title: Intake of sugar-sweetened beverages and weight gain: a systematic review1–3

abstract: Consumption of sugar-sweetened beverages (SSBs), particularly carbonated soft drinks, may be a key contributor to the epidemic of overweight and obesity, by virtue of these beverages’ high added sugar content, low satiety, and incomplete compensation for total energy. Whether an association exists between SSB intake and weight gain is unclear. We searched English-language MEDLINE publications from 1966 through May 2005 for cross-sectional, prospective cohort, and experimental studies of the relation between SSBs and the risk of weight gain (ie, overweight, obesity, or both). Thirty publications (15 cross-sectional, 10 prospective, and 5 experimental) were selected on the basis of relevance and quality of design and methods. Findings from large cross-sectional studies, in conjunction with those from well-powered prospective cohort studies with long periods of follow-up, show a positive association between greater intakes of SSBs and weight gain and obesity in both children and adults. Findings from short-term feeding trials in adults also support an induction of positive energy balance and weight gain by intake of sugar-sweetened sodas, but these trials are few. A school-based intervention found significantly less soft-drink consumption and prevalence of obese and overweight children in the intervention group than in control subjects after 12 mo, and a recent 25-week randomized controlled trial in adolescents found further evidence linking SSB intake to body weight. The weight of epidemiologic and experimental evidence indicates that a greater consumption of SSBs is associated with weight gain and obesity. Although more research is needed, sufficient evidence exists for public health strategies to discourage consumption of sugary drinks as part of a healthy lifestyle.


title: Factors influencing clients in the selection of offshore software outsourcing vendors: An exploratory study using a systematic literature review

abstract: Context: Offshore software development outsourcing is a modern business strategy for developing high quality software at low cost. Objective: The objective of this research paper is to identify and analyse factors that are important in terms of the competitiveness of vendor organisations in attracting outsourcing projects. Method: We performed a systematic literature review (SLR) by applying our customised search strings which were derived from our research questions. We performed all the SLR steps, such as the protocol development, initial selection, final selection, quality assessment, data extraction and data synthesis. Results: We have identified factors such as cost-saving, skilled human resource, appropriate infrastructure, quality of product and services, efficient outsourcing relationships management, and an organisation's track record of successful projects which are generally considered important by the outsourcing clients. Our results indicate that appropriate infrastructure, cost-saving, and skilled human resource are common in three continents, namely Asia, North America and Europe. We identified appropriate infrastructure, cost-saving, and quality of products and services as being common in three types of organisations (small, medium and large). We have also identified four factors-appropriate infrastructure, cost-saving, quality of products and services, and skilled human resource as being common in the two decades (1990-1999 and 2000-mid 2008). Conclusions: Cost-saving should not be considered as the driving factor in the selection process of software development outsourcing vendors. Vendors should rather address other factors in order to compete in the OSDO business, such as skilled human resource, appropriate infrastructure and quality of products and services.


title: Design and kinematic analysis of a novel upper limb exoskeleton for rehabilitation of stroke patients

abstract: This paper details the design process and features of a novel upper limb rehabilitation exoskeleton named CLEVER (Compact, Low-weight, Ergonomic, Virtual/Augmented Reality Enhanced Rehabilitation) ARM. The research effort is focused on designing a lightweight and ergonomic upper-limb rehabilitation exoskeleton capable of producing diverse and perceptually rich training scenarios. To this end, the knowledge available in the literature of rehabilitation robotics is used along with formal conceptual design techniques. This paper briefly reviews the systematic approach used for design of the exoskeleton, and elaborates on the specific details of the proposed design concept and its advantages over other design possibilities. The kinematic structure of CLEVER ARM has eight degrees of freedom supporting the motion of shoulder girdle, glenohumeral joint, elbow and wrist. Six degrees of freedom of the exoskeleton are active, and the two degrees of freedom supporting the wrist motion are passive. Kinematics of the proposed design is studied analytically and experimentally with the aid of a 3D printed prototype. The paper is concluded by some remarks on the optimization of the design, motorization of device, and the fabrication challenges.


title: Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

abstract: Deep learning is at the heart of the current rise of machine learning and artificial intelligence. In the field of Computer Vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas deep neural networks have demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has lead to a large influx of contributions in this direction. This article presents the first comprehensive survey on adversarial attacks on deep learning in Computer Vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them. To emphasize that adversarial attacks are possible in practical conditions, we separately review the contributions that evaluate adversarial attacks in the real-world scenarios. Finally, we draw on the literature to provide a broader outlook of the research direction.


title: Nonlinear model predictive control for aerial manipulation

abstract: This paper presents a nonlinear model predictive controller to follow desired 3D trajectories with the end effector of an unmanned aerial manipulator (i.e., a multirotor with a serial arm attached). To the knowledge of the authors, this is the first time that such controller runs online and on board a limited computational unit to drive a kinematically augmented aerial vehicle. Besides the trajectory following target, we explore the possibility of accomplishing other tasks during flight by taking advantage of the system redundancy. We define several tasks designed for aerial manipulators and show in simulation case studies how they can be achieved by either a weighting strategy, within a main optimization process, or a hierarchical approach consisting on nested optimizations. Moreover, experiments are presented to demonstrate the performance of such controller in a real robot.


title: Combined prosthetic incisional hernioplasty and panniculectomy—a 5-year single-centre experience

abstract: Background ::: Ventral incisional hernia patients develop limitation in physical activities as the hernia enlarges, leading to alteration in their lifestyle, quality of life, aesthetic deformities, and occasionally to complications. Cosmetic improvement of the abdomen, an important objective of hernia repair, can be achieved when hernia repair is combined with panniculectomy. The authors undertook this study to review their experience of the integration of hernia repair and panniculectomy to improve the understanding and treatment of this condition.


title: Face Detection and Tagging Using Deep Learning

abstract: With the social media boom in today's world, we see people constantly uploading photos of themselves along with their friends and family on various social media platforms such as Facebook, Instagram, Twitter, Google+, etc. What if they want to see all the photos in a categorized form such as photos with a particular person. In this paper, we extend the concept of Multiview Face Detection using Convolution Neural Networks (CNN) used by Farfade et al. by providing a tagging system for the detected faces. For the face detection, we use Deep Dense Face Detector, which uses a single model based on deep convolutional neural networks. All the detected faces are recognized using Local Binary Patterns Histograms (LBPH) method. Precision, recall, and F-measure are the parameters used to measure the performance of the algorithm. An accuracy of 85% is achieved for tagging the faces which are successfully detected.


title: Single-Feed Wideband Circularly Polarized Antenna with Novel Parasitic Elements

abstract: A novel wideband crossed dipole loaded with four parasitic elements is investigated in this letter. The printed crossed dipole is incorporated with a pair of vacant quarter rings to feed the antenna. The antenna is backed by a metallic plate to provide an unidirectional radiation pattern with a wide axial-ratio (AR) bandwidth. To verify the proposed design, a prototype is fabricated and measured. The final design with an overall size of $0.46\ \lambda_{0}\times 0.46\ \lambda_{0}\times 0.23\ \lambda_{0} (> \lambda_{0}$ is the free-space wavelength of circularly polarized center frequency) yields a 10-dB impedance bandwidth of approximately 62.7% and a 3-dB AR bandwidth of approximately 47.2%. In addition, the proposed antenna has a stable broadside gain of 7.9 ± 0.5 dBi within passband.


title: A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation

abstract: We present a novel and unified deep learning framework which is capable of learning domain-invariant representation from data across multiple domains. Realized by adversarial training with additional ability to exploit domain-specific information, the proposed network is able to perform continuous cross-domain image translation and manipulation, and produces desirable output images accordingly. In addition, the resulting feature representation exhibits superior performance of unsupervised domain adaptation, which also verifies the effectiveness of the proposed model in learning disentangled features for describing cross-domain data.


title: A virtual manipulator model for space robotic systems

abstract: Future robotic manipulators carried by a spacecraft will be required to perform complex tasks in space, like repairing satellites. Such applications of robotic manipulators will encounter a number of kinematic, dynamic and control problems due to the dynamic coupling between the manipulators and the spacecraft. A new analytical modeling method for studying the kinematics and dynamics of manipulators in space is presented. The problem is treated by introducing the concept of a Virtual Manipulator (VM). The kinematic and dynamic motions of the manipulator, vehicle and payload, can be described relatively easily in terms of the Virtual Manipulator movements, which have a fixed base in inertial space at a point called a Virtual Ground. It is anticipated that the approach described here will aid in the design and development of future space manipulator systems.


title: S.: Towards a linguistic linked open data cloud: The Open Linguistics Working Group

abstract: The Open Linguistics Working Group (OWLG) is an initiative of experts from different fields concerned with linguistic data, including academic linguistics (e.g. typology, corpus linguistics), applied linguistics (e.g. computational linguistics, lexicography and language documentation), and NLP (e.g. from the Semantic Web community). The primary goals of the working group are 1) promoting the idea of open linguistic resources, 2) developing means for their representation, and 3) encouraging the exchange of ideas across different disciplines. To a certain extent, the activities of the Open Linguistics Working Group converge towards the creation of a Linguistic Linked Open Data cloud, which is a topic addressed from different angles by several members of the Working Group. In this article, some of these currently on-going activities are presented and described. RESUME. Le groupe OWLG est une initiative d’experts provenant de differents domaines linguistiques, comprenant la linguistique academique (typologie, corpus), la linguistique appliquee (linguistique computationelle, lexicographie, documentation de langues) et le traitement automatique des langues (p.ex. Web Semantique). Les objectifs principaux de ce groupe sont 1) la promotion de l’idee de ressources ouvertes et accessibles, 2) le developpement de moyens pour representer lesdites ressources et 3) la stimulation d’echanges entre les diverses disciplines et subdisciplines. Les activites de l’OWLG sont pour la plupart liees a la creation d’un nuage de donnees linguistiques Linked Open Data. Les membres du groupe abordent ce theme sous des aspects differents, dont nous presenterons quelques-uns ci-dessous.


title: A Review of Hybrid Fiber-Optic Distributed Simultaneous Vibration and Temperature Sensing Technology and Its Geophysical Applications

abstract: Distributed sensing systems can transform an optical fiber cable into an array of sensors, allowing users to detect and monitor multiple physical parameters such as temperature, vibration and strain with fine spatial and temporal resolution over a long distance. Fiber-optic distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) systems have been developed for various applications with varied spatial resolution, and spectral and sensing range. Rayleigh scattering-based phase optical time domain reflectometry (OTDR) for vibration and Raman/Brillouin scattering-based OTDR for temperature and strain measurements have been developed over the past two decades. The key challenge has been to find a methodology that would enable the physical parameters to be determined at any point along the sensing fiber with high sensitivity and spatial resolution, yet within acceptable frequency range for dynamic vibration, and temperature detection. There are many applications, especially in geophysical and mining engineering where simultaneous measurements of vibration and temperature are essential. In this article, recent developments of different hybrid systems for simultaneous vibration, temperature and strain measurements are analyzed based on their operation principles and performance. Then, challenges and limitations of the systems are highlighted for geophysical applications.


title: Parallel DBSCAN Algorithm Using a Data Partitioning Strategy with Spark Implementation

abstract: DBSCAN is a well-known clustering algorithm which is based on density and is able to identify arbitrary shaped clusters and eliminate noise data. However, existing parallel implementation strategies based on MPI lack fault tolerance and there is no guarantee that their workload is balanced. Although some of Hadoop-based approaches have been proposed, they do not perform well in terms of scalability since the merge process is not efficient.We propose a scalable parallel DBSCAN algorithm by applying a partitioning strategy. It is implemented in Apache Spark. In order to reduce search time, kdtree is used in our algorithm. To achieve better performance and scalability based on kdtree, we adopt an effective partitioning technique aimed at producing balanced sub-domains which can be computed within Spark executors. Moreover, we came up with a new merging technique: through mapping the relationship between the local points and their bordering neighbors, all the partial clusters which are generated in executors are merged to form the final complete clusters. We have observed and verified (through experiments) that this merging approach is very effective in reducing the time taken for the merge phase and very scalable with increasing the number of processing cores and the generated partial clusters.We implemented the algorithm in Java, evaluated its scalability by using different number of processing cores, and using real and synthetic datasets containing up to several hundred million high-dimensional points. We used three scales of datasets to evaluate our implementation. For small scale, we use 50k, 100k, and 500k data points, obtaining up to a factor of 14.9 speedup when using 16 cores. For medium scale, we use 1.0m, 1.5m, and 1.9m data points, obtaining a factor of 109.2 speedup when using 128 cores. For large scale, we use 61.0m, 91.5m, and 115.9m data points, obtaining a factor of 8344.5 speedup when using 16384 cores.


title: Explainable Sentiment Analysis with Applications in Medicine

abstract: Sentiment Analysis can help to extract knowledge related to opinions and emotions from user generated text information. It can be applied in medical field for patients monitoring purposes. With the availability of large datasets, deep learning algorithms have become a state of the art also for sentiment analysis. However, deep models have the drawback of not being non human-interpretable, raising various problems related to model’s interpretability. Very few work have been proposed to build models that explain their decision making process and actions. In this work, we review the current sentiment analysis approaches and existing explainable systems. Moreover, we present a critical review of explainable sentiment analysis models and discussed the insight of applying explainable sentiment analysis in the medical field.


title: Subthreshold Mechanisms Underlying State-Dependent Modulation of Visual Responses

abstract: The processing of sensory information varies widely across behavioral states. However, little is known about how behavioral states modulate the intracellular activity of cortical neurons to effect changes in sensory responses. Here, we performed whole-cell recordings from neurons in upper-layer primary visual cortex of awake mice during locomotion and quiet wakefulness. We found that the signal-to-noise ratio for sensory responses was improved during locomotion by two mechanisms: (1) a decrease in membrane potential variability leading to a reduction in background firing rates and (2) an enhancement in the amplitude and reliability of visually evoked subthreshold responses mediated by an increase in total conductance and a depolarization of the stimulus-evoked reversal potential. Consistent with the enhanced signal-to-noise ratio for visual responses during locomotion, we demonstrate that performance is improved in a visual detection task during this behavioral state.


Part 5: Machine Translation Evaluation


title: Nitro: hardware-based system call tracing for virtual machines

abstract: Virtual machine introspection (VMI) describes the method of monitoring and analyzing the state of a virtual machine from the hypervisor level. This lends itself well to security applications, though the hardware virtualization support from Intel and AMD was not designed with VMI in mind. This results in many challenges for developers of hardware-supported VMI systems. This paper describes the design and implementation of our prototype framework, Nitro, for system call tracing and monitoring. Since Nitro is a purely VMI-based system, it remains isolated from attacks originating within the guest operating system and is not directly visible from within the guest. Nitro is extremely flexible as it supports all three system call mechanisms provided by the Intel ×86 architecture and has been proven to work in Windows, Linux, 32-bit, and 64-bit environments. The high performance of our system allows for real-time capturing and dissemination of data without hindering usability. This is supported by extensive testing with various guest operating systems. In addition, Nitro is resistant to circumvention attempts due to a construction called hardware rooting. Finally, Nitro surpasses similar systems in both performance and functionality.


title: Quantum error correction and entanglement spectrum in tensor networks

abstract: A sort of planar tensor networks with tensor constraints is investigated as a model for holography. We study the greedy algorithm generated by tensor constraints and propose the notion of critical protection (CP) against the action of greedy algorithm. For given tensor constraints, a CP tensor chain can be defined. We further find that the ability of quantum error correction (QEC), the non-flatness of entanglement spectrum (ES) and the correlation function can be quantitatively evaluated by the geometric structure of CP tensor chain. Four classes of tensor networks with different properties of entanglement is discussed. Thanks to tensor constraints and CP, the correlation function is reduced into a bracket of Matrix Production State and the result agrees with the one in conformal field theory.


title: Fatal child neglect.

abstract: Child neglect results from either acts of omission or of commission. Fatalities from neglect account for 30% to 40% of deaths caused by child maltreatment. Deaths may occur from failure to provide the basic needs of infancy such as food or medical care. Medical care may also be withheld because of parental religious beliefs. Inadequate supervision may contribute to a child's injury or death through adverse events involving drowning, fires, and firearms. Recognizing the factors contributing to a child's death is facilitated by the action of multidisciplinary child death review teams. As with other forms of child maltreatment, prevention and early intervention strategies are needed to minimize the risk of injury and death to children.


title: Automating the Approximate Record Matching Process

abstract: Abstract Data quality has many dimensions one of which is accuracy. Accuracy is usually compromised by errors accidentally or intensionally introduced in a database system. These errors result in inconsistent, incomplete, or erroneous data elements. For example, a small variation in the representation of a data object, produces a unique instantiation of the object being represented. In order to improve the accuracy of the data stored in a database system, we need to compare them either with real-world counterparts or with other data stored in the same or a different system. In this paper, we address the problem of matching records which refer to the same entity by computing their similarity. Exact record matching has limited applicability in this context since even simple errors like character transpositions cannot be captured in the record-linking process. Our methodology deploys advanced data-mining techniques for dealing with the high computational and inferential complexity of approximate record matching.


title: Genetics and Marker-assisted Selection of the Chemotype in Cannabis sativa L.

abstract: Cannabis sativa is an interesting crop for several industrial uses, but the legislations in Europe and USA require a tight control of cannabinoid type and content for cultivation and subsidies release. Therefore, cannabinoid survey by gas chromatography of materials under selection is an important step in hemp breeding. In this paper, a number of Cannabis accessions were examined for their cannabinoid composition. Their absolute and relative content was examined, and results are discussed in the light of both the current genetic model for cannabinoid’s inheritance, and the legislation’s requirements. In addition, the effectiveness of two different types of markers associated to the locus determining the chemotype in Cannabis was evaluated and discussed, as possible tools in marker-assisted selection in hemp, but also for possible applications in the forensic and pharmaceutical fields.


title: Reinforcement learning for sensing strategies

abstract: Since sensors have limited range and coverage, mobile robots often have to make decisions on where to point their sensors. A good sensing strategy allows a robot to collect information that is useful for its tasks. Most existing solutions to this active sensing problem choose the direction that maximally reduces the uncertainty in a single state variable. In more complex problem domains, however, uncertainties exist in multiple state variables, and they affect the performance of the robot in different ways. The robot thus needs to have more sophisticated sensing strategies in order to decide which uncertainties to reduce, and to make the correct trade-offs. In this work, we apply a least squares reinforcement learning method to solve this problem. We implemented and tested the learning approach in the RoboCup domain, where the robot attempts to reach a ball and accurately kick it into the goal. We present experimental results that suggest our approach is able to learn highly effective sensing strategies.


title: Unbiased offline recommender evaluation for missing-not-at-random implicit feedback

abstract: Implicit-feedback Recommenders (ImplicitRec) leverage positive only user-item interactions, such as clicks, to learn personalized user preferences. Recommenders are often evaluated and compared offline using datasets collected from online platforms. These platforms are subject to popularity bias (i.e., popular items are more likely to be presented and interacted with), and therefore logged ground truth data are Missing-Not-At-Random (MNAR). As a result, the widely used Average-Over-All (AOA) evaluator is biased toward accurately recommending trendy items. In this paper, we (a) investigate evaluation bias of AOA and (b) develop an unbiased and practical offline evaluator for implicit MNAR datasets using the Inverse-Propensity-Scoring (IPS) technique. Through extensive experiments using four real-world datasets and four widely used algorithms, we show that (a) popularity bias is widely manifested in item presentation and interaction; (b) evaluation bias due to MNAR data pervasively exists in most cases where AOA is used to evaluate ImplicitRec; and (c) the unbiased estimator significantly reduces the AOA evaluation bias by more than 30% in the Yahoo! music dataset in terms of the Mean Absolute Error (MAE).


title: Big Data Enabled Mobile Network Design for 5G and Beyond

abstract: Mobile communication networks are more and more characterized by the integration of distributed and centralized computing and storage resources. Big data capability thus available throughout such networks will not only deliver enhanced system performance, but also profoundly impact the design and standardization of the next-generation network architecture, protocol stack, signaling procedure, and physical- layer processing. In this article, a mobile network architecture enabled by big data analytics is proposed, which is capable of efficient resource orchestration, content distribution, and radio access network optimization. The protocol stack configuration at each access point and the processing optimization of each layer are presented. Key physical layer designs including reference signals and frame structure are discussed. Moreover, utilizing signals in the transform domains, such as delay, Doppler, and angle, may bring enlarged coherence time of the effective channels. It enables much simpler physical layer design, and effectively bridges the latency gap between big data cloud computing and real-time network optimization.


Prostaglandin E2 increases the tolerance of the rat liver to warm ischemia in absence of splanchnic congestion.


title: A hierarchical approach to recognition of handwritten Bangla characters

abstract: A novel hierarchical approach is presented here for optical character recognition (OCR) of handwritten Bangla words. Instead of dealing with isolated characters as found in selected works [T.K. Bhowmik, U. Bhattacharya, S.K. Parui, Recognition of Bangla handwritten characters using an MLP classifier based on stroke features, in: Proceedings of the ICONIP, Kolkata, India, 2004, pp. 814-819; K. Roy, U. Pal, F. Kimura, Bangla handwritten character recognition, in: Proceedings of the Second Indian International Conference on Artificial Intelligence (IICAI), 2005, pp. 431-443; S. Basu, N. Das, R. Sarkar, M. Kundu, M. Nasipuri, D.K. Basu, Handwritten Bangla alphabet recognition using an MLP based classifier, in: Proceedings of the Second National Conference on Computer Processing of Bangla, Dhaka, 2005, pp. 285-291; A.F.R. Rahman, R. Rahman, M.C. Fairhurst, Recognition of handwritten Bengali characters: a novel multistage approach, Pattern Recognition 35, 2002, pp. 997-1006; U. Bhattacharya, S.K. Parui, M. Sridhar, F. Kimura, Two-stage recognition of handwritten Bangla alphanumeric characters using neural classifiers, in: Proceedings of the Second Indian International Conference on Artificial Intelligence (IICAI), 2005, pp. 1357-1376; U. Bhattacharya, M. Sridhar, S.K. Parui, On recognition of handwritten Bangla characters, in: Proceedings of the ICVGIP-06, Lecture Notes in Computer Science, vol. 4338, 2006, pp. 817-828], the present approach segments a word image on Matra hierarchy, then recognizes the individual word segments and finally identifies the constituent characters of the word image through intelligent combination of recognition decisions of the associated word segments. Due to possible appearances of consecutive characters of Bangla words on overlapping character positions, segmentation of Bangla word images is not easy. For successful OCR of handwritten Bangla text, not only recognition but also segmentation of word images are important. In this respect the present hierarchical approach deals with both segmentation and recognition of handwritten Bangla word images for a complete solution to handwritten word recognition problem, an essential area of OCR of handwritten Bangla text. In dealing with certain category of word segments, created on Matra hierarchy, a sophisticated recognition technique, viz., two-pass approach [S. Basu, C. Chaudhury, M. Kundu, M. Nasipuri, D.K. Basu, A two pass approach to pattern classification, in: N.R. Pal et al. (Ed.), Lecture Notes in Computer Science, vol. 3316, ICONIP, Kolkata, 2004, pp. 781-786] is employed here. The degree of sophistication of the classification technique is also rationally tuned depending on various categories of word segments to be recognized. For example, the two-pass approach is employed here for recognizing middle zone character segments, whereas recognition of middle zone modified shapes of Bangla script is done through simple template matching. Considering learning and generalization abilities of multi layer perceptrons (MLPs), MLP based pattern classifiers are used here for most of the classification related tasks. A powerful feature set is also designed under this work for recognition of complex character patterns using three types of topological features, viz., longest-run features, modified shadow features and octant-centroid features. In a nutshell, the work deals with a practical problem of OCR of Bangla text involving recognition as well as segmentation of constituent characters of handwritten Bangla words.


title: Surgical treatment of Peyronie's disease by plaque incision and grafting with buccal mucosa.

abstract: BACKGROUND ::: Plaque incision and tunical grafting is widely used to correct penile curvatures secondary to Peyronie's disease (PD), but there is no consensus on the ideal graft to be used. ::: ::: ::: OBJECTIVE ::: To evaluate the efficacy, safety, and reproducibility of plaque incision and buccal mucosa grafting (BMG) in the correction of severe penile curvatures secondary to PD. ::: ::: ::: DESIGN, SETTING, AND PARTICIPANTS ::: Fifteen patients reporting normal erections and stable curvature (>12-mo duration) entered this prospective study carried out at two university hospitals. ::: ::: ::: INTERVENTION ::: All patients underwent plaque incision and BMG. ::: ::: ::: MEASUREMENTS ::: Preoperative evaluation included the International Index of Erectile Function (IIEF-5) and penile duplex ultrasounds with measurement of curvature and length of affected side. Follow-up visits were scheduled at 1, 3, 6, and 12 mo postoperatively, then yearly. Three-mo postoperative evaluation included IIEF-5, patient and partner satisfaction, and intracavernous injection test with evaluation of penile rigidity, straightness, and length; patient and partner satisfaction was recorded at all subsequent visits. ::: ::: ::: RESULTS AND LIMITATIONS ::: Mean patient age was 56.3 yr and mean penile curvature 72 degrees ; five patients had a two-sided curvature with mean second curvature of 37 degrees . There were no complications. All patients resumed unassisted intercourse 1 mo after surgery. Three-mo postoperative evaluation showed 100% penile straightening, 1.8-cm mean increase in length of affected side, no curvature recurrence or de novo erectile dysfunction, 1.6 mean increase in IIEF-5 score, and patient and partner satisfaction of 93.3% and 100%, respectively. Although results remained stable at subsequent follow-up (mean 13.1 mo), a greater number of patients and longer follow-up are needed before drawing any definite conclusions. ::: ::: ::: CONCLUSIONS ::: BMG provided excellent short-term results, probably because its prompt revascularisation, suggested by the fast return of spontaneous erections, prevented shrinkage, which is the main cause of graft failure. It also proved to be safe and reproducible, thus representing a valuable treatment option for PD.


title: A hybrid multi-criteria decision making model for ERP system selection

abstract: ERP system selection is one of the most important steps in an ERP implementation program and is critical to the system success. Due to inherent complexity of ERP systems, diversity of available alternatives in the market and limitation in resources selecting an appropriate ERP project is a risky, difficult and time-consuming task for the organizations. Although ERP system selection has been investigated in previous works vastly, the proposed models have some limitations and inadequacies. In this research, an in-depth study is implemented to identify and categorize ERP selection factors in four main groups. The degree to which the organizational requirements are satisfied by an ERP system is also embedded in the model and discussed as the fifth main decision factor. Then a comprehensive hybrid multi-criteria decision making model is developed in two stages using TOPSIS and PROMETHEE to evaluate and rank the alternatives. The proposed model is practical and quite simple in real world problems of selecting the most appropriate ERP system for the organizations.


title: Critical Success Factors for Adoption of Electronic Health Record Systems: Literature Review and Prescriptive Analysis

abstract: Adoption of electronic health record systems in healthcare has been relatively slow. This article proposes critical success factors for electronic health record adoption based on a comprehensive literature review and prescriptive analysis. The proposed 26 critical success factors for electronic health records are of value to both practice and research: practitioners are offered guidance with regard to managing electronic health record implementations toward adoption success, while researchers are offered a basis for further research about electronic health record implementation and adoption.


title: Kinect cane: an assistive system for the visually impaired based on the concept of object recognition aid

abstract: This paper proposes a novel concept to assist visually impaired individuals in recognizing three-dimensional objects in everyday environments. This concept is realized as a portable system that consists of a white cane, a Microsoft Kinect sensor, a numeric keypad, a tactile feedback device, and other components. By the use of the Kinect sensor, the system searches for an object that a visually impaired user instructs the system to find and then returns a searching result to the user via the tactile feedback device. The major advantage of the system is the ability to recognize the objects of various classes, such as chairs and staircases, out of detectable range of white canes. Furthermore, the system is designed to return minimum required information related to the instruction of a user so that the user can obtain necessary information more efficiently. The system is evaluated through two types of experiment: object recognition test and user study. The experimental results indicate that the system is promising as a means of helping visually impaired users recognize objects.


title: A User-Level Approach for ARINC 653 Temporal Partitioning in seL4

abstract: ARINC 653 provides a strong isolation mechanism for safety computing fields, such as aircrafts. seL4, a 3rd generation microkernel, was formally verified for its functional correctness and provides a desirable code base for partitioning operating systems. But there is a long way from seL4 to partitioning. We take the first step and focus on the temporal aspect, i.e., implementing a partitioned scheduler based on seL4. However, seL4 implements a stiff scheduler inside the kernel, which conflicts with the genera principles(eg. ARINC 653 standard) of temporal partitioning. To address this problem, we propose a user-mode approach. We also elaborate performance of our scheduler.


Mixed-initiative interaction


title: A 3D laser and vision based classifier

abstract: This paper presents a method for modelling semantic content in scenes, in order to facilitate urban driving. More specifically, it presents a 3D classifier based on Velodyne data and monocular color imagery. The system contains two main components: a ground model and an object model. The ground model is a novel extension of elevation maps using Conditional Random Fields. It allows estimation of ground type (grass vs. asphalt) in addition to modelling the geometry of the scene. The object model involves two segmentation procedures. The first is a novel extension of elevation maps to a hierarchical clustering algorithm. The second is a new algorithm for defining regions of interest in images, which reasons jointly in the 3D Cartesian frame and the image plane. These two procedures provide a segmentation of the objects in the 3D laser data and in the images. Based on the resulting segmentation, object classification is implemented using a rule based system to combine binary deterministic and probabilistic features. The overall 3D classifier is tested on logs acquired by the MIT Urban Grand Challenge 2007 vehicle. The classifier achieves an accuracy of 89% on a set of 500 scenes involving 16 classes. The proposed approach is evaluated against seven other standard classification algorithms, and is shown to produce superior performance.


title: Direct Neural-Network Hardware-Implementation Algorithm

abstract: An algorithm for compact neural-network hardware implementation is presented, which exploits the special properties of the Boolean functions describing the operation of artificial neurons with step activation function. The algorithm contains three steps: artificial-neural-network (ANN) mathematical model digitization, conversion of the digitized model into a logic-gate structure, and hardware optimization by elimination of redundant logic gates. A set of C++ programs automates algorithm implementation, generating an optimized very high speed integrated circuit hardware description language code. This strategy bridges the gap between ANN design software and hardware design packages (Xilinx). Although the method is directly applicable only to neurons with step activation functions, it can be extended to sigmoidal functions.


title: Comparator Networks

abstract: The objective of this work is set-based verification, e.g. to decide if two sets of images of a face are of the same person or not. The traditional approach to this problem is to learn to generate a feature vector per image, aggregate them into one vector to represent the set, and then compute the cosine similarity between sets. Instead, we design a neural network architecture that can directly learn set-wise verification. Our contributions are: (i) We propose a Deep Comparator Network (DCN) that can ingest a pair of sets (each may contain a variable number of images) as inputs, and compute a similarity between the pair--this involves attending to multiple discriminative local regions (landmarks), and comparing local descriptors between pairs of faces; (ii) To encourage high-quality representations for each set, internal competition is introduced for recalibration based on the landmark score; (iii) Inspired by image retrieval, a novel hard sample mining regime is proposed to control the sampling process, such that the DCN is complementary to the standard image classification models. Evaluations on the IARPA Janus face recognition benchmarks show that the comparator networks outperform the previous state-of-the-art results by a large margin.


title: Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review

abstract: Falls in older adults present a major growing healthcare challenge and reliable detection ::: of falls is crucial to minimise their consequences. The majority of development and testing has ::: used laboratory simulations. As simulations do not cover the wide range of real-world scenarios ::: performance is poor when retested using real-world data. There has been a move from the use of ::: simulated falls towards the use of real-world data. This review aims to assess the current methods ::: for real-world evaluation of fall detection systems, identify their limitations and propose improved ::: robust methods of evaluation. Twenty-three articles met the inclusion criteria and were assessed with ::: regard to the composition of the datasets, data processing methods and the measures of performance. ::: Real-world tests of fall detection technology are inherently challenging and it is clear the field is in ::: it’s infancy. Most studies used small datasets and studies differed on how to quantify the ability to ::: avoid false alarms and how to identify non-falls, a concept which is virtually impossible to define and ::: standardise. To increase robustness and make results comparable, larger standardised datasets are ::: needed containing data from a range of participant groups. Measures which depend on the definition ::: and identification of non-falls should be avoided. Sensitivity, precision and F-measure emerged as the ::: most suitable robust measures for evaluating the real-world performance of fall detection systems.


title: Nearest-neighbor Projected-Distance Regression (NPDR) for detecting network interactions with adjustments for multiple tests and confounding

abstract: Machine learning feature selection methods are needed to detect complex interaction-network effects in complicated modeling scenarios in high-dimensional data, such as GWAS, gene expression, eQTL, and structural/functional neuroimage studies for case-control or continuous outcomes. In addition, many machine learning methods have limited ability to address the issues of controlling false discoveries and adjusting for covariates. To address these challenges, we develop a new feature selection technique called Nearest-neighbor Projected-Distance Regression (NPDR) that calculates the importance of each predictor using generalized linear model (GLM) regression of distances between nearest-neighbor pairs projected onto the predictor dimension. NPDR captures the underlying interaction structure of data using nearest-neighbors in high dimensions, handles both dichotomous and continuous outcomes and predictor data types, statistically corrects for covariates, and permits statistical inference and penalized regression. We use realistic simulations with interactions and other effects to show that NPDR has better precision-recall than standard Relief-based feature selection and random forest importance, with the additional benefit of covariate adjustment and multiple testing correction. Using RNA-Seq data from a study of major depressive disorder (MDD), we show that NPDR with covariate adjustment removes spurious associations due to confounding. We apply NPDR to eQTL data to identify potentially interacting variants that regulate transcripts associated with MDD and demonstrate NPDR’s utility for GWAS and continuous outcomes.


title: Unsupervised Document Classification with Informed Topic Models

abstract: Document classification is an important and common application in natural language processing. Scaling classification approaches to many targets faces a bottleneck in acquiring gold standard labels. In this work, we develop and evaluate a method for using informed topic models to noisily label documents, creating a noisy but usable set of labels for training discriminative classifiers. We investigate multiple ways to train this noisy classifier, and the best performing method uses Wikipedia-seeded topic models to approximately label training instances without any supervision. We evaluate these methods on the classification task as well as in an active learning setting, in which they are shown to improve learning rates over traditional active learning.


title: The World of Emotions is not Two-Dimensional

abstract: For more than half a century, emotion researchers have attempted to establish the dimensional space that most economically accounts for similarities and differences in emotional experience. Today, many researchers focus exclusively on two-dimensional models involving valence and arousal. Adopting a theoretically based approach, we show for three languages that four dimensions are needed to satisfactorily represent similarities and differences in the meaning of emotion words. In order of importance, these dimensions are evaluationpleasantness, potency-control, activation-arousal, and unpredictability. They were identified on the basis of the applicability of 144 features representing the six components of emotions: (a) appraisals of events, (b) psychophysiological changes, (c) motor expressions, (d) action tendencies, (e) subjective experiences, and (f) emotion regulation.


title: Cross-view activity recognition using Hankelets

abstract: Human activity recognition is central to many practical applications, ranging from visual surveillance to gaming interfacing. Most approaches addressing this problem are based on localized spatio-temporal features that can vary significantly when the viewpoint changes. As a result, their performances rapidly deteriorate as the difference between the viewpoints of the training and testing data increases. In this paper, we introduce a new type of feature, the “Hankelet” that captures dynamic properties of short tracklets. While Hankelets do not carry any spatial information, they bring invariant properties to changes in viewpoint that allow for robust cross-view activity recognition, i.e. when actions are recognized using a classifier trained on data from a different viewpoint. Our experiments on the IXMAS dataset show that using Hanklets improves the state of the art performance by over 20%.


title: Detecting Interesting Event Sequences for Sports Reporting

abstract: Hand-crafted approaches to content determination are expensive to port to new domains. Machine-learned approaches, on the other hand, tend to be limited to relatively simple selection of items from data sets. We observe that in time series domains, textual descriptions often aggregate a series of events into a compact description. We present a simple technique for automatically determining sequences of events that are worth reporting, and evaluate its effectiveness.


title: Consensus decision making in animals.

abstract: Individual animals routinely face decisions that are crucial to their fitness. In social species, however, many of these decisions need to be made jointly with other group members because the group will split apart unless a consensus is reached. Here, we review empirical and theoretical studies of consensus decision making, and place them in a coherent framework. In particular, we classify consensus decisions according to the degree to which they involve conflict of interest between group members, and whether they involve either local or global communication; we ask, for different categories of consensus decision, who makes the decision, what are the underlying mechanisms, and what are the functional consequences. We conclude that consensus decision making is common in non-human animals, and that cooperation between group members in the decision-making process is likely to be the norm, even when the decision involves significant conflict of interest.


title: Lens based 77 GHz TDM MIMO radar sensor for angular estimation in multitarget environments

abstract: The demanding tasks for automotive radar systems in multi target scenarios require an increased target separation performance and new sensor concepts. In this paper a highly integrated 77 GHz TDM MIMO radar is presented. The sensor is feasible for advanced DOA estimation in azimuth and elevation. For efficient and high quality measurements a fractional-n PLL with integrated waveform generator, enabling chirp and FMCW modulations, is implemented. Spatial beamforming is done with series feed array patch antennas in combination with a dielectric cylindrical lens. Due to the usage of switchable MIMO techniques the sensor performance can be significantly increased.


title: An equilibrium model with restricted stock market participation

abstract: This article solves the equilibrium problem in a pure-exchange, continuous-time economy in which some agents face information costs or other types of frictions effectively preventing them from investing in the stock market. Under the assumption that the restricted agents have logarithmic utilities, a complete characterization of equilibrium prices and consumption/investment policies is provided. A simple calibration shows that the model can help resolve some of the empirical asset pricing puzzles. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.


title: A robust multiframe super-resolution algorithm based on half-quadratic estimation with modified BTV regularization

abstract: Multiframe image super-resolution is a technique to reconstruct a high-resolution image by fusing a sequence of low-resolution images of the same scene. In this paper, we propose a new multiframe image super-resolution algorithm built on the regularization framework. The objective functional to be minimized for the regularization framework consists of a fidelity term and a regularization term. A new adaptive norm combining the advantages of traditional L"1 and L"2 norms is used in both terms. The fidelity term is then formed by an adaptive strategy depending on the accuracies of the estimated low-resolution image observation models. This strategy serves to adaptively weight low-resolution images according to their reliability and can add robustness in practical implementation of super-resolution. The proposed regularization term can preserve sharp edges well without producing visual artifacts. Our experimental results using both synthetic and real data show the performance improvement of the proposed algorithm over other methods.


title: Unfolding the Earth : Myriahedral Projections

abstract: Myriahedral projections are a new class of methods for mapping the earth. The globe is projected on a myriahedron, a polyhedron with a very large number of faces. Next, this polyhedron is cut open and unfolded. The resulting maps have a large number of interrupts, but are (almost) conformal and conserve areas. A general approach is presented to decide where to cut the globe, followed by three different types of solution. These follow from the use of meshes based on the standard graticule, the use of recursively subdivided polyhedra and meshes derived from the geography of the earth. A number of examples are presented, including maps for tutorial purposes, optimal foldouts of Platonic solids, and a map of the coastline of the earth.


title: Real Time Systems Design Principles For Distributed Embedded Applications

abstract: "This book is a comprehensive text for the design of safety critical, hard real-time embedded systems. It offers a splendid example for the balanced, integrated treatment of systems and software engineering, helping readers tackle the hardest problems of advanced real-time system design, such as determinism, compositionality, timing and fault management. This book is an essential reading for advanced undergraduates and graduate students in a wide range of disciplines impacted by embedded computing and software. Its conceptual clarity, the style of explanations and the examples make the abstract conceptsaccessible for a wide audience."Janos Sztipanovits, DirectorE. Bronson Ingram Distinguished Professor of EngineeringInstitute for Software Integrated SystemsVanderbilt UniversityReal-Time Systems focuses on hard real-time systems, which are computing systems that must meet their temporal specification in all anticipated load and fault scenarios. The book stresses the system aspects of distributed real-time applications, treating the issues of real-time, distribution and fault-tolerance from an integral point of view. A unique cross-fertilization of ideas and concepts between the academic and industrial worlds has led to the inclusion of many insightful examples from industry to explain the fundamental scientific concepts in a real-world setting. Compared to the first edition, new developments incomplexity management,energy and power management, dependability, security, andthe internet of things, are addressed. The book is written as a standard textbook for a high-level undergraduate or graduate course on real-time embedded systems or cyber-physical systems. Its practical approach to solving real-time problems, along with numerous summary exercises, makes it an excellent choice for researchers and practitioners alike.


title: Efficient SVM Regression Training with SMO

abstract: The sequential minimal optimization algorithm (SMO) has been shown to be an effective method for training support vector machines (SVMs) on classification tasks defined on sparse data sets. SMO differs from most SVM algorithms in that it does not require a quadratic programming solver. In this work, we generalize SMO so that it can handle regression problems. However, one problem with SMO is that its rate of convergence slows down dramatically when data is non-sparse and when there are many support vectors in the solution—as is often the case in regression—because kernel function evaluations tend to dominate the runtime in this case. Moreover, caching kernel function outputs can easily degrade SMO's performance even more because SMO tends to access kernel function outputs in an unstructured manner. We address these problems with several modifications that enable caching to be effectively used with SMO. For regression problems, our modifications improve convergence time by over an order of magnitude.


title: Task adapted reconstruction for inverse problems

abstract: The paper considers the problem of performing a task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem. A key aspect is to formalize the steps of reconstruction and task as appropriate estimators (non-randomized decision rules) in statistical estimation problems. The implementation makes use of (deep) neural networks to provide a differentiable parametrization of the family of estimators for both steps. These networks are combined and jointly trained against suitable supervised training data in order to minimize a joint differentiable loss function, resulting in an end-to-end task adapted reconstruction method. The suggested framework is generic, yet adaptable, with a plug-and-play structure for adjusting both the inverse problem and the task at hand. More precisely, the data model (forward operator and statistical model of the noise) associated with the inverse problem is exchangeable, e.g., by using neural network architecture given by a learned iterative method. Furthermore, any task that is encodable as a trainable neural network can be used. The approach is demonstrated on joint tomographic image reconstruction, classification and joint tomographic image reconstruction segmentation.


title: Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints

abstract: Neural conversation models tend to generate safe, generic responses for most inputs. This is due to the limitations of likelihood-based decoding objectives in generation tasks with diverse outputs, such as conversation. To address this challenge, we propose a simple yet effective approach for incorporating side information in the form of distributional constraints over the generated responses. We propose two constraints that help generate more content rich responses that are based on a model of syntax and topics (Griffiths et al., 2005) and semantic similarity (Arora et al., 2016). We evaluate our approach against a variety of competitive baselines, using both automatic metrics and human judgments, showing that our proposed approach generates responses that are much less generic without sacrificing plausibility. A working demo of our code can be found at this https URL.


title: Design pattern mining enhanced by machine learning

abstract: Design patterns present good solutions to frequently occurring problems in object-oriented software design. Thus their correct application in a system's design may significantly improve its internal quality attributes such as reusability and maintainability. In software maintenance the existence of up-to-date documentation is crucial, so the discovery of as yet unknown design pattern instances can help improve the documentation. Hence a reliable design pattern recognition system is very desirable. However, simpler methods (based on pattern matching) may give imprecise results due to the vague nature of the patterns' structural description. In previous work we presented a pattern matching-based system using the Columbus framework with which we were able to find pattern instances from the source code by considering the patterns' structural descriptions only, and therefore we could not identify false hits and distinguish similar design patterns such as state and strategy. In the present work we use machine learning to enhance pattern mining by filtering out as many false hits as possible. To do so we distinguish true and false pattern instances with the help of a learning database created by manually tagging a large C++ system.


title: AdapNet: Adaptive semantic segmentation in adverse environmental conditions

abstract: Robust scene understanding of outdoor environments using passive optical sensors is a onerous and essential task for autonomous navigation. The problem is heavily characterized by changing environmental conditions throughout the day and across seasons. Robots should be equipped with models that are impervious to these factors in order to be operable and more importantly to ensure safety in the real-world. In this paper, we propose a novel semantic segmentation architecture and the convoluted mixture of deep experts (CMoDE) fusion technique that enables a multi-stream deep neural network to learn features from complementary modalities and spectra, each of which are specialized in a subset of the input space. Our model adaptively weighs class-specific features of expert networks based on the scene condition and further learns fused representations to yield robust segmentation. We present results from experimentation on three publicly available datasets that contain diverse conditions including rain, summer, winter, dusk, fall, night and sunset, and show that our approach exceeds the state-of-the-art. In addition, we evaluate the performance of autonomously traversing several kilometres of a forested environment using only the segmentation for perception.


Exercise in Computer Vision A Comparison of Thresholding Methods – NTNU –


title: Co-regularized least square regression for multi-view multi-class classification

abstract: Many classification problems involve instances that are unlabeled, multi-view and multi-class. However, few technique has been benchmarked for this complex scenario, with a notable exception that combines co-trained naive bayes (CoT-NB) with BCH coding. In this paper, we benchmark the performance of co-regularized least square regression (CoR-LS) for semi-supervised multi-view multi-class classification. We find it performed consistently and significantly better than CoT-NB over eight data sets at different scales. We also find for CoR-LS identity coding is optimal on large data sets and BCH coding is optimal on small data sets. Optimal scoring, a data-dependent coding scheme, often provides near-optimal performance.


title: Applying The Proactor Pattern To High-Performance Web Servers

abstract: Modern operating systems provide multiple concurrency mechanisms to develop high-performance Web servers. Synchronous multi-threading is a popular mechanism for developing Web servers that must perform multiple operations simultaneously to meet their performance requirements. In addition, an increasing number of operating systems support asynchronous mechanisms that provide the benefits of concurrency, while alleviating much of the performance overhead of synchronous multi-threading. This paper provides two contributions to the study of high-performance Web servers. First, it examines how synchronous and asynchronous event dispatching mechanisms impact the design and performance of JAWS, which is our high-performance Web server framework. The results reveal significant performance improvements when a proactive concurrency model is used to combine lightweight concurrency with asynchronous event dispatching. In general, however, the complexity of the proactive concurrency model makes it harder to program applications that can utilize asynchronous concurrency mechanisms effectively. Therefore, the second contribution of this paper describes how to reduce the software complexity of asynchronous concurrent applications by applying the Proactor pattern. This pattern describes the steps required to structure object-oriented applications that seamlessly combine concurrency with asynchronous event dispatching. The Proactor pattern simplifies concurrent programming and improves performance by allowing concurrent application to have multiple operations running simultaneously without requiring a large number of threads. This work was supported in part by Siemens Med and Siemens Corporate Research.


title: Detecting dynamical changes in time series using the permutation entropy

abstract: Timely detection of unusual and/or unexpected events in natural and man-made systems has deep scientific and practical relevance. We show that the recently proposed conceptually simple and easily calculated measure of permutation entropy can be effectively used to detect qualitative and quantitative dynamical changes. We illustrate our results on two model systems as well as on clinically characterized brain wave data from epileptic patients.


title: A simulation study of the number of events per variable in logistic regression analysis

abstract: Abstract We performed a Monte Carlo study to evaluate the effect of the number of events per variable (EPV) analyzed in logistic regression analysis. The simulations were based on data from a cardiac trial of 673 patients in which 252 deaths occurred and seven variables were cogent predictors of mortality; the number of events per predictive variable was (2527 = 36) for the full sample. For the simulations, at values of EPV=2, 5, 10, 15, 20, and 25, we randomly generated 500 samples of the 673 patients, chosen with replacement, according to a logistic model derived from the full sample. Simulation results for the regression coefficients for each variable in each group of 500 samples were compared for bias, precision, and significance testing against the results of the model fitted to the original sample. For EPV values of 10 or greater, no major problems occurred. For EPV values less than 10, however, the regression coefficients were biased in both positive and negative directions; the large sample variance estimates from the logistic model both overestimated and underestimated the sample variance of the regression coeffi-cients; the 90% confidence limits about the estimated values did not have proper coverage; the Wald statistic was conservative under the null hypothesis; and paradoxical associations (significance in the wrong direction) were increased. Although other factors (such as the total number of events, or sample size) may influence the validity of the logistic model, our findings indicate that low EPV can lead to major problems.


title: Recurrent Network Models Of Sequence Generation And Memory

abstract: Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for sequences and memory emphasized specialized architectures in which a principled mechanism is pre-wired into their connectivity. Here we demonstrate that, starting from random connectivity and modifying a small fraction of connections, a largely disordered recur- rent network can produce sequences and implement working memory efficiently. We use this process, called Partial In-Network Training (PINning), to model and match cellular resolution imaging data from the posterior parietal cortex during a virtual memory- guided two-alternative forced-choice task. Analysis of the connectivity reveals that sequences propagate by the cooperation between recurrent synaptic interactions and external inputs, rather than through feedforward or asymmetric connections. Together our results suggest that neural sequences may emerge through learning from largely unstructured network architectures.


title: Drift-free dynamic height sensor using MEMS IMU aided by MEMS pressure sensor

abstract: We demonstrate a low-cost, low-power, and small form factor solution to drift-free high-resolution vertical positioning by fusing MEMS accelerometers with MEMS barometric altimeter. In this system, the highly responsive but drift-prone aspect of the MEMS accelerometers is stabilized by barometric altimeter and high-fidelity height tracking is achieved. Typical vertical human movements such as walking up or down a staircase can be tracked in real-time with this system. The height tracking performance is benchmarked against a reference system using a tactical-grade IMU and an error analysis is performed.


title: Four Neighbourhood Cellular Automata as Better Cryptographic Primitives.

abstract: Three-neighbourhood Cellular Automata (CA) are widely studied and accepted as suitable cryptographic primitive. Rule 30, a 3-neighbourhood CA rule, was proposed as an ideal candidate for cryptographic primitive by Wolfram. However, rule 30 was shown to be weak against Meier-Staffelbach attack [7]. The cryptographic properties like diffusion and randomness increase with increase in neighbourhood radius and thus opens the avenue of exploring the cryptographic properties of 4neighbourhood CA. This work explores whether four-neighbourhood CA can be a better cryptographic primitive. We construct a class of cryptographically suitable 4-neighbourhood nonlinear CA rules that resembles rule 30. One 4-neighbourhood nonlinear CA from this selected class is shown to be resistant against Meier-Staffelbach attack on rule 30, justifying the applicability of 4-neighbourhood CA as better cryptographic primitives.


title: Image Denoising and Decomposition with Total Variation Minimization and Oscillatory Functions

abstract: In this paper, we propose a new variational model for image denoising and decomposition, witch combines the total variation minimization model of Rudin, Osher and Fatemi from image restoration, with spaces of oscillatory functions, following recent ideas introduced by Meyer. The spaces introduced here are appropriate to model oscillatory patterns of zero mean, such as noise or texture. Numerical results of image denoising, image decomposition and texture discrimination are presented, showing that the new models decompose better a given image, possible noisy, into cartoon and oscillatory pattern of zero mean, than the standard ones. The present paper develops further the models previously introduced by the authors in Vese and Osher (Modeling textures with total variation minimization and oscillating patterns in image processing, UCLA CAM Report 02-19, May 2002, to appear in Journal of Scientific Computing, 2003). Other recent and related image decomposition models are also discussed.


title: Mining StackOverflow to turn the IDE into a self-confident programming prompter

abstract: Developers often require knowledge beyond the one they possess, which often boils down to consulting sources of information like Application Programming Interfaces (API) documentation, forums, Q&A websites, etc. Knowing what to search for and how is non- trivial, and developers spend time and energy to formulate their problems as queries and to peruse and process the results. We propose a novel approach that, given a context in the IDE, automatically retrieves pertinent discussions from Stack Overflow, evaluates their relevance, and, if a given confidence threshold is surpassed, notifies the developer about the available help. We have implemented our approach in Prompter, an Eclipse plug-in. Prompter has been evaluated through two studies. The first was aimed at evaluating the devised ranking model, while the second was conducted to evaluate the usefulness of Prompter.


title: Analyzing the dynamics of free recall: An integrative review of the empirical literature

abstract: Relatively few experiments have measured the time course of free recall from episodic or se mantic memory. Of those that have, most report that cumulative recall is a negatively acceler ated exponential (or hyperbolic) function that is characterized by two properties: asymptotic re call and rate of approach to asymptote. The most common measure of free recall performance (viz., the number of items recalled) provides a reasonably good estimate of asymptotic recall if a relatively long recall period is used (which is rare), but the effect of experimental manipula tions on the rate of approach to asymptote cannot be determined without timing when recall re sponses occur. The research reviewed herein suggests that the rate of approach to asymptote may offer an estimate of the breadth of search through long-term memory. The search in question, unlike most ofthose investigated in the memory literature, is unique in that it requires minutes rather than milliseconds to complete. When subjects attempt to generate items from a seman tic category or to recall a recently presented list of words, they do not complete the task in an instant. Instead, their performance is almost invariably characterized by inter mittent successes occurring over an extended period of time. In spite of this interesting fact, memory researchers have exhibited an overwhelming preference for record ing which (or how many) items are recalled during some brief period of time, regardless of the speed with which those items have been retrieved. Does the time course of free recall offer any useful information about the nature of retrieval? Because this subject is an old one that com mands little attention today, one might assume that the answer is no. However, a review ofthe extant literature, limited as it is, suggests otherwise. Exactly what can be learned that is not already known by studying the dynamics of free recall? First, the studies that have tracked the time course of recall usually reveal that subjects continue to make progress far beyond the 1- or 2-min recall period typically provided. Indeed, Roediger and Thorpe (1978)-to take one example that will be considered in detail later-reported that recall on an episodic memory task continued to increase more than 20 min into the recall period. Moreover, a clear differ ence in the level of recall between two conditions (easily named pictures vs. words) became evident only after sev eral minutes had elapsed. Had these authors used the stan dard brief recall period, they might have mistakenly con cluded that the two conditions produced equal levels of performance. How often are incorrect conclusions arrived at because the recall period is too brief? The answer is


title: MPPT of photovoltaic systems using extremum - seeking control

abstract: A stability analysis for a maximum power point tracking (MPPT) scheme based on extremum-seeking control is developed for a photovoltaic (PV) array supplying a dc-to-dc switching converter. The global stability of the extremum-seeking algorithm is demonstrated by means of Lyapunov's approach. Subsequently, the algorithm is applied to an MPPT system based on the "perturb and observe" method. The steady-state behavior of the PV system with MPPT control is characterized by a stable oscillation around the maximum power point. The tracking algorithm leads the array coordinates to the maximum power point by increasing or decreasing linearly with time the array voltage. Off-line measurements are not required by the control law, which is implemented by means of an analog multiplier, standard operational amplifiers, a flip-flop circuit and a pulsewidth modulator. The effectiveness of the proposed MPPT scheme is demonstrated experimentally under different operating conditions.


title: Leveraging existing instrumentation to automatically infer invariant-constrained models

abstract: Computer systems are often difficult to debug and understand. A common way of gaining insight into system behavior is to inspect execution logs and documentation. Unfortunately, manual inspection of logs is an arduous process and documentation is often incomplete and out of sync with the implementation. This paper presents Synoptic, a tool that helps developers by inferring a concise and accurate system model. Unlike most related work, Synoptic does not require developer-written scenarios, specifications, negative execution examples, or other complex user input. Synoptic processes the logs most systems already produce and requires developers only to specify a set of regular expressions for parsing the logs. Synoptic has two unique features. First, the model it produces satisfies three kinds of temporal invariants mined from the logs, improving accuracy over related approaches. Second, Synoptic uses refinement and coarsening to explore the space of models. This improves model efficiency and precision, compared to using just one approach. In this paper, we formally prove that Synoptic always produces a model that satisfies exactly the temporal invariants mined from the log, and we argue that it does so efficiently. We empirically evaluate Synoptic through two user experience studies, one with a developer of a large, real-world system and another with 45 students in a distributed systems course. Developers used Synoptic-generated models to verify known bugs, diagnose new bugs, and increase their confidence in the correctness of their systems. None of the developers in our evaluation had a background in formal methods but were able to easily use Synoptic and detect implementation bugs in as little as a few minutes.


title: Computer-Aided Diagnosis of Thyroid Nodule: A Review

abstract: Diagnostic imaging is an important tool in medical science due to the constrained observations of the expert and uncertainties in medical knowledge. A thyroid ultrasound is a non-invasive imaging study used to detect and classify abnormalities of the thyroid gland. Computerized system is a valuable and beneficial means for feature extraction and classification of thyroid nodule in order to eliminate operator dependency and to improve the diagnostic accuracy. The aim of this paper is to review existing approaches to the automatic classification of nodules in thyroid ultrasound images, highlighting the keypoints and main differences between the used strategies. The aim of this paper is to review existing approaches for the diagnosis of Nodules in thyroid ultrasound images with their performance measures.


title: Dual-Band CPW-Fed Transparent Antenna for Active RFID Tags

abstract: A dual-band coplanar-waveguide (CPW)-fed transparent antenna for active radio-frequency identification (RFID) tags working at 2.45 GHz and 5.8 GHz is proposed. AgHT-8 thin film, includes the silver-coated polyester as the conductive layer, and polyethylene terephthalate as the substrate, is used to design and fabricate the antenna with high transparency of above 80%, which is sufficient to provide the ability to laminate the surface of the tag devices with little chance of detection. The overall dimension size of the proposed design is ${\hbox {36}} \times {\hbox {39}} \times {\hbox {0.175}}~\hbox{mm}^{3}$ . The measured bandwidth of 59.34% (1.6 to 2.95 GHz) and 16.95% (5.4 to 6.4 GHz) is achieved for the lower band and the upper band, respectively. The proposed antenna has considerably less thickness and a lighter structure compared with the nontransparent designs.


title: Hobbit, a care robot supporting independent living at home

abstract: One option to address the challenge of demographic transition is to build robots that enable aging in place. Falling has been identified as the most relevant factor to cause a move to a care facility. The Hobbit project combines research from robotics, gerontology, and human-robot interaction to develop a care robot which is capable of fall prevention and detection as well as emergency detection and handling. Moreover, to enable daily interaction with the robot, other functions are added, such as bringing objects, offering reminders, and entertainment. The interaction with the user is based on a multimodal user interface including automatic speech recognition, text-to-speech, gesture recognition, and a graphical touch-based user interface. We performed controlled laboratory user studies with a total of 49 participants (aged 70 plus) in three EU countries (Austria, Greece, and Sweden). The collected user responses on perceived usability, acceptance, and affordability of the robot demonstrate a positive reception of the robot from its target user group. This article describes the principles and system components for navigation and manipulation in domestic environments, the interaction paradigm and its implementation in a multimodal user interface, the core robot tasks, as well as the results from the user studies, which are also reflected in terms of lessons we learned and we believe are useful to fellow researchers. We present a care robot for aging in place by means of fall prevention/detection.Detailed description of sensor set-up, hardware, and the multimodal user interface.Detailed description of major software components and implemented robot tasks.Proof-of-concept user study (49 user) on usability, acceptance, and affordability.


title: Capturing and Rendering with Incident Light Fields

abstract: This paper presents a process for capturing spatially and directionally varying illumination from a real-world scene and using this lighting to illuminate computer-generated objects. We use two devices for capturing such illumination. In the first we photograph an array of mirrored spheres in high dynamic range to capture the spatially varying illumination. In the second, we obtain higher resolution data by capturing images with an high dynamic range omnidirectional camera as it traverses across a plane. For both methods we apply the light field technique to extrapolate the incident illumination to a volume. We render computer-generated objects as illuminated by this captured illumination using a custom shader within an existing global illumination rendering system. To demonstrate our technique we capture several spatially-varying lighting environments with spotlights, shadows, and dappled lighting and use them to illuminate synthetic scenes. We also show comparisons to real objects under the same illumination.


title: Optical character recognition for cursive handwriting

abstract: A new analytic scheme, which uses a sequence of image segmentation and recognition algorithms, is proposed for the off-line cursive handwriting recognition problem. First, some global parameters, such as slant angle, baselines, stroke width and height, are estimated. Second, a segmentation method finds character segmentation paths by combining gray-scale and binary information. Third, a hidden Markov model (HMM) is employed for shape recognition to label and rank the character candidates. For this purpose, a string of codes is extracted from each segment to represent the character candidates. The estimation of feature space parameters is embedded in the HMM training stage together with the estimation of the HMM model parameters. Finally, information from a lexicon and from the HMM ranks is combined in a graph optimization problem for word-level recognition. This method corrects most of the errors produced by the segmentation and HMM ranking stages by maximizing an information measure in an efficient graph search algorithm. The experiments indicate higher recognition rates compared to the available methods reported in the literature.


title: Treadmill exercise ameliorates motor dysfunction through inhibition of Purkinje cell loss in cerebellum of valproic acid-induced autistic rats

abstract: Autism is a complex developmental disorder with impairments in social interaction, communication, repetitive behavior and motor skills. Exercise enhances cognitive function, ameliorates motor dysfunction, and provides protective profits against neurodegeneration. In the present study, we evaluated the effect of treadmill exercise on the motor coordination and Purkinje cell loss in relation with reactive astrocytes and microglial activation in the cerebellum using valproic acid (VPA)-induced autism rat model. On the 12th day of pregnancy, the pregnant rats in the VPA-exposed group received intraperitoneal injections of 600-mg/kg VPA. After birth, the rat pups were divided into four groups: the control group, the exercise group, the VPA-treated group, the VPA-treated and exercise group. The rat pups in the exercise groups were forced to run on a treadmill for 30 min once a day, 5 times a week for 4 weeks. In the present results, motor balance and coordination was disturbed by induction of autism, in contrast, treadmill exercise alleviated motor dysfunction in the autistic rats. Purkinje cell loss, reactive astrocytes, and microglial activation were occurred by induction of autism, in contrast, treadmill exercise enhanced survival rate of Purkinje neurons through inhibition of reactive astrocytes and microglia in the autistic rats. The present study showed that exercise may provide a potential therapeutic strategy for the alleviation of motor dysfunction in autistic patients.


title: Present and future of flexible automation: Towards new paradigms

abstract: Abstract Automation has been one of the key drivers of the modern Manufacturing Industry and it has been present in various forms from the beginning of the industrial era until today passing through different evolutions responding to human's needs. Therefore automation and the manufacturing industry have undergone several paradigm changes in the last century. They were driven by the market conditions and society needs and were realized by timely developed engineering enabling technologies that fitted the paradigm requirements. This paper maps the different paradigms in terms of market and societal drivers and process technology enablers in order to show a consistent model of paradigm development, a model that links the product, and the process with the appropriate business model. The Flexible Manufacturing Systems (FMS) have been especially analysed as the major enabler to the mass customization paradigm. Summing up, a mapping methodology, able to map all past, present and future production paradigms, is presented. An example on the footwear sector has also been mapped and presented. The analysis is based on a survey conducted in Europe and the USA mechanical industries during 2002. The analysis, carried out within the CIRP Working Group on “Flexible Automation - Assessment and Future” has shown that new paradigms are emerging beyond flexible automation, paradigms that require addressing new technological challenges. Developing these new enabling technologies requires the establishment of new national RTD programmes. Therefore, the role of past national RTD programmes in developing previous enabling technologies that eventually elevated human wealth and life quality is also briefly mentioned. Foresight scenario building and “roadmapping” activities -taking place in different relevant economic regions- are presented. They point to new paradigms and technologies to be developed and call for new RTD programmes to be launched.


title: Data-Driven Feature Characterization Techniques for Laser Printer Attribution

abstract: Laser printer attribution is an increasing problem with several applications, such as pointing out the ownership of crime proofs and authentication of printed documents. However, as commonly proposed methods for this task are based on custom-tailored features, they are limited by modeling assumptions about printing artifacts. In this paper, we explore solutions able to learn discriminant-printing patterns directly from the available data during an investigation, without any further feature engineering, proposing the first approach based on deep learning to laser printer attribution. This allows us to avoid any prior assumption about printing artifacts that characterize each printer, thus highlighting almost invisible and difficult printer footprints generated during the printing process. The proposed approach merges, in a synergistic fashion, convolutional neural networks (CNNs) applied on multiple representations of multiple data. Multiple representations, generated through different pre-processing operations, enable the use of the small and lightweight CNNs whilst the use of multiple data enable the use of aggregation procedures to better determine the provenance of a document. Experimental results show that the proposed method is robust to noisy data and outperforms existing counterparts in the literature for this problem.


title: Smart TV interaction system using face and hand gesture recognition

abstract: In this paper, a face and hand gesture recognition system which can be applied to a smart TV interaction system is proposed. Human face and natural hand gesture are the key component to interact with smart TV system. The face recognition system is used in viewer authentication and the hand gesture recognition in control of smart TV, for example, volume up/down, channel changing. Personalized service such as favorite channels recommendation or parental guidance can be provided using face recognition. We show that the face recognition detection rate is about 99% and the face recognition rate is about 97% by using DGIST database. Also, hand detection rate is about 98% at distance of 1 meter, 1.5 meter, and 2 meter, respectively. Overall 5 type hand gesture recognition rate is about 80% using support vector machine (SVM).


title: A New Measure of Consumer Expectations, Perceptions and Satisfaction for Patients and Carers of Older People With Mental Health Problems

abstract: Objective: To describe the development and evaluation of the Consumer Expectations, Perceptions and Satisfaction Scale (CEPAS).Methods: The content of CEPAS was developed from a focus group of patients and carers. A total of 115 consecutive patients discharged from a community mental health service for older people and their carers was asked to complete a postal survey.Results: The CEPAS was appropriate for 49% of patients (56/115) and 51% of carers (59/115). The response rates were 69% (39/56) for patients and 76% (45/59) for carers. Overall satisfaction had a significant association with the Client Satisfaction Questionnaire (r = 0.67, p < 0.001) and the Clinical Global Impression Scale (r = 0.29, p < 0.01).Conclusions: The CEPAS was an adequate measure of patient and carer satisfaction in this setting and for this population. More research is needed to evaluate CEPAS in other settings and with other populations.


title: A health monitoring system using multiple non-contact ECG sensors for automotive drivers

abstract: Health and alert-level monitoring of automotive driver is very important to reduce the number of vehicle accidents and associated fatalities. The proposed work focuses on the development of a reliable and low-cost health monitoring system for automotive drivers. It is based on non-contact electrocardiogram (ECG) principle. Multiple signal acquisition ECG electrodes are placed on the seat and seat-belt of the automotive. The signals from the different electrodes are interfaced to simple analog and digital signal processing units through a switching logic. The frequency domain-based digital processing and the switching logic ensures that best quality ECG signal is selected for heart rate (HR) estimation. A prototype of the proposed system is build and tested on several volunteers. These tests show that the accuracy is 2 bpm. Additional tests to determine the system performance in various conditions was conducted and results reported.


title: Adaptive nonlinear Volterra equalizer for mitigation of chirp-induced distortions in cost effective IMDD OFDM systems

abstract: We report experimental validations of an adaptive 2nd order Volterra equalization scheme for cost effective IMDD OFDM systems. This equalization scheme was applied to both uplink and downlink transmission. Downlink settings were optimized for maximum bitrate where we achieved 34Gb/s over 10km of SSMF using an EML with 10GHz bandwidth. For the uplink, maximum reach was optimized achieving 14Gb/s using a low-cost DML with 2.5GHz bandwidth.


title: Socially Constrained Structural Learning for Groups Detection in Crowd

abstract: Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of individuals. In this work, we propose a novel algorithm for detecting social groups in crowds by means of a Correlation Clustering procedure on people trajectories. The affinity between crowd members is learned through an online formulation of the Structural SVM framework and a set of specifically designed features characterizing both their physical and social identity, inspired by Proxemic theory, Granger causality, DTW and Heat-maps. To adhere to sociological observations, we introduce a loss function ( $G$ -MITRE) able to deal with the complexity of evaluating group detection performances. We show our algorithm achieves state-of-the-art results when relying on both ground truth trajectories and tracklets previously extracted by available detector/tracker systems.


title: Agonistic Properties of Cannabidiol at 5-HT1a Receptors

abstract: Cannabidiol (CBD) is a major, biologically active, but psycho-inactive component of cannabis. In this cell culture-based report, CBD is shown to displace the agonist, [3H]8-OH-DPAT from the cloned human 5-HT1a receptor in a concentration-dependent manner. In contrast, the major psychoactive component of cannabis, tetrahydrocannabinol (THC) does not displace agonist from the receptor in the same micromolar concentration range. In signal transduction studies, CBD acts as an agonist at the human 5-HT1a receptor as demonstrated in two related approaches. First, CBD increases [35S]GTPgammaS binding in this G protein coupled receptor system, as does the known agonist serotonin. Second, in this GPCR system, that is negatively coupled to cAMP production, both CBD and 5-HT decrease cAMP concentration at similar apparent levels of receptor occupancy, based upon displacement data. Preliminary comparative data is also presented from the cloned rat 5-HT2a receptor suggesting that CBD is active, but less so, relative to the human 5-HT1a receptor, in binding analyses. Overall, these studies demonstrate that CBD is a modest affinity agonist at the human 5-HT1a receptor. Additional work is required to compare CBD's potential at other serotonin receptors and in other species. Finally, the results indicate that cannabidiol may have interesting and useful potential beyond the realm of cannabinoid receptors.


title: ExtensionSticker: A Proposal for a Striped Pattern Sticker to Extend Touch Interfaces and its Assessment

abstract: In this study, we propose a striped pattern sticker called ExtensionSticker that allows a touch input to be transferred from an external source by simply attaching the sticker to a touch panel. This allows the user to input touches or continuous scrolling actions by touching a sticker printed with stripes of conductive ink, without directly touching the touch panel. This method could be applied to the sides or back of a touch panel, or even the surface upon which a device is located as a touch interface, allowing us to freely construct interfaces in shapes matching the demands of the user. This paper also reports the results of evaluation experiments conducted to assess the recognition accuracy of scroll and tap actions using the proposed method.


title: Generic and Scalable Framework for Automated Time-series Anomaly Detection

abstract: This paper introduces a generic and scalable framework for automated anomaly detection on large scale time-series data. Early detection of anomalies plays a key role in maintaining consistency of person's data and protects corporations against malicious attackers. Current state of the art anomaly detection approaches suffer from scalability, use-case restrictions, difficulty of use and a large number of false positives. Our system at Yahoo, EGADS, uses a collection of anomaly detection and forecasting models with an anomaly filtering layer for accurate and scalable anomaly detection on time-series. We compare our approach against other anomaly detection systems on real and synthetic data with varying time-series characteristics. We found that our framework allows for 50-60% improvement in precision and recall for a variety of use-cases. Both the data and the framework are being open-sourced. The open-sourcing of the data, in particular, represents the first of its kind effort to establish the standard benchmark for anomaly detection.


title: CNC-NOS: Class noise cleaning by ensemble filtering and noise scoring

abstract: Abstract Obtaining data in the real world is subject to imperfections and the appearance of noise is a common consequence of such flaws. In classification, class noise will deteriorate the performance of a classifier, as it may severely mislead the model building. Among the strategies emerged to deal with class noise, the most popular is that of filtering. However, instance filtering can be harmful as it may eliminate more examples than necessary or produce loss of information. An ideal option would be relabeling the noisy instances, avoiding losing data, but instance correcting is harder to achieve and may lead to wrong information being introduced in the dataset. For this reason, we advance a new proposal based on an ensemble of noise filters with the goal not only of accurately filtering the mislabeled instances, but also correcting them when possible. A noise score is also applied to support the filtering and relabeling process. The proposal, named CNC-NOS (Class Noise Cleaner with Noise Scoring), is compared against state-of-the-art noise filters and correctors, showing that it is able to deliver a quality training instance set that overcomes the limitations of such techniques, both in terms of classification accuracy and properly treated instances.


title: Taxonomy of DoS and DDoS attacks and desirable defense mechanism in a Cloud computing environment

abstract: As Cloud computing is reforming the infrastructure of IT industries, it has become one of the critical security concerns of the defensive mechanisms applied to secure Cloud environment. Even if there are tremendous advancements in defense systems regarding the confidentiality, authentication and access control, there is still a challenge to provide security against availability of associated resources. Denial-of-service (DoS) attack and distributed denial-of-service (DDoS) attack can primarily compromise availability of the system services and can be easily started by using various tools, leading to financial damage or affecting the reputation. These attacks are very difficult to detect and filter, since packets that cause the attack are very much similar to legitimate traffic. DoS attack is considered as the biggest threat to IT industry, and intensity, size and frequency of the attack are observed to be increasing every year. Therefore, there is a need for stronger and universal method to impede these attacks. In this paper, we present an overview of DoS attack and distributed DoS attack that can be carried out in Cloud environment and possible defensive mechanisms, tools and devices. In addition, we discuss many open issues and challenges in defending Cloud environment against DoS attack. This provides better understanding of the DDoS attack problem in Cloud computing environment, current solution space, and future research scope to deal with such attacks efficiently.


title: Design of Doherty Power Amplifiers for Handset Applications

abstract: In this paper, we analyze the power drive of a Doherty power amplifier (PA), and introduce a technique for proper input dividing without a coupler. For the proper Doherty operation, we place a phase compensation circuit at the input of the carrier amplifier. We also propose an output matching of Doherty PA to reduce the number of matching components, and to match the output impedances to enhance efficiency and linearity in consideration of the uneven input drive. The PA circuit is fabricated using a 2-μm InGaP/GaAs heterojunction bipolar transistor process and combined for Doherty operation using merged lumped components. For the IEEE 802.16e m-WiMAX signal, which has a 9.54-dB crest factor and 8.75-MHz bandwidth, the PA has an error vector magnitude of 3% and a power-added efficiency of 40.2% at an output power of 26 dBm.