvement of upper limb function as a result of distal training.


title: Adaptive security mechanisms for safety applications in Internet of Vehicles

abstract: Internet of Vehicles (IoV) is an important constituent of next generation smart cities that enables city wide connectivity of vehicles for traffic management applications. A secure and reliable communications is an important ingredient of safety applications in IoV. While the use of a more robust security algorithm makes communications for safety applications secure, it could reduce application QoS due to increased packet overhead and security processing delays. Particularly, in high density scenarios where vehicles receive large number of safety packets from neighborhood, timely signature verification of these packets could not be guaranteed. As a result, critical safety packets remain unverified resulting in cryptographic loss. In this paper, we propose two security mechanisms that aim to reduce cryptographic loss rate. The first mechanism is random transmitter security level section whereas the second one is adaptive scheme that iteratively selects the best possible security level at the transmitter depending on the current cryptographic loss rate. Simulation results show the effectiveness of the proposed mechanisms in comparison with the static security technique recommended by the ETSI standard.


title: Automatic Exploration of Machine Learning Experiments on OpenML

abstract: Understanding the influence of hyperparameters on the performance of a machine learning algorithm is an important scientific topic in itself and can help to improve automatic hyperparameter tuning procedures. Unfortunately, experimental meta data for this purpose is still rare. This paper presents a large, free and open dataset addressing this problem, containing results on 38 OpenML data sets, six different machine learning algorithms and many different hyperparameter configurations. Results where generated by an automated random sampling strategy, termed the OpenML Random Bot. Each algorithm was cross-validated up to 20.000 times per dataset with different hyperparameters settings, resulting in a meta dataset of around 2.5 million experiments overall.


title: Quaternion Domain $k$ -Means Clustering for Improved Real Time Classification of E-Nose Data

abstract: This paper proposes a novel clustering technique implemented in the quaternion domain for qualitative classification of E-nose data. The proposed technique is in many ways similar to the popular $ {k}$ -means clustering algorithm. However, computations carried out in the quaternion space have yielded better class separability and higher cluster validity. A pool of possible cluster centers was created by subjecting each initial center to a fixed rotation in the quaternion space. The test samples were then compared with each of the centers in the pool and assigned to an appropriate center using minimum Euclidean distance criterion. The evolving clusters have been evaluated periodically for their compactness and interclass separation using the Davis–Bouldin (DB) index. The set of clusters having minimum DB index was chosen as an optimal one. It was observed that using the proposed technique the inverse DB index remains significantly higher with successive iterations implying a consistent performance on the cluster validity front. Furthermore, clusters formed using quaternion algebra have been observed to have a smaller DB index. Finally, when compared with the traditional $ {k}$ -means algorithm, the proposed technique performed significantly better in terms of percentage classification of unlabeled samples.


title: Dance Composition Using Microsoft Kinect

abstract: In this work, we propose a novel approach in which a system autonomously composes dance sequences from previously taught dance moves with the help of the well-known differential evolution algorithm. Initially, we generated a large population of dance sequences. The fitness of each of these sequences was determined by calculating the total inter-move transition abruptness of the adjacent dance moves. The transition abruptness was calculated as the difference of corresponding slopes formed by connected body joint co-ordinates. By visually evaluating the dance sequences created, it was observed that the fittest dance sequence had the least abrupt inter-move transitions. Computer simulation undertaken revealed that the developed dance video frames do not have significant inter-move transition abruptness between two successive frames, indicating the efficacy of the proposed approach. Gestural data specific of dance moves is captured using a Microsoft Kinect sensor. The algorithm developed by us was used to fuse the dancing styles of various ‘Odissi’ dancers dancing to the same rasa (theme) and tala (beats) and loy (rhythm). In future, it may be used to fuse different forms of dance.


title: Probabilistic reasoning for assembly-based 3D modeling

abstract: Assembly-based modeling is a promising approach to broadening the accessibility of 3D modeling. In assembly-based modeling, new models are assembled from shape components extracted from a database. A key challenge in assembly-based modeling is the identification of relevant components to be presented to the user. In this paper, we introduce a probabilistic reasoning approach to this problem. Given a repository of shapes, our approach learns a probabilistic graphical model that encodes semantic and geometric relationships among shape components. The probabilistic model is used to present components that are semantically and stylistically compatible with the 3D model that is being assembled. Our experiments indicate that the probabilistic model increases the relevance of presented components.


title: A review of more electric aircraft technology

abstract: Air travel is the main focus of attention with respect to reducing greenhouse gas emissions in the transport sector. This creates the necessity for having environmentally friendly flights to facilitate local and global transport. To address this issue, several initiatives are being taken by the aircraft industry to promote the use of electrical power to drive most of the aircraft systems and subsystems that were conventionally driven by hydraulic, pneumatic and mechanical systems. The development of aircraft designs with improved performance to make them more efficient and environmentally-friendly is the motivation underlying the concept of More Electric Aircraft (MEA). In this context, this paper reviews some of the notable MEA initiatives undertaken by the aerospace industry.


title: Abstraction hierarchy in deep learning neural networks

abstract: We develop a methodology to assess knowledge representation in deep neural networks trained to recognize classes of objects. We measure the abstraction level by studying correlations between the neuron activation levels of different layers based on image class. The approach is developed and tested using CIFAR-10 dataset and MatConvNet toolbox. The results show that different kinds of layers, convolutional or pooling, have different effect on the representation. The observations also point to a tendency for incremental increase in the abstraction measure, sometimes interrupted by more significant jumps, which may indicate a qualitative transition between abstraction levels. We describe and interpret current results and outline the direction of future work.


title: OmniTouch: wearable multitouch interaction everywhere

abstract: OmniTouch is a wearable depth-sensing and projection system that enables interactive multitouch applications on everyday surfaces. Beyond the shoulder-worn system, there is no instrumentation of the user or environment. Foremost, the system allows the wearer to use their hands, arms and legs as graphical, interactive surfaces. Users can also transiently appropriate surfaces from the environment to expand the interactive area (e.g., books, walls, tables). On such surfaces - without any calibration - OmniTouch provides capabilities similar to that of a mouse or touchscreen: X and Y location in 2D interfaces and whether fingers are "clicked" or hovering, enabling a wide variety of interactions. Reliable operation on the hands, for example, requires buttons to be 2.3cm in diameter. Thus, it is now conceivable that anything one can do on today's mobile devices, they could do in the palm of their hand.


title: Design of high-speed induction machine for the 6 kW, 120 000 rpm helium turbo-circulator

abstract: High-speed ranging requires various equipment, for example compressors, turbo-compressors and turbo-circulators nowadays. Solutions using electrical drive with gearboxes is limited by technical feasibility, lifetime and maintenance of gearbox systems. Thanks to development of frequency converters is possible to build direct high-speed electrical drive. This paper is dealing with design of high-speed solid rotor cage induction machine for running 6 kW, 120 000 rpm helium turbo-circulator. Design of high speed electrical machine for TC is described from requirement specification to manufacturing. Results of mechanical, electromagnetic and thermal calculations are given. Proposed design approached can be used in various application in which high speed electrical machine is required.


title: Self-paced curriculum learning

abstract: Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training. The two methods share a similar conceptual learning paradigm, but differ in specific learning schemes. In CL, the curriculum is predetermined by prior knowledge, and remain fixed thereafter. Therefore, this type of method heavily relies on the quality of prior knowledge while ignoring feedback about the learner. In SPL, the curriculum is dynamically determined to adjust to the learning pace of the leaner. However, SPL is unable to deal with prior knowledge, rendering it prone to overfitting. In this paper, we discover the missing link between CL and SPL, and propose a unified framework named self-paced curriculum leaning (SPCL). SPCL is formulated as a concise optimization problem that takes into account both prior knowledge known before training and the learning progress during training. In comparison to human education, SPCL is analogous to "instructor-student-collaborative" learning mode, as opposed to "instructor-driven" in CL or "student-driven" in SPL. Empirically, we show that the advantage of SPCL on two tasks.


Distributional Smoothing by Virtual Adversarial Examples.


title: Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures

abstract: Major winning Convolutional Neural Networks (CNNs), such as AlexNet, VGGNet, ResNet, GoogleNet, include tens to hundreds of millions of parameters, which impose considerable computation and memory overhead. This limits their practical use for training, optimization and memory efficiency. On the contrary, light-weight architectures, being proposed to address this issue, mainly suffer from low accuracy. These inefficiencies mostly stem from following an ad hoc procedure. We propose a simple architecture, called SimpleNet, based on a set of designing principles, with which we empirically show, a well-crafted yet simple and reasonably deep architecture can perform on par with deeper and more complex architectures. SimpleNet provides a good tradeoff between the computation/memory efficiency and the accuracy. Our simple 13-layer architecture outperforms most of the deeper and complex architectures to date such as VGGNet, ResNet, and GoogleNet on several well-known benchmarks while having 2 to 25 times fewer number of parameters and operations. This makes it very handy for embedded system or system with computational and memory limitations. We achieved state-of-the-art result on CIFAR10 outperforming several heavier architectures, near state of the art on MNIST and competitive results on CIFAR100 and SVHN. Models are made available at: this https URL


Deep Hybrid Models : Bridging Discriminative and Generative Approaches


title: The Ph1 and Ph2 loci play different roles in the synaptic behaviour of hexaploid wheat Triticum aestivum

abstract: Triticum aestivum is an allohexaploid wheat (AABBDD) that shows diploid-like behaviour at metaphase-I. This behaviour is influenced by the action of several loci, Ph1 and Ph2 being the main loci involved. To study the effect of these two loci on chromosome pairing in T. aestivum we have analysed the synaptic pattern in fully traced spread nuclei at mid- and late-zygotene, and at pachytene, of three different genotypes of cv Chinese Spring: standard line, ph1b and ph2b mutants. The analysis of the synaptic progression showed that only a few nuclei accomplish synapsis in the ph2b genotype, whereas most nuclei completed synapsis in the standard and ph1b genotypes. This result indicates that the Ph2 locus affects synaptic progression. The number of synaptonemal complex (SC) bivalents and of the different SC multivalent associations were determined in each nucleus. The mean number of lateral elements involved in SC multivalent associations (LEm) at mid- zygotene was relatively high and showed similar values in the three genotypes. These values decreased progressively between mid-zygotene and pachytene in the genotypes with the Ph1 locus because of the transformation of multivalents into bivalents. In the ph1b genotype, this value only decreased between late-zygotene and pachytene. Therefore, multivalent correction was more efficient in the presence than in the absence of the Ph1 locus.It is concluded that the Ph1 and Ph2 loci bring about diploidization of allohexaploid wheat via a different mechanism.


title: Placebos without Deception: A Randomized Controlled Trial in Irritable Bowel Syndrome

abstract: Background: Placebo treatment can significantly influence subjective symptoms. However, it is widely believed that response to placebo requires concealment or deception. We tested whether open-label placebo (non-deceptive and nonconcealed administration) is superior to a no-treatment control with matched patient-provider interactions in the treatment of irritable bowel syndrome (IBS). Methods: Two-group, randomized, controlled three week trial (August 2009-April 2010) conducted at a single academic center, involving 80 primarily female (70%) patients, mean age 47618 with IBS diagnosed by Rome III criteria and with a score $150 on the IBS Symptom Severity Scale (IBS-SSS). Patients were randomized to either open-label placebo pills presented as ‘‘placebo pills made of an inert substance, like sugar pills, that have been shown in clinical studies to produce significant improvement in IBS symptoms through mind-body self-healing processes’’ or no-treatment controls with the same quality of interaction with providers. The primary outcome was IBS Global Improvement Scale (IBS-GIS). Secondary measures were IBS Symptom Severity Scale (IBS-SSS), IBS Adequate Relief (IBS-AR) and IBS Quality of Life (IBS-QoL). Findings: Open-label placebo produced significantly higher mean (6SD) global improvement scores (IBS-GIS) at both 11day midpoint (5.261.0 vs. 4.061.1, p,.001) and at 21-day endpoint (5.061.5 vs. 3.961.3, p=.002). Significant results were also observed at both time points for reduced symptom severity (IBS-SSS, p=.008 and p=.03) and adequate relief (IBS-AR, p=.02 and p=.03); and a trend favoring open-label placebo was observed for quality of life (IBS-QoL) at the 21-day endpoint (p=.08). Conclusion: Placebos administered without deception may be an effective treatment for IBS. Further research is warranted in IBS, and perhaps other conditions, to elucidate whether physicians can benefit patients using placebos consistent with informed consent. Trial Registration: ClinicalTrials.gov NCT01010191


title: High Bandwidth Visible Light Communications Based on a Post-Equalization Circuit

abstract: A research in extending bandwidth of the visible light communication (VLC) system that uses phosphorescent white LED has been reported in this letter. Slow response of the phosphorescent component limits the modulation bandwidth of white LED to the lower MHz range. In this letter, we present a post-equalization circuit that contains two passive equalizers and one active equalizer. With blue-filtering and the post-equalization circuit, a bandwidth of 151 MHz has been achieved in our VLC system, which allows OOK-NRZ data transmission up to 340 Mb/s. The VLC link operates at 43 cm using a single one Watt white LED, and the bit-error-ratio was below 2×10-3, which is within the forward error correction limit.


title: Domain Adaptation: Learning Bounds and Algorithms

abstract: This paper addresses the general problem of domain adaptation which arises in a variety of applications where the distribution of the labeled sample available somewhat differs from that of the test data. Building on previous work by Ben-David et al. (2007), we introduce a novel distance between distributions, discrepancy distance, that is tailored to adaptation problems with arbitrary loss functions. We give Rademacher complexity bounds for estimating the discrepancy distance from finite samples for different loss functions. Using this distance, we derive new generalization bounds for domain adaptation for a wide family of loss functions. We also present a series of novel adaptation bounds for large classes of regularization-based algorithms, including support vector machines and kernel ridge regression based on the empirical discrepancy. This motivates our analysis of the problem of minimizing the empirical discrepancy for various loss functions for which we also give several algorithms. We report the results of preliminary experiments that demonstrate the benefits of our discrepancy minimization algorithms for domain adaptation.


title: Overvoltage analysis at the terminals of rotating-diode bridges of three-stage synchronous machine

abstract: This paper deals with the behavior of the rotating diodes of the three stages synchronous machines during load transient in generation and starting mode; the purpose is to analyze and to quantify the constraints applied to the rotating diodes in the various use of functioning.