title: Automatic parallelization for graphics processing units

abstract: Accelerated graphics cards, or Graphics Processing Units (GPUs), have become ubiquitous in recent years. On the right kinds of problems, GPUs greatly surpass CPUs in terms of raw performance. However, because they are difficult to program, GPUs are used only for a narrow class of special-purpose applications; the raw processing power made available by GPUs is unused most of the time. This paper presents an extension to a Java JIT compiler that executes suitable code on the GPU instead of the CPU. Both static and dynamic features are used to decide whether it is feasible and beneficial to off-load a piece of code on the GPU. The paper presents a cost model that balances the speedup available from the GPU against the cost of transferring input and output data between main memory and GPU memory. The cost model is parameterized so that it can be applied to different hardware combinations. The paper also presents ways to overcome several obstacles to parallelization inherent in the design of the Java bytecode language: unstructured control flow, the lack of multi-dimensional arrays, the precise exception semantics, and the proliferation of indirect references.


title: A Sinusoidal Encoder-to-Digital Converter Based on an Improved Tangent Method

abstract: Sinusoidal encoders are used for the measurement of angular position and linear displacement. These provide electrical signals dependent on the sine and cosine of the position $\theta $ of their moving part relative to the stationary one. Conventional tangent/cotangent converters for determining $\theta $ from the encoder signals produce alternating highly non-linear segments of tangent and cotangent. Lookup tables are used for determining $\theta $ . This paper presents a novel converter for estimating $\theta $ without the lookup table. The method is based on dividing the circle angular interval into $N$ sections. A crude or coarse measure $\theta _{C}$ of the unknown angle $\theta $ is first determined by identifying the section in which $\theta $ is located. This is followed by the determination of a fine measure $\theta _{F}$ . These measures are combined to yield a precise measure of $\theta $ ; the maximum error of the method depends on $N$ . The converter has been implemented on a dSPACE set and has been successfully tested both with a sensor emulator and a real Hall-Effect sinusoidal encoder. This paper describes full details of the theory, simulation, and experimental results. The experimental results demonstrate excellent agreement with theory and simulation.


title: Predictive Analytics for Complex IoT Data Streams

abstract: The requirements of analyzing heterogeneous data streams and detecting complex patterns in near real-time have raised the prospect of complex event processing (CEP) for many Internet of Things (IoT) applications. Although CEP provides a scalable and distributed solution for analyzing complex data streams on the fly, it is designed for reactive applications as CEP acts on near real-time data and does not exploit historical data. In this regard, we propose a proactive architecture which exploits historical data using machine learning for prediction in conjunction with CEP. We propose an adaptive prediction algorithm called adaptive moving window regression for dynamic IoT data and evaluated it using a real-world use case with an accuracy of over 96%. It can perform accurate predictions in near real-time due to reduced complexity and can work along CEP in our architecture. We implemented our proposed architecture using open source components which are optimized for big data applications and validated it on a use-case from intelligent transportation systems. Our proposed architecture is reliable and can be used across different fields in order to predict complex events.


title: Person Re-identification: System Design and Evaluation Overview

abstract: Person re-identification has important applications in video surveillance. It is particularly challenging because observed pedestrians undergo significant variations across camera views, and there are a large number of pedestrians to be distinguished given small pedestrian images from surveillance videos. This chapter discusses different approaches of improving the key components of a person re-identification system, including feature design, feature learning, and metric learning, as well as their strength and weakness. It provides an overview of various person re-identification systems and their evaluation on benchmark datasets. Multiple benchmark datasets for person re-identification are summarized and discussed. The performance of some state-of-the-art person identification approaches on benchmark datasets is compared and analyzed. It also discusses a few future research directions on improving benchmark datasets, evaluation methodology, and system design.


title: Learning constraints in spreadsheets and tabular data

abstract: Spreadsheets, comma separated value files and other tabular data representations are in wide use today. However, writing, maintaining and identifying good formulas for tabular data and spreadsheets can be time-consuming and error-prone. We investigate the automatic learning of constraints (formulas and relations) in raw tabular data in an unsupervised way. We represent common spreadsheet formulas and relations through predicates and expressions whose arguments must satisfy the inherent properties of the constraint. The challenge is to automatically infer the set of constraints present in the data, without labeled examples or user feedback. We propose a two-stage generate and test method where the first stage uses constraint solving techniques to efficiently reduce the number of candidates, based on the predicate signatures. Our approach takes inspiration from inductive logic programming, constraint learning and constraint satisfaction. We show that we are able to accurately discover constraints in spreadsheets from various sources.


title: Response Styles in Marketing Research: A Cross-National Investigation

abstract: Abstract Response styles are a source of contamination in questionnaire ratings, and therefore they threaten the validity of conclusions drawn from marketing research data. In this article, the authors examine five forms of stylistic responding (acquiescence and disacquiescence response styles, extreme response style/response range, midpoint responding, and noncontingent responding) and discuss their biasing effects on scale scores and correlations between scales. Using data from large, representative samples of consumers from 11 countries of the European Union, the authors find systematic effects of response styles on scale scores as a function of two scale characteristics (the proportion of reverse-scored items and the extent of deviation of the scale mean from the midpoint of the response scale) and show that correlations between scales can be biased upward or downward depending on the correlation between the response style components. In combination with the apparent lack of concern with response styl...


title: Dual-Processing Accounts of Reasoning, Judgment, and Social Cognition

abstract: This article reviews a diverse set of proposals for dual processing in higher cognition within largely disconnected literatures in cognitive and social psychology. All these theories have in common the distinction between cognitive processes that are fast, automatic, and unconscious and those that are slow, deliberative, and conscious. A number of authors have recently suggested that there may be two architecturally (and evolutionarily) distinct cognitive systems underlying these dual-process accounts. However, it emerges that (a) there are multiple kinds of implicit processes described by different theorists and (b) not all of the proposed attributes of the two kinds of processing can be sensibly mapped on to two systems as currently conceived. It is suggested that while some dual-process theories are concerned with parallel competing processes involving explicit and implicit knowledge systems, others are concerned with the influence of preconscious processes that contextualize and shape deliberative reasoning and decision-making.


title: Cost-effective gateway selection scheme for delay-tolerant data transmission in Internet of Things

abstract: Every connected smart object with sensors will be generating and transmitting data, hence a staggering amount of data needs to be collected and processed in the cloud. All these data must be sent to its application in a timely fashion. Depending on the types of application, some applications need real-time data while some can accommodate delay-tolerant data. However, although an application supports delay-tolerant data, the data still have to be sent before its delay is beyond tolerable. Our aim is to propose a mesh-type IoT gateways architecture which allows the gateways to aggregate bandwidth and provide a high-availability internet access to all the connected things. The objective of this work is to show that with the proposed architecture, IoT gateway selection scheme can be applied to reduce the WAN (Wide Area Network) data transfer cost. Data which can be delayed will be relayed to the cloud through the most cost-effective IoT gateway. We evaluated our architecture through simulations with various IoT gateway selection schemes using 3 different scenarios. Compared to direct transfer which only meets 65.8% of the data deadline, we are able to meet 98.6% of the data deadline and reduce the WAN cost by 5.74% under the normal scenario, when the average Internet connection speed is higher than the average data incoming rate.


title: Probabilistic segmentation of white matter lesions in MR imaging

abstract: Abstract A new method has been developed for fully automated segmentation of white matter lesions (WMLs) in cranial MR imaging. The algorithm uses information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It is based on the K-Nearest Neighbor (KNN) classification technique that builds a feature space from voxel intensities and spatial information. The technique generates images representing the probability per voxel being part of a WML. By application of thresholds on these probability maps, binary segmentations can be obtained. ROC curves show that the segmentations achieve both high sensitivity and specificity. A similarity index (SI), overlap fraction (OF) and extra fraction (EF) are calculated for additional quantitative analysis of the result. The SI is also used for determination of the optimal probability threshold for generation of the binary segmentation. Using probabilistic equivalents of the SI, OF and EF, the probability maps can be evaluated directly, providing a powerful tool for comparison of different classification results. This method for automated WML segmentation reaches an accuracy that is comparable to methods for multiple sclerosis (MS) lesion segmentation and is suitable for detection of WMLs in large and longitudinal population studies.


STOCK MARKET PREDICTION USING BIO-INSPIRED COMPUTING: A SURVEY


title: Building Intelligent Learning Database Systems

abstract: Induction and deduction are two opposite operations in data-mining applications. Induction extracts knowledge in the form of, say, rules or decision trees from existing data, and deduction applies induction results to interpret new data. An intelligent learning database (ILDB) system integrates machine-learning techniques with database and knowledge base technology. It starts with existing database technology and performs both induction and deduction. The integration of database technology, induction (from machine learning), and deduction (from knowledge-based sys-tems) plays a key role in the construction of ILDB systems, as does the design of efficient induction and deduction algorithms. This article presents a system structure for ILDB systems and discusses practical issues for ILDB applications, such as instance selection and structured induction.


Securing Vehicular On-Board IT Systems : The EVITA Project


title: Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks

abstract: Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT and MRI abdomen images using cascaded fully convolutional neural networks (CFCNs) enabling the segmentation of a large-scale medical trial or quantitative image analysis. We train and cascade two FCNs for a combined segmentation of the liver and its lesions. In the first step, we train a FCN to segment the liver as ROI input for a second FCN. The second FCN solely segments lesions within the predicted liver ROIs of step 1. CFCN models were trained on an abdominal CT dataset comprising 100 hepatic tumor volumes. Validations on further datasets show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for liver with computation times below 100s per volume. We further experimentally demonstrate the robustness of the proposed method on an 38 MRI liver tumor volumes and the public 3DIRCAD dataset.


title: Conscious and unconscious perception: Experiments on visual masking and word recognition

abstract: Five experiments are presented which explore the relation of masking to consciousness and visual word processing. In Experiment 1 a single word or blank field was followed by a pattern mask. Subjects had to make one of three decisions: Did anything precede the mask? To which of two probe words was what preceded the mask more similar graphically? To which of two probe words was it more similar semantically? As word-mask stimulus onset asynchrony (SOA) was reduced, subjects reached chance performance on the detection, graphic, and semantic decisions in that order. In Experiment 2, subjects again had to choose which of two words was more similar either graphically or semantically to a nondetectable masked word, but the forced-choice stimuli now covaried negatively on graphic and semantic similarity. Subjects were now unable to choose selectively on each dimension, suggesting that their ability to choose in Experiment 1 was passively rather than intentionally mediated. In Experiment 3 subjects had to make manual identification responses to color patches which were either accompanied or preceded by words masked to prevent awareness. Color-congruent words facilitated reaction time (RT), color-incongruent words delayed RT. Experiment 4 used a lexical decision task where a trial consisted of the critical letter string following another not requiring a response. When both were words they were either semantically associated or not. The first letter string was either left unmasked, energy masked monoptically, or pattern masked dichoptically to prevent awareness. The effect of association was equal in the unmasked and pattern masked cases, but absent with energy masking. In Experiment 5 repeating a word-plus-mask (where the SOA precluded detection) from 1 to 20 times (a) increased the association effect on a subsequent lexical decision, but had no effect on (b) detectability or (c) the semantic relatedness of forced guesses of the masked word. It is proposed that central pattern masking has little effect on visual processing itself (while peripheral energy masking does), but affects availability of records of the results of those processes to consciousness. Perceptual processing itself is unconscious and automatically proceeds to all levels of analysis and redescription available to the perceiver. The general importance of these findings is to cast doubt on the paradigm assumption that representations yielded by perceptual analysis are identical to and directly reflected by phenomenal percepts.


title: PD mechanism and pattern investigation for stator winding insulation of HV motors

abstract: The high voltage (HV) motor suffers from insulation failure due to long term partial discharge (PD) which is a great threat to its normal operation. This is why online monitoring and fault diagnosis of PD activities of HV motors is becoming more and more important to date. To perform the online monitoring of the PD activity, pattern distributions of the harmful PD should be understood to guarantee detecting accuracy in the fault identification process. In this paper, three types of partial discharge, including slot discharge, bar-to-bar discharge, and corona discharge at the junction section of the semi-conductive tapes and the corona release tapes, are induced using stator coils embedded into a stator core slot model. The phase resolved partial discharge (PRPD) representation is adopted to get distribution characteristics of the PD. The PD mechanisms are illustrated using dielectric barrier discharge model. The experimental results show that a sharp increase at the onset of the positive discharge could be helpful to distinguish slot discharge from corona discharge. The relatively high amplitude and the narrow section of the phase distribution of partial discharge at the positive half voltage cycle are chosen to characterize the bar-to-bar discharge. Because of the block of insulating medium, the discharge current cannot develop into spark or electric arc but could be turned to several visible stable discharge filaments.


Get Me Out of This Slump! Visual Illusions Improve Sports Performance:


title: Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut

abstract: Dimensionality reduction, cluster analysis, and sparse representation are basic components in machine learning. However, their relationships have not yet been fully investigated. In this paper, we find that the spectral graph theory underlies a series of these elementary methods and can unify them into a complete framework. The methods include PCA, K-means, Laplacian eigenmap (LE), ratio cut (Rcut), and a new sparse representation method developed by us, called spectral sparse representation (SSR). Further, extended relations to conventional over-complete sparse representations (e.g., method of optimal directions, KSVD), manifold learning (e.g., kernel PCA, multidimensional scaling, Isomap, locally linear embedding), and subspace clustering (e.g., sparse subspace clustering, low-rank representation) are incorporated. We show that, under an ideal condition from the spectral graph theory, PCA, K-means, LE, and Rcut are unified together. And when the condition is relaxed, the unification evolves to SSR, which lies in the intermediate between PCA/LE and K-mean/Rcut. An efficient algorithm, NSCrt, is developed to solve the sparse codes of SSR. SSR combines merits of both sides: its sparse codes reduce dimensionality of data meanwhile revealing cluster structure. For its inherent relation to cluster analysis, the codes of SSR can be directly used for clustering. Scut, a clustering approach derived from SSR reaches the state-of-the-art performance in the spectral clustering family. The one-shot solution obtained by Scut is comparable to the optimal result of K-means that are run many times. Experiments on various data sets demonstrate the properties and strengths of SSR, NSCrt, and Scut.


title: Do offensive words harm people

abstract: The harm thesis—the assumption that words harm people—is a defining feature of sexual harassment, hate speech, verbal abuse, and obscene telephone call (OTC) offenses. This thesis ignores the possibility that swearing can be advantageous, cathartic, or an acceptable substitute for physical aggression. Observational data, courtroom evidence and verbal abuse research reviewed here produce conflicting conclusions on the question of harm. The best evidence of harm resides in harassment and OTC studies, but verbal abuse research is indeterminate because of flawed research methodology. Public swearing research reveals that swearing is a common conversational practice resulting in no obvious harm. "Common sense" (folk psychology) views of swearing are mistaken and inadequate for some decisions regarding harm. Meanwhile, efforts to restrict speech in media and instructional settings continue, despite the lack of a convincing need to do so. Harm from offensive speech is contextually determined; therefore attempts to restrict speech on a universal basis are misguided. Psychologists’ research needs to be informed by public policy and courtroom practices, and public policy and litigation need to be better informed by psychologists’ research.


title: RobustFill: Neural Program Learning under Noisy I/O

abstract: The problem of automatically generating a computer program from some specification has been studied since the early days of AI. Recently, two competing approaches for automatic program learning have received significant attention: (1) neural program synthesis, where a neural network is conditioned on input/output (I/O) examples and learns to generate a program, and (2) neural program induction, where a neural network generates new outputs directly using a latent program representation. Here, for the first time, we directly compare both approaches on a large-scale, real-world learning task and we additionally contrast to rule-based program synthesis, which uses hand-crafted semantics to guide the program generation. Our neural models use a modified attention RNN to allow encoding of variable-sized sets of I/O pairs, which achieve 92% accuracy on a real-world test set, compared to the 34% accuracy of the previous best neural synthesis approach. The synthesis model also outperforms a comparable induction model on this task, but we more importantly demonstrate that the strength of each approach is highly dependent on the evaluation metric and end-user application. Finally, we show that we can train our neural models to remain very robust to the type of noise expected in real-world data (e.g., typos), while a highly-engineered rule-based system fails entirely.


title: Model-based whitebox fuzzing for program binaries

abstract: Many real-world programs take highly structured and very complex inputs. The automated testing of such programs is non-trivial. If the test input does not adhere to a specific file format, the program returns a parser error. For symbolic execution-based whitebox fuzzing the corresponding error handling code becomes a significant time sink. Too much time is spent in the parser exploring too many paths leading to trivial parser errors. Naturally, the time is better spent exploring the functional part of the program where failure with valid input exposes deep and real bugs in the program. In this paper, we suggest to leverage information about the file format and the data chunks of existing, valid files to swiftly carry the exploration beyond the parser code. We call our approach Model-based Whitebox Fuzzing (MoWF) because the file format input model of blackbox fuzzers can be exploited as a constraint on the vast input space to rule out most invalid inputs during path exploration in symbolic execution. We evaluate on 13 vulnerabilities in 8 large program binaries with 6 separate file formats and found that MoWF exposes all vulnerabilities while both, traditional whitebox fuzzing and model-based blackbox fuzzing, expose only less than half, respectively. Our experiments also demonstrate that MoWF exposes 70% vulnerabilities without any seed inputs.


title: A Comparative Study of Load Balancing Algorithms in Cloud Computing Environment

abstract: Cloud Computing is a new trend emerging in IT environment with huge requirements of infrastructure and resources. Load Balancing is an important aspect of cloud computing environment. Efficient load balancing scheme ensures efficient resource utilization by provisioning of resources to cloud users on demand basis in pay as you say manner. Load Balancing may even support prioritizing users by applying appropriate scheduling criteria. This paper presents various load balancing schemes in different cloud environment based on requirements specified in Service Level Agreement (SLA).


title: A 40 nm Fully Integrated 82 mW Stereo Headphone Module for Mobile Applications

abstract: An 82 mW fully integrated stereo ground-referenced headphone module is designed in 40 nm CMOS. Lower platform cost is enabled by integrating the headphone module on the same SoC as the baseband functions. Maintaining device reliability with direct battery hook-up and providing large output swing are major challenges for this work, and several techniques were employed to guarantee safe operation for all of the devices under various conditions. Area reduction techniques were utilized to reduce the die cost and achieve lower platform cost. The module supports direct battery hookup with a battery range from 3.1 to 4.5 V and achieves a minimum low frequency, i.e., 217 Hz, PSRR of 110 dB at the lowest battery voltage. Audio quality is preserved by achieving a dynamic range of 100 dB, THD+N of -84 dB at 10 mW output power, and 160 μV pop-and-click noise level during power-up and power-down. The module occupies an area of 0.675 mm 2 ::: on the SoC.


title: Reducing Visual Discomfort with HMDs Using Dynamic Depth of Field

abstract: Although head-mounted displays (HMDs) are ideal devices for personal viewing of immersive stereoscopic content, exposure to VR applications on them results in significant discomfort for the majority of people, with symptoms including eye fatigue, headaches, nausea, and sweating. A conflict between accommodation and vergence depth cues on stereoscopic displays is a significant cause of visual discomfort. This article describes the results of an evaluation used to judge the effectiveness of dynamic depth-of-field (DoF) blur in an effort to reduce discomfort caused by exposure to stereoscopic content on HMDs. Using a commercial game engine implementation, study participants report a reduction of visual discomfort on a simulator sickness questionnaire when DoF blurring is enabled. The study participants reported a decrease in symptom severity caused by HMD exposure, indicating that dynamic DoF can effectively reduce visual discomfort.


title: Exponential Stability of Linear Delay Impulsive Differential Equations

abstract: For ordinary differential equations and functional differential equations the following result is well known. Suppose any solution is bounded on the half-line for each bounded on the half-line right-hand side. Then under certain conditions the equations is exponentially stable. ::: We prove the same result for a delay differential equation ::: $$ \dot{x}(t) + \sum_{i=1}^k A_i (t)x[h_i(t)] = f(t), $$ ::: with impulses ::: $$ x(\tau_i + 0) = B_i x(\tau_i - 0) $$ ::: at fixed moments $\tau_i$. The proof is based on a solution representation formula obtained here.


title: Novel compact net-type resonators and their applications to microstrip bandpass filters

abstract: Novel compact net-type resonators and their practical applications to microstrip bandpass filters have been presented in this paper. Three kinds of filters are designed and fabricated to demonstrate the practicality of the proposed compact net-type resonators. In addition, by adjusting the structural parameters of the net-type resonators, the spurious frequencies can be properly shifted to higher frequencies. As a result, a three-pole Chebyshev net-type resonator filter with a fractional bandwidth (FBW) of 6.8% has a spurious resonance of up to 4.1f/sub 0/, and it has more than 80% size reduction in comparison with the conventional U-shaped resonator filter. A four-pole quasi-elliptic net-type resonator filter with a FBW of 3.5% has a spurious resonance of up to 5f/sub 0/, and it has approximately 67% size reduction in comparison with the cross-coupled open-loop resonator filter. A three-pole trisection net-type resonator filter with a FBW of 4.7% has a spurious resonance of up to 6.5f/sub 0/, and its size is reduced by 68% in comparison with the trisection open-loop resonator filter. Consequently, each of the designed filters occupies a very small circuit size and has a good stopband response. The measured results are in good agreement with the full-wave simulation results by IE3D.


title: 3D Hough transform for sphere recognition on point clouds

abstract: Three-dimensional object recognition on range data and 3D point clouds is becoming more important nowadays. Since many real objects have a shape that could be approximated by simple primitives, robust pattern recognition can be used to search for primitive models. For example, the Hough transform is a well-known technique which is largely adopted in 2D image space. In this paper, we systematically analyze different probabilistic/randomized Hough transform algorithms for spherical object detection in dense point clouds. In particular, we study and compare four variants which are characterized by the number of points drawn together for surface computation into the parametric space and we formally discuss their models. We also propose a new method that combines the advantages of both single-point and multi-point approaches for a faster and more accurate detection. The methods are tested on synthetic and real datasets.


title: Few-exemplar Information Extraction for Business Documents

abstract: The automatic extraction of relevant information from business documents (sender, recipient, date, etc.) is a valuable task in the application domain of document management and archiving. Although current scientific and commercial self-learning solutions for document classification and extraction work pretty well, they still require a high effort of on-site configuration done by domain experts and administrators. Small office/home office (SOHO) users and private individuals do often not benefit from such systems. A low extraction effi- ciency especially in the starting period due to a small number of initially available example documents and a high effort to annotate new documents, drastically lowers their acceptance to use a self-learning information extraction system. Therefore we present a solution for information extraction that fits the requirements of these users. It adopts the idea of one-shot learning from computer vision to the domain of business document processing and requires only a minimal number of training to reach competitive extraction efficiency. Our evaluation on a document set of 12,500 documents following 399 different layouts/templates shows extraction results of 88% F1 score on 10 commonly used fields like document type, sender, recipient, and date. We already reach an F1 score of 78% with only one document of each template in the training set.


title: An overview of MetaMap: historical perspective and recent advances.

abstract: MetaMap is a widely available program providing access to the concepts in the unified medical language system (UMLS) Metathesaurus from biomedical text. This study reports on MetaMap’s evolution over more than a decade, concentrating on those features arising out of the research needs of the biomedical informatics community both within and outside of the National Library of Medicine. Such features include the detection of author-defined acronyms/abbreviations, the ability to browse the Metathesaurus for concepts even tenuously related to input text, the detection of negation in situations in which the polarity of predications is important, word sense disambiguation (WSD), and various technical and algorithmic features. Near-term plans for MetaMap development include the incorporation of chemical name recognition and enhanced WSD.


title: Towards semantic diff of XML documents

abstract: Applications are increasingly using XML to represent semi-structured data and, consequently, a large amount of XML documents is available worldwide. As XML documents evolve over time, comparing XML documents to understand their evolution becomes fundamental. The main focus of existing research for comparing XML documents resides in identifying syntactic changes. However, a deeper notion of the change meaning is usually desired. This paper presents an inference-based XML evolution approach using Prolog to deal with this problem. Differently from existing XML diff approaches, our approach composes multiple syntactic changes, which usually have a common purpose, to infer semantic changes. We evaluated our approach through ten versions of an employment XML document. In this evaluation, we could observe that each new version introduced syntactic changes that could be summarized into semantic changes.


title: Iot Forensics: Challenges for the Ioa Era

abstract: Challenges for IoT-based forensic investigations include the increasing amount of objects of forensic interest, relevance of identified and collected devices, blurry network boundaries, and edgeless networks. As we look ahead to a world of expanding ubiquitous computing, the challenge of forensic processes such as data acquisition (logical and physical) and extraction and analysis of data grows in this space. Containing an IoT breach is increasingly challenging - evidence is no longer restricted to a PC or mobile device, but can be found in vehicles, RFID cards, and smart devices. Through the combination of cloud-native forensics with client-side forensics (forensics for companion devices), we can study and develop the connection to support practical digital investigations and tackle emerging challenges in digital forensics. With the IoT bringing investigative complexity, this enhances challenges for the Internet of Anything (IoA) era. IoA brings anything and everything "online" in a connectedness that generates an explosion of connected devices, from fridges, cars and drones, to smart swarms, smart grids and intelligent buildings. Research to identify methods for performing IoT-based digital forensic analysis is essential. The long-term goal is the development of digital forensic standards that can be used as part of overall IoT and IoA security and aid IoT-based investigations.


Sentence Extraction by tf/idf and Position Weighting from Newspaper Articles


title: An Efficient Elliptic Curve Cryptography Signature Server With GPU Acceleration

abstract: Over the Internet, digital signature has been an indispensable approach to securing e-commerce and other online transactions requiring authentication. Concerning the computing costs of signature generation and verification, it has become a more and more common practice for security practitioners to outsource such computations from heavily loaded application servers called tenants to dedicated proxies like signature servers in the enterprise private cloud. In this paper, we present our high-performance signature server called Guess . It implements the elliptic curve digital signature algorithm (ECDSA) with 256-b key size on a Linux-powered commodity computer, harnessing a desktop graphics processing unit as a featured cryptographic accelerator. We demonstrate our experience in maximizing the computing power of Guess and also its capability to deliver such power to the tenants, which includes down-to-earth customization and optimization considering various hardware and software factors. Our comprehensive implementation of ECDSA is tested against intensive network traffic. Field experiments show that Guess achieves $T_{s} = 8.71 \times 10^{6}$ operations per second (OPS) for signature generation or $T_{v} = 9.29 \times 10^{5}$ OPS for verification, which is significantly faster than existent prototypes and products. Guess is a universal server that readily supports various categories of elliptic curve cryptographic schemes, such as digital signature, key agreement, and encryption.


title: A Binary Integer Programming Model for Global Optimization of Learning Path Discovery

abstract: This paper introduces a method based on graph theory and operations research techniques to optimize learning path discovery. In this method, learning objects are considered as nodes and competencies as vertices of a learning graph. A first step consists in reducing the solution space by obtaining an induced subgraph H. In a second step, the search of an optimal learning path in H is considered as a binary integer programming problem which we propose to solve using an exact method based on the well-known branch-and-bound algorithm. The method detailed in the paper takes into account the prerequisite and gained competencies as constraints of the optimization problem by minimizing the total competencies needed to reach the learning objective.


title: Statistical learning in children with Specific Language Impairment

abstract: Purpose In this study, the authors examined (a) whether children with specific language impairment (SLI) can implicitly compute the probabilities of adjacent sound sequences, (b) if this ability is...


title: Arabic Dialect Identification with an Unsupervised Learning (Based on a Lexicon). Application Case: ALGERIAN Dialect

abstract: The identification of Arabic dialects is considered to be the first pre-processing component by any NLP problem. This identification is useful for automatic translation, information retrieval, opinion mining and sentiment analysis. Most of the work on the identification treat this issue like any classification problem. These work are based on supervised learning. The majority of them are based on the EGYPTIAN (EGY), the TUNISIAN (TUN), the IRAQI (IRAQI) dialect, etc., by omitting the Algerian dialect. The purpose of this paper is to identify the Arabic dialects within social media in an unsupervised manner. In order to do so, we use an Algerian dialect lexicon and based on Algeriers Dialect. We also propose an approach based on an algorithm that perform three kinds of identification: 1) total (when the term is totally identify), 2) partial (when the term is identified only partially with prefix and suffix) and by using the improved Levenshtein distance (based on classical Levenshtein distance) and with considering the number of character of the words compared). We applied our algorithm on a corpus of 100 messages that were collected using the Facebook API, we obtain a rate exceeding 60%.


Efforts Made in Enhancing Communication Skills of Student University Librarian in the Digital Age


BEHAVIOURAL RESPONSES OF DABCHICKS PODICEPS RUFICOLLIS TO DISTURBANCES WHILE INCUBATING


title: Obesity and diabetes in Western Samoa.

abstract: A study of the effect of obesity on glucose tolerance in a Polynesian population (Western Samoa) has not shown a consistent relationship between the two. Although there was a trend for increasing age-standardized prevalence of impaired glucose tolerance and diabetes mellitus with increasing body mass index in males, no such trend was observed in females. The impressive differences in prevalence of abnormal glucose tolerance between rural and urban groups was altered little by adjusting for differences in distribution of body mass index. Environmental factors, such as diet, physical activity, and stress, acting independently of adiposity must have contributed significantly to the observed rural/urban differences in prevalence of abnormal glucose tolerance in Western Samoans.


title: Efficient object localization using Convolutional Networks

abstract: Recent state-of-the-art performance on human-body pose estimation has been achieved with Deep Convolutional Networks (ConvNets). Traditional ConvNet architectures include pooling and sub-sampling layers which reduce computational requirements, introduce invariance and prevent over-training. These benefits of pooling come at the cost of reduced localization accuracy. We introduce a novel architecture which includes an efficient ‘position refinement’ model that is trained to estimate the joint offset location within a small region of the image. This refinement model is jointly trained in cascade with a state-of-the-art ConvNet model [21] to achieve improved accuracy in human joint location estimation. We show that the variance of our detector approaches the variance of human annotations on the FLIC [20] dataset and outperforms all existing approaches on the MPII-human-pose dataset [1].


title: Form and Meaning in the Earliest Cities: A New Approach to Ancient Urban Planning:

abstract: This article describes a new model for urban planning in ancient and preindustrial cities that moves beyond the traditional simplistic dichotomy of planned versus organic cities. The model has two components: coordination of buildings and spaces, and standardization among cities. A variety of coordinated arrangements of buildings reflect urban planning, including simple coordination, formality and monumentality, orthogonal layouts, other forms of geometric order, and access and visibility (viewshed). Standardization among cities is analyzed in terms of architectural inventories, spatial patterns, orientation, and metrology. The political and social significance of ancient urban planning is then discussed using Amos Rapoport’s model of levels of meaning in the built environment.


2 Retrieval as a Self-limiting Process : Part II


title: RoboCup: A Challenge Problem for AI

abstract: The Robot World-Cup Soccer (RoboCup) is an attempt to foster AI and intelligent robotics research by providing a standard problem where a wide range of technologies can be integrated and examined. The first RoboCup competition will be held at the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan. A robot team must actually perform a soccer game, incorporating various technologies, including design principles of autonomous agents, multiagent collaboration, strategy acquisition, real-time reasoning, robotics, and sensor fusion. RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. Although RoboCup's final target is a world cup with real robots, RoboCup offers a software platform for research on the software aspects of RoboCup. This article describes technical challenges involved in RoboCup, rules, and the simulation environment.


title: Virtual Network Function placement for resilient Service Chain provisioning

abstract: Virtualization technologies are changing the way network operators deploy and manage Internet services. In particular in this study we focus on the new Network Function Virtualization (NFV) paradigm, which consists in instantiating Virtual Network Function (VNFs) in Commercial-Off-The-Shelf (COSTS) hardware. Adopting NFV network operators can dynamically instantiate Network Functions (NFs) based on current demands and network conditions, allowing to save capital and operational costs. Typically, VNFs are concatenated together in a sequential order to form Service Chains (SCs) that provide specific Internet Services to the users. In this paper we study different approaches to provide the resiliency of SCs against single-link and single-node failures. We propose three Integer Linear Programming (ILP) models to solve the VNF placement problem with the VNF service chaining while guaranteeing resiliency against single-node/link, single-link and single-node failures. Moreover we evaluate the impact of latency of SCs on the VNFs distribution. We show that providing resiliency against both single-link and single-node failures necessitates the activation of twice the amount of resources in terms of nodes, and that for latency critical services providing resiliency against single-node failures comes at the same cost with respect to resiliency against single-link and single-nodes failures.


title: Using WordNetTM to Disambiguate Word Senses for Text Retrieval

abstract: This paper describes an automatic indexing procedure that uses the “IS-A” relations contained within WordNet and the set of nouns contained in a text to select a sense for each plysemous noun in the text. The result of the indexing procedure is a vector in which some of the terms represent word senses instead of word stems. Retrieval experiments comparing the effectivenss of these sense-based vectors vs. stem-based vectors show the stem-based vectors to be superior overall, although the sense-based vectors do improve the performance of some queries. The overall degradation is due in large part to the difficulty of disambiguating senses in short query statements. An analysis of these results suggests two conclusions: the IS-A links define a generalization/specialization hierarchy that is not sufficient to reliably select the correct sense of a noun from the set of fine sense distinctions in WordNet; and missing correct matches because of incorrect sense resolution has a much more deleterious effect on retrieval performance than does making spurious matches.


title: Optimizing Multi-Relational Factorization Models for Multiple Target Relations

abstract: Multi-matrix factorization models provide a scalable and effective approach for multi-relational learning tasks such as link prediction, Linked Open Data (LOD) mining, recommender systems and social network analysis. Such models are learned by optimizing the sum of the losses on all relations in the data. Early models address the problem where there is only one target relation for which predictions should be made. More recent models address the multi-target variant of the problem and use the same set of parameters to make predictions for all target relations. In this paper, we argue that a model optimized for each target relation individually has better predictive performance than models optimized for a compromise on the performance on all target relations. We introduce specific parameters for each target but, instead of learning them independently from each other, we couple them through a set of shared auxiliary parameters, which has a regularizing effect on the target specific ones. Experiments on large Web datasets derived from DBpedia, Wikipedia and BlogCatalog show the performance improvement obtained by using target specific parameters and that our approach outperforms competitive state-of-the-art methods while being able to scale gracefully to big data.


title: The Impact of Dynamic Capabilities on Resource Access and Development

abstract: This study analyzes dynamic capabilities that support activities directed toward accessing resources and further developing resources to make them commercially usable. We develop theory and empirically investigate the impact of dynamic capabilities on the amount and success of these activities and whether the impact of dynamic capabilities differs between the two types of activities. Using unique data from the upstream oil industry, we develop an objective measure of dynamic capability that is distinct from the outcomes of utilizing these capabilities. We find that firms with more sophisticated dynamic capabilities undertake greater amounts of activity to access resources and further develop them prior to commercial use; they also have greater success in these activities. Finally, these effects of dynamic capabilities are larger for activities directed toward initially accessing resources than those directed toward further developing them, which is consistent with the potential for firms to rely on knowledge gained through resource access activity when conducting subsequent activity to further develop resources.


Incentive Instruments in Agricultural Contracts: Input Control, Monitoring, Quality Measurement, and Price Risk


title: Deep self-organizing reservoir computing model for visual object recognition

abstract: Reservoir computing becomes increasingly a hot spot in recent years. In this paper, we propose a deep self-organizing reservoir computing model for visual object recognition. First, through combination of Kohonen's self-organizing map and SHESN network, we present a self-organizing SHESN (SO-SHESN). In the new model, we adopt the same mechanism of generating reservoir as SHESN, but McCulloch-Pitts type reservoir neuron is replaced with radial basis function neuron. Correspondingly, unsupervised competitive learning is exploited to train both input weights and reservoir weights of SO-SHESN. Second, we propose a deep SO-SHESN model through a stack of well-trained reservoir layers. In such a stacked structure, a novel trial-and-readout learning algorithm is used for pre-training of layer-wise reservoir, in which each layer is trained independently from each other. Finally, the experimental results obtained on MNIST benchmark dataset show that our SO-SHESN achieves the test recognition error rate of 5.66%, which improves classical ESN and SHESN by 6.44% and 1.74%, respectively. Furthermore, the test error rate of our deep SO-SHESN could reach up to 1.39%, which outperforms SO-SHESN with single reservoir layer by 4.27% and approximately approaches the state-of-the-art result of 1% among existing traditional machine learning approaches with non-CNN features.


title: Semantic Reasoning for Context-Aware Internet of Things Applications

abstract: Acquiring knowledge from continuous and heterogeneous data streams is a prerequisite for Internet of Things (IoT) applications. Semantic technologies provide comprehensive tools and applicable methods for representing, integrating, and acquiring knowledge. However, resource-constraints, dynamics, mobility, scalability, and real-time requirements introduce challenges for applying these methods in IoT environments. We study how to utilize semantic IoT data for reasoning of actionable knowledge by applying state-of-the-art semantic technologies. For performing these studies, we have developed a semantic reasoning system operating in a realistic IoT environment. We evaluate the scalability of different reasoning approaches, including a single reasoner, distributed reasoners, mobile reasoners, and a hybrid of them. We evaluate latencies of reasoning introduced by different semantic data formats. We verify the capabilities of promising semantic technologies for IoT applications through comparing the scalability and real-time response of different reasoning approaches with various semantic data formats. Moreover, we evaluate different data aggregation strategies for integrating distributed IoT data for reasoning processes.


title: Feature Subset Selection Using a Genetic Algorithm

abstract: Practical pattern-classification and knowledge-discovery problems require the selection of a subset of attributes or features to represent the patterns to be classified. The authors' approach uses a genetic algorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.


CONTROL STRATEGIES FOR MICROGRIDS BLACK START AND ISLANDED OPERATION


Stance Detection for the Fake News Challenge


Knowledge-Based Semantic Embedding for Machine Translation


title: Sobel Edge Detection Algorithm

abstract: Edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. Sobel which is a popular edge detection algorithm is considered in this work. There exists a function, edge.m which is in the image toolbox. In the edge function, the Sobel method uses the derivative approximation to find edges. Therefore, it returns edges at those points where the gradient of the considered image is maximum. The Sobel operator performs a 2-D spatial gradient measurement on images. It uses a pair of horizontal and vertical gradient matrices whose dimensions are 3×3 for edge detection operations. It will also demonstrate how to build a Sobel detector function of 5 ×5 dimension in matlab to find edges.


title: Characteristic mode based pattern reconfigurable antenna for mobile handset

abstract: Pattern reconfigurability is a desirable feature of mobile handset antennas, as it enables the antennas to adapt to non-stationary propagation environments. However, it is difficult to vary the radiation pattern of a handset antenna at frequencies below 1 GHz because the entire chassis commonly acts as the main radiator. Hence, designing pattern reconfigurability based on the antenna element (secondary radiator) alone can severely limit the achievable pattern diversity. In this work, a dual-band (0.85-0.90 GHz and 1.93-2.11 GHz) handset antenna with pattern reconfigurability at the low band is designed by loading the periphery of the chassis with a metal bezel. The bezel loading adds a new resonant characteristic mode to the chassis, on top of the fundamental resonant mode. These two modes are selectively excited for pattern reconfigurability using a suitable feed and two PIN diodes, which yield an envelope correlation of below 0.2 in the low band (0.07 at 0.87 GHz). Moreover, since the proposed antenna is fully integrated into the bezel structure, it does not occupy any extra space on the chassis, which also allows more antennas to be implemented, e.g., for MIMO applications. (Less)


title: Real-Time Big Data Processing Framework: Challenges and Solutions

abstract: Data type and amount in human society is growing in amazing speed which is caused by emerging new services as cloud computing, internet of things and location-based services, the era of big data has arrived. As data has been fundamental resource, how to manage and utilize big data better has attracted much attention. Especially, with the development of internet of things, how to processing large amount real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention with high-performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. This paper studied the challenges of big data firstly and concludes all these challenges into six iss ues. In order to improve the performance of real-time processing of large data, this paper builds a kind of real-time big data processi ng (RTDP) architecture based on the cloud computing technology and then proposed the four layers of the architecture, and hierarc hical computing model. This paper proposed a multi-level storage model and the LMA-based application deployment method to meet the real-time and heterogeneity requirements of RTDP system. We use DSMS, CEP, batch-based MapReduce and other processing mode and FPGA, GPU, CPU, ASIC technologies differently to processing the data at the terminal of data collection. We structured the dat a and then upload to the cloud server and MapReduce the data combined with the powerful computing capabilities cloud architecture. This paper points out the general framework for future RTDP system and calculation methods, is currently the general method RTDP system design.


title: A calibration model for fingerprint sensor interoperability

abstract: Biometric sensor interoperability refers to the ability of a system to compensate for the variability introduced in the biometric data of an individual due to the deployment of different sensors. We demonstrate that a simple non-linear calibration scheme, based on Thin Plate Splines (TPS), is sufficient to facilitate sensor interoperability in the context of fingerprints. In the proposed technique, the variation between the images acquired using two different sensors is modeled using non-linear distortions. Experiments indicate that the proposed calibration scheme can significantly improve inter-sensor matching performance.


title: PRoportional Fair Scheduling of Uplink Single-Carrier FDMA Systems

abstract: We apply novel utility-based scheduling schemes to uplink single carrier frequency division multiple access (SC-FDMA) systems. Two utility functions are used for managing two dimensional resources (time and frequency): user data rate for maximizing system capacity and logarithmic user data rate for proportional fairness. To develop utility-based scheduling algorithms, we revise channel-dependent scheduling (CDS) schemes derived in our previous work (J. Lim et al.). The results show that proportional fair scheduling with logarithmic user data rate can improve the rate-sum capacity up to 100% for localized FDMA and 30% for interleaved FDMA, with the capacity gains equally shared among all users


title: Design of 1.3 GHz Microstrip Highpass Filter Using Optimum Distributed Short Circuited Stubs

abstract: This paper presents an optimum distributed microstrip structure highpass filter having a cutoff frequency 1.3 GHz. The distributed elements used for this design are such as commensurate (equal electrical length) transmission line elements. The proposed design consists of shorted circuited stub microstrip transmission lines on a commercial substrate (RT/D 5880) with a relative dielectric constant 2.2 and 1.57 mm thickness. This filter exhibits a highpass band with steep cutoff. A sample filter is designed based upon the theory presented and it has been simulated with the S-parameters and IE3D software. Advanced computer aided design tools and a high precision thin film process make such filters predictable and repeatable. These filters have less radiation, dispersion and sensitivity to nearby objects.


title: Topic Cropping: Leveraging Latent Topics for the Analysis of Small Corpora

abstract: Topic modeling has gained a lot of popularity as a means for identifying and describing the topical structure of textual documents and whole corpora. There are, however, many document collections such as qualitative studies in the digital humanities that cannot easily benefit from this technology. The limited size of those corpora leads to poor quality topic models. Higher quality topic models can be learned by incorporating additional domain-specific documents with similar topical content. This, however, requires finding or even manually composing such corpora, requiring considerable effort. For solving this problem, we propose a fully automated adaptable process of topic cropping. For learning topics, this process automatically tailors a domain-specific Cropping corpus from a general corpus such as Wikipedia. The learned topic model is then mapped to the working corpus via topic inference. Evaluation with a real world data set shows that the learned topics are of higher quality than those learned from the working corpus alone. In detail, we analyzed the learned topics with respect to coherence, diversity, and relevance.


title: Deep Mixture of Diverse Experts for Large-Scale Visual Recognition

abstract: In this paper, a deep mixture of diverse experts algorithm is developed for seamlessly combining a set of base deep CNNs (convolutional neural networks) with diverse outputs (task spaces), e.g., such base deep CNNs are trained to recognize different subsets of tens of thousands of atomic object classes. First, a two-layer (category layer and object class layer) ontology is constructed to achieve more effective solution for task group generation, e.g., assigning the semantically-related atomic object classes at the sibling leaf nodes into the same task group because they may share similar learning complexities. Second, one particular base deep CNNs with $M+1$ ($M \leq 1,000$) outputs is learned for each task group to recognize its $M$ atomic object classes effectively and identify one special class of "not-in-group" automatically, and the network structure (numbers of layers and units in each layer) of the well-designed AlexNet is directly used to configure such base deep CNNs. A deep multi-task learning algorithm is developed to leverage the inter-class visual similarities to learn more discriminative base deep CNNs and multi-task softmax for enhancing the separability of the atomic object classes in the same task group. Finally, all these base deep CNNs with diverse outputs (task spaces) are seamlessly combined to form a deep mixture of diverse experts for recognizing tens of thousands of atomic object classes. Our experimental results have demonstrated that our deep mixture of diverse experts algorithm can achieve very competitive results on large-scale visual recognition.


The Usage of Genomic Selection Strategy in Plant Breeding


title: The roles of network communities in social information diffusion

abstract: Online social connections allow users to share and spread information with others. Different users play various roles on information diffusion: some are influential within a community and some make information widely spread across communities. This paper aims to unveil the hidden relationships between nodes and information diffusion in the context of communities from the big Twitter diffusion data with a million-scale social network, as a complement to existing link-based diffusion analysis (e.g. the strength of weak ties). We identify six types of well-known community-aware roles of nodes, and examine their diffusion capability on propagating information within/across communities, using information flows via retweet and mention in Twitter. We find nodes acting as community bridges are exposed to more information and more dominative on spread information within/across communities. Community-aware roles are also validated via supervised learning to be effective for detecting the most diffusive information propagators.


title: Joint Word Segmentation and POS Tagging Using a Single Perceptron

abstract: For Chinese POS tagging, word segmentation is a preliminary step. To avoid error propagation and improve segmentation by utilizing POS information, segmentation and tagging can be performed simultaneously. A challenge for this joint approach is the large combined search space, which makes efficient decoding very hard. Recent research has explored the integration of segmentation and POS tagging, by decoding under restricted versions of the full combined search space. In this paper, we propose a joint segmentation and POS tagging model that does not impose any hard constraints on the interaction between word and POS information. Fast decoding is achieved by using a novel multiple-beam search algorithm. The system uses a discriminative statistical model, trained using the generalized perceptron algorithm. The joint model gives an error reduction in segmentation accuracy of 14.6% and an error reduction in tagging accuracy of 12.2%, compared to the traditional pipeline approach.


title: Technical Section: Inverse lighting design for interior buildings integrating natural and artificial sources

abstract: In this paper we propose a new method for solving inverse lighting design problems that can include diverse sources such as diffuse roof skylights or artificial light sources. Given a user specification of illumination requirements, our approach provides optimal light source positions as well as optimal shapes for skylight installations in interior architectural models. The well known huge computational effort that involves searching for an optimal solution is tackled by combining two concepts: exploiting the scene coherence to compute global illumination and using a metaheuristic technique for optimization. Results and analysis show that our method provides both fast and accurate results, making it suitable for lighting design in indoor environments while supporting interactive visualization of global illumination.


title: Conversation Modeling on Reddit Using a Graph-Structured LSTM

abstract: This paper presents a novel approach for modeling threaded discussions on social media using a graph-structured bidirectional LSTM (long-short term memory) which represents both hierarchical and te...


title: Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums

abstract: The Internet is frequently used as a medium for exchange of information and opinions, as well as propaganda dissemination. In this study the use of sentiment analysis methodologies is proposed for classification of Web forum opinions in multiple languages. The utility of stylistic and syntactic features is evaluated for sentiment classification of English and Arabic content. Specific feature extraction components are integrated to account for the linguistic characteristics of Arabic. The entropy weighted genetic algorithm (EWGA) is also developed, which is a hybridized genetic algorithm that incorporates the information-gain heuristic for feature selection. EWGA is designed to improve performance and get a better assessment of key features. The proposed features and techniques are evaluated on a benchmark movie review dataset and U.S. and Middle Eastern Web forum postings. The experimental results using EWGA with SVM indicate high performance levels, with accuracies of over 91p on the benchmark dataset as well as the U.S. and Middle Eastern forums. Stylistic features significantly enhanced performance across all testbeds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments.


Predicting Problem-Solving Behavior and Performance Levels from Visual Attention Data


title: Prolate spheroidal wave functions, fourier analysis, and uncertainty — V: the discrete case

abstract: A discrete time series has associated with it an amplitude spectrum which is a periodic function of frequency. This paper investigates the extent to which a time series can be concentrated on a finite index set and also have its spectrum concentrated on a subinterval of the fundamental period of the spectrum. Key to the analysis are certain sequences, called discrete prolate spheroidal sequences, and certain functions of frequency called discrete prolate spheroidal functions. Their mathematical properties are investigated in great detail, and many applications to signal analysis are pointed out.


title: 57.5GHz bandwidth 4.8Vpp swing linear modulator driver for 64GBaud m-PAM systems

abstract: A novel series-stacked large swing push-pull MOS-HBT driver was implemented in 55nm SiGe BiCMOS technology. The circuit achieves 4.8Vpp differential swing, 57.5GHz band-width and has an output compression point of 12 dBm per side. 4-PAM and 8-PAM eye diagrams were measured at 56 GBaud for a record data rate of 168 Gb/s. 4-PAM 64GBaud eye diagrams were also demonstrated. The circuit consumes 820/600 mW with/without the predriver, for an energy efficiency of 4.88/3.57 pJ/b.


title: Inflammation and Atherosclerosis

abstract: Atherosclerosis, formerly considered a bland lipid storage disease, actually involves an ongoing inflammatory response. Recent advances in basic science have established a fundamental role for inflammation in mediating all stages of this disease from initiation through progression and, ultimately, the thrombotic complications of atherosclerosis. These new findings provide important links between risk factors and the mechanisms of atherogenesis. Clinical studies have shown that this emerging biology of inflammation in atherosclerosis applies directly to human patients. Elevation in markers of inflammation predicts outcomes of patients with acute coronary syndromes, independently of myocardial damage. In addition, low-grade chronic inflammation, as indicated by levels of the inflammatory marker C-reactive protein, prospectively defines risk of atherosclerotic complications, thus adding to prognostic information provided by traditional risk factors. Moreover, certain treatments that reduce coronary risk also limit inflammation. In the case of lipid lowering with statins, this anti-inflammatory effect does not appear to correlate with reduction in low-density lipoprotein levels. These new insights into inflammation in atherosclerosis not only increase our understanding of this disease, but also have practical clinical applications in risk stratification and targeting of therapy for this scourge of growing worldwide importance.


title: Effect of Inductive Coil Shape on Sensing Performance of Linear Displacement Sensor Using Thin Inductive Coil and Pattern Guide

abstract: This paper discusses the effect of inductive coil shape on the sensing performance of a linear displacement sensor. The linear displacement sensor consists of a thin type inductive coil with a thin pattern guide, thus being suitable for tiny space applications. The position can be detected by measuring the inductance of the inductive coil. At each position due to the change in inductive coil area facing the pattern guide the value of inductance is different. Therefore, the objective of this research is to study various inductive coil pattern shapes and to propose the pattern that can achieve good sensing performance. Various shapes of meander, triangular type meander, square and circle shape with different turn number of inductive coils are examined in this study. The inductance is measured with the sensor sensitivity and linearity as a performance evaluation parameter of the sensor. In conclusion, each inductive coil shape has its own advantages and disadvantages. For instance, the circle shape inductive coil produces high sensitivity with a low linearity response. Meanwhile, the square shape inductive coil has a medium sensitivity with higher linearity.


title: The Tomato Cf-2 Disease Resistance Locus Comprises Two Functional Genes Encoding Leucine-Rich Repeat Proteins

abstract: In plants, resistance to pathogens is frequently determined by dominant resistance genes, whose products are proposed to recognize pathogen-encoded avirulence gene (Avr) products. The tomato resistance locus Cf-2 was isolated by positional cloning and found to contain two almost identical genes, each conferring resistance to isolates of tomato leaf mould (C. fulvum) expressing the corresponding Avr2 gene. The two Cf-2 genes encode protein products that differ from each other by only three amino acids and contain 38 leucine-rich repeat (LRR) motifs. Of the LRRs, 20 show extremely conserved alternating repeats. The C-terminus of Cf-2 carries regions of pronounced homology to the protein encoded by the unlinked Cf-9 gene. We suggest that this conserved region interacts with other proteins involved in activating plant defense mechanisms.


title: Deep Interactive Object Selection

abstract: Interactive object selection is a very important research problem and has many applications. Previous algorithms require substantial user interactions to estimate the foreground and background distributions. In this paper, we present a novel deep-learning-based algorithm which has much better understanding of objectness and can reduce user interactions to just a few clicks. Our algorithm transforms user-provided positive and negative clicks into two Euclidean distance maps which are then concatenated with the RGB channels of images to compose (image, user interactions) pairs. We generate many of such pairs by combining several random sampling strategies to model users' click patterns and use them to finetune deep Fully Convolutional Networks (FCNs). Finally the output probability maps of our FCN-8s model is integrated with graph cut optimization to refine the boundary segments. Our model is trained on the PASCAL segmentation dataset and evaluated on other datasets with different object classes. Experimental results on both seen and unseen objects demonstrate that our algorithm has a good generalization ability and is superior to all existing interactive object selection approaches.


title: [Imperforate hymen: a series of 13 cases].

abstract: BACKGROUND ::: Imperforate hymen is a rare congenital malformation. Patients often remain asymptomatic until puberty and present in early adolescence with cyclic abdominal pain. Early diagnosis and treatment must be performed in order to prevent morbidity. THE AIM of this study was to asses management of this disease. ::: ::: ::: METHODS ::: A retrospective study of 13 cases of imporforate hymen diagnosed in the department of obstetrics and gynecology "A" of Charles Nicolle hospital of Tunis from January 1980 to December 2008. The clinical features and the management are discussed. ::: ::: ::: RESULTS ::: The mean age was 14 years. All patients were single and had primary amenorrhea. They presented with pelvic pain in 9 cases and bladder urinary retention in 4 cases. Secondary sexual characters were present and normal in all cases. Inspection of the vulva could establish the diagnosis in all cases. Pelvic ultrasounds showed the hematocolpos in all cases. The latter was associated to a hematometria and a Douglas pouch liquid in 2 cases. Hematocolpos was evacuated by hymeneotomy under oxytocin infusion in all cases. Eight patients were treated by cruciform incisions and five patients were treated by radial incisions of the hymen. The volume of hematocolpos varied from 250 ml to 2000 ml. One patient underwent surgery twice for restenosis of the imperforate hymen. ::: ::: ::: CONCLUSION ::: Imperforate hymen is a rare anomaly. Its diagnosis is simple and could be established at birth by a systematic screening. More frequently, the diagnosis must be suspected in front of a primary amenorrhea associated to abdominal pain in order to prevent complications.


title: Development of sleep patterns in early adolescence

abstract: This study examines the developmental changes of sleep patterns as a function of gender and puberty and assesses the prevalence of sleep habits and sleep disturbances in early adolescence. It also investigates the relationship between sleep patterns, sleep habits and difficulty falling asleep and nocturnal awakenings. The present analyses are based on results available for 588 boys and 558 girls for whom mothers completed questions concerning demographics and sleep at annual intervals when their child was aged 10–13 years. The results indicated that nocturnal sleep times decreased, bedtimes were delayed and differences between weekend and school day sleep schedules progressively increased with age. Gender and puberty were both associated with the timing of sleep on weekends. Girls presented longer weekend time in bed (TIB) and later weekend wake time than boys. Similarly, subjects with higher pubertal status showed longer weekend TIB and later weekend wake time than subjects with lower pubertal status. Difficulty falling asleep was associated with later weekend wake time and with sleeping with a night light. In conclusion, the gender differences commonly reported in adolescents’ sleep patterns are most likely explained by girls’ higher pubertal status. This study emphasizes the link between puberty and a putative physiological need for more sleep, in presence of a general reduction of sleep times during adolescence. From age 10–13 years, the delay and lengthening of the sleep period on weekends in comparison to schooldays is associated with difficulty falling asleep.


title: A Novel Axial Flux Magnetic-Field-Modulated Dual-Mechanical-Port Dual-Electrical-Port Machine for Hybrid Electric Vehicle

abstract: Hybrid electrical vehicles (HEVs) are becoming more and more popular nowadays in automobile market due to virtues such as energy saving, environmental friendly and low noise. A simple mechanical structure called "planetary gears" are widely used in HEVs to allow the internal combustion engine (ICE) constantly working at its high efficiency region and save energy by decoupling the speed of the ICE and the wheel-load. Despite its advantages, planetary gears also introduce several problems to the HEV system such as vibration, abrasion and mechanical maintenance fee. In this paper, a novel axial flux magnetic-field-modulated dual-mechanical- port dual-electrical-port (AF-MFM-DMP-DEP) machine is proposed for HEVs applications to replace the whole hybrid-electric powertrain in HEVs. By cooperating a magnetic-field-modulated (MFM) machine and a conventional synchronous PM machine, the proposed machine is capable of decoupling both speed and torque of the ICE and the wheel-load electrically. Without the usage of mechanical speed decoupling devices, the hybrid system mechanical transmission loss could be avoided while the vibration, abrasion and maintenance fee of the system could be reduced. Based on the proposed machine, a HEV system is constructed and its operating modes are illustrated. Firstly, the machine structure, operation principles and operation modes are introduced. After that the torque transmission capability is verified by quasi- 3D FEA. Four models of different gear ratio are established to determine the influence of gear ratio variation and the optimization gear ratio is 2:13.


title: A Multiband Dual-Polarized Omnidirectional Antenna for 2G/3G/LTE Applications

abstract: A multiband dual-polarized omnidirectional antenna for 2G/3G/Long Term Evolution (LTE) mobile communications is proposed in this letter, which consists of horizontal polarization (HP) and vertical polarization (VP) element with separate feeds. The VP element consists of three polygonal radiation patches with three equally spaced legs shorted to the ground plane. The HP element consists of three wideband slot loop structures, which is nested on the top of the VP element. Three slot loop structures provide a 360° coverage for HP and enhance its bandwidth. Both the simulated and measured results indicate that the frequency bands of 1650–2900 MHz for HP and 780–2700 MHz for VP can be achieved. The reflection of VP improves the gain of the HP element at least 1 dBi after nesting. The gain of HP element is more than 3 dBi for LTE, and the gain of VP element is more than 5 dBi in the LTE band and 1.5 dBi in the 2G band. Port isolation larger than 30 dB and low-gain variation levels are also obtained. The proposed antenna can be applied in mobile communications.


title: Automatic Twitter Topic Summarization With Speech Acts

abstract: With the growth of the social media service of Twitter, automatic summarization of Twitter messages (tweets) is in urgent need for efficient processing of the massive tweeted information. Unlike multi-document summarization in general, Twitter topic summarization must handle the numerous, short, dissimilar, and noisy nature of tweets. To address this challenge, we propose a novel speech act-guided summarization approach in this work. Speech acts characterize tweeters' communicative behavior and provide an organized view of their messages. Speech act recognition is a multi-class classification problem, which we solve by using word-based and symbol-based features that capture both the linguistic features of speech acts and the particularities of Twitter text. The recognized speech acts in tweets are then used to direct the extraction of key words and phrases to fill in templates designed for speech acts. Leveraging high-ranking words and phrases as well as topic information for major speech acts, we propose a round-robin algorithm to generate template-based summaries. Different from the extractive method adopted in most previous works, our summarization method is abstractive. Evaluated on two 100-topic datasets, the summaries generated by our method outperform two kinds of representative extractive summaries and rival human-written summaries in terms of explanatoriness and informativeness.


title: Deploying PaaS for Accelerating Cloud Uptake in the Guifi Community Network

abstract: Community networks is an emerging model in which communities of citizens build and own open shared networks. But currently, cloud computing infrastructures, common in today's Internet, hardy exist in community networks. We present in this paper our approach for bringing clouds into community networks. The main element of our strategy for achieving cloud uptake is the deployment of the Guifi-Community-Distribution (GCODIS) on all cloud nodes, a distribution containing common services and applications. We argue in the paper the reasons why this approach is appropriate for the scenario of community networks, where the user acceptance needs to be gained. We show the steps and the on-going process with which we actually implement our approach in the Guifi community network. With these key elements in place, we conclude that we are closer to the vision that the users of community networks ultimately will not need to consume cloud applications from the Internet, but find them within the community network. An interesting open issue is to what extend our approach can be applied to the more generic volunteer computing scenario.


title: ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector

abstract: Given the ability to directly manipulate image pixels in the digital input space, an adversary can easily generate imperceptible perturbations to fool a Deep Neural Network (DNN) image classifier, as demonstrated in prior work. In this work, we propose ShapeShifter, an attack that tackles the more challenging problem of crafting physical adversarial perturbations to fool image-based object detectors like Faster R-CNN. Attacking an object detector is more difficult than attacking an image classifier, as it needs to mislead the classification results in multiple bounding boxes with different scales. Extending the digital attack to the physical world adds another layer of difficulty, because it requires the perturbation to be robust enough to survive real-world distortions due to different viewing distances and angles, lighting conditions, and camera limitations. We show that the Expectation over Transformation technique, which was originally proposed to enhance the robustness of adversarial perturbations in image classification, can be successfully adapted to the object detection setting. ShapeShifter can generate adversarially perturbed stop signs that are consistently mis-detected by Faster R-CNN as other objects, posing a potential threat to autonomous vehicles and other safety-critical computer vision systems.


title: Non-Invasive Glucose Meter for Android-Based Devices

abstract: This study helps in monitoring blood glucose level of a patient with the aid of an android device non-invasively. Diabetes is a metabolic disease characterized by high level of sugar in the blood, and considered as the fastest growing long-term disease affecting millions of people globally. The study measures the blood glucose level using sensor patch through diffused reflectance spectra on the inner side of the forearm. The Arduino microcontroller does the processing of the information from the sensor patch while the Bluetooth module wirelessly transmits to the android device the measured glucose level for storing, interpreting and displaying. Results showed that there is no significant between the measured values using the commercially available glucose meter and the created device. Based on ISO 15197 standard 39 of the 40 trials conducted, or 97.5% fell within the acceptable range.


title: Real time health monitoring of industrial machine using multiclass support vector machine

abstract: Most of the failures in the industrial systems are due to motor faults which can be catastrophic and cause major downtimes. Hence, continuous health monitoring, precise fault detection and advance failure warning for motors are pivotal and cost-effective. The identification of motor faults requires sophisticated signal processing techniques for quick fault detection and isolation. This paper presents a real time health monitoring technique for induction motor using pattern recognition method. The proposed fault detection and isolation scheme comprises three stages: data acquisition, feature extraction and multiclass support vector machine classifier. This paper investigates single and multiple faults in single-phase induction motor including bearing fault, load fault and their combination. The testbed consists of 1/2 hp, 220V squirrel cage induction motor with load, vibration sensor, current sensor, data acquisition system and controller. Two features standard deviation and average value are computed for each sensor's data. Multiclass support vector machine classifier is implemented using a low-cost arduino controller for fault detection and isolation. The performance analysis of the classifier with real time sensor's data is presented which shows superior capabilities of developed method.


title: Printed Drug-Delivery Systems for Improved Patient Treatment

abstract: The use of various types of printing technologies offer potential solutions for personalized medicine and tailored dosage forms to meet the needs of individual treatments of the future. Many types of scenario for printed dosage form exist and the concepts include, on the simplest level, accurately deposited doses of drug substances. In addition, computer design allows endless opportunities to create suitable geometries with tailored functionality and different levels of complexity to control the release properties of one or multiple drug substances. It will take some time to convert these technological developments in printing to better treatments for patients, because challenges exist. However, printing technologies are developing fast and have the potential to allow the use of versatile materials to manufacture sophisticated drug-delivery systems and biofunctional constructs for personalized treatments.


title: Combining Top-Down and Bottom-Up Segmentation

abstract: In this work we show how to combine bottom-up and top-down approaches into a single figure-ground segmentation process. This process provides accurate delineation of object boundaries that cannot be achieved by either the top-down or bottom-up approach alone. The top-down approach uses object representation learned from examples to detect an object in a given input image and provide an approximation to its figure-ground segmentation. The bottom-up approach uses image-based criteria to define coherent groups of pixels that are likely to belong together to either the figure or the background part. The combination provides a final segmentation that draws on the relative merits of both approaches: The result is as close as possible to the top-down approximation, but is also constrained by the bottom-up process to be consistent with significant image discontinuities. We construct a global cost function that represents these top-down and bottom-up requirements. We then show how the global minimum of this function can be efficiently found by applying the sum-product algorithm. This algorithm also provides a confidence map that can be used to identify image regions where additional top-down or bottom-up information may further improve the segmentation. Our experiments show that the results derived from the algorithm are superior to results given by a pure top-down or pure bottom-up approach. The scheme has broad applicability, enabling the combined use of a range of existing bottom-up and top-down segmentations.


title: Benchmarking of LSTM Networks

abstract: LSTM (Long Short-Term Memory) recurrent neural networks have been highly successful in a number of application areas. This technical report describes the use of the MNIST and UW3 databases for benchmarking LSTM networks and explores the effect of different architectural and hyperparameter choices on performance. Significant findings include: (1) LSTM performance depends smoothly on learning rates, (2) batching and momentum has no significant effect on performance, (3) softmax training outperforms least square training, (4) peephole units are not useful, (5) the standard non-linearities (tanh and sigmoid) perform best, (6) bidirectional training combined with CTC performs better than other methods.


title: Cinnamon Converts Poor Learning Mice to Good Learners: Implications for Memory Improvement

abstract: This study underlines the importance of cinnamon, a commonly used natural spice and flavoring material, and its metabolite sodium benzoate (NaB) in converting poor learning mice to good learning ones. NaB, but not sodium formate, was found to upregulate plasticity-related molecules, stimulate NMDA- and AMPA-sensitive calcium influx and increase of spine density in cultured hippocampal neurons. NaB induced the activation of CREB in hippocampal neurons via protein kinase A (PKA), which was responsible for the upregulation of plasticity-related molecules. Finally, spatial memory consolidation-induced activation of CREB and expression of different plasticity-related molecules were less in the hippocampus of poor learning mice as compared to good learning ones. However, oral treatment of cinnamon and NaB increased spatial memory consolidation-induced activation of CREB and expression of plasticity-related molecules in the hippocampus of poor-learning mice and converted poor learners into good learners. These results describe a novel property of cinnamon in switching poor learners to good learners via stimulating hippocampal plasticity.


Analysis of Circular Microstrip Patch Antenna Using Radial Basis Artificial Neural Network


title: Scaling Up Programming by Demonstration for Intelligent Tutoring Systems Development: An Open-Access Web Site for Middle School Mathematics Learning

abstract: Intelligent tutoring systems (ITSs), which provide step-by-step guidance to students in complex problem-solving activities, have been shown to enhance student learning in a range of domains. However, they tend to be difficult to build. Our project investigates whether the process of authoring an ITS can be simplified, while at the same time maintaining the characteristics that make ITS effective, and also maintaining the ability to support large-scale tutor development. Specifically, our project tests whether authoring tools based on programming-by-demonstration techniques (developed in prior research) can support the development of a large-scale, real-world tutor. We are creating an open-access Web site, called Mathtutor (http://webmathtutor.org), where middle school students can solve math problems with step-by-step guidance from ITS. The Mathtutor site fields example-tracing tutors, a novel type of ITS that are built "by demonstration," without programming, using the cognitive tutor authoring tools (CTATs). The project's main contribution will be that it represents a stringent test of large-scale tutor authoring through programming by demonstration. A secondary contribution will be that it tests whether an open-access site (i.e., a site that is widely and freely available) with software tutors for math learning can attract and sustain user interest and learning on a large scale.


title: Danger of injudicious use of tui-na therapy in ankylosing spondylitis

abstract: Tui-na is a very important component of Chinese medicine. It is a well-respected treatment modality known to be helpful and safe for a wide range of conditions. It can be considered as a certain kind of massage which is performed at acupoints, meridians, and collaterals by pushing, finger twisting, grasping thumb waving pressing, patting, palm twisting, and other manipulation techniques. At present, it is extensively used for relieving pain and stiffness associated with ankylosing spondylitis in China, even though there is a lack of evidence to support its validation and feasibility. The patient in this case was treated by tui-na massage at acute flare-up of ankylosing spondylitis and ended up with catastrophic results.


title: Semi-supervised Region Metric Learning for Person Re-identification

abstract: In large-scale camera networks, label information for person re-identification is usually not available under a large amount of cameras due to expensive human labor efforts. Semi-supervised learning could be employed to train a discriminative classifier by using unlabeled data and unmatched image pairs (negatives) generated from non-overlapping camera views, but existing methods suffer from the problem of imbalanced unlabeled data. In this context, this paper proposes a novel semi-supervised region metric learning method to improve person re-identification performance under imbalanced unlabeled data. Firstly, instead of seeking for matched image pairs (positives) from the unlabeled data, we propose to estimate positive neighbors by label propagation with cross person score distribution alignment. Secondly, multiple positive regions are generated using sets of positive neighbors to learn a discriminative region-to-point metric. Experimental results demonstrate that the superiority of the proposed method over existing unsupervised, semi-supervised and person re-identification methods.


title: ICP-based pose-graph SLAM

abstract: Odometry-like localization solutions can be built upon Light Detection And Ranging (LIDAR) sensors, by sequentially registering the point clouds acquired along a robot trajectory. Yet such solutions inherently drift over time: we propose an approach that adds a graphical model layer on top of such LIDAR odometry layer, using the Iterative Closest Points (ICP) algorithm for registration. Reference frames called keyframes are defined along the robot trajectory, and ICP results are used to build a pose graph, that in turn is used to solve an optimization problem that provides updates for the keyframes upon loop closing, enabling the correction of the path of the robot and of the map of the environment. We present in details the configuration used to register data from the Velodyne High Definition LIDAR (HDL), and a strategy to build local maps upon which current frames are registered, either when discovering new areas or revisiting previously mapped areas. Experiments show that it is possible to build the graph using data from ICP and that the loop closings in the graph level reduces the overall drift of the system.


title: A Subthreshold to Above-Threshold Level Shifter Comprising a Wilson Current Mirror

abstract: In this brief, we propose a novel level shifter circuit that is capable of converting subthreshold to above-threshold signal levels. In contrast to other existing implementations, it does not require a static current flow and can therefore offer considerable static power savings. The circuit has been optimized and simulated in a 90-nm process technology. It operates correctly across process corners for supply voltages from 100 mV to 1 V on the low-voltage side. At the target design voltage of 200 mV, the level shifter has a propagation delay of 18.4 ns and a static power dissipation of 6.6 nW. For a 1-MHz input signal, the total energy per transition is 93.9 fJ. Simulation results are compared to an existing subthreshold to above-threshold level shifter implementation from the paper of Chen et al.


title: Understanding metonymies in discourse

abstract: We propose a new computational model for the resolution of metonymies, a particular type of figurative language. Typically, metonymies are considered as a violation of semantic constraints (e.g., those expressed by selectional restrictions) that require some repair mechanism (e.g., type coercion) for proper interpretation. We reject this view, arguing that it misses out on the interpretation of a considerable number of utterances. Instead, we treat literal and figurative language on a par, by computing both kinds of interpretation independently from each other as long as their semantic representation structures are consistent with the underlying knowledge representation structures of the domain of discourse. The following general heuristic principles apply for making reasonable selections from the emerging readings. We argue that the embedding of utterances in a coherent discourse context is as important for recognizing and interpreting metonymic utterances as intrasentential semantic constraints. Therefore, in our approach, (metonymic or literal) interpretations that establish referential cohesion are preferred over ones that do not. In addition, metonymic interpretations that conform to a metonymy schema are preferred over metonymic ones that do not, and metonymic interpretations that are in conformance with knowledge-based aptness conditions are preferred over metonymic ones that are not. We lend further credit to our model by discussing empirical data from an evaluation study which highlights the importance of the discourse embedding of metonymy interpretation for both anaphora and metonymy resolution.


title: Structured variational methods for distributed inference in wireless ad hoc and sensor networks

abstract: In this paper, a variational message passing framework is proposed for Markov random fields, which is computationally more efficient and admits wider applicability compared to the belief propagation algorithm. Based on this framework, structured variational methods are explored to take advantage of both the simplicity of variational approximation (for inter-cluster processing) and the accuracy of exact inference (for intra-cluster processing). Its performance is elaborated on a Gaussian Markov random field, through both theoretical analysis and simulation results.


title: SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking

abstract: Publish/subscribe systems on the traditional Internet suffer from poor scalability and high delay in the face of the Internet of Things (IoT) environment. Being customizable, the paradigm of software-defined networking (SDN) provides a chance to establish an IoT-specific network. In this paper, we propose an SDN-based publish/subscribe system named SDNPS, which can construct and fine-tune topic-connected overlays for the sake of disseminating events efficiently and non-redundantly based on a global topology overview. It organizes topics as a Huffman-like topic tree and codes them into binary strings so that filtering and forwarding events can be operated directly on SDN-configurable switches, which helps to reduce end-to-end latency. This hierarchical organization form of topic tree makes it possible to incrementally construct and store overlays, which contribute to reducing the time and space complexity of routing computation. More specifically, it achieves a better tradeoff between load-balancing of the overall optimization objective and the minimal forwarding cost of per-topic overlay.


title: A Modified Doherty Configuration for Broadband Amplification Using Symmetrical Devices

abstract: A new Doherty amplifier configuration with an intrinsically broadband characteristic is presented based on the synthesis of key ideas derived from the analyses of the load modulation concept and the conventional Doherty amplifier. Important building blocks to implement the proposed Doherty amplifier structure are outlined, which include the quasi-lumped quarter-wave transmission line, as well as the Klopfenstein taper for broadband impedance matching. A 90-W GaN broadband Doherty amplifier was designed and fabricated and achieved an average peak output power of 49.9 dBm, an average gain of 15.3 dB, and average peak and 6-dB back-off efficiencies of 67.3% and 60.6%, respectively, from 700 to 1000 MHz (35.3% bandwidth). The amplifier is shown to be highly linearizable when driven with 20-MHz WCDMA and long-term evolution signals, achieving adjacent channel power ratio of better than -48 dBc after digital predistortion.