title: Psychological and educational interventions for atopic eczema in children

abstract: Atopic eczema is an itchy inflammatory skin condition which affects the quality of life of children with eczema and their parents; it can affect up to 15% of school children in the UK. Psychological and educational approaches to treating eczema have been used to complement medication in managing eczema by, for example, promoting relaxation and educating parents and children to understand the condition and their role in its successful management. However, the effectiveness of these approaches has not been systematically reviewed. ::: ::: The main finding of the review is that there is currently only limited research evidence about the effectiveness of educational and psychological approaches when used with medicines for the treatment of childhood eczema. We were only able to include one study on the effectiveness of psychological approaches in the review. We included four educational studies, of which three identified that education decreased the severity of the eczema, and one study found that education improved quality of life for parents of children with eczema. Relaxation methods reduced the severity of the eczema, compared to discussion only, in the psychological study. Two different approaches have been used to deliver education; one led by a nurse and the other by a team of health professionals. ::: ::: Due to weaknesses in the quality of most of the research studies and the fact that different measures were used to evaluate effectiveness of the approaches, we cannot draw strong conclusions about whether psychological and educational approaches work or which is the best approach to use. More details are needed about the psychological and educational approaches used, to allow a greater understanding of the key factors that might help reduce eczema. Better description of the research methods used are also needed. Research priority should also be given to comparing the relative cost effectiveness of health professionals educating parents either in teams or by nurses alone. ::: ::: No adverse effects have been reported. ::: ::: Limitations of the review: We were able to find only five studies eligible for inclusion in the review and we were not able to combine findings from these studies due to the different ways in which effectiveness of the approaches were measured.


title: Linear PM Generator system for wave energy conversion in the AWS

abstract: The Archimedes Wave Swing is a system that converts ocean wave energy into electric energy. A pilot plant of this system has been built. The generator system consists of a permanent-magnet linear synchronous generator with a current source inverter (CSI). The correlation between the measured and the calculated parameters of the designed generator is reasonable. The annual energy yield of the pilot plant is calculated from the wave distribution as 1.64 GWh. Using a voltage source inverter instead of a CSI improves the power factor, the current waveforms, the efficiency and the generator force, so that the annual energy yield increases with 18%.


title: Fast gapped-read alignment with Bowtie 2

abstract: As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.


title: Fiji: an open-source platform for biological-image analysis

abstract: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.


title: The effect of films with and without subtitles on listening comprehension of EFL learners

abstract: The present study represented a preliminary effort to empirically examine the efficacy of subtitled movie on listening comprehension of intermediate English as a Foreign Language students. To achieve this purpose, out of a total of 200 intermediate students, 90 were picked based on a proficiency test. The material consisted of six episodes (approximately 5 minutes each) of a DVD entitled 'Wild Weather'. The students viewed only one of the three treatment conditions: English subtitles, Persian subtitles, no subtitles. After each viewing session, six sets of multiple-choice tests were administered to examine listening comprehension rates. The results revealed that the English subtitles group performed at a considerably higher level than the Persian subtitles group, which in turn performed at a substantially higher level than the no subtitle group on the listening test. [ABSTRACT FROM AUTHOR]


title: Manipulating the epileptic brain using stimulation: a review of experimental and clinical studies.

abstract: Neurostimulation represents an interesting alternative therapy for patients resistant to drug treatment or who cannot benefit from resective surgery. Theoretically, neurostimulation allows the control of seizures to be tailored to the individual patient and specific form of epilepsy. Here, we review both experimental and clinical studies that have reported the possible control of epileptic seizures by means of different approaches using electrical stimulation (vagus nerve stimulation, deep brain stimulation and repetitive transcranial magnetic stimulation). The rationale for targeting specific areas that have thus far been considered (i.e., vagus nerve, cerebellum, anterior or centromedial thalamus, basal ganglia, cortex and temporal lobe) is addressed in the light of experimental data and clinical effectiveness in different models and forms of epilepsy. The type of seizures that can be considered for neurostimulation, as well as the optimal parameters such as stimulation frequency and modes of stimulation (chronic, continuous or adaptative), are discussed to determine the best candidates for such a therapeutic strategy. This review points out the need for improved knowledge of neural circuits that generate seizures and/or allow their propagation, as well as a better understanding of the mechanisms of action of neurostimulation.


title: SDN and Virtualization-Based LTE Mobile Network Architectures: A Comprehensive Survey

abstract: Software-defined networking (SDN) features the decoupling of the control plane and data plane, a programmable network and virtualization, which enables network infrastructure sharing and the "softwarization" of the network functions. Recently, many research works have tried to redesign the traditional mobile network using two of these concepts in order to deal with the challenges faced by mobile operators, such as the rapid growth of mobile traffic and new services. In this paper, we first provide an overview of SDN, network virtualization, and network function virtualization, and then describe the current LTE mobile network architecture as well as its challenges and issues. By analyzing and categorizing a wide range of the latest research works on SDN and virtualization in LTE mobile networks, we present a general architecture for SDN and virtualization in mobile networks (called SDVMN) and then propose a hierarchical taxonomy based on the different levels of the carrier network. We also present an in-depth analysis about changes related to protocol operation and architecture when adopting SDN and virtualization in mobile networks. In addition, we list specific use cases and applications that benefit from SDVMN. Last but not least, we discuss the open issues and future research directions of SDVMN.


title: Reconfiguring the firm’s core technological portfolio through open innovation: focusing on technological M & A

abstract: The purpose of this study is to investigate the effects of open innovation, especially focusing on technological M&A, on subsequent innovation and changes to the firm’s core technological portfolio.,The study suggests three types of core technological areas, based on prior focus and experience in technological categories. These are 1) the existing core area, in which the acquirer firm retains its knowledge and expertise, 2) the enhanced core area, where knowledge and expertise in the acquirer firm’s insufficient areas are strengthened, and 3) the new core area, i.e. new knowledge fields in which the acquirer firm ventures into. The study then analyzes the effects of two key knowledge characteristics of the target firm, similarity and complementarity, on post-M&A innovation outcomes in each of the three core technological areas.,The results confirm that while none of the investigated knowledge characteristics of the target firm is advantageous for post-M&A innovation outcomes in existing core areas, similarity of the target firm does facilitate post-M&A innovation outcomes in enhanced core areas. Moreover, the results confirm that complementarity of the target firm is beneficial for post-M&A innovation outcomes in new core areas.,The study explains the reconfiguration mechanism of a firm’s core technological portfolio. It also suggests an extended framework to analyze innovation outcomes in more detail. Moreover, the study helps to explain why most M&As result in failure.


title: Enhancing privacy through caching in location-based services

abstract: Privacy protection is critical for Location-Based Services (LBSs). In most previous solutions, users query service data from the untrusted LBS server when needed, and discard the data immediately after use. However, the data can be cached and reused to answer future queries. This prevents some queries from being sent to the LBS server and thus improves privacy. Although a few previous works recognize the usefulness of caching for better privacy, they use caching in a pretty straightforward way, and do not show the quantitative relation between caching and privacy. In this paper, we propose a caching-based solution to protect location privacy in LBSs, and rigorously explore how much caching can be used to improve privacy. Specifically, we propose an entropy-based privacy metric which for the first time incorporates the effect of caching on privacy. Then we design two novel caching-aware dummy selection algorithms which enhance location privacy through maximizing both the privacy of the current query and the dummies' contribution to cache. Evaluations show that our algorithms provide much better privacy than previous caching-oblivious and caching-aware solutions.


title: The Painful Shoulder: Shoulder Impingement Syndrome

abstract: Rotator cuff disorders are considered to be among the most common causes of shoulder pain and disability encountered in both primary and secondary care. ::: ::: The general pathology of subacromial impingment generally relates to a chronic repetitive process in which the conjoint tendon of the rotator cuff undergoes repetitive compression and micro trauma as it passes under the coracoacromial arch. However acute traumatic injuries may also lead to this condition. ::: ::: Diagnosis remains a clinical one, however advances in imaging modalities have enabled clinicians to have an increased understanding of the pathological process. Ultrasound scanning appears to be a justifiable and cost effective assessment tool following plain radiographs in the assessment of shoulder impingment, with MRI scans being reserved for more complex cases. ::: ::: A period of observed conservative management including the use of NSAIDs, physiotherapy with or without the use of subacromial steroid injections is a well-established and accepted practice. However, in young patients or following any traumatic injury to the rotator cuff, surgery should be considered early. If surgery is to be performed this should be done arthroscopically and in the case of complete rotator cuff rupture the tendon should be repaired where possible.


title: Efficient Inkjet Printing of Graphene

abstract: An efficient and mature inkjet printing technology is introduced for mass production of coffee-ring-free patterns of high-quality graphene at high resolution (unmarked scale bars are 100 μm). Typic ...


title: Improved Neural Network Performance Using Principal Component Analysis on Matlab

abstract: Most of the real-world data samples used to train artificial neural networks (ANNs) consist of correlated information caused by overlapping input instances. Correlation in sampled data normally creates confusion over ANNs during the learning process and thus, degrades their generalization capability. This paper proposes the Principal Component Analysis (PCA) method for elimination of correlated information in data. Since it has been wellknown that Electrical Capacitance Tomography (ECT) data are highly correlated due to overlapping sensing areas, the PCA technique has been examined on the ECT data for oil fraction estimation from gas-oil flows. After application of PCA, the uncorrelated ECT data were used to train a Multi-Layer Perceptron (MLP) ANN system. Then, the trained MLP was tested upon unseen ECT data. The results demonstrated that the elimination of correlated information in the sample data by way of the PCA method improved the MLP’s estimation performance and reduced the training time.


title: Nonlinear robust adaptive Cartesian impedance control of UAVs equipped with a robot manipulator

abstract: In this paper, a new nonlinear robust adaptive impedance controller is addressed for Unmanned Aerial Vehicles (UAVs) equipped with a robot manipulator that physically interacts with environment. A UAV equipped with a robot manipulator is a novel system that can perform different tasks instead of human being in dangerous and/or inaccessible environments. The objective of the proposed robust adaptive controller is control of the UAV and its robotic manipulator’s end-effector impedance in Cartesian space in order to have a stable physical interaction with environment. The proposed controller is robust against parametric uncertainties in the nonlinear dynamics model of the UAV and the robot manipulator. Moreover, the controller has robustness against the bounded force sensor inaccuracies and bounded unstructured modeling (nonparametric) uncertainties and/or disturbances in the system. Tracking performance and stability of the system are proved via Lyapunov stability theorem. Using simulations on a quadrotor U...


title: Hierarchical Behavioral Repertoires with Unsupervised Descriptors

abstract: Enabling artificial agents to automatically learn complex, versatile and high-performing behaviors is a long-lasting challenge. This paper presents a step in this direction with hierarchical behavioral repertoires that stack several behavioral repertoires to generate sophisticated behaviors. Each repertoire of this architecture uses the lower repertoires to create complex behaviors as sequences of simpler ones, while only the lowest repertoire directly controls the agent's movements. This paper also introduces a novel approach to automatically define behavioral descriptors thanks to an unsupervised neural network that organizes the produced high-level behaviors. The experiments show that the proposed architecture enables a robot to learn how to draw digits in an unsupervised manner after having learned to draw lines and arcs. Compared to traditional behavioral repertoires, the proposed architecture reduces the dimensionality of the optimization problems by orders of magnitude and provides behaviors with a twice better fitness. More importantly, it enables the transfer of knowledge between robots: a hierarchical repertoire evolved for a robotic arm to draw digits can be transferred to a humanoid robot by simply changing the lowest layer of the hierarchy. This enables the humanoid to draw digits although it has never been trained for this task.


title: Herpes simplex virus encephalitis is a trigger of brain autoimmunity

abstract: In five prospectively diagnosed patients with relapsing post-herpes simplex encephalitis (HSE), NMDAR-antibodies were identified. Antibody synthesis started 1–4 weeks post-HSE, preceding the neurological relapse. Three of five patients improved post-immunotherapy, one spontaneously, and one has started to improve. Two additional patients with NMDAR-antibodies, 9 with unknown neuronal surface-antibodies, and one with NMDAR and unknown antibodies were identified during retrospective assessment of 34 HSE-patients; the frequency of autoantibodies increased over time (serum p=0.004, CSF p=0.04). The three retrospectively identified NMDAR-antibody positive patients also had evidence of relapsing post-HSE. Overall, these findings indicate that HSE triggers NMDAR-antibodies and potentially other brain autoimmunity.


title: Using Lexical Chains for Keyword Extraction

abstract: Keywords can be considered as condensed versions of documents and short forms of their summaries. In this paper, the problem of automatic extraction of keywords from documents is treated as a supervised learning task. A lexical chain holds a set of semantically related words of a text and it can be said that a lexical chain represents the semantic content of a portion of the text. Although lexical chains have been extensively used in text summarization, their usage for keyword extraction problem has not been fully investigated. In this paper, a keyword extraction technique that uses lexical chains is described, and encouraging results are obtained.


title: Australian scholarly journals: a situation report

abstract: In early 1987 a sample of Australian journals in the natural and social sciences was surveyed to examine their editorial procedures, costs, revenues, marketing, production methods and general well-being. The survey has shown that many scholarly journals admit to problems with management and marketing, and do not cover their costs. Most journals are increasingly subsidized by members of their parent Societies or from other sources, and their longer-term future is unclear. The separation in most Societies of the policy, editing and financial functions is thought to contnbute to this situation, which in many cases is simply not yet recognized by those closest to it.


title: I know your MAC address: targeted tracking of individual using Wi-Fi

abstract: This work is about wireless communications technologies embedded in portable devices, namely Wi-Fi, Bluetooth and GSM. Focusing on Wi-Fi, we study the privacy issues and potential missuses that can affect the owners of wireless-enabled portable devices. Wi-Fi enable-devices periodically broadcast in plain-text their unique identifier along with other sensitive information. As a consequence, their owners are vulnerable to a range of privacy breaches such as the tracking of their movement and inference of private information (Cunche et al. in Pervasive Mobile Comput, 2013; Greenstein in Proceedings of the 11th USENIX workshop on hot topics in operating systems, pp 10:1–10:6. USENIX Association, Berkeley, 2007). As serious as those information leakage can be, linking a device with an individual and its real world identity is not a straightforward task. Focusing on this problem, we present a set of attacks that allow an attacker to link a Wi-Fi device to its owner identity. We present two methods that, given an individual of interest, allow identifying the MAC address of its Wi-Fi enabled portable device. Those methods do not require a physical access to the device and can be performed remotely, reducing the risks of being noticed. Finally we present scenarios in which the knowledge of an individual MAC address could be used for mischief.


Interactive Visualization for Linguistic Structure.


title: word2vec Skip-Gram with Negative Sampling is a Weighted Logistic PCA

abstract: We show that the skip-gram formulation of word2vec trained with negative sampling is equivalent to a weighted logistic PCA. This connection allows us to better understand the objective, compare it to other word embedding methods, and extend it to higher dimensional models.


title: Part of Speech Tagging in Bengali Using Support Vector Machine

abstract: Part of speech (POS) tagging is the task of labeling each word in a sentence with its appropriate syntactic category called part of speech. POS tagging is a very important preprocessing task for language processing activities. This paper reports about task of POS tagging for Bengali using support vector machine (SVM). The POS tagger has been developed using a tagset of 26 POS tags, defined for the Indian languages. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the various POS classes. The POS tagger has been trained, and tested with the 72,341, and 20 K wordforms, respectively. Experimental results show the effectiveness of the proposed SVM based POS tagger with an accuracy of 86.84%. Results show that the lexicon, named entity recognizer and different word suffixes are effective in handling the unknown word problems and improve the accuracy of the POS tagger significantly. Comparative evaluation results have demonstrated that this SVM based system outperforms the three existing systems based on the hidden markov model (HMM), maximum entropy (ME) and conditional random field (CRF).


title: Grid Cells and Neural Coding in High-End Cortices

abstract: An ultimate goal of neuroscience is to understand the mechanisms of mammalian intellectual functions, many of which are thought to depend extensively on the cerebral cortex. While this may have been considered a remote objective when Neuron was launched in 1988, neuroscience has now evolved to a stage where it is possible to decipher neural-circuit mechanisms in the deepest parts of the cortex, far away from sensory receptors and motoneurons. In this review, we show how studies of place cells in the hippocampus and grid cells in the entorhinal cortex may provide some of the first glimpses into these mechanisms. We shall review the events that led up to the discovery of grid cells and a functional circuit in the entorhinal cortex and highlight what we currently see as the big questions in this field—questions that, if resolved, will add to our understanding of cortical computation in a general sense.


title: Estimating Alertness from the EEG Power Spectrum

abstract: In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, the authors show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEC; measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.


title: Can peer instruction be effective in upper-division computer science courses?

abstract: Peer Instruction (PI) is an active learning pedagogical technique. PI lectures present students with a series of multiple-choice questions, which they respond to both individually and in groups. PI has been widely successful in the physical sciences and, recently, has been successfully adopted by computer science instructors in lower-division, introductory courses. In this work, we challenge readers to consider PI for their upper-division courses as well. We present a PI curriculum for two upper-division computer science courses: Computer Architecture and Theory of Computation. These courses exemplify several perceived challenges to the adoption of PI in upper-division courses, including: exploration of abstract ideas, development of high-level judgment of engineering design trade-offs, and exercising advanced mathematical sophistication. This work includes selected course materials illustrating how these challenges are overcome, learning gains results comparing these upper-division courses with previous lower-division results in the literature, student attitudinal survey results (N = 501), and pragmatic advice to prospective developers and adopters. We present three main findings. First, we find that these upper-division courses achieved student learning gains equivalent to those reported in successful lower-division computing courses. Second, we find that student feedback for each class was overwhelmingly positive, with 88p of students recommending PI for use in other computer science classes. Third, we find that instructors adopting the materials introduced here were able to replicate the outcomes of the instructors who developed the materials in terms of student learning gains and student feedback.


title: More is Less: A More Complicated Network with Less Inference Complexity

abstract: In this paper, we present a novel and general network structure towards accelerating the inference process of convolutional neural networks, which is more complicated in network structure yet with less inference complexity. The core idea is to equip each original convolutional layer with another low-cost collaborative layer (LCCL), and the element-wise multiplication of the ReLU outputs of these two parallel layers produces the layer-wise output. The combined layer is potentially more discriminative than the original convolutional layer, and its inference is faster for two reasons: 1) the zero cells of the LCCL feature maps will remain zero after element-wise multiplication, and thus it is safe to skip the calculation of the corresponding high-cost convolution in the original convolutional layer, 2) LCCL is very fast if it is implemented as a 1*1 convolution or only a single filter shared by all channels. Extensive experiments on the CIFAR-10, CIFAR-100 and ILSCRC-2012 benchmarks show that our proposed network structure can accelerate the inference process by 32% on average with negligible performance drop.


title: Maintaining alertness and performance during sleep deprivation: modafinil versus caffeine

abstract: Rationale: The performance and alertness effects of modafinil were evaluated to determine whether modafinil should replace caffeine for restoring performance and alertness during total sleep deprivation in otherwise healthy adults. Objectives: Study objectives were to determine (a) the relative efficacy of three doses of modafinil versus an active control dose of caffeine 600 mg; (b) whether modafinil effects are dose-dependent; and (c) the extent to which both agents maintain performance and alertness during the circadian trough. Methods: Fifty healthy young adults remained awake for 54.5 h (from 6:30 a.m. day 1 to 1:00 p.m. on day 3) and performance and alertness tests were administered bi-hourly from 8:00 a.m. day 1 until 10:00 p.m. day 2. At 11:55 p.m. on day 2 (after 41.5 h awake), subjects received double blind administration of one of five drug doses: placebo; modafinil 100, 200, or 400 mg; or caffeine 600 mg (n=10 per group), followed by hourly testing from midnight through 12:00 p.m. on day 3. Results: Performance and alertness were significantly improved by modafinil 200 and 400 mg relative to placebo, and effects were comparable to those obtained with caffeine 600 mg. Although a trend toward better performance at higher modafinil doses suggested a dose-dependent effect, differences between modafinil doses were not significant. Performance enhancing effects were especially salient during the circadian nadir (6:00 a.m. through 10:00 a.m.). Few instances of adverse subjective side effects (nausea, heart pounding) were reported. Conclusions: Like caffeine, modafinil maintained performance and alertness during the early morning hours, when the combined effects of sleep loss and the circadian trough of performance and alertness trough were manifest. Thus, equivalent performance- and alertness-enhancing effects were obtained with drugs possessing different mechanisms of action. However, modafinil does not appear to offer advantages over caffeine (which is more readily available and less expensive) for improving performance and alertness during sleep loss in otherwise normal, healthy adults.


title: High frequency half-bridge converter using multilayered coreless Printed Circuit Board step-down power transformer

abstract: This paper introduces high frequency half bridge DC-DC converter, using multilayered coreless Printed Circuit Board (PCB) step down power transformer. The converter is simulated and then implemented on a PCB. Complementary Pulse Width Modulated (PWM) signals are generated to turn on high and low side Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) alternately by a micro-controller. The isolated gate drive signals are provided to the high side MOSFET by using high frequency coreless PCB isolation transformer. We tested the converter for switching frequency range of 2 to 3 MHz, and for maximum input voltage up-to 170 V. The maximum output power achieved is 40 W and the maximum energy efficiency is approximately 82 %.


title: Three stage 6–18 GHz high gain and high power amplifier based on GaN technology

abstract: A monolithic three stage HPA has been developed for wide band applications. This MMIC is fabricated on UMS 0.25 µm GaN technology based on SiC substrate. At 18GHz, the MMIC achieved in CW mode 10W of output power with 20dB linear gain and 20% power added efficiency. The HPA provided 6 to 10W output power over 6 to 18GHz with minimum small signal gain of 18dB. These obtained performances are very promising and very close to the simulations, this will allow a very short term further improvement. This demonstration is the first MMIC on the UMS 0.25µm GaN technology.


title: Joint Haar-like features for face detection

abstract: In this paper, we propose a new distinctive feature, called joint Haar-like feature, for detecting faces in images. This is based on co-occurrence of multiple Haar-like features. Feature co-occurrence, which captures the structural similarities within the face class, makes it possible to construct an effective classifier. The joint Haar-like feature can be calculated very fast and has robustness against addition of noise and change in illumination. A face detector is learned by stagewise selection of the joint Haar-like features using AdaBoost. A small number of distinctive features achieve both computational efficiency and accuracy. Experimental results with 5, 676 face images and 30,000 nonface images show that our detector yields higher classification performance than Viola and Jones' detector; which uses a single feature for each weak classifier. Given the same number of features, our method reduces the error by 37%. Our detector is 2.6 times as fast as Viola and Jones' detector to achieve the same performance


title: Survey on OFDMA based MAC protocols for the next generation WLAN

abstract: The physical (PHY) layer peak rate of the wireless local area network (WLAN) has been almost exponentially improved over the past 15 years since 1999. However, it is proved that the throughput is very low comparing to the PHY peak rate, and the media access control (MAC) efficiency is very low in the current WLANs specification, especially in dense deployment scenarios. Therefore, to achieve high MAC efficiency the IEEE Standards Association Standards Board (IEEE-SA) approves IEEE 802.11ax in March 2014, to draw up a brand new amendment for the next generation WLAN. One of the promising technologies to improve MAC efficiency is Orthogonal Frequency Division Multiple Access (OFDMA). In this paper, we firstly investigate the existing OFDMA based MAC protocols in the literature. Then, a framework of OFDMA based MAC protocol for the next generation WLAN is proposed. Finally, all of the existing OFDMA based MAC protocols listed in this paper are compared according to the proposed design issues. To the best of our knowledge this paper is the first survey focusing on OFDMA based MAC protocols for the next generation WLAN.


title: Real moral problems in the use of virtual reality

abstract: In this paper, we argue that, under a specific set of circumstances, designing and employing certain kinds of virtual reality (VR) experiences can be unethical. After a general discussion of simulations and their ethical context, we begin our argument by distinguishing between the experiences generated by different media (text, film, computer game simulation, and VR simulation), and argue that VR experiences offer an unprecedented degree of what we call “perspectival fidelity” that prior modes of simulation lack. Additionally, we argue that when VR experiences couple this perspectival fidelity with what we call “context realism,” VR experiences have the ability to produce “virtually real experiences.” We claim that virtually real experiences generate ethical issues for VR technologies that are unique to the medium. Because subjects of these experiences treat them as if they were real, a higher degree of ethical scrutiny should be applied to any VR scenario with the potential to generate virtually real experiences. To mitigate this unique moral hazard, we propose and defend what we call “The Equivalence Principle.” This principle states that “if it would be wrong to allow subjects to have a certain experience in reality, then it would be wrong to allow subjects to have that experience in a virtually real setting.” We argue that such a principle, although limited in scope, should be part of the risk analysis conducted by any Institutional Review Boards, psychologists, empirically oriented philosophers, or game designers who are using VR technology in their work.


title: Pulmonary rehabilitation improves long-term outcomes in interstitial lung disease: A prospective cohort study

abstract: Summary Background Pulmonary rehabilitation improves outcomes in patients with interstitial lung disease (ILD), however it is unclear whether these effects are long lasting and which patients benefit most. Methods Patients with ILD were recruited into this prospective cohort study from three pulmonary rehabilitation programs. Patients completed functional assessments (6-minute walk distance (6MWD), and 4-meter walk time) and surveys (quality of life, dyspnea, depression, and physical activity) before rehabilitation, after rehabilitation, and at six months. Changes from baseline were compared using a paired t -test. Independent predictors of change in 6MWD and quality of life were determined using multivariate analysis. Results Fifty-four patients were recruited (22 with idiopathic pulmonary fibrosis), 50 patients (93%) completed the rehabilitation program, and 39 returned for six-month follow-up. 6MWD improved 57.6 m immediately after rehabilitation (95% confidence interval (CI) 40.2–75.1 m, p p = 0.005). The majority of patients achieved the minimum clinically important difference for quality of life (51%), dyspnea (65%), and depression score (52%) immediately after rehabilitation, and improvements were still significant at 6-month follow-up for quality of life, depression, and physical activity. A low baseline 6MWD was the only independent predictor of improvement in 6MWD during rehabilitation ( r = −0.49, p r = −0.36, p = 0.01). Conclusions Pulmonary rehabilitation improved multiple short- and long-term outcomes in patients with ILD. While all patients appear to benefit, ILD patients with a low baseline 6MWD had greater benefit from rehabilitation. Clinical trials registration number NCT01055730 (clinicaltrials.gov).


HarmTrace: Automatic functional harmonic analysis


title: AI2-THOR: An Interactive 3D Environment for Visual AI

abstract: We introduce The House Of inteRactions (THOR), a framework for visual AI research, available at this http URL AI2-THOR consists of near photo-realistic 3D indoor scenes, where AI agents can navigate in the scenes and interact with objects to perform tasks. AI2-THOR enables research in many different domains including but not limited to deep reinforcement learning, imitation learning, learning by interaction, planning, visual question answering, unsupervised representation learning, object detection and segmentation, and learning models of cognition. The goal of AI2-THOR is to facilitate building visually intelligent models and push the research forward in this domain.


title: Knowledge, Attitudes, and Beliefs of Youth Club Athletes Toward Sport Specialization and Sport Participation

abstract: Background:There are a variety of proposed motivations for sport specialization, such as improving sport skills to an elite level, making all-star or travel teams, or receiving a scholarship or professional contract. However, there has not been a quantitative examination of the attitudes and beliefs that may be contributing to the trend of sport specialization and year-round sport participation.Purpose:The primary aim was to describe the attitudes and beliefs of youth club sport athletes regarding sport specialization and sport participation. A secondary objective was to investigate whether an association exists between the level of sport specialization and the belief in receiving a college scholarship.Study Design:Cross-sectional study; Level of evidence, 3.Methods:A total of 974 youth athletes (578 female; mean age, 14.2 ± 1.6 years) completed an anonymous questionnaire that focused on attitudes and beliefs toward sport specialization and sport participation. Questions were developed utilizing the feedb...


SLIDE - a Sentiment Lexicon of Common Idioms.


title: Improving Vector Space Word Representations Using Multilingual Correlation

abstract: The distributional hypothesis of Harris (1954), according to which the meaning of words is evidenced by the contexts they occur in, has motivated several effective techniques for obtaining vector space semantic representations of words using unannotated text corpora. This paper argues that lexico-semantic content should additionally be invariant across languages and proposes a simple technique based on canonical correlation analysis (CCA) for incorporating multilingual evidence into vectors generated monolingually. We evaluate the resulting word representations on standard lexical semantic evaluation tasks and show that our method produces substantially better semantic representations than monolingual techniques.


title: Parametric t-Distributed Stochastic Exemplar-centered Embedding

abstract: Parametric embedding methods such as parametric t-SNE (pt-SNE) have been widely adopted for data visualization and out-of-sample data embedding without further computationally expensive optimization or approximation. However, the performance of pt-SNE is highly sensitive to the hyper-parameter batch size due to conflicting optimization goals, and often produces dramatically different embeddings with different choices of user-defined perplexities. To effectively solve these issues, we present parametric t-distributed stochastic exemplar-centered embedding methods. Our strategy learns embedding parameters by comparing given data only with precomputed exemplars, resulting in a cost function with linear computational and memory complexity, which is further reduced by noise contrastive samples. Moreover, we propose a shallow embedding network with high-order feature interactions for data visualization, which is much easier to tune but produces comparable performance in contrast to a deep neural network employed by pt-SNE. We empirically demonstrate, using several benchmark datasets, that our proposed methods significantly outperform pt-SNE in terms of robustness, visual effects, and quantitative evaluations.


Chapter 15 SECURITY ANALYSIS OF MULTILAYER SCADA PROTOCOLS : A MODBUS TCP CASE STUDY


title: Cross-subject classification of cognitive loads using a recurrent-residual deep network

abstract: The problem of automatically learning temporal and spectral feature representations for EEG signals has been intensely studied in the last years. However, most solutions are focused on extracting representations used for training classifiers in particular subjects. This is not well suitable for applications involving several subjects, since it requires high computing times and costs at labeling data and training classifiers for each new subject involved. To address this problem, we propose an improvement of a deep neural network architecture, using residual layers and Gated Recurrent Units (GRU), able to extract feature representations for “cross-subject” classification. Our architecture, called RecResNet, achieved a better accuracy (0.907±0.124) and F-measure (0.896±0.148) than other baseline methods, when applied to the classification of four levels of cognitive loads using 13 subjects.


title: A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

abstract: This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. In addition to being one of the largest corpora available for the task of NLI, at 433k examples, this corpus improves upon available resources in its coverage: it offers data from ten distinct genres of written and spoken English--making it possible to evaluate systems on nearly the full complexity of the language--and it offers an explicit setting for the evaluation of cross-genre domain adaptation.


title: Video-Based Abnormal Human Behavior Recognition—A Review

abstract: Modeling human behaviors and activity patterns for recognition or detection of special event has attracted significant research interest in recent years. Diverse methods that are abound for building intelligent vision systems aimed at scene understanding and making correct semantic inference from the observed dynamics of moving targets. Most applications are in surveillance, video content retrieval, and human-computer interfaces. This paper presents not only an update extending previous related surveys, but also a focus on contextual abnormal human behavior detection especially in video surveillance applications. The main purpose of this survey is to extensively identify existing methods and characterize the literature in a manner that brings key challenges to attention.


title: Label-dependency coding in Simple Recurrent Networks for Spoken Language Understanding

abstract: Modelling target label dependencies is important for sequence labelling tasks. This may become crucial in the case of Spoken Language Understanding (SLU) applications, especially for the slot-filling task where models have to deal often with a high number of target labels. Conditional Random Fields (CRF) were previously considered as the most efficient algorithm in these conditions. More recently, different architectures of Recurrent Neural Networks (RNNs) have been proposed for the SLU slot-filling task. Most of them, however, have been successfully evaluated on the simple ATIS database, on which it is difficult to draw significant conclusions. In this paper we propose new variants of RNNs able to learn efficiently and effectively label dependencies by integrating label embeddings. We show first that modeling label dependencies is useless on the (simple) ATIS database and unstructured models can produce state-of-the-art results on this benchmark. On ATIS our new variants achieve the same results as state-of-the-art models, while being much simpler. On the other hand, on the MEDIA benchmark, we show that the modification introduced in the proposed RNN outperforms traditional RNNs and CRF models.


abstract: Visual Question Answering (VQA) has recently attracted considerable attention from researchers in the trending field of deep learning. The need to improve VQA models by focusing on local regions of images, has resulted in the development of various attention models. This paper proposes the Affective Visual Question Answering Network (AVQAN), an attention model that combines the locality of the image features, the question and the mood detected from the specific region of the image to produce an affective answer using a preprocessed image dataset. The experimental results depict that AVQAN enriches the analysis and understanding of images by adding affective information to the answer, while still managing to maintain the accuracy levels within the range of recent ordinary VQA baseline models. The proposed model significantly contributes towards the development of rapidly improving emotion-aware machines that are becoming increasingly vital in everyday life.


title: Automatic Extraction of Clusters from Hierarchical

abstract: Hierarchical clustering algorithms are typically more effective in detecting the true clustering structure of a data set than partitioning algorithms. However, hierarchical clustering algorithms do not actually create clusters, but compute only a hierarchical representation of the data set. This makes them unsuitable as an automatic pre-processing step for other algorithms that operate on detected clusters. This is true for both dendrograms and reachability plots, which have been proposed as hierarchical clustering representations, and which have different advantages and disadvantages. In this paper we first investigate the relation between dendrograms and reachability plots and introduce methods to convert them into each other showing that they essentially contain the same information. Based on reachability plots, we then introduce a technique that automatically determines the significant clusters in a hierarchical cluster representation. This makes it for the first time possible to use hierarchical clustering as an automatic pre-processing step that requires no user interaction to select clusters from a hierarchical cluster representation.


title: HW/SW Codesign and FPGA Acceleration of Visual Odometry Algorithms for Rover Navigation on Mars

abstract: Future Mars exploration missions rely heavily on high-mobility autonomous rovers equipped with sophisticated scientific instruments and possessing advanced navigational capabilities. Increasing their navigation velocity and localization accuracy is essential for enabling these rovers to explore large areas on Mars. Contemporary Mars rovers move slowly, partially due to the long execution time of complex computer vision algorithms running on their slow space-grade CPUs. This paper exploits the advent of high-performance space-grade field-programmable gate arrays (FPGAs) to accelerate the navigation of future rovers. Specifically, it focuses on visual odometry (VO) and performs HW/SW codesign to achieve one order of magnitude faster execution and improved accuracy. Conforming to the specifications of the European Space Agency, we build a proof-of-concept system on an HW/SW platform with processing power resembling that to be available onboard future rovers. We develop a codesign methodology adapted to the rover’s specifications, design parallel architectures, and customize several feature extraction, matching, and motion estimation algorithms. We implement and evaluate five distinct HW/SW pipelines on a Virtex6 FPGA and a 150 MIPS CPU. We provide a detailed analysis of their cost–time–accuracy tradeoffs and quantify the benefits of employing FPGAs for implementing VO. Our solution achieves a speedup factor of $16\times $ over a CPU-only implementation, handling a stereo image pair in less than 1 s, with a 1.25% mean positional error after a 100 m traverse and an FPGA cost of 54 K LUTs and 1.46-MB RAM.


title: Image De-raining Using a Conditional Generative Adversarial Network

abstract: Severe weather conditions such as rain and snow adversely affect the visual quality of images captured under such conditions thus rendering them useless for further usage and sharing. In addition, such degraded images drastically affect performance of vision systems. Hence, it is important to solve the problem of single image de-raining/de-snowing. However, this is a difficult problem to solve due to its inherent ill-posed nature. Existing approaches attempt to introduce prior information to convert it into a well-posed problem. In this paper, we investigate a new point of view in addressing the single image de-raining problem. Instead of focusing only on deciding what is a good prior or a good framework to achieve good quantitative and qualitative performance, we also ensure that the de-rained image itself does not degrade the performance of a given computer vision algorithm such as detection and classification. In other words, the de-rained result should be indistinguishable from its corresponding clear image to a given discriminator. This criterion can be directly incorporated into the optimization framework by using the recently introduced conditional generative adversarial networks (GANs). To minimize artifacts introduced by GANs and ensure better visual quality, a new refined loss function is introduced. Based on this, we propose a novel single image de-raining method called Image De-raining Conditional General Adversarial Network (ID-CGAN), which considers quantitative, visual and also discriminative performance into the objective function. Experiments evaluated on synthetic images and real images show that the proposed method outperforms many recent state-of-the-art single image de-raining methods in terms of quantitative and visual performance.


title: Design and construction of organic computing systems

abstract: The next generation of embedded computing systems will have to meet new challenges. The systems are expected to act mainly autonomously, to dynamically adapt to changing environments and to interact with one another if necessary. Such systems are called organic. Organic Computing systems are similar to autonomic computing systems. In addition Organic Computing systems often behave life-like and are inspired by nature/biological phenomena. Design and construction of such systems brings new challenges for the software engineering process. In this paper we present a framework for design, construction and analysis of organic computing systems. It can facilitate design and construction as well as it can be used to (semi-)formally define organic properties like self-configuration or self-adaptation. We illustrate the framework on a real-world case study from production automation.


title: Enriching speech recognition with automatic detection of sentence boundaries and disfluencies

abstract: Effective human and automatic processing of speech requires recovery of more than just the words. It also involves recovering phenomena such as sentence boundaries, filler words, and disfluencies, referred to as structural metadata. We describe a metadata detection system that combines information from different types of textual knowledge sources with information from a prosodic classifier. We investigate maximum entropy and conditional random field models, as well as the predominant hidden Markov model (HMM) approach, and find that discriminative models generally outperform generative models. We report system performance on both broadcast news and conversational telephone speech tasks, illustrating significant performance differences across tasks and as a function of recognizer performance. The results represent the state of the art, as assessed in the NIST RT-04F evaluation


title: Order Picking: A Survey of Planning Problems and Methods

abstract: Order picking — as a warehouse function — is often considered to be critical for the public manifestation of a supply chain. Underperformance can result both in unsatisfactory customer service and in high cost. In this paper the author will give an overview of planning problems and corresponding methods which have been suggested in the literature for the reduction of cost and the improvement of customer service in picker-to-product systems. In particular, the following central issues will be addressed: item location, order batching, and picker routing.


title: An appropriate radius for reduced-complexity sphere decoding

abstract: Sphere decoding (SD) for multiple-input multiple-output (MIMO) systems has been shown to achieve maximum-likelihood (ML) performance with low complexity. However, the achievement of such an excellent performance-complexity tradeoff is highly dependent on initial choice of sphere radius. In this paper, we propose a new scheme of initial radius selection which has less computational complexity than the original SD. Moreover, the new expanding method of sphere radius that we propose can detect symbols efficiently. In our simulations, new method can reduce computational complexity approximately 35 percent compared with original SD when SNR is smaller than 10dB.


title: Discovering evolutionary theme patterns from text: an exploration of temporal text mining

abstract: Temporal Text Mining (TTM) is concerned with discovering temporal patterns in text information collected over time. Since most text information bears some time stamps, TTM has many applications in multiple domains, such as summarizing events in news articles and revealing research trends in scientific literature. In this paper, we study a particular TTM task -- discovering and summarizing the evolutionary patterns of themes in a text stream. We define this new text mining problem and present general probabilistic methods for solving this problem through (1) discovering latent themes from text; (2) constructing an evolution graph of themes; and (3) analyzing life cycles of themes. Evaluation of the proposed methods on two different domains (i.e., news articles and literature) shows that the proposed methods can discover interesting evolutionary theme patterns effectively.


title: Concise Integer Linear Programming Formulations for Dependency Parsing

abstract: We formulate the problem of non-projective dependency parsing as a polynomial-sized integer linear program. Our formulation is able to handle non-local output features in an efficient manner; not only is it compatible with prior knowledge encoded as hard constraints, it can also learn soft constraints from data. In particular, our model is able to learn correlations among neighboring arcs (siblings and grandparents), word valency, and tendencies toward nearly-projective parses. The model parameters are learned in a max-margin framework by employing a linear programming relaxation. We evaluate the performance of our parser on data in several natural languages, achieving improvements over existing state-of-the-art methods.


title: From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities

abstract: Recent advancements in signal processing (SP) and machine learning, coupled with electronic medical record keeping in hospitals and the availability of extensive sets of medical images through internal/external communication systems, have resulted in a recent surge of interest in radiomics. Radiomics , an emerging and relatively new research field, refers to extracting semiquantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic models. In the near future, it is expected to be a critical component for integrating image-derived information used for personalized treatment. The conventional radiomics workflow is typically based on extracting predesigned features (also referred to as handcrafted or engineered features ) from a segmented region of interest (ROI). Nevertheless, recent advancements in deep learning have inspired trends toward deep-learning-based radiomics (DLRs) (also referred to as discovery radiomics ). In addition to the advantages of these two approaches, there are also hybrid solutions that exploit the potential of multiple data sources. Considering the variety of approaches to radiomics, further improvements require a comprehensive and integrated sketch, which is the goal of this article. This article provides a unique interdisciplinary perspective on radiomics by discussing state-of-the-art SP solutions in the context of radiomics.


title: Motion Control of the CyberCarpet Platform

abstract: The CyberCarpet is an actuated platform that allows unconstrained locomotion of a walking user for Virtual Reality exploration. The platform consists of a linear treadmill covered by a ball-array carpet and mounted on a turntable, and is equipped with two actuating devices for linear and angular motion. The main control objective is to keep the walker close to the platform center in the most natural way, counteracting his/her voluntary motion while satisfying perceptual constraints. The motion control problem for this platform is not trivial since the system kinematics is subject to a nonholonomic constraint. In the first part of the paper we describe the kinematic control design devised within the CyberWalk project, where the linear and angular platform velocities are used as input commands and feedback is based only on walker's position measurements obtained by an external visual tracking system. In particular, we present a globally stabilizing control scheme that combines a feedback and a feedforward action, based on a disturbance observer of the walker's intentional velocity. We also discuss possible extensions to acceleration-level control and the related assessment of dynamic issues affecting a walker during his/her motion. The second part of the paper is devoted to the actual implementation of the overall system. As a proof of concept of a final full-scale platform, the mechanical design and realization of a small-scale prototype of the CyberCarpet is presented, as well as the visual localization method used to obtain the human walker's position on the platform by an overhead camera. To validate the proposed motion control design, experimental results are reported and discussed for a series of motion tasks performed using a small tracked vehicle representative of a moving user.


ON DATA BANKS AND PRIVACY HOMOMORPHISMS


title: Can cloud computing reach the top500?

abstract: Computing as a utility has reached the mainstream. Scientists can now rent time on large commercial clusters through several vendors. The cloud computing model provides flexible support for "pay as you go" systems. In addition to no upfront investment in large clusters or supercomputers, such systems incur no maintenance costs. Furthermore, they can be expanded and reduced on-demand in real-time. Current cloud computing performance falls short of systems specifically designed for scientific applications. Scientific computing needs are quite different from those of web applications--composed primarily of database queries--that have been the focus of cloud computing vendors. In this paper we investigate the use of cloud computing for high-performance numerical applications. In particular, we assume unlimited monetary resources to answer the question, "How high can a cloud computing service get in the TOP500 list?" We show results for the Linpack benchmark on different allocations on Amazon EC2.


title: Scene Segmentation with DAG-Recurrent Neural Networks

abstract: In this paper, we address the challenging task of scene segmentation. In order to capture the rich contextual dependencies over image regions, we propose Directed Acyclic Graph-Recurrent Neural Networks (DAG-RNN) to perform context aggregation over locally connected feature maps. More specifically, DAG-RNN is placed on top of pre-trained CNN (feature extractor) to embed context into local features so that their representative capability can be enhanced. In comparison with plain CNN (as in Fully Convolutional Networks-FCN), DAG-RNN is empirically found to be significantly more effective at aggregating context. Therefore, DAG-RNN demonstrates noticeably performance superiority over FCNs on scene segmentation. Besides, DAG-RNN entails dramatically less parameters as well as demands fewer computation operations, which makes DAG-RNN more favorable to be potentially applied on resource-constrained embedded devices. Meanwhile, the class occurrence frequencies are extremely imbalanced in scene segmentation, so we propose a novel class-weighted loss to train the segmentation network. The loss distributes reasonably higher attention weights to infrequent classes during network training, which is essential to boost their parsing performance. We evaluate our segmentation network on three challenging public scene segmentation benchmarks: Sift Flow, Pascal Context and COCO Stuff. On top of them, we achieve very impressive segmentation performance.


title: Automatic Keyphrase Extraction based on NLP and Statistical Methods

abstract: In this article we would like to present our exper imental approach to automatic keyphrase extraction based on statistical methods and Wordnet-based pattern evaluation. Automatic keyphrases are import ant for automatic tagging and clustering because manually assigned keyphrases are not sufficient in most cases. Keyphrase candidates are extracted in a new way derived from a combination of graph methods (TextRank) and statistical methods (TF*IDF). Keyword candidates are merged with named entities and stop words according to NL POS (Part Of a Speech) patterns. Automatic keyphrases are generated as TF*IDF weighted unigrams. Keyphrases describe the main ideas of documents in a human-readable way. Evaluation of this approac h is presented in articles extracted from News web sites. Each article contain s manually assigned topics/categories which are used for keyword evalua tion.


title: A novel double layer NCF for highly reliable micro-bump interconnection

abstract: 40 μm pitch Cu-pillar/Sn-Ag bump to Ni pad thermo-compression bonding was performed using non-conductive films (NCFs) with different curing agents. The joint morphology of Cu-pillar/Sn-Ag bump/Ni pad was dependent on the viscosity and curing speed of NCFs. Imidazole NCF with high viscosity and fast curing speed showed no solder wetting on both Cu-pillar and Ni pad. However, anhydride NCF with low viscosity and slow curing speed showed both solder wetting on Cu-pillar and Ni pad. Double layer NCF consisting of imidazole NCF as top layer and anhydride NCF as bottom layer was designed and optimized to eliminate solder wetting on Cu-pillar. Joints bonded using double layer NCF showed ideal joint shape with no solder wetting on Cu pillar and good wetting on pad. Thermal aging at 150 C was performed and IMC growth rate of Cu 6 Sn 5 and Cu 3 Sn was slower for double layer NCF compared to single layer NCF due to reduced reacting area for Cu and Sn.


title: Performance of a Crossed Exponentially Tapered Slot Antenna for UWB Systems

abstract: A compact printed antenna is described that exhibits adequate transient performance for ultrawideband (UWB) applications and it is further adequate for polarization diversity schemes. The antenna is based on an original combination of two crossed exponentially tapered slots plus a star-shaped slot to produce a stable radiation pattern with very stable polarization over the 3.1-10.6 GHz FCC assigned band. Results are confirmed with measurements. Figures of merit like output pulse fidelity and time window containing 90% of the transmitted energy are analyzed over the entire solid angle and showed to remain quite stable, in line with envisaged UWB system requirements. Compact dual-antenna arrangements are also analyzed in view of potential use for UWB multiple-input-multiple-output implementations.


title: Design and implementation of encrypted SMS on Android smartphone combining ECDSA - ECDH and AES

abstract: A Short Message Service (SMS) is a popular mechanism to communicate. However, for an important purpose, a mechanism to protect SMS messages is needed so the SMS messages cannot be easily read by entities who are not permitted to see. Keeping an authentication of messages, and ensuring messages are received by a right entity are also an important aspect. Maintaining confidentiality of messages, by combining symmetric and asymmetric scheme are used. The combination can be implemented by using the Advanced Encryption Standard (AES) and Elliptic Curve Diffie-Hellman (ECDH). Even so, there is a weakness while using ECDH protocol. It does not provide an authentication mechanism between entities. This means an authenticity and an integrity of messages are not fully protected. This research proposes a solution to maintain it by adding the Elliptic Curve Digital Signature algorithm (ECDSA). ECDSA is used to handle the authentication of key exchange.


title: A novel secure hash algorithm for public key digital signature schemes

abstract: Hash functions are the most widespread among all cryptographic primitives, and are currently used in multiple cryptographic schemes and in security protocols. This paper presents a new Secure Hash Algorithm called (SHA(192). It uses a famous secure hash algorithm given by the National Institute of Standard and Technology (NIST).The basic design of SHA( 192 is to have the output length of 192.The SHA(192 has been designed to satisfy the different level of enhanced security and to resist the advanced SHA attacks. The security analysis of the SHA(192 is compared to the old one given by NIST and gives more security and excellent results as shown in our discussion. In this paper the digital signature algorithm which is given by NIST has been modified using the proposed algorithms SHA(192. Using proposed SHA(192 hash algorithm a new digital signature schemes is also proposed. The SHA(192 can be used in many applications such s public key cryptosystem, digital signcryption, message authentication code, random generator and in security architecture of upcoming wireless devices like software defined radio etc.


title: Fostering Intrinsic Motivation in Early Childhood Classrooms

abstract: Young children are born with an innate curiosity to learn about their world. This intrinsically instigated learning is often called mastery motivation. Patterns of motivation are established at an early age. The early childhood years are crucial for establishing robust intrinsic motivational orientations which will last a lifetime. By the time many children reach school, much of their motivation has been lost or replaced with extrinsically motivated learning strategies. Preschools and elementary schools have been criticized for contributing to such negative motivational patterns in children. This can be changed. Early child care situations and preschools can instead be instrumental in the strengthening of children's motivation. The goal of this paper is to show that through an understanding of the beginnings of motivation, we can begin to find ways to build strong motivational patterns in children that can carry on to later years of learning.


title: From the School Yard to the Squad Car: School Discipline, Truancy, and Arrest

abstract: Since the 1990’s, implementation of zero tolerance policies in schools has led to increased use of school suspension and expulsion as disciplinary techniques for students with varying degrees of infractions. An unintended consequence of zero tolerance policies is that school suspension or expulsion may increase risk for contact with the juvenile justice system. In the present study, we test how forced absence from school via suspension or expulsion and chosen absence from school (truancy) are associated with the likelihood of being arrested. Using month-level data from 6,636 months from a longitudinal study of delinquent adolescents (N = 1,354; 13.5 % female; 41.5 % Black, 33.5 % Hispanic-American, 20.2 % White), we compare the likelihood of being arrested, within individuals, for months when youth were and were not suspended or expelled from school and for months when youth were and were not truant. Finally, we test if these associations were moderated by stable demographic characteristics (sex, race, age, history of problem behaviors) and time-varying contextual factors (peer delinquency, parental monitoring, and commitment to school). Being suspended or expelled from school increased the likelihood of arrest in that same month and this effect was stronger among youth who did not have a history of behavior problems and when youth associated with less delinquent peers. Truancy independently contributed to the likelihood of arrest, but this association was explained by differences in parental monitoring and school commitment. Thus, school disciplinary action places youth at risk for involvement in the juvenile justice system and this may be especially true for less risky youth.


title: An evolutionary strategy enhanced with a local search technique for the space allocation problem in architecture, Part 2: Validation and performance tests

abstract: The first part of this paper proposed a hybrid evolutionary technique which helps architects to generate sets of floor plans in the early design stage. The algorithm is an enhanced Evolutionary Strategy (ES) with a Stochastic Hill Climbing (SHC) technique. In this second part, the validity and performance of the technique is examined. Four tests are conducted. Three validation tests are used to determine the ability of the algorithm to replicate a floor plan made by an architect; the ability to produce a set of different floor plan designs by comparing the results with a well-known enumerated floor plan solution; its capability to work with less constrained problems, and to control the variation in the floor plan form with different compactness evaluators' weights. The last test determines the evolving behavior, the breakdown of the evaluators' performance for the objective function, and the robustness of the algorithm. The methodologies used in the tests and the problem specifications are presented. Results and observations on the evaluators' weights and their importance and the limitations of the technique are analyzed and discussed. Finally, the conclusion to this part of the paper is made.


title: Hardware based design and implementation of a bottle recycling machine using FPGA

abstract: Conventional reverse vending machines use complex image processing technology to detect the bottles which make it more expensive. In this paper the design of a Smart Bottle Recycle Machine (SBRM) is presented. It is designed on a Field Programmable Gate Array (FPGA) using an ultrasonic range sensor which is readily available at a low cost. The sensor was used to calculate the number of bottles and distinguish between them. The main objective of this project is to build a SBRM at a cheaper production cost. This project was implemented on Altera DE2-115 board using Verilog HDL. This prototype enables the user to recycle plastic bottles and receive reward points. FPGA was chosen because hardware based implementation on a FPGA is usually much faster than the software based implementation on a microcontroller. The former is also capable of executing concurrent parallel processes at a high speed where the latter can only do a limited amount of parallel execution. So, overall FPGAs are more efficient than the microcontrollers for development of reliable and real time applications. The developed project is environment friendly and cost effective.


title: Inertial Sensors and Their Applications

abstract: Due to the universal presence of motion, vibration, and shock, inertial motion sensors can be applied in various contexts. Development of the microelectromechanical (MEMS) technology opens up many new consumer and automotive applications for accelerometers and gyroscopes. The large variety of application creates different requirements to inertial sensors in terms of accuracy, size, power consumption and cost. It makes it difficult to choose sensors that are suited best for the particular application. Signal processing methods depend on the application and should reflect on the physical principles behind this application. This chapter describes the principles of operation of accelerometers and gyroscopes, different applications involving the inertial sensors. It also gives examples of signal processing algorithms for pedestrian navigation and motion classification.


title: 3D-2D projective registration of free-form curves and surfaces

abstract: Some medical interventions require knowing the correspondence between an MRI/CT pre-operative image and the actual position of the patient. Examples occur in neurosurgery, radiotherapy, interventional radiology, but also in video surgery (laparoscopy). We present in this article three new techniques for performing this task without artificial markers. We find the 3D-2D projective transformation (composition of a rigid displacement and a perspective projection) which maps a 3D object onto a 2D image of this object. Depending on the object model (curve or surface), and on the 2D image acquisition system (X-Ray, video), the techniques are different but the framework is common. It does not depend on the initial relative positions of the objects and deals with the occlusions and the outliers. Results are presented on real medical data to demonstrate the validity of our approach. >


title: Learning Sentential Paraphrases from Bilingual Parallel Corpora for Text-to-Text Generation

abstract: Previous work has shown that high quality phrasal paraphrases can be extracted from bilingual parallel corpora. However, it is not clear whether bitexts are an appropriate resource for extracting more sophisticated sentential paraphrases, which are more obviously learnable from monolingual parallel corpora. We extend bilingual paraphrase extraction to syntactic paraphrases and demonstrate its ability to learn a variety of general paraphrastic transformations, including passivization, dative shift, and topicalization. We discuss how our model can be adapted to many text generation tasks by augmenting its feature set, development data, and parameter estimation routine. We illustrate this adaptation by using our paraphrase model for the task of sentence compression and achieve results competitive with state-of-the-art compression systems.


title: Inverted Index Compression Using Word-Aligned Binary Codes

abstract: We examine index representation techniques for document-based inverted files, and present a mechanism for compressing them using word-aligned binary codes. The new approach allows extremely fast decoding of inverted lists during query processing, while providing compression rates better than other high-throughput representations. Results are given for several large text collections in support of these claims, both for compression effectiveness and query efficiency.


title: Design of a high-performance humanoid dual arm system with inner shoulder joints

abstract: This paper presents the design of the KIT Dual Arm System, which consists of two high-performance, humanoid robot arms. Based on human arm kinematics, each arm has 8 degrees of freedom (DOF) including a clavicle joint of the inner shoulder. In comparison to classical 7 DOF robot arms, the incorporation of the clavicle joint results in a larger workspace and an increased dexterity in bimanual tasks. The arm structure is based on an exoskeleton design approach: Highly modular and highly integrated sensor-actuator-control units in each joint are linked by a hollow structure, which allows a stiff construction at low weight. Combined with its length of 1 m and a maximum payload of 11 kg at stretched configuration, the performance of the KIT Arm is comparable to state-of-the art industrial robot arms. Thereby, it combines the strengths of humanoid and industrial robot arms.


title: Resource sharing in continuous sliding-window aggregates

abstract: We consider the problem of resource sharing when processing large numbers of continuous queries. We specifically address sliding-window aggregates over data streams, an important class of continuous operators for which sharing has not been addressed. We present a suite of sharing techniques that cover a wide range of possible scenarios: different classes of aggregation functions (algebraic, distributive, holistic), different window types (time-based, tuple-based, suffix, historical), and different input models (single stream, multiple substreams). We provide precise theoretical performance guarantees for our techniques, and show their practical effectiveness through experimental study.


title: Better Malware Ground Truth: Techniques for Weighting Anti-Virus Vendor Labels

abstract: We examine the problem of aggregating the results of multiple anti-virus (AV) vendors' detectors into a single authoritative ground-truth label for every binary. To do so, we adapt a well-known generative Bayesian model that postulates the existence of a hidden ground truth upon which the AV labels depend. We use training based on Expectation Maximization for this fully unsupervised technique. We evaluate our method using 279,327 distinct binaries from VirusTotal, each of which appeared for the first time between January 2012 and June 2014. Our evaluation shows that our statistical model is consistently more accurate at predicting the future-derived ground truth than all unweighted rules of the form "k out of n" AV detections. In addition, we evaluate the scenario where partial ground truth is available for model building. We train a logistic regression predictor on the partial label information. Our results show that as few as a 100 randomly selected training instances with ground truth are enough to achieve 80% true positive rate for 0.1% false positive rate. In comparison, the best unweighted threshold rule provides only 60% true positive rate at the same false positive rate.


title: A.: Dialog Learning in Conversational CBR

abstract: Case based reasoning (CBR) is a problem solving method that reuses the previous problem solving experiences (represented as cases) to solve the new problem. As a type of interactive CBR, conversational CBR has been proposed to help users construct their problem descriptions incrementally through a mixed-initiative question-answering sequence. In software component retrieval, users meet the difficulty in well-defining their component queries. As a solution to release this difficulty, we proposed and implemented a conversational component retrieval model (CCRM) using a knowledge-intensive conversational CBR method in the thesis.The research activities and contributions followed two directions: theoretical research on conversational CBR to provide an efficient and natural conversation process, and applying and adapting conversational CBR to support software component retrieval.In the theoretical research direction, we first provided a framework to classify the similarity methods in CBR from the perspective of what features were taken into account during the similarity calculation, and further analyzed and illustrated that the query-biased similarity methods (only considering the features appearing in the query during the similarity calculation) were more suitable for conversational CBR applications. A knowledge-intensive conversational CBR method was designed that was able to utilize the general domain knowledge to improve the efficiency and naturalness of the conversation process. Four knowledge-intensive question selection tasks, including the feature inferencing, the integrated question ranking, the consistent question clustering, and the coherent question sequencing, were identified and handled in this method. We also proposed a lazy dialog learning mechanism that could continuously improve the performance of conversational CBR.Following the conversational component retrieval application direction, we reviewed and analyzed the current software component retrieval methods and proposed a conversational component retrieval model. In order to represent both the software components and the component queries as cases, it is necessary for a case to have multiple values on some features (generalized cases). In the research, we analyzed the feasibilities and discussed the methods to extend conversational CBR to support generalized cases from three aspects: the case representation, the similarity calculation metric, and the question selection method. At the end, a knowledge-intensive conversational software component retrieval system (TrollCCRM), enhanced by the above research findings, was implemented and evaluated on the image processing software component retrieval application.The evaluation results so far gave us positive results that the TrollCCRM system provided an efficient and natural conversation process guiding users to find their desired software components.


title: A 550–2260MHz self-adjustable clock generator in 28nm FDSOI

abstract: A self-adjustable clock generator that closely tracks the voltage dependence of the critical path delay in a microprocessor is presented. Real-time frequency modulation performed by a tunable replica circuit (TRC) reduces the response time to a single cycle, by picking the appropriate phases of the delay-locked loop (DLL). The generator has wide tuning range of 550–2260 MHz at 1V supply voltage, works down to 35 MHz at 0.4V supply, and can continuously synthesize the clock signal for arbitrary voltage waveforms between 0.4V and 1V. The proposed design was implemented in a 28nm FDSOI, and occupies an area of 1120μm2 with 2.7mW of power consumption.


title: Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter

abstract: With the recent rise in popularity and scale of social media, a growing need exists for systems that can extract useful information from huge amounts of data. We address the issue of detecting influenza epidemics. First, the proposed system extracts influenza related tweets using Twitter API. Then, only tweets that mention actual influenza patients are extracted by the support vector machine (SVM) based classifier. The experiment results demonstrate the feasibility of the proposed approach (0.89 correlation to the gold standard). Especially at the outbreak and early spread (early epidemic stage), the proposed method shows high correlation (0.97 correlation), which outperforms the state-of-the-art methods. This paper describes that Twitter texts reflect the real world, and that NLP techniques can be applied to extract only tweets that contain useful information.


title: HARPOON: An Obfuscation-Based SoC Design Methodology for Hardware Protection

abstract: Hardware intellectual-property (IP) cores have emerged as an integral part of modern system-on-chip (SoC) designs. However, IP vendors are facing major challenges to protect hardware IPs from IP piracy. This paper proposes a novel design methodology for hardware IP protection using netlist-level obfuscation. The proposed methodology can be integrated in the SoC design and manufacturing flow to simultaneously obfuscate and authenticate the design. Simulation results for a set of ISCAS-89 benchmark circuits and the advanced-encryption-standard IP core show that high levels of security can be achieved at less than 5% area and power overhead under delay constraint.


title: An Analytical Approach for a Novel Coupled-Line Dual-Band Wilkinson Power Divider

abstract: A novel generalized coupled-line circuit structure for a dual-band Wilkinson power divider is proposed. The proposed power divider is composed of two coupled lines with different even- and odd-mode characteristic impedances and two lumped resistors. Using rigorous even- and odd-mode analysis, the analytical design equations for this proposed power divider are obtained and the ideal closed-form scattering parameters are constructed. Since the traditional transmission line is a special case of coupled line (coupled coefficient is zero), it is found that traditional noncoupled-line dual-band (including single band) Wilkinson power dividers and previous dual-band coupled-line power dividers are special cases of this generalized power divider. As a typical example, which could only be designed by using this given design equations, a compact microstrip 3-dB power divider operating at both 1.1 and 2.2 GHz is designed, fabricated, and measured. There is good agreement between calculated and measured results.


title: AliMe Assist : An Intelligent Assistant for Creating an Innovative E-commerce Experience

abstract: We present AliMe Assist, an intelligent assistant designed for creating an innovative online shopping experience in E-commerce. Based on question answering (QA), AliMe Assist offers assistance service, customer service, and chatting service. It is able to take voice and text input, incorporate context to QA, and support multi-round interaction. Currently, it serves millions of customer questions per day and is able to address 85% of them. In this paper, we demonstrate the system, present the underlying techniques, and share our experience in dealing with real-world QA in the E-commerce field.


title: Evaluation on quality consistency of Ganoderma lucidum dietary supplements collected in the United States

abstract: Ganoderma lucidum is a well-known medicinal mushroom. At present, numerous G. lucidum products have emerged in the form of dietary supplements in the United States due to its various benefits. However, the quality consistency of these products based on their label ingredients has seldom been evaluated due to the lack of a suitable toolkit. In this study, 19 batches of products of G. lucidum (Red Reishi, Reishi), herbal/mushroom supplements purchased in the United States, were evaluated based on their bioactive components including triterpenes and polysaccharides by using chromatographic methods and saccharide mapping. The results showed that the measured ingredients of only 5 tested samples (26.3%) were in accordance with their labels, which suggested the quality consistency of G. lucidum dietary supplements in the U.S. market was poor, which should be carefully investigated.


title: Observations on intra-abdominal pressure and patterns of abdominal intra-muscular activity in man.

abstract: The aim was to investigate possible relationships between activities of the individual muscles of the ventrolateral abdominal wall and the development of pressure within the abdominal cavity. Intra-muscular activity was recorded bilaterally from transversus abdominis, obliquus internus, obliquus externus and rectus abdominis with fine-wire electrodes guided into place using real-time ultrasound. Intra-abdominal pressure was measured intragastrically using a micro tip pressure transducer. Six males were studied during loading and movement tasks with varied levels of intra-abdominal pressure. During both maximal voluntary isometric trunk flexion and extension, transversus abdominis activity and intra-abdominal pressure remained constant, while all other abdominal muscles showed a marked reduction during extension. When maximal isometric trunk flexor or extensor torques were imposed upon a maximal Valsalva manoeuvre, transversus abdominis activity and intra-abdominal pressure remained comparable within and across conditions, whereas obliquus internus, obliquus externus and rectus abdominis activities either markedly increased (flexion) or decreased (extension). Trunk twisting movements showed reciprocal patterns of activity between the left and right sides of transversus abdominis, indicating an ability for torque development. During trunk flexion--extension, transversus abdominis showed less distinguished changes of activity possibly relating to a general stabilizing function. In varied pulsed Valsalva manoeuvres, changes in peak intra-abdominal pressure were correlated with mean amplitude electromyograms of all abdominal muscles, excluding rectus abdominis. It is concluded that the co-ordinative patterns shown between the muscles of the ventrolateral abdominal wall are task specific based upon demands of movement, torque and stabilization. It appears that transversus abdominis is the abdominal muscle whose activity is most consistently related to changes in intra-abdominal pressure.


Use, selection, and home range properties: complex patterns of individual habitat utilization


title: The Asynchronous PALM Algorithm for Nonsmooth Nonconvex Problems

abstract: We introduce the Asynchronous PALM algorithm, a new extension of the Proximal Alternating Linearized Minimization (PALM) algorithm for solving nonsmooth, nonconvex optimization problems. Like the PALM algorithm, each step of the Asynchronous PALM algorithm updates a single block of coordinates; but unlike the PALM algorithm, the Asynchronous PALM algorithm eliminates the need for sequential updates that occur one after the other. Instead, our new algorithm allows each of the coordinate blocks to be updated asynchronously and in any order, which means that any number of computing cores can compute updates in parallel without synchronizing their computations. In practice, this asynchronization strategy often leads to speedups that increase linearly with the number of computing cores. ::: We introduce two variants of the Asynchronous PALM algorithm, one stochastic and one deterministic. In the stochastic \textit{and} deterministic cases, we show that cluster points of the algorithm are stationary points. In the deterministic case, we show that the algorithm converges globally whenever the Kurdyka-{\L}ojasiewicz property holds for a function closely related to the objective function, and we derive its convergence rate in a common special case. Finally, we provide a concrete case in which our assumptions hold.


A cross-cultural test of the Maluma-Takete phenomenon.


title: PSYCHOLOGICAL SCIENCE Research Article GESTURE, SPEECH, AND LEXICAL ACCESS: The Role of Lexical Movements in Speech Production

abstract: In a within-subjects design that varied whether speakers were allowed to gesture and the difficulty of lexical access, speakers were videotaped as they described animated action cartoons to a listener When speakers were permitted to gesture, they gestured more often during phrases with spatial content than during phrases with other content Speech with spatial content was less fluent when speakers could not gesture than when they could gesture, speech with nonspatial content was not affected by gesture condition Preventing gesturing increased the relative frequency of nonjuncture filled pauses in speech with spatial content, but not in speech with other content Overall, the effects of preventing speakers from gesturing resembled those of increasing the difficulty of lexical access by other means, except that the effects of gesture restriction were specific to speech with spatial content The findings support the hypothesis that gestural accompaniments to spontaneous speech can facilitate access to the menta...


title: NIMFA: A Python Library for Nonnegative Matrix Factorization

abstract: NIMFA is an open-source Python library that provides a unified interface to nonnegative matrix factorization algorithms. It includes implementations of state-of-the-art factorization methods, initialization approaches, and quality scoring. It supports both dense and sparse matrix representation. NIMFA's component-based implementation and hierarchical design should help the users to employ already implemented techniques or design and code new strategies for matrix factorization tasks.


title: TimeSlice: Interactive Faceted Browsing of Timeline Data

abstract: Temporal events with multiple sets of metadata attributes, i. e., facets, are ubiquitous across different domains. The capabilities of efficiently viewing and comparing events data from various perspectives are critical for revealing relationships, making hypotheses, and discovering patterns. In this paper, we present TimeSlice, an interactive faceted visualization of temporal events, which allows users to easily compare and explore timelines with different attributes on a set of facets. By directly manipulating the filtering tree, a dynamic visual representation of queries and filters in the facet space, users can simultaneously browse the focused timelines and their contexts at different levels of detail, which supports efficient navigation of multi-dimensional events data. Also presented is an initial evaluation of TimeSlice with two datasets - famous deceased people and US daily flight delays.


title: Neurofibrillary tangles in Niemann-Pick disease type C

abstract: Niemann-Pick disease type C (NPC) is an autosomal recessive disease, belonging to a clinically heterogeneous group of lipid storage diseases, distinguished by a unique error in cellular trafficking of exogenous cholesterol, associated with lysosomal accumulation of unesterified cholesterol. Unlike Niemann-Pick disease types A and B, there is no primary genetic defect in sphingomyelinase in NPC. During the routine neuropathological study of NPC patients, we found neurofibrillary tangles (NFT) in a series of cases with a slowly progressive chronic course. These were not associated with beta-amyloid deposits. The NFT were most frequent in the orbital gyrus, cingulate gyrus and entorhinal region of the cerebral cortex, but were also frequently found in the basal ganglia, thalamus and hypothalamus. In one of the most severely affected case, the NFT were even found in the neurons in the inferior olivary nucleus and in the spinal cord. The NFT were immunostained with Alz 50, and consisted of paired helical filaments. The distribution of the neurons bearing the NFT was generally similar to that of the swollen storage neurons, and storage neurons often contained NFT in their perikarya and/or in the meganeurites. However, neurons with NFT could be noted without swollen perikarya. The coexistence of neuronal storage and NFT in NPC without amyloid deposits suggests that perturbed cholesterol metabolism and/or lysosomal membrane trafficking may play a role in the formation of NFT, and that amyloid deposits are not necessarily the prerequisite for NFT formation. The results of our study also suggest that NFT formation may be a rather nonspecific cellular reaction of neurons to certain slowly progressive metabolic perturbations of an as yet undefined nature.


title: Utilization of building information modeling (BIM) in planning an adaptive reuse project of a Traditional Malay House (TMH)

abstract: The Traditional Malay Houses (TMH) in Malaysia are considered as vernacular architecture, built and designed by the locals with thoughtful considerations and appreciation for nature. There are various beneficial features in technical and environmental contexts of a TMH that should be investigated further and applied in the present-day built environment. Due to its modularity and ingenious construction method, it is possible for a TMH to be dismantled, relocated and reconstructed in a new environment, also known as adaptive reuse practice. In Malaysia, a new trend has already emerged among built heritage lovers, business owners, individuals and universities to take the challenge of adaptive reusing abandoned traditional Malay houses for heritage conservation and tourism purposes. This paper aims to explore Building Information Modeling (BIM) usage to analyze the building and environment performance of a typical TMH in an adaptive reuse experiment. The analysis is important in order to maximize the building performance of the TMH once it has been constructed at the new location. Building Information Modeling (BIM) is the process of creating and organizing building data during its life cycle. Characteristically it uses three-dimensional building modeling software with smart parametric object features to increase efficiency in building design and construction. With the development of simulation technology in BIM, it is possible to evaluate the environmental performance of a building before the real construction begins. An on-going research project to dismantle, relocate and reconstruct a TMH has been used as a case study to explore the aim of this study. The Malaysia's Green Building Index (GBI) and Leadership in Energy and Environmental Design (LEED) guidelines were used to evaluate the design and performance of the TMH. A framework was acquired from literatures to establish the correlation between BIM for sustainable design analysis and the GBI and LEED assessment. The focus of this study is the building orientation and daylight performance. The framework was then justified by the case study. Through the usage of BIM software, in this case Autodesk Revit Architecture, the selected TMH was parametrically remodeled in order to coordinate any changes and maintain the consistency of the building components. The BIM model was then simulated for its building performance through secondary software, Autodesk Green Building Studio and the results were analyzed. The study eventually recommended improvements on the performance of the TMH through modification of some of the architectural components without losing the originality and heritage values of the building. Besides that, this study may also be served as a platform to promote the practice of adaptive reuse for heritage and conservation purposes through effective planning using BIM and its simulation tools.


A Review on Technical and Clinical Impact of Microsoft Kinect on Physical Therapy and Rehabilitation


title: Liquid biopsies come of age: towards implementation of circulating tumour DNA

abstract: Improvements in genomic and molecular methods are expanding the range of potential applications for circulating tumour DNA (ctDNA), both in a research setting and as a 'liquid biopsy' for cancer management. Proof-of-principle studies have demonstrated the translational potential of ctDNA for prognostication, molecular profiling and monitoring. The field is now in an exciting transitional period in which ctDNA analysis is beginning to be applied clinically, although there is still much to learn about the biology of cell-free DNA. This is an opportune time to appraise potential approaches to ctDNA analysis, and to consider their applications in personalized oncology and in cancer research.


State of the Art of Parallel Coordinates