title: RSSI-based relative localisation for mobile robots

abstract: In this work, we develop an anchor-less relative localisation algorithm aimed to be used in multi-robot teams. The localisation is performed based on the Received Signal Strength Indicator (RSSI) readings collected from the messages exchanged between nodes. We use the RSSI as a rough estimate of the inverse of distance between any pair of communicating nodes, and we claim that such estimates provide a coarse information of the nodes relative localisation that is still suitable to support several coordination tasks. In addition, we introduce a relative velocity estimation framework based on the RSSI measurements. This framework uses consecutive distance measurements and position estimates to provide the relative velocity vectors for all the nodes in the network. To accomplish this, we propose using a Kalman filter and the Floyd-Warshall algorithm to generate smooth RSSI pairwise signal distance for all nodes. Then we use Multidimensional Scaling to obtain relative positions from the pairwise distances. Finally, due to anchor unavailability, relative positions are adjusted to reflect the continuous mobility by using geometric transformations, thus obtaining smoother trajectories for mobile nodes. This allows us to estimate velocity and to establish a correspondence between orientation in the physical world and in the relative coordinates system. Additionally, we study the impact of several parameters in calculating the network topology, namely different approaches to provide a symmetric distances matrix, the period of the matrix dissemination, the use of synchronisation of the transmissions, and the filtering of the RSSI data. Experimental results, with a set of MicaZ motes, show that the period of matrix dissemination is the most relevant of the parameters, specifically with larger periods providing the best results, however, shorter periods are shown to be possible as long as the transmissions are synchronised.


title: Front Vehicle Detection and Distance Estimation Using Single-Lens Video Camera

abstract: This paper is dedicated to the front vehicle detection and its distance estimation using one single-lens car video camera on the urban & suburb road roads. The proposed method mainly consists of road area detection and the front vehicle detection & distance calculation. Firstly, Hough transform is used to detect lines in which the appropriate straight lines are selected and their intersection points are exploited for obtaining the vanishing point. Then, both strong right and left edges are extracted and connected to the vanishing point for segmenting the road area, and on such area the bottom shadow of the front vehicle is utilized to locate the vehicle's position. Finally, the distance between the host vehicle and the front vehicle is calculated based on the position of a vehicle and the vanishing point. Experimental results show that the proposed technique can moderately detect the front vehicles on the urban & suburb roads with detection rate of 78% at least.


title: Oral manifestations of secondary syphilis in the elderly - a timely reminder for dentists

abstract: Syphilis is a sexually transmitted infectious disease caused by Treponema pallidum. Cases of syphilis have increased in frequency and are challenging when affecting the elderly. The main causes of increased prevalence of syphilis are sexual promiscuity, lack of knowledge about the disease and decreasing use of barrier protection. Clinically, the oral manifestation of syphilis may resemble other entities, which hampers the correct diagnosis. We report a case of a 79-year-old male with weight loss and feeding difficulties. In the oral cavity there were ulcerative lesions in the hard palate and bilaterally in the buccal mucosa. The incisional biopsy revealed only a non-specific ulceration of the oral mucosa. After 20 days, the patient was re-evaluated and presented maculopapular lesions in the palmar and plantar areas. Positive serological venereal disease reference laboratory (VDRL) tests confirmed the diagnosis of secondary syphilis. The patient was treated with Benzathine penicillin G. After two weeks of treatment the oral lesion disappeared and the patient returned to normal feeding and gained weight. This case report reinforces the need to alert physicians and dentists to include sexually transmitted infections such as syphilis in the differential diagnosis of oral ulcerative lesions in elderly sexually active patients.


title: Sensor relocation in mobile sensor networks

abstract: Recently there has been a great deal of research on using mobility in sensor networks to assist in the initial deployment of nodes. Mobile sensors are useful in this environment because they can move to locations that meet sensing coverage requirements. This paper explores the motion capability to relocate sensors to deal with sensor failure or respond to new events. We define the problem of sensor relocation and propose a two-phase sensor relocation solution: redundant sensors are first identified and then relocated to the target location. We propose a Grid-Quorum solution to quickly locate the closest redundant sensor with low message complexity, and propose to use cascaded movement to relocate the redundant sensor in a timely, efficient and balanced way. Simulation results verify that the proposed solution outperforms others in terms of relocation time, total energy consumption, and minimum remaining energy.


title: Perfusion CT in Acute Stroke: A Comprehensive Analysis of Infarct and Penumbra

abstract: In a well-characterized and large data set, we have found considerable variation in the accuracy of core and penumbra detection, and the perfusion thresholds used to determine them, with commonly used perfusion algorithms.


title: A band-notched UWB log-periodic dipole antenna fed by strip line

abstract: This paper presents a compact ultra-wideband (UWB) antenna fed by strip line. The antenna is a log-periodic dipole antenna with 18 elements. Simulation by Ansoft High Frequency Structure Simulator (HFSS) 10.0, the in-band impedances are quite stable and satisfactory. A rejected narrow frequency band is further obtained within the wideband width by inserting an H-shaped slot into the central fed line of the antenna. The antenna has a dimension of 47.8mm by 42 mm by 2mm. The simulation results show that the proposed antenna has stable directional radiation patterns, very low-profile and low fabrication cost, which are suitable for UWB system.


title: Learning to gather information via imitation

abstract: The budgeted information gathering problem — where a robot with a fixed fuel budget is required to maximize the amount of information gathered from the world — appears in practice across a wide range of applications in autonomous exploration and inspection with mobile robots. Although there is an extensive amount of prior work investigating effective approximations of the problem, these methods do not address the fact that their performance is heavily dependent on distribution of objects in the world. In this paper, we attempt to address this issue by proposing a novel data-driven imitation learning framework. We present an efficient algorithm, EXPLORE, that trains a policy on the target distribution to imitate a clairvoyant oracle — an oracle that has full information about the world and computes non-myopic solutions to maximize information gathered. We validate the approach on a spectrum of results on a number of 2D and 3D exploration problems that demonstrates the ability of EXPLORE to adapt to different object distributions. Additionally, our analysis provides theoretical insight into the behavior of EXPLORE. Our approach paves the way forward for efficiently applying data-driven methods to the domain of information gathering.


title: ESTScan: A Program for Detecting, Evaluating, and Reconstructing Potential Coding Regions in EST Sequences

abstract: One of the problems associated with the large-scale analysis of unannotated, low quality EST sequences is the detection of coding regions and the correction of frameshift errors that they often contain. We introduce a new type of hidden Markov model that explicitly deals with the possibility of errors in the sequence to analyze, and incorporates a method for correcting these errors. This model was implemented in an efficient and robust program, ESTScan. We show that ESTScan can detect and extract coding regions from low-quality sequences with high selectivity and sensitivity, and is able to accurately correct frameshift errors. In the framework of genome sequencing projects, ESTScan could become a very useful tool for gene discovery, for quality control, and for the assembly of contigs representing the coding regions of genes.


title: Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings

abstract: A review and meta-analysis was performed of seventy-five articles concerned with innovation characteristics and their relationship to innovation adoption and implementation. One part of the analysis consisted of constructing a methodological profile of the existing studies, and contrasting this with a hypothetical optimal approach. A second part of the study employed meta-analytic statistical techniques to assess the generality and consistency of existing empirical findings. Three innovation characteristics (compatibility, relative advantage, and complexity) had the most consistent significant relationship to innovation adoption. Suggestions for future research in the area were made.


title: Prevention of Cooperative Black Hole Attack in Wireless Ad Hoc Networks

abstract: Mobile ad hoc networks (MANETs) are extensively used in military and civilian applications. The dynamic topology of MANETs allows nodes to join and leave the network at any point of time. This generic characteristic of MANET has rendered it vulnerable to security attacks. In this paper, we address the problem of coordinated attack by multiple black holes acting in group. We present a technique to identify multiple black holes cooperating with each other and a solution to discover a safe route avoiding cooperative black hole attack.


title: High-Temperature GaN Active-Clamp Flyback Converter With Resonant Operation Mode

abstract: An isolated active-clamp flyback power supply with a new resonant operation mode is designed and experimentally tested. A full soft-switching design based on dead-time optimization new approach is implemented. Each topology phase is detailed. An experimental board is presented for high ambient temperature (200 °C) operation. The power supply is dedicated to provide the driver power of wide bandgap power components in a phase-leg configuration. The constraint for galvanic isolation is high and guides the design of the transformer. The power-supply capabilities are 15 V input voltage, 6 V output voltage, 2 W output power, 75% peak efficiency, 11 cm2 footprint, and nominal operation at 200 °C ambient temperature.


title: Practicing a Science of Security: A Philosophy of Science Perspective

abstract: Our goal is to refocus the question about cybersecurity research from 'is this process scientific' to 'why is this scientific process producing unsatisfactory results'. We focus on five common complaints that claim cybersecurity is not or cannot be scientific. Many of these complaints presume views associated with the philosophical school known as Logical Empiricism that more recent scholarship has largely modified or rejected. Modern philosophy of science, supported by mathematical modeling methods, provides constructive resources to mitigate all purported challenges to a science of security. Therefore, we argue the community currently practices a science of cybersecurity. A philosophy of science perspective suggests the following form of practice: structured observation to seek intelligible explanations of phenomena, evaluating explanations in many ways, with specialized fields (including engineering and forensics) constraining explanations within their own expertise, inter-translating where necessary. A natural question to pursue in future work is how collecting, evaluating, and analyzing evidence for such explanations is different in security than other sciences.


title: Additively Manufactured Scaffolds for Bone Tissue Engineering and the Prediction of their Mechanical Behavior: A Review

abstract: Additive manufacturing (AM), nowadays commonly known as 3D printing, is a revolutionary materials processing technology, particularly suitable for the production of low-volume parts with high shape complexities and often with multiple functions. As such, it holds great promise for the fabrication of patient-specific implants. In recent years, remarkable progress has been made in implementing AM in the bio-fabrication field. This paper presents an overview on the state-of-the-art AM technology for bone tissue engineering (BTE) scaffolds, with a particular focus on the AM scaffolds made of metallic biomaterials. It starts with a brief description of architecture design strategies to meet the biological and mechanical property requirements of scaffolds. Then, it summarizes the working principles, advantages and limitations of each of AM methods suitable for creating porous structures and manufacturing scaffolds from powdered materials. It elaborates on the finite-element (FE) analysis applied to predict the mechanical behavior of AM scaffolds, as well as the effect of the architectural design of porous structure on its mechanical properties. The review ends up with the authors’ view on the current challenges and further research directions.


title: Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification

abstract: We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the other two visualize network activations on a spatial map. Visual checks and a quantitative localization benchmark indicate that all approaches identify important brain parts for Alzheimer's disease diagnosis. Comparative analysis show that the sensitivity analysis based approach has difficulty handling loosely distributed cerebral cortex, and approaches based on visualization of activations are constrained by the resolution of the convolutional layer. The complementarity of these methods improves the understanding of 3D-CNNs in Alzheimer's disease classification from different perspectives.


Automatic detection of creaky voice using epoch parameters.


title: A Decomposable Attention Model for Natural Language Inference

abstract: We propose a simple neural architecture for natural language inference. Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable. On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information. Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements.


title: Physics-of-Failure, Condition Monitoring, and Prognostics of Insulated Gate Bipolar Transistor Modules: A Review

abstract: Recent growth of the insulated gate bipolar transistor (IGBT) module market has been driven largely by the increasing demand for an efficient way to control and distribute power in the field of renewable energy, hybrid/electric vehicles, and industrial equipment. For safety-critical and mission-critical applications, the reliability of IGBT modules is still a concern. Understanding the physics-of-failure of IGBT modules has been critical to the development of effective condition monitoring (CM) techniques as well as reliable prognostic methods. This review paper attempts to summarize past developments and recent advances in the area of CM and prognostics for IGBT modules. The improvement in material, fabrication, and structure is described. The CM techniques and prognostic methods proposed in the literature are presented. This paper concludes with recommendations for future research topics in the CM and prognostics areas.


Vaccination against Bm86 Homologues in Rabbits Does Not Impair Ixodes ricinus Feeding or Oviposition


title: High-precision camera distortion measurements with a"calibration harp"

abstract: This paper addresses the high-precision measurement of the distortion of a digital camera from photographs. Traditionally, this distortion is measured from photographs of a flat pattern that contains aligned elements. Nevertheless, it is nearly impossible to fabricate a very flat pattern and to validate its flatness. This fact limits the attainable measurable precisions. In contrast, it is much easier to obtain physically very precise straight lines by tightly stretching good quality strings on a frame. Taking literally “plumb-line methods,” we built a “calibration harp” instead of the classic flat patterns to obtain a high-precision measurement tool, demonstrably reaching 2/100 pixel precisions. The harp is complemented with the algorithms computing automatically from harp photographs two different and complementary lens distortion measurements. The precision of the method is evaluated on images corrected by state-of-the-art distortion correction algorithms, and by popular software. Three applications are shown: first an objective and reliable measurement of the result of any distortion correction. Second, the harp permits us to control state-of-the art global camera calibration algorithms: it permits us to select the right distortion model, thus avoiding internal compensation errors inherent to these methods. Third, the method replaces manual procedures in other distortion correction methods, makes them fully automatic, and increases their reliability and precision.


title: SCREAM: sketch resource allocation for software-defined measurement

abstract: Software-defined networks can enable a variety of concurrent, dynamically instantiated, measurement tasks, that provide fine-grain visibility into network traffic. Recently, there have been many proposals for using sketches for network measurement. However, sketches in hardware switches use constrained resources such as SRAM memory, and the accuracy of measurement tasks is a function of the resources devoted to them on each switch. This paper presents SCREAM, a system for allocating resources to sketch-based measurement tasks that ensures a user-specified minimum accuracy. SCREAM estimates the instantaneous accuracy of tasks so as to dynamically adapt the allocated resources for each task. Thus, by finding the right amount of resources for each task on each switch and correctly merging sketches at the controller, SCREAM can multiplex resources among network-wide measurement tasks. Simulations with three measurement tasks (heavy hitter, hierarchical heavy hitter, and super source/destination detection) show that SCREAM can support more measurement tasks with higher accuracy than existing approaches.


title: A multi-stream ASR framework for BLSTM modeling of conversational speech

abstract: We propose a novel multi-stream framework for continuous conversational speech recognition which employs bidirectional Long Short-Term Memory (BLSTM) networks for phoneme prediction. The BLSTM architecture allows recurrent neural nets to model long-range context, which led to improved ASR performance when combined with conventional triphone modeling in a Tandem system. In this paper, we extend the principle of joint BLSTM and triphone modeling to a multi-stream system which uses MFCC features and BLSTM predictions as observations originating from two independent data streams. Using the COSINE database, we show that this technique prevails over a recently proposed single-stream Tandem system as well as over a conventional HMM recognizer.


title: Google Driverless Car

abstract: This paper explores the impact that has been working towards the goal of vehicles that can shoulder the entire burden of driving. Google driverless cars are designed to operate safely and autonomously without requiring human intervention. They won’t have a steering wheel, accelerator or a brake pedal because they don’t need them, software and sensors do all the work. It takes you where you want to go at the push of a button. This Technology step towards improving road safety and transforming mobility for millions of people.


title: Natural Language Processing: A Historical Review

abstract: This paper reviews natural language processing (NLP) from the late 1940’s to the present, seeking to identify its successive trends as these reflect concerns with different problems or the pursuit of different approaches to solving these problems and building systems as wholes. The review distinguishes four phases in the history of NLP, characterised respectively by an emphasis on machine translation, by the influence of artificial intelligence, by the adoption of a logico-grammatical style, and by an attack on massive language data. The account considers the significant and salient work in each phase, and concludes with an assessment of where we stand after more than forty years of effort in the field.


title: Grid Computing for Bioinformatics and Computational Biology

abstract: Preface. Chapter 1: Open computing Grid for molecular sciences (M. Romberg, E. Benfenati, and W. Dubitzky). Chapter 2: Designing high-performance concurrent strategies for biological sequence alignment problems on networked computing platforms (B. Veeravalli). Chapter 3: Optimized cluster-enabled HMMER searches (J. P. Walters, J. Landman, and V. Chaudhary). Chapter 4: Expanding the rich of Grid computing: combining Globus and BOINC based systems (D. S. Myers, A. L. Bazinet, and M. P. Cummings). Chapter 5: Hierarchical Grid computing for high performance bioinformatics (B. Schmidt, C.X. Chen and W. Liu). Chapter 6:Multiple sequence alignment and phylogenetic inference (D. Trystram, and J. Zola). Chapter 7: Data syndication techniques for bioinformatics applications (C. Wang, A. Y. Zomaya, and B. B. Zhou). Chapter 8: Conformational sampling and docking on Grids (A. Tantar, N. Melab, and E-G. Talbi). Chapter 9: Deployment of Grid life sciences applications (V. Breton, N. Jacq , V. Kasam, and J. Salzemann). Chapter 10: Grid-based interactive decision support in biomedicine (A. Tirado-Ramos, P. M. A. Sloot, and M. Bubak). Chapter 11: Database-driven grid computing and distributed web applications: a comparison (H. De Sterck, A.Papo, C. Zhang, M. Hamady, and R. Knight). Chapter 12: A semantic mediation architecture for a clinical Data Grid (K. Kumpf, A. Wohrer, S. Benkner, G. Engelbrecht, and Jochen Fingberg). Chapter 13: Bioinformatics applications in Grid computing environments (A. Boukerche, A. C. Magalhaes and Alves De Melo). Chapter 14: Recent advances in solving the protein threading problem (R. Andonov, G. Collet, J-F. Gibrat, A. Marin, V. Poirriez, and N. Yanev). Chapter 15: DNA fragment assembly using Grid systems (A. J. Nebro, G. Luque, and E. Alba). Chapter 16: Seeing is knowing: Visualization of parameter-parameter dependencies in biomedical network models (A. Konagaya, R. Azuma, R. Umetsu, S. Ohki, F. Konishi, K. Matsumura, and S. Yoshikawa).


title: Malware Detection Using Perceptrons and Support Vector Machines

abstract: In this paper we explore the capabilities of a framework that can use different machine learning algorithms to successfully detect malware files, aiming to minimize the number of false positives. We report the results obtained in our framework, working firstly with cascades of one-sided perceptron and kernelized one-sides perceptrons and secondly with cascade of one-sided support vector machines.


title: Recognizing complex activities by a probabilistic interval-based model

abstract: A key challenge in complex activity recognition is the fact that a complex activity can often be performed in several different ways, with each consisting of its own configuration of atomic actions and their temporal dependencies. This leads us to define an atomic activity-based probabilistic framework that employs Allen's interval relations to represent local temporal dependencies. The framework introduces a latent variable from the Chinese Restaurant Process to explicitly characterize these unique internal configurations of a particular complex activity as a variable number of tables. It can be analytically shown that the resulting interval network satisfies the transitivity property, and as a result, all local temporal dependencies can be retained and are globally consistent. Empirical evaluations on benchmark datasets suggest our approach significantly outperforms the state-of-the-art methods.


title: ISBN 978-80-7378-067-8 © MATFYZPRESS Au Interaction with CeO2 (111) Thin Film

abstract: The interaction of gold with CeO2(111) layer was investigated with photeelectron spectroscopy (PES) and by means of resonant photoelectron spectroscopy (RPES). Gold was deposited in steps onto a 1.5 nm thick CeO2(111) layer, epitaxially grown on a Cu(111) substrate. The RPES showed a partial Ce 4+ → Ce 3+ reduction, observed as a resonant enhancement of the 4f level of the Ce 3+ species. This can be explained by creation of a new Au + ionic state located in Au 4f spectra. The observed effects are stronger for Au deposition at room temperature than 250 °C. The results can be interpreted as a formation of a CeAuxO2 compound produced by gold - cerium oxide interaction. The formed compound is probably located at the gold - cerium oxide interface.


Conjugate Priors for Exponential Families Having Cubic Variance Functions


title: Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases

abstract: A new image feature description based on the local wavelet pattern (LWP) is proposed in this paper to characterize the medical computer tomography (CT) images for content-based CT image retrieval. In the proposed work, the LWP is derived for each pixel of the CT image by utilizing the relationship of center pixel with the local neighboring information. In contrast to the local binary pattern that only considers the relationship between a center pixel and its neighboring pixels, the presented approach first utilizes the relationship among the neighboring pixels using local wavelet decomposition, and finally considers its relationship with the center pixel. A center pixel transformation scheme is introduced to match the range of center value with the range of local wavelet decomposed values. Moreover, the introduced local wavelet decomposition scheme is centrally symmetric and suitable for CT images. The novelty of this paper lies in the following two ways: 1) encoding local neighboring information with local wavelet decomposition and 2) computing LWP using local wavelet decomposed values and transformed center pixel values. We tested the performance of our method over three CT image databases in terms of the precision and recall. We also compared the proposed LWP descriptor with the other state-of-the-art local image descriptors, and the experimental results suggest that the proposed method outperforms other methods for CT image retrieval.


title: Memory systems do not divide on consciousness: reinterpreting memory in terms of activation and binding

abstract: There is a popular hypothesis that performance on implicit and explicit memory tasks reflects 2 distinct memory systems. Explicit memory is said to store those experiences that can be consciously recollected, and implicit memory is said to store experiences and affect subsequent behavior but to be unavailable to conscious awareness. Although this division based on awareness is a useful taxonomy for memory tasks, the authors review the evidence that the unconscious character of implicit memory does not necessitate that it be treated as a separate system of human memory. They also argue that some implicit and explicit memory tasks share the same memory representations and that the important distinction is whether the task (implicit or explicit) requires the formation of a new association. The authors review and critique dissociations from the behavioral, amnesia, and neuroimaging literatures that have been advanced in support of separate explicit and implicit memory systems by highlighting contradictory evidence and by illustrating how the data can be accounted for using a simple computational memory model that assumes the same memory representation for those disparate tasks.


title: How are WEEE doing? A global review of the management of electrical and electronic wastes.

abstract: This paper presents and critically analyses the current waste electrical and electronic equipment (WEEE) management practices in various countries and regions. Global trends in (i) the quantities and composition of WEEE; and (ii) the various strategies and practices adopted by selected countries to handle, regulate and prevent WEEE are comprehensively examined. The findings indicate that for (i), the quantities of WEEE generated are high and/or on the increase. IT and telecommunications equipment seem to be the dominant WEEE being generated, at least in terms of numbers, in Africa, in the poorer regions of Asia and in Latin/South America. However, the paper contends that the reported figures on quantities of WEEE generated may be grossly underestimated. For (ii), with the notable exception of Europe, many countries seem to be lacking or are slow in initiating, drafting and adopting WEEE regulations. Handling of WEEE in developing countries is typified by high rate of repair and reuse within a largely informal recycling sector. In both developed and developing nations, the landfilling of WEEE is still a concern. It has been established that stockpiling of unwanted electrical and electronic products is common in both the USA and less developed economies. The paper also identifies and discusses four common priority areas for WEEE across the globe, namely: (i) resource depletion; (ii) ethical concerns; (iii) health and environmental issues; and (iv) WEEE takeback strategies. Further, the paper discusses the future perspectives on WEEE generation, treatment, prevention and regulation. Four key conclusions are drawn from this review: global amounts of WEEE will continue unabated for some time due to emergence of new technologies and affordable electronics; informal recycling in developing nations has the potential of making a valuable contribution if their operations can be changed with strict safety standards as a priority; the pace of initiating and enacting WEEE specific legislation is very slow across the globe and in some cases non-existent; and globally, there is need for more accurate and current data on amounts and types of WEEE generated.


title: A 5.5-7.3 GHz Analog Fractional-N Sampling PLL in 28-nm CMOS with 75 fsrmsJitter and −249.7 dB FoM

abstract: We present a low jitter, DTC-based analog fractional-N PLL with novel, background DTC gain calibration and reference clock duty cycle correction for high performance applications. The PLL achieves a 75-fs rms jitter, integrated from 10 kHz to 10 MHz and a −249.7 dB figure of merit at fractional mode. The measured fractional-N spurs are less than −64 dBc across the 5.5-7.3 GHz output frequency. Implemented in 28-nm CMOS, this PLL consumes 18.9 mW and occupies 0.5 mm2.


title: Automatic photo-from-panorama for Google Maps

abstract: We introduce a technique for extracting interesting photographs from 360° panoramas. We build on the success of convolutional neural networks for classification to train a model that scores a given view, using this score to find a best view. Training data for this classification model is generated automatically from landmark detections within Street View panoramas. We validate that our selected views are often preferred over manually chosen ones and have experienced an increase in user interaction when automatically selected views are shown on Google Maps.


title: Neural Network Encapsulation

abstract: A capsule is a collection of neurons which represents different variants of a pattern in the network. The routing scheme ensures only certain capsules which resemble lower counterparts in the higher layer should be activated. However, the computational complexity becomes a bottleneck for scaling up to larger networks, as lower capsules need to correspond to each and every higher capsule. To resolve this limitation, we approximate the routing process with two branches: a master branch which collects primary information from its direct contact in the lower layer and an aide branch that replenishes master based on pattern variants encoded in other lower capsules. Compared with previous iterative and unsupervised routing scheme, these two branches are communicated in a fast, supervised and one-time pass fashion. The complexity and runtime of the model are therefore decreased by a large margin. Motivated by the routing to make higher capsule have agreement with lower capsule, we extend the mechanism as a compensation for the rapid loss of information in nearby layers. We devise a feedback agreement unit to send back higher capsules as feedback. It could be regarded as an additional regularization to the network. The feedback agreement is achieved by comparing the optimal transport divergence between two distributions (lower and higher capsules). Such an add-on witnesses a unanimous gain in both capsule and vanilla networks. Our proposed EncapNet performs favorably better against previous state-of-the-arts on CIFAR10/100, SVHN and a subset of ImageNet.


title: The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks

abstract: Recently, more and more social network data have been published in one way or another. Preserving privacy in publishing social network data becomes an important concern. With some local knowledge about individuals in a social network, an adversary may attack the privacy of some victims easily. Unfortunately, most of the previous studies on privacy preservation data publishing can deal with relational data only, and cannot be applied to social network data. In this paper, we take an initiative toward preserving privacy in social network data. Specifically, we identify an essential type of privacy attacks: neighborhood attacks. If an adversary has some knowledge about the neighbors of a target victim and the relationship among the neighbors, the victim may be re-identified from a social network even if the victim’s identity is preserved using the conventional anonymization techniques. To protect privacy against neighborhood attacks, we extend the conventional k-anonymity and l-diversity models from relational data to social network data. We show that the problems of computing optimal k-anonymous and l-diverse social networks are NP-hard. We develop practical solutions to the problems. The empirical study indicates that the anonymized social network data by our methods can still be used to answer aggregate network queries with high accuracy.


title: A restart CMA evolution strategy with increasing population size

abstract: In this paper we introduce a restart-CMA-evolution strategy, where the population size is increased for each restart (IPOP). By increasing the population size the search characteristic becomes more global after each restart. The IPOP-CMA-ES is evaluated on the test suit of 25 functions designed for the special session on real-parameter optimization of CEC 2005. Its performance is compared to a local restart strategy with constant small population size. On unimodal functions the performance is similar. On multi-modal functions the local restart strategy significantly outperforms IPOP in 4 test cases whereas IPOP performs significantly better in 29 out of 60 tested cases.


title: Decorrelation in Interferometric Radar Echoes

abstract: A radar interferometric technique for topographic mapping of surfaces, implemented utilizing a single synthetic aperture radar (SAR) system in a nearly repeating orbit, is discussed. The authors characterize the various sources contributing to the echo correlation statistics, and isolate the term which most closely describes surficial change. They then examine the application of this approach to topographic mapping of vegetated surfaces which may be expected to possess varying backscatter over time. It is found that there is decorrelation increasing with time but that digital terrain model generation remains feasible. The authors present such a map of a forested area in Oregon which also includes some nearly unvegetated lava flows. Such a technique could provide a global digital terrain map. >


title: Challenges in 5G: how to empower SON with big data for enabling 5G

abstract: While an al dente character of 5G is yet to emerge, network densification, miscellany of node types, split of control and data plane, network virtualization, heavy and localized cache, infrastructure sharing, concurrent operation at multiple frequency bands, simultaneous use of different medium access control and physical layers, and flexible spectrum allocations can be envisioned as some of the potential ingredients of 5G. It is not difficult to prognosticate that with such a conglomeration of technologies, the complexity of operation and OPEX can become the biggest challenge in 5G. To cope with similar challenges in the context of 3G and 4G networks, recently, self-organizing networks, or SONs, have been researched extensively. However, the ambitious quality of experience requirements and emerging multifarious vision of 5G, and the associated scale of complexity and cost, demand a significantly different, if not totally new, approach toward SONs in order to make 5G technically as well as financially feasible. In this article we first identify what challenges hinder the current self-optimizing networking paradigm from meeting the requirements of 5G. We then propose a comprehensive framework for empowering SONs with big data to address the requirements of 5G. Under this framework we first characterize big data in the context of future mobile networks, identifying its sources and future utilities. We then explicate the specific machine learning and data analytics tools that can be exploited to transform big data into the right data that provides a readily useable knowledge base to create end-to-end intelligence of the network. We then explain how a SON engine can build on the dynamic models extractable from the right data. The resultant dynamicity of a big data empowered SON (BSON) makes it more agile and can essentially transform the SON from being a reactive to proactive paradigm and hence act as a key enabler for 5G's extremely low latency requirements. Finally, we demonstrate the key concepts of our proposed BSON framework through a case study of a problem that the classic 3G/4G SON fails to solve.


title: Intelligent diagnosis of open and short circuit faults in electric drive inverters for real-time applications

abstract: This study presents a machine learning technique for fault diagnostics in induction motor drives. A normal model and an extensive range of faulted models for the inverter-motor combination were developed and implemented using a generic commercial simulation tool to generate voltages and current signals at a broad range of operating points selected by a machine learning algorithm. A structured neural network system has been designed, developed and trained to detect and isolate the most common types of faults: single switch open circuit faults, post short-circuits, short circuits and the unknown faults. Extensive simulation experiments were conducted to test the system with added noise, and the results show that the structured neural network system which was trained by using the proposed machine learning approach gives high accuracy in detecting whether a faulty condition has occurred, thus isolating and pin-pointing to the type of faulty conditions occurring in power electronics inverter-based electrical drives. Finally, the authors show that the proposed structured neural network system has the capability of real-time detection of any of the faulty conditions mentioned above within 20 ms or less.


title: Through-Wall Opportunistic Sensing System Utilizing a Low-Cost Flat-Panel Array

abstract: A UWB through-wall imaging system is proposed based on a planar low profile aperture array operating from 0.9 GHz to 2.3 GHz. The goal is to provide a lightweight, fixed array to serve as an alternative to synthetic aperture radars (SAR) that require continuous array movement while collecting data. The proposed system consists of 12 dual-linear printed elements arranged within a triangular lattice, each forming a “flower” shape and backed by a ground plane. The array delivers half-space radiation with wideband performance, necessary for imaging applications. UWB capability is realized by suppressing grating lobes via the introduction of virtual phase centers interwoven within the actual array feeds. The proposed system is demonstrated for through-wall imaging via a non-coherent process. Distinctively, several coherent images are forged from various fixed aperture locations (referred to as “snapshot” locations) and appropriately combined to create a composite scene image. In addition to providing a unique wideband imaging capability (as an alternative to SAR), the system is portable and inexpensive for collecting/storing scattering data. The array design and data collection system is described, and several through-wall images are presented to demonstrate functionality.


An Efficient Architectural Realization of a Specialization Engine for Neural Networks


title: Extending the possibilities of corpus-based research on English in the twentieth century: a prequel to LOB and FLOB

abstract: This paper explains the rationale for a new corpus being assembled at Lancaster University to complement the existing Brown ‘family’ of corpora: that is, English language corpora modelled on the original Brown University corpus, such as LOB, Frown, FLOB, Wellington, etc. The purpose of the new corpus, called Lancaster1931, is to extend the chronological span of these corpora into the first half of the twentieth century, and so to afford researchers a stronger empirical basis for examining recent changes in grammatical usage in English. We discuss some methodological issues encountered in extending the Brown model to earlier historical periods. We also outline some developments under way to permit more rigorous computer-assisted analyses within and across these corpora, namely (i) encoding of all the corpora with XML, (ii) adoption of a common grammatical tagset, known as ‘C8’, and (iii) implementation of a semantic annotation scheme.


Genetic Clustering Algorithms for Detecting Money-Laundering


title: A SIP-based conference control framework

abstract: Conference control has been an area of intensive research over the years but widely accepted robust and scalable solutions and standards are still lacking. The main conference control components are conference management and floor (resource) control. We identify the requirements for conference control and propose a component-based scalable conference control framework employing the Session Initiation Protocol (SIP) and the Simple Object Access Protocol (SOAP). The framework assumes a single control point, but our architecture can scale to large groups by distributing media via a tree-shaped hierarchy of conference servers.


title: Multiple Kernel Fuzzy Clustering

abstract: While fuzzy c-means is a popular soft-clustering method, its effectiveness is largely limited to spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to address this problem by mapping data with nonlinear relationships to appropriate feature spaces. Kernel combination, or selection, is crucial for effective kernel clustering. Unfortunately, for most applications, it is uneasy to find the right combination. We propose a multiple kernel fuzzy c-means (MKFC) algorithm that extends the fuzzy c-means algorithm with a multiple kernel-learning setting. By incorporating multiple kernels and automatically adjusting the kernel weights, MKFC is more immune to ineffective kernels and irrelevant features. This makes the choice of kernels less crucial. In addition, we show multiple kernel k-means to be a special case of MKFC. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed MKFC algorithm.


How big are the tax benefits of debt?


title: Visual and semantic similarity in ImageNet

abstract: Many computer vision approaches take for granted positive answers to questions such as “Are semantic categories visually separable?” and “Is visual similarity correlated to semantic similarity?”. In this paper, we study experimentally whether these assumptions hold and show parallels to questions investigated in cognitive science about the human visual system. The insights gained from our analysis enable building a novel distance function between images assessing whether they are from the same basic-level category. This function goes beyond direct visual distance as it also exploits semantic similarity measured through ImageNet. We demonstrate experimentally that it outperforms purely visual distances.


title: Social Media: A Tool for Open Innovation

abstract: Despite the exponential rise of social media use in external stakeholder engagement, academic research and managerial practice have paid little attention to how it can be used for open innovation across the entire innovation funnel, spanning ideation, R&D, and commercialization. As a result, there is little understanding of how companies can organize for and implement social media for open innovation. Utilizing a multiple case study design, this article examines its application across the entire innovation process. It proposes a range of organizational and technological adaptations that managers can implement to ensure they realize the innovative benefits of social media application.


title: Energy and Memory Efficient Mapping of Bitonic Sorting on FPGA

abstract: Parallel sorting networks are widely employed in hardware implementations for sorting due to their high data parallelism and low control overhead. In this paper, we propose an energy and memory efficient mapping methodology for implementing bitonic sorting network on FPGA. Using this methodology, the proposed sorting architecture can be built for a given data parallelism while supporting continuous data streams. We propose a streaming permutation network (SPN) by "folding" the classic Clos network. We prove that the SPN is programmable to realize all the interconnection patterns in the bitonic sorting network. A low cost design for sorting with minimal resource usage is obtained by reusing one SPN . We also demonstrate a high throughput design by trading off area for performance. With a data parallelism of p (2 ≤ p ≤ N/ log2 N), the high throughput design sorts an N-key sequence with latency O(N/p), throughput (# of keys sorted per cycle) O(p) and uses O(N) memory. This achieves optimal memory efficiency (defined as the ratio of throughput to the amount of on-chip memory used by the design) of O(p/N). Another noteworthy feature of the high throughput design is that only single-port memory rather than dual-port memory is required for processing continuous data streams. This results in 50% reduction in memory consumption. Post place-and-route results show that our architecture demonstrates 1.3x ∼1.6x improvment in energy efficiency and 1.5x ∼ 5.3x better memory efficiency compared with the state-of-the-art designs.


title: Challenges in a future IP/ethernet-based in-car network for real-time applications

abstract: In current vehicles, a large number of control units are connected by several automotive specific communication buses, facilitating innovative distributed applications. At the same time, computers and entertainment devices use IP and commodity communications technology like Ethernet to connect to the Internet, allowing for innovative solutions and maintaining fast innovation cycles. Today, one can see first applications of Ethernet for in-vehicle communication in contemporary cars. In next generation vehicles, many innovative applications could benefit from the increased bandwidth Ethernet can offer. Therefore, a examination of Ethernet usage for additional in-vehicle communication use cases is needed. In this paper, we show simulation results of promising use cases for in-car Ethernet, while looking at different realistic topologies, types of traffic, and configurations.


title: Automatic Personality and Interaction Style Recognition from Facebook Profile Pictures

abstract: In this paper, we address the issue of personality and interaction style recognition from profile pictures in Facebook. We recruited volunteers among Facebook users and collected a dataset of profile pictures, labeled with gold standard self-assessed personality and interaction style labels. Then, we exploited a bag-of-visual-words technique to extract features from pictures. Finally, different machine learning approaches were used to test the effectiveness of these features in predicting personality and interaction style traits. Our good results show that this task is very promising, because profile pictures convey a lot of information about a user and are directly connected to impression formation and identity management.


title: Do miniscrews remain stationary under orthodontic forces?

abstract: Miniscrews have been used in recent years for anchorage in orthodontic treatment. However, it is not clear whether the miniscrews are absolutely stationary or move when force is applied. Sixteen adult patients with miniscrews (diameter = 2 mm, length = 17 mm) as the maxillary anchorage were included in this study. Miniscrews were inserted on the maxillary zygomatic buttress as a direct anchorage for en masse anterior retraction. Nickel-titanium closed-coil springs were placed for the retraction 2 weeks after insertion of the miniscrews. Cephalometric radiographs were taken immediately before force application (T1) and 9 months later (T2). The cephalometric tracings at T1 and T2 were superimposed for the overall best fit on the structures of the maxilla, cranial base, and cranial vault to determine any movement of the miniscrews. The miniscrews were also evaluated clinically for their mobility (0: no movement, 1: < or =0.5 mm, 2: 0.5-1.0 mm, 3: >1.0 mm). The mobility of all miniscrews was 0 at T1 and T2. On average, the miniscrews tipped forward significantly, by 0.4 mm at the screw head. The miniscrews were extruded and tipped forward (-1.0 to 1.5 mm) in 7 of the 16 patients. Miniscrews are a stable anchorage but do not remain absolutely stationary throughout orthodontic loading. They might move according to the orthodontic loading in some patients. To prevent miniscrews hitting any vital organs because of displacement, it is recommended that they be placed in a non-tooth-bearing area that has no foramen, major nerves, or blood vessel pathways, or in a tooth-bearing area allowing 2 mm of safety clearance between the miniscrew and dental root.


title: Genital Injuries Acute Evaluation And Management

abstract: Abstract Genital trauma may result in external injuries to the labia, vulva or vagina, urethra and anus and internal injuries to the bony pelvis, bladder, bowels and reproductive organs. Worldwide, the most common cause of genital trauma in reproductive age women is injury sustained during childbirth, but in this chapter we will focus on accidental genital injuries as well as those arising from sexual violence, and female genital mutilation. While genital injuries alone rarely result in death; if not properly managed, chronic discomfort, dyspareunia, infertility, or fistula formation may result. Clinicians need to be able to recognize these injuries and provide initial management, and assure that the patient's mental, emotional and physical needs are addressed.


title: Automatic text recognition for video indexing

abstract: Efficient indexing and retrieval of digital video is an important aspect of video databases. One powerful index for retrieval is the text appearing in them. It enables content- based browsing. We present our methods for automatic segmentation and recognition of text in digital videos. The algorithms we propose make use of typical characteristics of text in videos in order to enable and enhance segmentation and recognition performance. Especially the inter-frame dependencies of the characters provide new possibilities for their refinement. Then, a straightforward indexing and retrieval scheme is introduced. It is used in the experiments to demonstrate that the proposed text segmentation and text recognition algorithms are suitable for indexing and retrieval of relevant video scenes in and from a video data base. Our experimental results are very encouraging and suggest that these algorithms can be used in video retrieval applications as well as to recognize higher semantics in video.


title: Fetal injury and abortion associated with occupational exposure to inhaled anesthetics.

abstract: While the actual risk associated with occupational exposure to inhaled anesthetics has not precisely been defined, a growing body of data has accumulated so that we may begin to make certain valid inferences. Published studies of the teratogenic, mutagenic and carcinogenic effects of exposure to nitrous oxide and isoflurane suggest that a causal role of these inhaled agents appears to be very low or nonexistent.


title: Female waist-to-hip ratio, body mass index and sexual attractiveness in China

abstract: Men and women at Northwest University (n=751), Xi'an, China were asked to judge the attractiveness of photo- graphs of female patients who had undergone micrograft surgery to reduce their waist-to-hip ratios (WHR). Micrograft surgery involves harvesting adipose tissue from the waist and reshaping the buttocks to produce a low WHR and an 'hourglass' female figure. This gynoid distribution of female body fat has been shown to correlate with measures of fertility and health. Significantly larger numbers of subjects, of both sexes, chose post-operative photographs, with lower WHRs, as more attractive than pre-operative photographs of the same women. Some patients had gained, and some had lost weight, post-operatively, with resul- tant changes in body mass index (BMI). However, these changes in BMI were not related to judgments of attractiveness. These results show that the hourglass female figure is rated as attractive in China, and that WHR, rather than BMI, plays a crucial role in such attractiveness judgments (Current Zoology 56 (2): 175-181, 2010).


title: The mechanisms of alloxan- and streptozotocin-induced diabetes

abstract: Alloxan and streptozotocin are toxic glucose analogues that preferentially accumulate in pancreatic beta cells via the GLUT2 glucose transporter. In the presence of intracellular thiols, especially glutathione, alloxan generates reactive oxygen species (ROS) in a cyclic redox reaction with its reduction product, dialuric acid. Autoxidation of dialuric acid generates superoxide radicals, hydrogen peroxide and, in a final iron-catalysed reaction step, hydroxyl radicals. These hydroxyl radicals are ultimately responsible for the death of the beta cells, which have a particularly low antioxidative defence capacity, and the ensuing state of insulin-dependent 'alloxan diabetes'. As a thiol reagent, alloxan also selectively inhibits glucose-induced insulin secretion through its ability to inhibit the beta cell glucose sensor glucokinase. Following its uptake into the beta cells, streptozotocin is split into its glucose and methylnitrosourea moiety. Owing to its alkylating properties, the latter modifies biological macromolecules, fragments DNA and destroys the beta cells, causing a state of insulin-dependent diabetes. The targeting of mitochondrial DNA, thereby impairing the signalling function of beta cell mitochondrial metabolism, also explains how streptozotocin is able to inhibit glucose-induced insulin secretion.


title: A Method for MPPT Control While Searching for Parameters Corresponding to Weather Conditions for PV Generation Systems

abstract: This paper describes a method for maximum power point tracking (MPPT) control while searching for optimal parameters corresponding to weather conditions at that time. The conventional method has problems in that it is impossible to quickly acquire the generation power at the maximum power (MP) point in low solar radiation (irradiation) regions. It is found theoretically and experimentally that the maximum output power and the optimal current, which give this maximum, have a linear relation at a constant temperature. Furthermore, it is also shown that linearity exists between the short-circuit current and the optimal current. MPPT control rules are created based on the findings from solar arrays that can respond at high speeds to variations in irradiation. The proposed MPPT control method sets the output current track on the line that gives the relation between the MP and the optimal current so as to acquire the MP that can be generated at that time by dividing the power and current characteristics into two fields. The method is based on the generated power being a binary function of the output current. Considering the experimental fact that linearity is maintained only at low irradiation below half the maximum irradiation, the proportionality coefficient (voltage coefficient) is compensated for only in regions with more than half the rated optimal current, which correspond to the maximum irradiation. At high irradiation, the voltage coefficient needed to perform the proposed MPPT control is acquired through the hill-climbing method. The effectiveness of the proposed method is verified through experiments under various weather conditions


What Trust means in the Sharing Economy: A provider perspective on Airbnb.com


title: Sparse code multiple access: An energy efficient uplink approach for 5G wireless systems

abstract: The rapid traffic growth and ubiquitous access requirements make it essential to explore the next generation (5G) wireless communication networks. In the current 5G research area, non-orthogonal multiple access has been proposed as a paradigm shift of physical layer technologies. Among all the existing non-orthogonal technologies, the recently proposed sparse code multiple access (SCMA) scheme is shown to achieve a better link level performance. In this paper, we extend the study by proposing an unified framework to analyze the energy efficiency of SCMA scheme and a low complexity decoding algorithm which is critical for prototyping. We show through simulation and prototype measurement results that SCMA scheme provides extra multiple access capability with reasonable complexity and energy consumption, and hence, can be regarded as an energy efficient approach for 5G wireless communication systems.


title: Dental implants for orthodontic anchorage.

abstract: The purpose of this article is to review and update current concepts involving the use of dental implants for orthodontic anchorage. Topics to be discussed include indications, implant requirements (eg, materials, size, designs of dental implants), surgery and healing time, biomechanics and forces, loading time, implant maintenance, posttreatment considerations, and disadvantages.


title: Supervoxel-Consistent Foreground Propagation in Video

abstract: A major challenge in video segmentation is that the foreground object may move quickly in the scene at the same time its appearance and shape evolves over time. While pairwise potentials used in graph-based algorithms help smooth labels between neighboring (super)pixels in space and time, they offer only a myopic view of consistency and can be misled by inter-frame optical flow errors. We propose a higher order supervoxel label consistency potential for semi-supervised foreground segmentation. Given an initial frame with manual annotation for the foreground object, our approach propagates the foreground region through time, leveraging bottom-up supervoxels to guide its estimates towards long-range coherent regions. We validate our approach on three challenging datasets and achieve state-of-the-art results.


title: Agroforestry for high value tree systems in Europe

abstract: Most farm-based agroforestry projects focus on the integration of trees on arable or livestock enterprises. This paper focuses on the integration of understorey crops and/or livestock within high value tree systems (e.g., apple orchards, olive groves, chestnut woodlands, and walnut plantations), and describes the components, structure, ecosystem services and economic value of ten case studies of this type of agroforestry across Europe. Although their ecological and socio-economic contexts vary, the systems share some common characteristics. The primary objective of the farmer is likely to remain the value of tree products like apples, olives, oranges, or nuts, or particularly high value timber. However there can still be production, environmental or economic benefits of integrating agricultural crops such as chickpeas and barley, or grazing an understorey grass crop with livestock. Three of the systems focused on the grazing of apple orchards with sheep in the UK and France. The introduction of sheep to apple orchards can minimise the need for mowing and provide an additional source of revenue. Throughout the Mediterranean, there is a need to improve the financial viability of olive groves. The case studies illustrate the possibility of intercropping traditional olive stands with chickpea in Greece, or the intercropping of wild asparagus in high density olive groves in Italy. Another system studied in Greece involves orange trees intercropped with chickpeas. Stands of chestnut trees in North-west Spain can provide feed for pigs when the fruit falls in November, and provide an excellent habitat for the commercial production of edible mushrooms. In Spain, in the production of high quality walnut trees using rotations of up to 50–60 years, there are options to establish a legume-based mixed pasture understorey and to introduce sheep to provide financial and environmental benefits.


title: Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms

abstract: The existence of large volumes of time series data in many applications has motivated data miners to investigate specialized methods for mining time series data. Clustering is a popular data mining method due to its powerful exploratory nature and its usefulness as a preprocessing step for other data mining techniques. This article develops two novel clustering algorithms for time series data that are extensions of a crisp c-shapes algorithm. The two new algorithms are heuristic derivatives of fuzzy c-means (FCM). Fuzzy c-Shapes plus (FCS+) replaces the inner product norm in the FCM model with a shape-based distance function. Fuzzy c-Shapes double plus (FCS++) uses the shape-based distance, and also replaces the FCM cluster centers with shape-extracted prototypes. Numerical experiments on 48 real time series data sets show that the two new algorithms outperform state-of-the-art shape-based clustering algorithms in terms of accuracy and efficiency. Four external cluster validity indices (the Rand index, Adjusted Rand Index, Variation of Information, and Normalized Mutual Information) are used to match candidate partitions generated by each of the studied algorithms. All four indices agree that for these finite waveform data sets, FCS++ gives a small improvement over FCS+, and in turn, FCS+ is better than the original crisp c-shapes method. Finally, we apply two tests of statistical significance to the three algorithms. The Wilcoxon and Friedman statistics both rank the three algorithms in exactly the same way as the four cluster validity indices.


title: Full explicit consistency constraints in uncalibrated multiple homography estimation

abstract: We reveal a complete set of constraints that need to be imposed on a set of 3-by-3 matrices to ensure that the matrices represent genuine homographies associated with multiple planes between two views. We also show how to exploit the constraints to obtain more accurate estimates of homography matrices between two views. Our study resolves a long-standing research question and provides a fresh perspective and a more in-depth understanding of the multiple homography estimation task.


title: A 13.3ns double-precision floating-point ALU and multiplier

abstract: One-bit pre-shifting before alignment shift, normalization with anticipated leading '1' bit and pre-rounding techniques have been developed for a floating-point arithmetic logic unit (ALU). In addition, carry select addition and pre-rounding techniques have been developed for a floating-point multiplier. A noise tolerant precharge (NTP) circuit was designed and applied to the ALU and multiplier. These techniques reduced the delay time of the critical path by 24%. Each unit was fabricated in 0.3 /spl mu/m 2.5 V four-layer-metal CMOS technology and achieved a two-cycle latency at 150 MHz.


Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading


title: Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection

abstract: Despite inherent ill-definition, anomaly detection is a research endeavour of great interest within machine learning and visual scene understanding alike. Most commonly, anomaly detection is considered as the detection of outliers within a given data distribution based on some measure of normality. The most significant challenge in real-world anomaly detection problems is that available data is highly imbalanced towards normality (i.e. non-anomalous) and contains at most a sub-set of all possible anomalous samples - hence limiting the use of well-established supervised learning methods. By contrast, we introduce an unsupervised anomaly detection model, trained only on the normal (non-anomalous, plentiful) samples in order to learn the normality distribution of the domain, and hence detect abnormality based on deviation from this model. Our proposed approach employs an encoder-decoder convolutional neural network with skip connections to thoroughly capture the multi-scale distribution of the normal data distribution in image space. Furthermore, utilizing an adversarial training scheme for this chosen architecture provides superior reconstruction both within image space and a lower-dimensional embedding vector space encoding. Minimizing the reconstruction error metric within both the image and hidden vector spaces during training aids the model to learn the distribution of normality as required. Higher reconstruction metrics during subsequent test and deployment are thus indicative of a deviation from this normal distribution, hence indicative of an anomaly. Experimentation over established anomaly detection benchmarks and challenging real-world datasets, within the context of X-ray security screening, shows the unique promise of such a proposed approach.


title: When is the area under the receiver operating characteristic curve an appropriate measure of classifier performance?

abstract: The area under the receiver operating characteristic curve is a widely used measure of the performance of classification rules. This paper shows that when classifications are based solely on data describing individual objects to be classified, the area under the receiver operating characteristic curve is an incoherent measure of performance, in the sense that the measure itself depends on the classifier being measured. It significantly extends earlier work by showing that this incoherence is not a consequence of a cost-based interpretation of misclassifications, but is a fundamental property of the area under the curve itself. The paper also shows that if additional information, such as the class assignments of other objects, is taken into account when making a classification, then the area under the curve is a coherent measure, although in those circumstances it makes an assumption which is seldom if ever appropriate.


title: MEMS accelerometer with all-optical readout based on twin-defect photonic crystal waveguide

abstract: A novel design is proposed for a Si accelerometer sensor chip where a stress-tunable photonic crystal is used as the sensitive element to optically measure the deflection of a bulk-micromachined diaphragm with a boss. The photonic crystal may be 1D stack, 2D channel type, microbridge, etc. The photoelasticity effect is used instead of customarily utilized piezoresistivity and thus an all-optical readout is obtained. A feasibility analysis of the proposed structure is performed. 3D finite element modeling is used to determine the mechanical properties of the structure and the transfer matrix method to calculate its electromagnetic behavior. The advantages of the proposed concept are its simplicity, all-optical operation, monolithic integration with the sensor chip and compatibility with contemporary fiber-optic and integrated optics systems and devices.


title: Anti-Sweep Jamming Design and Implementation Using Multi-Channel Harmonic Timing Sequence Detection for Short-Range FMCW Proximity Sensors

abstract: Currently, frequency-modulated continuous-wave (FMCW) proximity sensors are widely used. However, they suffer from a serious sweep jamming problem, which significantly reduces the ranging performance. To improve its anti-jamming capability, this paper analyzed the response mechanism of a proximity sensor with the existence of real target echo signals and sweep jamming, respectively. Then, a multi-channel harmonic timing sequence detection method, using the spectrum components' distribution difference between the real echo signals and sweep jamming, is proposed. Moreover, a novel fast Fourier transform (FFT)-based implementation was employed to extract multi-channel harmonic information. Compared with the traditional band-pass filter (BPF) implementation, this novel realization scheme only computes FFT once, in each transmission cycle, which significantly reduced hardware resource consumption and improved the real-time performance of the proximity sensors. The proposed method was implemented and proved to be feasible through the numerical simulations and prototype experiments. The results showed that the proximity sensor utilizing the proposed method had better anti-sweep jamming capability and ranging performance.


title: Finding Structure in Reinforcement Learning

abstract: Reinforcement learning addresses the problem of learning to select actions in order to maximize one's performance in unknown environments. To scale reinforcement learning to complex real-world tasks, such as typically studied in AI, one must ultimately be able to discover the structure in the world, in order to abstract away the myriad of details and to operate in more tractable problem spaces. ::: ::: This paper presents the SKILLS algorithm. SKILLS discovers skills, which are partially defined action policies that arise in the context of multiple, related tasks. Skills collapse whole action sequences into single operators. They are learned by minimizing the compactness of action policies, using a description length argument on their representation. Empirical results in simple grid navigation tasks illustrate the successful discovery of structure in reinforcement learning.


title: Vision based robot localization by ground to satellite matching in GPS-denied situations

abstract: This paper studies the problem of matching images captured from an unmanned ground vehicle (UGV) to those from a satellite or high-flying vehicle. We focus on situations where the UGV navigates in remote areas with few man-made structures. This is a difficult problem due to the drastic change in perspective between the ground and aerial imagery and the lack of environmental features for image comparison. We do not rely on GPS, which may be jammed or uncertain. We propose a two-step approach: (1) the UGV images are warped to obtain a bird's eye view of the ground, and (2) this view is compared to a grid of satellite locations using whole-image descriptors. We analyze the performance of a variety of descriptors for different satellite map sizes and various terrain and environment types. We incorporate the air-ground matching into a particle-filter framework for localization using the best-performing descriptor. The results show that vision-based UGV localization from satellite maps is not only possible, but often provides better position estimates than GPS estimates, enabling us to improve the location estimates of Google Street View.


title: Rotation Forest: A New Classifier Ensemble Method

abstract: We propose a method for generating classifier ensembles based on feature extraction. To create the training data for a base classifier, the feature set is randomly split into K subsets (K is a parameter of the algorithm) and principal component analysis (PCA) is applied to each subset. All principal components are retained in order to preserve the variability information in the data. Thus, K axis rotations take place to form the new features for a base classifier. The idea of the rotation approach is to encourage simultaneously individual accuracy and diversity within the ensemble. Diversity is promoted through the feature extraction for each base classifier. Decision trees were chosen here because they are sensitive to rotation of the feature axes, hence the name "forest". Accuracy is sought by keeping all principal components and also using the whole data set to train each base classifier. Using WEKA, we examined the rotation forest ensemble on a random selection of 33 benchmark data sets from the UCI repository and compared it with bagging, AdaBoost, and random forest. The results were favorable to rotation forest and prompted an investigation into diversity-accuracy landscape of the ensemble models. Diversity-error diagrams revealed that rotation forest ensembles construct individual classifiers which are more accurate than these in AdaBoost and random forest, and more diverse than these in bagging, sometimes more accurate as well


title: Specification Inference from Demonstrations

abstract: Learning from expert demonstrations has received a lot of attention in artificial intelligence and machine learning. The goal is to infer the underlying reward function that an agent is optimizing given a set of observations of the agent's behavior over time in a variety of circumstances, the system state trajectories, and a plant model specifying the evolution of the system state for different agent's actions. The system is often modeled as a Markov decision process, that is, the next state depends only on the current state and agent's action, and the the agent's choice of action depends only on the current state. While the former is a Markovian assumption on the evolution of system state, the later assumes that the target reward function is itself Markovian. In this work, we explore learning a class of non-Markovian reward functions, known in the formal methods literature as specifications. These specifications offer better composition, transferability, and interpretability. We then show that inferring the specification can be done efficiently without unrolling the transition system. We demonstrate on a 2-d grid world example.


title: Incremental learning with self-organizing maps

abstract: We present a novel use for self-organizing maps (SOMs) as an essential building block for incremental learning algorithms. SOMs are very well suited for this purpose because they are inherently online learning algorithms, because their weight updates are localized around the best-matching unit, which inherently protects them against catastrophic forgetting, and last but not least because they have fixed model complexity limiting execution time and memory requirements for processing streaming data. However, in order to perform incremental learning which is usually supervised in nature, SOMs need to be complemented by a readout layer as well as a self-referential control mechanism for prototype updates in order to be protected against negative consequences of concept drift. We present the PROPRE architecture which implements these functions, thus realizing incremental learning with SOMs in very high-dimensional data domains, and show its capacity for incremental learning on several known and new classification problems. In particular, we discuss the required control of SOM parameters in detail and validate our choices by experimental results.


title: A 13.56 MHz 40 mW CMOS High-Efficiency Inductive Link Power Supply Utilizing On-Chip Delay-Compensated Voltage Doubler Rectifier and Multiple LDOs for Implantable Medical Devices

abstract: In this paper, a 13.56 MHz CMOS near-field inductive link power supply (ILPS) that can deliver 20 mA output current for implantable medical devices (IMDs) is proposed and fabricated. In the proposed ILPS, the pair of inductive link coils is constructed in the spiral shape with a ferrite core to save space and increase efficiency. Experimental results have shown that the near-field coils can transmit power at the resonant frequency of 13.56 MHz with the transmission efficiency up to 76.3%. The CMOS power regulator is composed of active voltage doubler rectifier (VD) and low-dropout regulators (LDOs). In the active VD with the comparator, the input offset voltage is adjustable for delay compensation and a start-up control circuit is added to achieve robust start-up mechanism. On-chip delay compensation control with SR-latches is proposed to prevent from error glitch switching on offset voltage control and achieve accurate delay compensation so that the reverse current conduction can be avoided and the efficiency can be increased. Three fully-integrated LDOs with rectifier output voltage of 2 V to 1.8 V are realized for analog (ALDO), digital (DLDO), and reference-voltage (RLDO) circuits. Thus the performance of individual LDO can be optimized. The measured output ripple voltage of the active VD is 10.4 mV. The power conversion efficiency (PCE) is 85% under 20 mA output current. The measured dropout voltage is 384 mV. As compared with other designs, the proposed ILPS has lower ripple voltages, lower dropout voltage, and higher PCE.


title: Dialog Act classification in Chinese spoken language

abstract: Dialog Act (DA) is an important pragmatics feature for us to understand speakers' intention. Many methods have been proposed to recognize DA tags. However, little work has been conducted to address the problem of DA tagging in Chinese spoken dialog language. In this work, we employ both the lexical features and the inter-utterance dependency features for DA tagging. And we propose three different methods: n-gram, extended HMM and n-gram+KNN. The experimental results show that these methods are effective for the task.


title: Metcalfe's Law after 40 Years of Ethernet

abstract: Critics have declared Metcalfe's law, which states that the value of a network grows as the square of the number of its users, a gross overestimation of the network effect, but nobody has tested the law with real data. Using a generalization of the sigmoid function called the netoid, Ethernet's inventor and the law's originator models Facebook user growth over the past decade and fits his law to the associated revenue. The Web extra at http://youtu.be/m_agCPNGOzU is a video interview with Bob Metcalfe about the creation of the first Ethernet local area network 40 years ago at Xerox Palo Alto Research Center.


title: LiveMaps: Learning Geo-Intent from Images of Maps on a Large Scale

abstract: Image search is a popular application on web search engines. Issuing a location-related query on an image search engine often returns multiple images of maps among the top ranked results. Traditionally, clicking on such images either opens the image in a new browser tab or takes users to a web page containing the image. However, finding the area of intent on an interactive web map (e.g., Bing Maps) is a manual process. In this paper, we describe a novel system, LiveMaps, for analyzing and retrieving an appropriate map viewport for a given image of a map. This provides annotation of images of maps returned by image search engines, allowing users to directly open a link to an interactive map centered on the location of interest. LiveMaps works in several stages. It first checks whether the input image represents a map. If yes, then the system attempts to identify what geographical area this map image represents. In the process, we use textual as well as visual information extracted from the image. Finally, we construct an interactive map object capturing the geographical area inferred for the image. Evaluation results on a dataset of labeled map images indicate our system constructs precise map representations while also achieving good levels of coverage.


title: Tenzing A SQL Implementation On The MapReduce Framework

abstract: Tenzing is a query engine built on top of MapReduce [9] for ad hoc analysis of Google data. Tenzing supports a mostly complete SQL implementation (with several extensions) combined with several key characteristics such as heterogeneity, high performance, scalability, reliability, metadata awareness, low latency, support for columnar storage and structured data, and easy extensibility. Tenzing is currently used internally at Google by 1000+ employees and serves 10000+ queries per day over 1.5 petabytes of compressed data. In this paper, we describe the architecture and implementation of Tenzing, and present benchmarks of typical analytical queries.


title: A Cost-based Optimizer for Gradient Descent Optimization

abstract: As the use of machine learning (ML) permeates into diverse application domains, there is an urgent need to support a declarative framework for ML. Ideally, a user will specify an ML task in a high-level and easy-to-use language and the framework will invoke the appropriate algorithms and system configurations to execute it. An important observation towards designing such a framework is that many ML tasks can be expressed as mathematical optimization problems, which take a specific form. Furthermore, these optimization problems can be efficiently solved using variations of the gradient descent (GD) algorithm. Thus, to decouple a user specification of an ML task from its execution, a key component is a GD optimizer. We propose a cost-based GD optimizer that selects the best GD plan for a given ML task. To build our optimizer, we introduce a set of abstract operators for expressing GD algorithms and propose a novel approach to estimate the number of iterations a GD algorithm requires to converge. Extensive experiments on real and synthetic datasets show that our optimizer not only chooses the best GD plan but also allows for optimizations that achieve orders of magnitude performance speed-up.


title: Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization

abstract: Military operations are turning to more complex and advanced automation technologies forminimumriskandmaximumefficiency.Acriticalpiecetothisstrategyisunmannedaerial vehicles. Unmanned aerial vehicles require the intelligence to safely maneuver along a path to an intended target and avoiding obstacles such as other aircrafts or enemy threats. This paper presents a unique three-dimensional path planning problem formulation and solution approach using particle swarm optimization. The problem formulation was designed with three objectives: 1) minimize risk owing to enemy threats, 2) minimize fuel consumption incurred by deviating from the original path, and 3) fly over defined reconnaissance targets. The initial design point is defined as the original path of the unmanned aerial vehicles. Using particle swarm optimization, alternate paths are generated using B-spline curves, optimized based on the three defined objectives. The resulting paths can be optimized with a preference toward maximum safety, minimum fuel consumption, or target reconnaissance. This method has been implemented in a virtual environment where the generated alternate paths can be visualized interactively to better facilitate the decision-making process. The problem formulation and solution implementation is described along with the results from several simulated scenarios demonstrating the effectiveness of the method.


title: The Salsa20 family of stream ciphers

abstract: Salsa20 is a family of 256-bit stream ciphers designed in 2005 and submitted to eSTREAM, the ECRYPT Stream Cipher Project. Salsa20 has progressed to the third round of eSTREAM without any changes. The 20-round stream cipher Salsa20/20 is consistently faster than AES and is recommended by the designer for typical cryptographic applications. The reduced-round ciphers Salsa20/12 and Salsa20/8 are among the fastest 256-bit stream ciphers available and are recommended for applications where speed is more important than confidence. The fastest known attacks use ≈ 2153simple operations against Salsa20/7, ≈ 2249simple operations against Salsa20/8, and ≈ 2255simple operations against Salsa20/9, Salsa20/10, etc. In this paper, the Salsa20 designer presents Salsa20 and discusses the decisions made in the Salsa20 design.


title: New monitoring approach for Neonatal Intensive Care Unit

abstract: The Neonatal Intensive Care Unit (NICU) represents a complex and multi-in/output context aimed at monitoring and controlling biological signals and parameters in premature newborns. This paper details some methodological and design options for developing technologies that allow end-user composition and control through new approaches that integrate wearable monitoring, pervasive and unobtrusive computing research that already are introducing new perspectives in a wide range of applications. These options enhance biosignals monitoring capabilities and provide consistent user experiences in environments where different devices, services and processes typically co-exist. In particular we describe the notion of assemblies of monitoring devices, interpreted as the combination of sensors, tools and services in a distributed monitoring environment where they interact. We report on the importance of flexibility and user control in the use of such technological assemblies in a NICU, describing a prototype and preliminary results of such monitoring system.


title: Building a Corpus of Temporal-Causal Structure

abstract: While recent corpus annotation efforts cover a wide variety of semantic structures, work on temporal and causal relations is still in its early stages. Annotation efforts have typically considered either temporal relations or causal relations, but not both, and no corpora currently exist that allow the relation between temporals and causals to be examined empirically. We have annotated a corpus of 1000 event pairs for both temporal and causal relations, focusing on a relatively frequent construction in which the events are conjoined by the word and. Temporal relations were annotated using an extension of the BEFORE and AFTER scheme used in the TempEval competition, and causal relations were annotated using a scheme based on connective phrases like and as a result. The annotators achieved 81.2% agreement on temporal relations and 77.8% agreement on causal relations. Analysis of the resulting corpus revealed some interesting findings, for example, that over 30% of CAUSAL relations do not have an underlying BEFORE relation. The corpus was also explored using machine learning methods, and while model performance exceeded all baselines, the results suggested that simple grammatical cues may be insufficient for identifying the more difficult temporal and causal relations.


title: PWM Power Audio Amplifier With Voltage/Current Mixed Feedback for High-Efficiency Speakers

abstract: High-efficiency speakers significantly improve electrical to sound power conversion efficiency by reducing the power dissipation over the speaker coil. However, they require some equalization of the input signal. In this paper, a new driving technique based on a pulsewidth-modulation switching amplifier is presented. This exploits a double voltage/current feedback to obtain automatic equalization. Furthermore, the double feedback allows the output resonant filter to be included in the feedback path, compensating for its nonlinearity. Finally, the mixed feedback acts as intrinsic current limiter. Both theoretical analysis and simulations demonstrate that this system is feasible. The results were also confirmed by measurements using a prototype


title: Differentially private recommender systems: building privacy into the net

abstract: We consider the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of privacy for these users. Specifically, we consider the Netflix Prize data set, and its leading algorithms, adapted to the framework of differential privacy. Unlike prior privacy work concerned with cryptographically securing the computation of recommendations, differential privacy constrains a computation in a way that precludes any inference about the underlying records from its output. Such algorithms necessarily introduce uncertainty--i.e., noise--to computations, trading accuracy for privacy. We find that several of the leading approaches in the Netflix Prize competition can be adapted to provide differential privacy, without significantly degrading their accuracy. To adapt these algorithms, we explicitly factor them into two parts, an aggregation/learning phase that can be performed with differential privacy guarantees, and an individual recommendation phase that uses the learned correlations and an individual's data to provide personalized recommendations. The adaptations are non-trivial, and involve both careful analysis of the per-record sensitivity of the algorithms to calibrate noise, as well as new post-processing steps to mitigate the impact of this noise. We measure the empirical trade-off between accuracy and privacy in these adaptations, and find that we can provide non-trivial formal privacy guarantees while still outperforming the Cinematch baseline Netflix provides.


title: Social network activity and social well-being

abstract: Previous research has shown a relationship between use of social networking sites and feelings of social capital. However, most studies have relied on self-reports by college students. The goals of the current study are to (1) validate the common self-report scale using empirical data from Facebook, (2) test whether previous findings generalize to older and international populations, and (3) delve into the specific activities linked to feelings of social capital and loneliness. In particular, we investigate the role of directed interaction between pairs---such as wall posts, comments, and "likes" --- and consumption of friends' content, including status updates, photos, and friends' conversations with other friends. We find that directed communication is associated with greater feelings of bonding social capital and lower loneliness, but has only a modest relationship with bridging social capital, which is primarily related to overall friend network size. Surprisingly, users who consume greater levels of content report reduced bridging and bonding social capital and increased loneliness. Implications for designs to support well-being are discussed.


title: Measurement of user experience: A Spanish language version of the user experience questionnaire (UEQ)

abstract: To improve a product you will need most likely developers, managers and user feedback. Besides the basic software qualities other important properties are usability and user experience for developing a good product. Usability is well known and can be tested with e.g. a usability test or an expert review. In contrast user experience describes the whole impact a product has on the end-user. The timeline goes from before, while and after the use of a product. We present a tool that allows to evaluate the user experience of a product with little effort. We show in addition how this tool can be used for a continuous user experience assessment.


title: Assuring Security through Attribute GSN

abstract: The NFR (Non Functional Requirements) framework was used to quantitatively evaluate software architectures. Assurance cases using GSN (Goal Structuring Notation) have not been applied to quantitatively evaluate software architectures. In this paper, GSN will be extended to define quantitative attributes for this purpose. Then, the architecture evaluation method based on the attributed GSN method will be proposed. A case study on a LAN device monitoring system is also shown to evaluate the usefulness of the proposed method.


title: Development of a Miniature Robot Finger with a Variable Stiffness Mechanism using Shape Memory

abstract: This paper describes a miniature robot finger which uses shape memory alloy as the actuator. Aiming at developing robot hands to perform human work instead of men, numerous research have been conducted. By miniaturizing robot hands, execution of more detailed work becomes possible. In developing miniature robot hands for dexterous manipulation, it is necessary to consider miniaturizing and simplification. The miniature robot finger proposed in this paper is driven by shape memory alloy (SMA) wires. The structure of the robot finger imitates the musculoskeletal system of humans, since SMA wires exhibit nonlinear features similar to human muscles. Highly precise position control of fingertip is performable by using SMA owing to its shape memory effect. Force control of the fingertip is performed by measuring the tension of the SMA wire. We used strain gages to measure tension. We confirmed that stiffness control of the finger joint can be conducted by controlling tension of the SMA simultaneously.


title: Modeling of 120W Monocrystalline Photovoltaic Module using MATLAB Simulink

abstract: This project aims to model a solar Photovoltaic (PV) Module using MATLAB Simulink. In Renewable Energy (RE) field, many studies have been carried out to determine the level of efficiency and performance of a specific PV module. Therefore, this research will carry out the modeling of the 120W Monocrystalline Photovoltaic Module by Su-Kam Solar using MATLAB Simulink to determine the efficiency and performance. The input parameters that consists of Solar Irradiance (G) and Temperature (T) data will be collected at location 1.8635° N, 103.1089° E which is in Parit Raja, Batu Pahat, Johor. The results are shown in I-V curve and P-V curve and compared with the theory of I-V and P-V curve. Other than that, the PV module have different performance in different value of irradiance and temperature. Lastly, the PV Module is work efficiently and full performance at Standard Test Conditon (STC).


title: Single-player Monte-Carlo tree search for SameGame

abstract: Classic methods such as A^* and IDA^* are a popular and successful choice for one-player games. However, without an accurate admissible evaluation function, they fail. In this article we investigate whether Monte-Carlo tree search (MCTS) is an interesting alternative for one-player games where A^* and IDA^* methods do not perform well. Therefore, we propose a new MCTS variant, called single-player Monte-Carlo tree search (SP-MCTS). The selection and backpropagation strategy in SP-MCTS are different from standard MCTS. Moreover, SP-MCTS makes use of randomized restarts. We tested IDA^* and SP-MCTS on the puzzle SameGame and used the cross-entropy method to tune the SP-MCTS parameters. It turned out that our SP-MCTS program is able to score a substantial number of points on the standardized test set.


title: Integration of OPC UA into a web-based platform to enhance interoperability

abstract: The paper presents a web-based platform able to offer access to one or more OPC UA Servers. The proposed platform may be used by web-users to exchange data with OPC UA Server without any knowledge of the standard. The software solution described in the paper is available on GitHub.


title: The impact of information technology on the banking industry

abstract: This paper analyses the effects of investment in information technologies (IT) in the banking sector using bank-level data from a panel of 68 US banks over the period 1986–2005. Although IT can improve bank's performance by reducing operational cost (supply side), it can bring in competition among banks in order to embrace new technology (demand side). Since most empirical studies have adopted the production function approach, it is difficult to identify which effect has dominated. In a differentiated model with network effects, this paper characterizes the conditions to identify these two effects. The results suggest that (at individual firm levels) the bank profits can decline due to adoption and diffusion of IT investment, reflecting negative network competition effects in this industry. Using panel cointegration tests, we confirm that the estimated profit equation is indeed a long-run equilibrium relation.


Feedback Strategies for Interactive Learning Tasks


title: Compact Dual-Band Branch-Line and Rat-Race Couplers With Stepped-Impedance-Stub Lines

abstract: This study constructs stepped-impedance-stub lines for a dual-band branch-line coupler design with improved design flexibility. The proposed structure demonstrates dual-band performance and a compact size due to additional stepped-impedance stubs to branches. The developed synthesis method has two degrees of freedom which can be exploited to miniaturize circuit size and/or replace impractical impedances with more realizable ones. Observations also show the advantage of a wide-range realizable frequency ratio of dual bands. The current work fabricates three experimental dual-band branch-line couplers, including a two-section coupler, and achieves a size reduction up to 21.7%, compared with conventional structures. The measured results validate good dual-band performance at 2.4/5.8 GHz with enhanced bandwidths up to 21% and 12%, respectively. This research also successfully applies the proposed circuit to synthesize a dual-band rat-race coupler.