title: Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions

abstract: The main question we address in this paper is how to use purely textual description of categories with no training images to learn visual classifiers for these categories. We propose an approach for zero-shot learning of object categories where the description of unseen categories comes in the form of typical text such as an encyclopedia entry, without the need to explicitly defined attributes. We propose and investigate two baseline formulations, based on regression and domain adaptation. Then, we propose a new constrained optimization formulation that combines a regression function and a knowledge transfer function with additional constraints to predict the classifier parameters for new classes. We applied the proposed approach on two fine-grained categorization datasets, and the results indicate successful classifier prediction.


title: Relative attributes

abstract: Human-nameable visual “attributes” can benefit various recognition tasks. However, existing techniques restrict these properties to categorical labels (for example, a person is ‘smiling’ or not, a scene is ‘dry’ or not), and thus fail to capture more general semantic relationships. We propose to model relative attributes. Given training data stating how object/scene categories relate according to different attributes, we learn a ranking function per attribute. The learned ranking functions predict the relative strength of each property in novel images. We then build a generative model over the joint space of attribute ranking outputs, and propose a novel form of zero-shot learning in which the supervisor relates the unseen object category to previously seen objects via attributes (for example, ‘bears are furrier than giraffes’). We further show how the proposed relative attributes enable richer textual descriptions for new images, which in practice are more precise for human interpretation. We demonstrate the approach on datasets of faces and natural scenes, and show its clear advantages over traditional binary attribute prediction for these new tasks.


title: Subspace Methods for Joint Sparse Recovery

abstract: We propose robust and efficient algorithms for the joint sparse recovery problem in compressed sensing, which simultaneously recover the supports of jointly sparse signals from their multiple measurement vectors obtained through a common sensing matrix. In a favorable situation, the unknown matrix, which consists of the jointly sparse signals, has linearly independent nonzero rows. In this case, the MUltiple SIgnal Classification (MUSIC) algorithm, originally proposed by Schmidt for the direction of arrival estimation problem in sensor array processing and later proposed and analyzed for joint sparse recovery by Feng and Bresler, provides a guarantee with the minimum number of measurements. We focus instead on the unfavorable but practically significant case of rank defect or ill-conditioning. This situation arises with a limited number of measurement vectors, or with highly correlated signal components. In this case, MUSIC fails and, in practice, none of the existing methods can consistently approach the fundamental limit. We propose subspace-augmented MUSIC (SA-MUSIC), which improves on MUSIC such that the support is reliably recovered under such unfavorable conditions. Combined with a subspace-based greedy algorithm, known as Orthogonal Subspace Matching Pursuit, which is also proposed and analyzed in this paper, SA-MUSIC provides a computationally efficient algorithm with a performance guarantee. The performance guarantees are given in terms of a version of the restricted isometry property. In particular, we also present a non-asymptotic perturbation analysis of the signal subspace estimation step, which has been missing in the previous studies of MUSIC.


title: NeuAR – A Review of the VR/AR Applications in the Neuroscience Domain

abstract: Since the 1980’s, computational applications based on virtual reality (VR) aimed at treating mental disorders and rehabilitating individuals with cognitive or motor disabilities have been around. They started off by focusing on simple phobias like acrophobia (Emmelkamp et al., 2002) and agoraphobia (Botella et al., 2004), fear of flying (Rothbaum, Hodges, Smith, Lee & Price, 2000), and evolved to fear of driving (Saraiva et al., 2007) or posttraumatic stress disorder (PTSD) (Gamito et al., 2010), schizophrenia (Costa & Carvalho, 2004) or traumatic brain injuries (Gamito et al., 2011a), among many others (Gamito et al., 2011b).


title: Language, music, syntax and the brain

abstract: The comparative study of music and language is drawing an increasing amount of research interest. Like language, music is a human universal involving perceptually discrete elements organized into hierarchically structured sequences. Music and language can thus serve as foils for each other in the study of brain mechanisms underlying complex sound processing, and comparative research can provide novel insights into the functional and neural architecture of both domains. This review focuses on syntax, using recent neuroimaging data and cognitive theory to propose a specific point of convergence between syntactic processing in language and music. This leads to testable predictions, including the prediction that that syntactic comprehension problems in Broca's aphasia are not selective to language but influence music perception as well.


title: A Ku/Ka dual-band dual-CP tracking feed

abstract: The current NASA TDRSS (Telemetry Data Relay Satellite System) provides service at S- and Ku-bands. The next generation TDRSS will extend the service to Ka-band, in addition to the existing S- and Ku-bands. One approach to achieve this enhancement is to replace the Ku-band feed assembly of the existing single access antennas (SAAs) with a new Ku/Ka dual-band feed. This approach has the advantage of minimum breakage to the existing hardware, which has seven flight heritage. The new Ku/Ka dual-band feed is required to provide efficient illumination on the deployable mesh reflector and perform the tracking function (in both Ku- and Ka-bands) and be capable of both RHCP and LHCP operations. In addition, the new feed assembly has to be sufficiently compact to fit into the existing feed tower so that the costly redesign of the reflector system and the deployment mechanism is avoided. This paper describes the design approach, the breadboard model fabrication, and the measured performance of such a feed assembly. The new feed is designed for the following operating frequencies: 13.775 to 13.805 GHz for Ku-transmit, 14.888 to 15.113 GHz for Ku-receive, 22.55 to 23.55 GHz for Ka-transmit, and 25.255 to 27.505 GHz for Ka-receive.


title: PN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptors

abstract: In this paper we propose a new approach for learning local descriptors for matching image patches. It has recently been demonstrated that descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Unfortunately their computational complexity is prohibitive for any practical application. We address this problem and propose a CNN based descriptor with improved matching performance, significantly reduced training and execution time, as well as low dimensionality. ::: We propose to train the network with triplets of patches that include a positive and negative pairs. To that end we introduce a new loss function that exploits the relations within the triplets. We compare our approach to recently introduced MatchNet and DeepCompare and demonstrate the advantages of our descriptor in terms of performance, memory footprint and speed i.e. when run in GPU, the extraction time of our 128 dimensional feature is comparable to the fastest available binary descriptors such as BRIEF and ORB.


title: A Flexible Millimeter-Wave Channel Sounder With Absolute Timing

abstract: This paper presents a novel ultrawideband wireless spread spectrum millimeter-wave (mmWave) channel sounder that supports both a wideband sliding correlator mode and a realtime spread spectrum mode, also known as wideband correlation or direct correlation. Both channel sounder modes are capable of absolute propagation delay (time of flight) measurements with up to 1 GHz of radio frequency null-to-null bandwidth, and can measure multipath with a 2-ns time resolution. The sliding correlator configuration facilitates long-distance measurements with angular spread and delay spread for up to 185 dB of maximum measurable path loss. The real-time spread spectrum mode is shown to support short-range, small-scale temporal, and Doppler measurements (minimum snapshot sampling interval of 32.753 μs) with a substantial dynamic fading range of 40 dB for human blockage and dynamic urban scenarios. The channel sounder uses field programmable gate arrays, analog-to-digital converters, digital-to-analog converters, and low-phase-noise rubidium standard references for frequency/time synchronization and absolute time delay measurements. Using propagation theory, several methods are presented here to calibrate and verify the accuracy of the channel sounder, and an improved diffraction model for human blockage, based on the METIS model but now including directional antenna gains, is developed from measurements using the channel sounder. The mmWave channel sounder described here may be used for accurate spatial and temporal raytracing calibration, to identify individual multipath components, to measure antenna patterns, for constructing spatial profiles of mmWave channels, and for developing statistical channel impulse response models in time and space.


title: PacGAN: The power of two samples in generative adversarial networks

abstract: Generative adversarial networks (GANs) are a technique for learning generative models of complex data distributions from samples. Despite remarkable advances in generating realistic images, a major shortcoming of GANs is the fact that they tend to produce samples with little diversity, even when trained on diverse datasets. This phenomenon, known as mode collapse, has been the focus of much recent work. We study a principled approach to handling mode collapse, which we call packing. The main idea is to modify the discriminator to make decisions based on multiple samples from the same class, either real or artificially generated. We draw analysis tools from binary hypothesis testing---in particular the seminal result of Blackwell---to prove a fundamental connection between packing and mode collapse. We show that packing naturally penalizes generators with mode collapse, thereby favoring generator distributions with less mode collapse during the training process. Numerical experiments on benchmark datasets suggest that packing provides significant improvements.


title: Distributed Liveness: Understanding How New Technologies Transform Performance Experiences

abstract: We identify emerging phenomena of distributed liveness, involving new relationships among performers, audiences, and technology. Liveness is a recent, technology-based construct, which refers to experiencing an event in real-time with the possibility for shared social realities. Distributed liveness entails multiple forms of physical, spatial, and social co-presence between performers and audiences across physical and virtual spaces. We interviewed expert performers about how they experience liveness in physically co-present and distributed settings. Findings show that distributed performances and technology need to support flexible social co-presence and new methods for sensing subtle audience responses and conveying engagement abstractly.


Texas stem cell rules may impede clinical research


title: An approach to validity indices for clustering techniques in Big Data

abstract: Clustering analysis is one of the most used Machine Learning techniques to discover groups among data objects. Some clustering methods require the number of clusters into which the data is going to be partitioned. There exist several cluster validity indices that help us to approximate the optimal number of clusters of the dataset. However, such indices are not suitable to deal with Big Data due to its size limitation and runtime costs. This paper presents two clustering validity indices that handle large amount of data in low computational time. Our indices are based on redefinitions of traditional indices by simplifying the intra-cluster distance calculation. Two types of tests have been carried out over 28 synthetic datasets to analyze the performance of the proposed indices. First, we test the indices with small and medium size datasets to verify that our indices have a similar effectiveness to the traditional ones. Subsequently, tests on datasets of up to 11 million records and 20 features have been executed to check their efficiency. The results show that both indices can handle Big Data in a very low computational time with an effectiveness similar to the traditional indices using Apache Spark framework.


title: An Improved Droop Control Method for DC Microgrids Based on Low Bandwidth Communication With DC Bus Voltage Restoration and Enhanced Current Sharing Accuracy

abstract: Droop control is the basic control method for load current sharing in dc microgrid applications. The conventional dc droop control method is realized by linearly reducing the dc output voltage as the output current increases. This method has two limitations. First, with the consideration of line resistance in a droop-controlled dc microgrid, since the output voltage of each converter cannot be exactly the same, the output current sharing accuracy is degraded. Second, the dc-bus voltage deviation increases with the load due to the droop action. In this paper, in order to improve the performance of the dc microgrid operation, a low-bandwidth communication (LBC)-based improved droop control method is proposed. In contrast with the conventional approach, the control system does not require a centralized secondary controller. Instead, it uses local controllers and the LBC network to exchange information between converter units. The droop controller is employed to achieve independent operation, and the average voltage and current controllers are used in each converter to simultaneously enhance the current sharing accuracy and restore the dc bus voltage. All of the controllers are realized locally, and the LBC system is only used for changing the values of the dc voltage and current. Hence, a decentralized control scheme is accomplished. The simulation test based on MATLAB/Simulink and the experimental validation based on a 2 × 2.2 kW prototype were implemented to demonstrate the proposed approach.


title: A Comprehensive Survey on Text Summarization Systems

abstract: Text summarization systems are among the most attractive research areas nowadays. Summarization systems offers the possibility of finding the main points of texts and so the user will spend less time on reading the whole document. Different types of summary might be useful in various applications and summarization systems can be categorized based on these types. This paper presents a taxonomy of summarization systems and defines the most important criteria for a summary which can be generated by a system. Additionally, different methods of text summarization as well as main steps for summarization process is discussed. we also go through main criteria for evaluating a text summarization.


title: Particle Swarm Optimization with Self-Adaptive Acceleration Coefficients

abstract: In this article we describe a novel Particle Swarm Optimization (PSO) approach to multi-objective optimization (MOO), called Time Variant Multi-Objective Particle Swarm Optimization (TV-MOPSO). TV-MOPSO is made adaptive in nature by allowing its vital parameters (viz., inertia weight and acceleration coefficients) to change with iterations. This adaptiveness helps the algorithm to explore the search space more efficiently. A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front. TV-MOPSO has been compared with some recently developed multi-objective PSO techniques and evolutionary algorithms for 11 function optimization problems, using different performance measures.


title: Early Childhood Predictors of the Social Competence of Adults with Autism

abstract: Longitudinal research into adult outcomes in autism remains limited. Unlike previous longitudinal examinations of adult outcome in autism, the twenty participants in this study were evaluated across multiple assessments between early childhood (M = 3.9 years) and adulthood (M = 26.6 years). In early childhood, responsiveness to joint attention (RJA), language, and intelligence were assessed. In adulthood, the parents of participants responded to interviews assessing the adaptive functioning, autistic symptomology and global functioning of their children. RJA and early childhood language predicted a composite measure of adult social functioning and independence. Early childhood language skills and intelligence predicted adult adaptive behaviors. RJA predicted adult non-verbal communication, social skills and symptoms. Adaptive behaviors changed with development, but symptoms of autism did not. Additional factors associated with adult outcomes are discussed.


title: Qualitative Analysis of the Application of Self-Determination Theory in Robotics Tournaments

abstract: To explore the application of Self-Determination Theory (SDT) in Human-Robot Interaction (HRI), this paper used robotics tournaments as a test bed to seek evidence of people's need for autonomy, competence, relatedness to other humans, as well as intrinsic motivation and emotions toward interactions with robots. The resulting seven categories of codes and verbal data analysis supported the SDT framework in robotics tournaments and provided a direction for further examination of SDT in HRI.


title: Directional Clustering Through Matrix Factorization

abstract: This paper deals with a clustering problem where feature vectors are clustered depending on the angle between feature vectors, that is, feature vectors are grouped together if they point roughly in the same direction. This directional distance measure arises in several applications, including document classification and human brain imaging. Using ideas from the field of constrained low-rank matrix factorization and sparse approximation, a novel approach is presented that differs from classical clustering methods, such as seminonnegative matrix factorization, $K$ -EVD, or $k$ -means clustering, yet combines some aspects of all these. As in nonnegative matrix factorization and $K$ -EVD, the matrix decomposition is iteratively refined to optimize a data fidelity term; however, no positivity constraint is enforced directly nor do we need to explicitly compute eigenvectors. As in $k$ -means and $K$ -EVD, each optimization step is followed by a hard cluster assignment. This leads to an efficient algorithm that is shown here to outperform common competitors in terms of clustering performance and/or computation speed. In addition to a detailed theoretical analysis of some of the algorithm’s main properties, the approach is empirically evaluated on a range of toy problems, several standard text clustering data sets, and a high-dimensional problem in brain imaging, where functional magnetic resonance imaging data are used to partition the human cerebral cortex into distinct functional regions.


title: Design of a Direct-Driven Linear Actuator for a High-Speed Quadruped Robot, Cheetaroid-I

abstract: Several legged robots have been developed in recent years and proved that they are an effective transportation system on an uneven terrain. In particular, quadruped robots are regarded as a new trend in robotics due to their superior gait stability and robustness to disturbances. More recently, many robotics researchers are making their best efforts to improve the locomotion speed, as well as the stability and robustness, of quadruped robots. The high-speed locomotion creates various challenges in the development of actuators, mechanical design, and control algorithms of the robot. In this paper, a linear actuation system for the high-speed locomotion of a quadruped robot is introduced. The proposed actuator is designed based on the principle of brushed direct-current electric motor systems. For the minimal impedance and improved power capacity, the actuator is designed with dual layers of cores, which are aligned parallel to permanent magnets. The mechanical and electrical properties of the actuation system, such as back-drivability, controllability, and response time, are verified by experimental results. A robotic leg, which is the rear leg of a Cheetah-like robot, is designed with the proposed actuator, and experimental results for trajectory tracking performance are presented.


title: Intermittency phenomena in electrical connectors

abstract: Fretting is known to be a major cause of contact deterioration and failure, particularly in tin-plated contacts. During fretting the contact resistance generally increases slowly with time. Superimposed on this slow increase in contact resistance are rapid changes in contact resistance within fractions of a second, called intermittences or short duration discontinuities. Although intermittences have been reported by several authors, they are frequently overlooked in traditional fretting experiments and not much is known about their origin. The present study aims at filling this gap. A test apparatus has been built to measure the contact voltage-drop profile during an intermittence and fretting experiments on tin-plated copper contacts have been carried out. The results lead to a set of requirements for a model to explain intermittency phenomena.


title: Pruned Neural Networks for Regression

abstract: Neural networks have been widely used as a tool for regression. They are capable of approximating any function and they do not require any assumption about the distribution of the data. The most commonly used architectures for regression are the feedforward neural networks with one or more hidden layers. In this paper, we present a network pruning algorithm which determines the number of units in the input and hidden layers of the networks. We compare the performance of the pruned networks to four regression methods namely, linear regression (LR), Naive Bayes (NB), k-nearest-neighbor (kNN), and a decision tree predictor M5 . On 32 pubhcly available data sets tested, the neural network method outperforms NB and kNN if the prediction errors are computed in terms of the root mean squared errors. Under this measurement metric, it also performs as well as LR and M5'. On the other hand, using the mean absolute error as the measurement metric, the neural network method outperforms all four other regression methods.


title: Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network

abstract: Abstract Screening for vision threatening diabetic retinopathy by grading digital retinal images reduces the risk of blindness in people with diabetes. Computer-aided diagnosis can aid human graders to cope with this mounting problem. We propose to use a 10-layer convolutional neural network to automatically, simultaneously segment and discriminate exudates, haemorrhages and micro-aneurysms. Input image is normalized before segmentation. The net is trained in two stages to improve performance. On average, our net on 30,275,903 effective points achieved a sensitivity of 0.8758 and 0.7158 for exudates and dark lesions on the CLEOPATRA database. It also achieved a sensitivity of 0.6257 and 0.4606 for haemorrhages and micro-aneurysms. This study shows that it is possible to get a single convolutional neural network to segment these pathological features on a wide range of fundus images with reasonable accuracy.


title: Unstable burst fractures of the thoraco-lumbar junction: treatment by posterior bisegmental correction/fixation and staged anterior corpectomy and titanium cage implantation

abstract: BACKGROUND ::: Controversy exists about the best treatment of unstable thoraco-lumbar (TL) burst fractures. Kyphosis correction and canal decompression in case of a neurological deficit are recognized treatment objectives, and various conservative and surgical strategies have been proposed. This prospective observational study evaluates the benefits and risks of a posterior bisegmental transpedicular correction/fixation and staged anterior corpectomy and titanium cage implantation in unstable TL junction burst fractures. ::: ::: ::: METHOD ::: 20 consecutive patients with a single-level traumatic unstable burst fracture at the TL junction were operated on by a bisegmental posterior correction/fixation, followed by anterior corpectomy and titanium cage implantation 7-10 days later. The radiological and clinical course is documented over a period of 24 months. ::: ::: ::: FINDINGS ::: The mean posttraumatic loss of anterior vertebral body height was 58% (45-70%). The posttraumatic mean regional kyphosis was 16 degrees and could be corrected by the posterior approach to a mean lordosis of 2 degrees. Mean secondary loss of the kyphosis correction was 3 degrees over 24 months. No hardware failure occurred, and construct stability was observed in all 20 patients. One surgical complication occurred during the posterior approach, and three transient surgical complications by the anterior approach. 12 of the 14 patients with an initial neurological deficit recovered an average of 1.5 grades on the ASIA scale. At 24 months postoperatively, the mean regional TL back pain on a VAS (0-10) was 1.6, and the mean pain at the anterior approach site was 1.2. ::: ::: ::: CONCLUSION ::: Posterior bisegmental transpedicular correction/fixation and staged anterior corpectomy and titanium cage implantation is a safe and reliable surgical treatment option in unstable TL junction burst fractures. The advantages of this technique are a complete kyphosis correction, immediate stability, maintenance of kyphosis correction, and complete spinal canal decompression in case of a neurological deficit. However, these advantages have to be carefully weighed against the double approach morbidity.


title: Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling

abstract: Deep neural networks (DNNs) are powerful machine learning models and have succeeded in various artificial intelligence tasks. Although various architectures and modules for the DNNs have been proposed, selecting and designing the appropriate network structure for a target problem is a challenging task. In this paper, we propose a method to simultaneously optimize the network structure and weight parameters during neural network training. We consider a probability distribution that generates network structures, and optimize the parameters of the distribution instead of directly optimizing the network structure. The proposed method can apply to the various network structure optimization problems under the same framework. We apply the proposed method to several structure optimization problems such as selection of layers, selection of unit types, and selection of connections using the MNIST, CIFAR-10, and CIFAR-100 datasets. The experimental results show that the proposed method can find the appropriate and competitive network structures.


title: Inferring Searcher Attention by Jointly Modeling User Interactions and Content Salience

abstract: Modeling and predicting user attention is crucial for interpreting search behavior. The numerous applications include quantifying web search satisfaction, estimating search quality, and measuring and predicting online user engagement. While prior research has demonstrated the value of mouse cursor data and other interactions as a rough proxy of user attention, precisely predicting where a user is looking on a page remains a challenge, exacerbated in Web pages beyond the traditional search results. To improve attention prediction on a wider variety of Web pages, we propose a new way of modeling searcher behavior data by connecting the user interactions to the underlying Web page content. Specifically, we propose a principled model for predicting a searcher's gaze position on a page, that we call Mixture of Interactions and Content Salience (MICS). To our knowledge, our model is the first to effectively combine user interaction data, such as mouse cursor and scrolling positions, with the visual prominence, or salience, of the page content elements. Extensive experiments on multiple popular types of Web content demonstrate that the proposed MICS model significantly outperforms previous approaches to searcher gaze prediction that use only the interaction information. Grounding the observed interactions to the underlying page content provides a general and robust approach to user attention modeling, enabling more powerful tool for search behavior interpretation and ultimately search quality improvements.


title: Skeletal muscle buffering capacity and endurance performance after high-intensity interval training by well-trained cyclists

abstract: Skeletal muscle buffering capacity (beta m), enzyme activities and exercise performance were measured before and after 4 weeks of high-intensity, submaximal interval training (HIT) undertaken by six well-trained competitive cyclists [mean maximal oxygen consumption (VO2max) = 66.2 ml.kg-1.min-1]. HIT replaced a portion of habitual endurance training and consisted of six sessions, each of six to eight repetitions of 5 min duration at 80% of peak sustained power output (PPO) separated by 1 min of recovery. beta m increased from 206.6 (17.9) to 240.4 (34.1) mumol H+.g muscle dw-1.pH-1 after HIT (P = 0.05). PPO, time to fatigue at 150% PPO (TF150) and 40-km cycle time trial performance (TT40) all significantly improved after HIT (P < 0.05). In contrast, there was no change in the activity of either phosphofructokinase or citrate synthase. In addition, beta m correlated significantly with TT40 performance before HIT (r = -0.82, P < 0.05) and the relationship between change in beta m and change in TT40 was close to significance (r = -0.74). beta m did not correlate with TF150. These results indicate that beta m may be an important determinant of relatively short-duration (< 60 min) endurance cycling activity and responds positively to just six sessions of high-intensity, submaximal interval training.


title: Warehouse Layout Method Based on Ant Colony and Backtracking Algorithm

abstract: Warehouse is one of the important aspects of a company. Therefore, it is necessary to improve Warehouse Management System (WMS) to have a simple function that can determine the layout of the storage goods. In this paper we propose an improved warehouse layout method based on ant colony algorithm and backtracking algorithm. The method works on two steps. First, it generates a solutions parameter tree from backtracking algorithm. Then second, it deducts the solutions parameter by using a combination of ant colony algorithm and backtracking algorithm. This method was tested by measuring the time needed to build the tree and to fill up the space using two scenarios. The method needs 0.294 to 33.15 seconds to construct the tree and 3.23 seconds (best case) to 61.41 minutes (worst case) to fill up the warehouse. This method is proved to be an attractive alternative solution for warehouse layout system.


title: Brainwave entrainment for better sleep and post-sleep state of young elite soccer players – A pilot study

abstract: AbstractThe effect of sleep deprivation on psychophysical performance and well-being is comprehensively investigated. Research investigating the effect of improved sleep is rare. Just as little exists about attempts to support athletic mental state and performance by improving sleep quality. This study aims to investigate whether sleep quality of top athletes can be improved by auditory brainwave entrainment and whether this leads to enhancements of post-sleep psychophysical states. In a pilot study, 15 young elite soccer players were stimulated for eight weeks during sleep with binaural beats around 2–8 Hz. Once a week after wake-up, participants completed three different questionnaires: a sleep diary, an adjective list for psychophysical and motivational state, and a self-assessment questionnaire for sleep and awakening quality. Fifteen sport students executed the same protocol sleeping on the same pillow, but without stimulation. Subjective ratings of sleep and awakening quality, sleepiness and motivat...


title: Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing

abstract: The Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a collection of human-human written conversations spanning over multiple domains and topics. The dataset was collected based on the Wizard of Oz experiment on Amazon MTurk. Each dialogue contains a goal label and several exchanges between a visitor and the system. Each system turn has labels from the set of slot-value pairs representing a coarse representation of dialogue state. There are in total 9855 dialogues.


title: Convolutional Neural Networks for Aerial Multi-Label Pedestrian Detection

abstract: The low resolution of objects of interest in aerial images makes pedestrian detection and action detection extremely challenging tasks. Furthermore, using deep convolutional neural networks to process large images can be demanding in terms of computational requirements. In order to alleviate these challenges, we propose a two-step, yes and no question answering framework to find specific individuals doing one or multiple specific actions in aerial images. First, a deep object detector, Single Shot Multibox Detector (SSD), is used to generate object proposals from small aerial images. Second, another deep network, is used to learn a latent common sub-space which associates the high resolution aerial imagery and the pedestrian action labels that are provided by the human-based sources.


title: Automatic sentiment analysis in on-line text

abstract: The growing stream of content placed on the Web provides a huge collection of textual resources. People share their experiences on-line, ventilate their opinions (and frustrations), or simply talk just about anything. The large amount of available data creates opportunities for automatic mining and analysis. The information we are interested in this paper, is how people feel about certain topics. We consider it as a classification task: their feelings can be positive, negative or neutral. A sentiment isn’t always stated in a clear way in the text; it is often represented in subtle, complex ways. Besides direct expression of the user's feelings towards a certain topic, he or she can use a diverse range of other techniques to express his or her emotions. On top of that, authors may mix objective and subjective information about a topic, or write down thoughts about other topics than the one we are investigating. Lastly, the data gathered from the World Wide Web often contains a lot of noise. All of this makes the task of automatic recognition of the sentiment in on-line text more difficult. We will give an overview of various techniques used to tackle the problems in the domain of sentiment analysis, and add some of our own results.


title: Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization

abstract: State-of-the-art methods treat pedestrian attribute recognition as a multi-label image classification problem. The location information of person attributes is usually eliminated or simply encoded in the rigid splitting of whole body in previous work. In this paper, we formulate the task in a weakly-supervised attribute localization framework. Based on GoogLeNet, firstly, a set of mid-level attribute features are discovered by novelly designed detection layers, where a max-pooling based weakly-supervised object detection technique is used to train these layers with only image-level labels without the need of bounding box annotations of pedestrian attributes. Secondly, attribute labels are predicted by regression of the detection response magnitudes. Finally, the locations and rough shapes of pedestrian attributes can be inferred by performing clustering on a fusion of activation maps of the detection layers, where the fusion weights are estimated as the correlation strengths between each attribute and its relevant mid-level features. Extensive experiments are performed on the two currently largest pedestrian attribute datasets, i.e. the PETA dataset and the RAP dataset. Results show that the proposed method has achieved competitive performance on attribute recognition, compared to other state-of-the-art methods. Moreover, the results of attribute localization are visualized to understand the characteristics of the proposed method.


title: BioRob-Arm: A Quickly Deployable and Intrinsically Safe, Light- Weight Robot Arm for Service Robotics Applications.

abstract: Using conventional rigid industrial manipulators for service robotics applications typically demands huge efforts for safety measurements resulting in high installation and operation costs. We present how the BioRob robot arm is based on a combination of compliant actuation and lightweight mechanical design to obtain the flexibility, mobility and, most important, the inherent safety properties needed to implement effective and safe service robotics applications. We discuss the sensors and control structure used to damp the oscillations caused by the significant joint compliance of the arm and to obtain the accuracy needed for the intended applications. The concluding example of a typical pick and place application with teaching by manual guidance illustrates the benefits of the BioRob design for service robotics applications.


title: Using association rules to guide a search for best fitting transfer models of student learning

abstract: A rechargeable aqueous metal-halogen cell is described which includes a casing, a pair of spaced apart porous electrode substrates in the casing, a micro-porous separator between the electrode substrates defining a positive and a negative electrode compartment, an aqueous electrolytic solution containing a zinc salt selected from the class consisting of zinc bromide, zinc iodide, and mixtures thereof in both compartments, and an organic halogen complexing additive of nitrobenzene in the electrolytic solution of at least the positive compartment.


title: Automatic gender recognition for “in the wild” facial images using convolutional neural networks

abstract: Automatic recognition of human demographical attributes has implications in a variety of domains, such as surveillance systems, human computer interaction, marketing etc. In this paper, we present an automatic gender recognition method from facial images based on convolutional neural networks. In order to train the network, we merged together several face databases and also gathered and annotated a ∼70000 facial images from the internet. We trained, evaluated and compared several network architectures that achieved impressive results on other computer vision tasks. The best accuracy is obtained using Inception-v4 network: 98.2% on our dataset, and 84% on Adience dataset.


title: Assisting the Visually Impaired: Obstacle Detection and Warning System by Acoustic Feedback

abstract: The aim of this article is focused on the design of an obstacle detection system for assisting visually impaired people. A dense disparity map is computed from the images of a stereo camera carried by the user. By using the dense disparity map, potential obstacles can be detected in 3D in indoor and outdoor scenarios. A ground plane estimation algorithm based on RANSAC plus filtering techniques allows the robust detection of the ground in every frame. A polar grid representation is proposed to account for the potential obstacles in the scene. The design is completed with acoustic feedback to assist visually impaired users while approaching obstacles. Beep sounds with different frequencies and repetitions inform the user about the presence of obstacles. Audio bone conducting technology is employed to play these sounds without interrupting the visually impaired user from hearing other important sounds from its local environment. A user study participated by four visually impaired volunteers supports the proposed system.


Dual camera intelligent sensor for high definition 360 degrees surveillance


title: Citizens as sensors: The world of volunteered geography

abstract: In recent months there has been an explosion of interest in using the Web to create, assemble, and disseminate geographic information provided voluntarily by individuals. Sites such as Wikimapia and OpenStreetMap are empowering citizens to create a global patchwork of geographic information, while Google Earth and other virtual globes are encouraging volunteers to develop interesting applications using their own data. I review this phenomenon, and examine associated issues: what drives people to do this, how accurate are the results, will they threaten individual privacy, and how can they augment more conventional sources? I compare this new phenomenon to more traditional citizen science and the role of the amateur in geographic observation.


title: Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution

abstract: We propose an image super resolution(ISR) method using generative adversarial networks (GANs) that takes a low resolution input fundus image and generates a high resolution super resolved (SR) image upto scaling factor of $16$. This facilitates more accurate automated image analysis, especially for small or blurred landmarks and pathologies. Local saliency maps, which define each pixel's importance, are used to define a novel saliency loss in the GAN cost function. Experimental results show the resulting SR images have perceptual quality very close to the original images and perform better than competing methods that do not weigh pixels according to their importance. When used for retinal vasculature segmentation, our SR images result in accuracy levels close to those obtained when using the original images.


title: A deep semantic framework for multimodal representation learning

abstract: Multimodal representation learning has gained increasing importance in various real-world multimedia applications. Most previous approaches focused on exploring inter-modal correlation by learning a common or intermediate space in a conventional way, e.g. Canonical Correlation Analysis (CCA). These works neglected the exploration of fusing multiple modalities at higher semantic level. In this paper, inspired by the success of deep networks in multimedia computing, we propose a novel unified deep neural framework for multimodal representation learning. To capture the high-level semantic correlations across modalities, we adopted deep learning feature as image representation and topic feature as text representation respectively. In joint model learning, a 5-layer neural network is designed and enforced with a supervised pre-training in the first 3 layers for intra-modal regularization. The extensive experiments on benchmark Wikipedia and MIR Flickr 25K datasets show that our approach achieves state-of-the-art results compare to both shallow and deep models in multimodal and cross-modal retrieval.


title: Automatic Target Detection and Tracking in FLIR Image Sequences Using Morphological Connected Operator

abstract: In this paper, we propose a method for detecting and tracking small targets in forward looking infrared (FLIR) image sequences taken from an airborne moving platform. Firstly, we adopt the morphological connected operator to remove the undesirable clutter in the background. Secondly, the image is decomposed by morphological Haar wavelet, and the wavelet energy image is computed from the horizontal and vertical detail images, and it is fused with the scaled image. Thirdly, the targets are extracted coarse-to-fine by adaptive double thresholding. Finally, targets are modeled by intensity probabilistic density function and tracked using mean shift algorithm. The experiments performed on the AMCOM FLIR data set verify the validity and robustness of the algorithm.


title: Toward a unified theory of the multitasking continuum: from concurrent performance to task switching, interruption, and resumption

abstract: Multitasking in user behavior can be represented along a continuum in terms of the time spent on one task before switching to another. In this paper, we present a theory of behavior along the multitasking continuum, from concurrent tasks with rapid switching to sequential tasks with longer time between switching. Our theory unifies several theoretical effects - the ACT-R cognitive architecture, the threaded cognition theory of concurrent multitasking, and the memory-for-goals theory of interruption and resumption - to better understand and predict multitasking behavior. We outline the theory and discuss how it accounts for numerous phenomena in the recent empirical literature.


title: Sample-Specific SVM Learning for Person Re-identification

abstract: Person re-identification addresses the problem of matching people across disjoint camera views and extensive efforts have been made to seek either the robust feature representation or the discriminative matching metrics. However, most existing approaches focus on learning a fixed distance metric for all instance pairs, while ignoring the individuality of each person. In this paper, we formulate the person re-identification problem as an imbalanced classification problem and learn a classifier specifically for each pedestrian such that the matching model is highly tuned to the individual's appearance. To establish correspondence between feature space and classifier space, we propose a Least Square Semi-Coupled Dictionary Learning (LSSCDL) algorithm to learn a pair of dictionaries and a mapping function efficiently. Extensive experiments on a series of challenging databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art approaches, especially on the rank-1 recognition rate.


title: METIS-II: low resource machine translation

abstract: METIS-II was an EU-FET MT project running from October 2004 to September 2007, which aimed at translating free text input without resorting to parallel corpora. The idea was to use "basic" linguistic tools and representations and to link them with patterns and statistics from the monolingual target-language corpus. The METIS-II project has four partners, translating from their "home" languages Greek, Dutch, German, and Spanish into English. The paper outlines the basic ideas of the project, their implementation, the resources used, and the results obtained. It also gives examples of how METIS-II has continued beyond its lifetime and the original scope of the project. On the basis of the results and experiences obtained, we believe that the approach is promising and offers the potential for development in various directions.


title: Predicting football results using Bayesian nets and other machine learning techniques

abstract: Bayesian networks (BNs) provide a means for representing, displaying, and making available in a usable form the knowledge of experts in a given field. In this paper, we look at the performance of an expert constructed BN compared with other machine learning (ML) techniques for predicting the outcome (win, lose, or draw) of matches played by Tottenham Hotspur Football Club. The period under study was 1995-1997 - the expert BN was constructed at the start of that period, based almost exclusively on subjective judgement. Our objective was to determine retrospectively the comparative accuracy of the expert BN compared to some alternative ML models that were built using data from the two-year period. The additional ML techniques considered were: MC4, a decision tree learner; Naive Bayesian learner; Data Driven Bayesian (a BN whose structure and node probability tables are learnt entirely from data); and a K-nearest neighbour learner. The results show that the expert BN is generally superior to the other techniques for this domain in predictive accuracy. The results are even more impressive for BNs given that, in a number of key respects, the study assumptions place them at a disadvantage. For example, we have assumed that the BN prediction is 'incorrect' if a BN predicts more than one outcome as equally most likely (whereas, in fact, such a prediction would prove valuable to somebody who could place an 'each way' bet on the outcome). Although the expert BN has now long been irrelevant (since it contains variables relating to key players who have retired or left the club) the results here tend to confirm the excellent potential of BNs when they are built by a reliable domain expert. The ability to provide accurate predictions without requiring much learning data are an obvious bonus in any domain where data are scarce. Moreover, the BN was relatively simple for the expert to build and its structure could be used again in this and similar types of problems.


title: Overview of the Multiview and 3D Extensions of High Efficiency Video Coding

abstract: The High Efficiency Video Coding (HEVC) standard has recently been extended to support efficient representation of multiview video and depth-based 3D video formats. The multiview extension, MV-HEVC, allows efficient coding of multiple camera views and associated auxiliary pictures, and can be implemented by reusing single-layer decoders without changing the block-level processing modules since block-level syntax and decoding processes remain unchanged. Bit rate savings compared with HEVC simulcast are achieved by enabling the use of inter-view references in motion-compensated prediction. The more advanced 3D video extension, 3D-HEVC, targets a coded representation consisting of multiple views and associated depth maps, as required for generating additional intermediate views in advanced 3D displays. Additional bit rate reduction compared with MV-HEVC is achieved by specifying new block-level video coding tools, which explicitly exploit statistical dependencies between video texture and depth and specifically adapt to the properties of depth maps. The technical concepts and features of both extensions are presented in this paper.


title: First Result on Arabic Neural Machine Translation

abstract: Neural machine translation has become a major alternative to widely used phrase-based statistical machine translation. We notice however that much of research on neural machine translation has focused on European languages despite its language agnostic nature. In this paper, we apply neural machine translation to the task of Arabic translation (Ar En) and compare it against a standard phrase-based translation system. We run extensive comparison using various configurations in preprocessing Arabic script and show that the phrase-based and neural translation systems perform comparably to each other and that proper preprocessing of Arabic script has a similar effect on both of the systems. We however observe that the neural machine translation significantly outperform the phrase-based system on an out-of-domain test set, making it attractive for real-world deployment.


title: Citizen Science for Mining the Biomedical Literature

abstract: Biomedical literature represents one of the largest and fastest growing collections of unstructured biomedical knowledge. Finding critical information buried in the literature can be challenging. To extract information from free-flowing text, researchers need to: 1. identify the entities in the text (named entity recognition), 2. apply a standardized vocabulary to these entities (normalization), and 3. identify how entities in the text are related to one another (relationship extraction). Researchers have primarily approached these information extraction tasks through manual expert curation and computational methods. We have previously demonstrated that named entity recognition (NER) tasks can be crowdsourced to a group of non-experts via the paid microtask platform, Amazon Mechanical Turk (AMT), and can dramatically reduce the cost and increase the throughput of biocuration efforts. However, given the size of the biomedical literature, even information extraction via paid microtask platforms is not scalable. With our web-based application Mark2Cure (http://mark2cure.org), we demonstrate that NER tasks also can be performed by volunteer citizen scientists with high accuracy. We apply metrics from the Zooniverse Matrices of Citizen Science Success and provide the results here to serve as a basis of comparison for other citizen science projects. Further, we discuss design considerations, issues, and the application of analytics for successfully moving a crowdsourcing workflow from a paid microtask platform to a citizen science platform. To our knowledge, this study is the first application of citizen science to a natural language processing task.


title: Celery root extract as an inducer of mania induction in a patient on venlafaxine and St John's Wort.

abstract: ABSTRACTCelery root belongs to a group of plants classified as the umbelliferous family, which contains phytoestrogens. Phytoestrogens are structurally similar to estrogen as they share a pair of hydroxyl groups and phenolic ring, which enables them to bind to estrogen receptors directly, making them a herbal remedy for low estrogen states such as menopause. We present a case of a female patient with depression who was stabilized on venlafaxine and St John’s Wort, and who developed a manic episode due to elevated serum venlafaxine levels after she started taking celery extracts for menopausal related issues. We proffer a hypothesis for this unusual occurrence.


title: Correction of Deep Bite in Adults Using the Invisalign System

abstract: full correction. 1 The amount of incisor exposure in smiling is an important consideration in deep-bite cases. When the maxillary incisor display is correct, maxillary incisor intrusion is unnecessary; treatment should involve intrusion and leveling of the mandibular anterior teeth to avoid flattening of the smile arc. 2 Any maxillary incisor intrusion or mandibular leveling should be carefully monitored during treatment to maintain proper incisor exposure while effectively correcting the deep bite. After the development of thermoformed appliances that could be used for minor tooth movements, 3-7 the Invisalign* system was introduced for treatment of moderate malocclusions. 8-13 In 2004, Wheeler reported that Invisalign could be used in patients with anterior and posterior crossbites, deep bites, anterior open bites or shallow overbites, and periodontal problems. 14 The present article describes the use of Invisalign appliances for the correction of deep bite in adult patients with normal skeletal patterns. Case 1 A 23-year-old female presented with a Class I malocclusion, a deep bite, and mild crowding in both arches (Fig. 1). Treatment involved 23 upper and 29 lower aligners. After 15 months of initial treatment, another six months of Case Refinement was needed to finish the lower alignment (Figs. 2,3). The upper and lower crowding was corrected with interproximal reduction. The deep bite was opened by leveling the mandibular arch, using controlled proclination of the mandibular incisors and only slight intrusion of the maxillary incisors, thus maintaining the smile arc. The patient’s good posterior occlusal relationship was preserved.


title: Glycerol (Byproduct of Biodiesel Production) as a Source for Fuels and Chemicals - Mini Review~!2009-08-26~!2010-02-02~!2010-04-09~!

abstract: Currently the large surplus of glycerol formed as a by-product during the production of biodiesel offered an abundant and low cost feedstock. Researchers showed a surge of interest in using glycerol as renewable feedstock to produce functional chemicals. This Minireview focuses on recent developments in the conversion of glycerol into value- added products, including citric acid, lactic acid, 1,3-dihydroxyacetone (DHA), 1,3-propanediol (1,3-PD), dichloro-2- propanol (DCP), acrolein, hydrogen, and ethanol etc. The versatile new applications of glycerol in the everyday life and chemical industry will improve the economic viability of the biodiesel industry.


Social media and Tourism Destination: TripAdvisor Case Study


Finger Vein Identification using Convolutional Neural Network and Supervised Discrete Hashing


DSM: A Low-Overhead, High-Performance, Dynamic Stream Mapping Approach for MongoDB.


title: Chapter 26 An Introduction to Physiological Player Metrics for Evaluating Games

abstract: Evaluating affective user experience in games is an important component of the growing field of game user research, because compelling gameplay experiences incorporate meaningful and, therefore, emotional player decisions. This makes evaluating player emotions and player visceral physiological reactions a fascinating field of study for game researchers. With their recent success in the human factors domain, physiological metrics, which complement game metrics, have been successfully used to study player engagement and emotion in research and industry. This chapter provides a brief introduction to and primer of physiological measures currently used in game research and discusses the benefits and challenges of this quantitative method of game user research.


title: Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors

abstract: As companies increase their efforts in retaining customers, being able to predict accurately ahead of time, whether a customer will churn in the foreseeable future is an extremely powerful tool for any marketing team. The paper describes in depth the application of Deep Learning in the problem of churn prediction. Using abstract feature vectors, that can generated on any subscription based company's user event logs, the paper proves that through the use of the intrinsic property of Deep Neural Networks (learning secondary features in an unsupervised manner), the complete pipeline can be applied to any subscription based company with extremely good churn predictive performance. Furthermore the research documented in the paper was performed for Framed Data (a company that sells churn prediction as a service for other companies) in conjunction with the Data Science Institute at Lancaster University, UK. This paper is the intellectual property of Framed Data.


title: Simulating IBM Watson in the Classroom

abstract: IBM Watson exemplifies multiple innovations in natural language processing and question answering. In addition, Watson uses most of the known techniques in these two domains as well as many methods from related domains. Hence, there is pedagogical value in a rigorous understanding of its function. The paper provides the description of a text analytics course focused on building a simulator of IBM Watson, conducted in Spring 2014 at UNC Charlotte. We believe this is the first time a simulation containing all the major Watson components was created in a university classroom. The system achieved a respectable (close to) 20% accuracy on Jeopardy! questions, and there remain many known and new avenues of improving performance that can be explored in the future. The code and documentation are available on GitHub. The paper is a joint effort of the teacher and some of the students who were leading teams implementing component technologies, and therefore deeply involved in making the class successful.


title: Improving read performance by isolating multiple queues in NVMe SSDs

abstract: As NVMe SSDs have become viable, recent researches have focused on optimizing the performance of NVMe SSDs, which provide multiple I/O queues to maximize the I/O parallelism of flash-chip, while traditional operating systems are designed ordinarily for single queue storage, such as HDD and SATA SSD. Unfortunately, no prior works have considered the write interference while NVMe SSD still has the possibility of the interference. This interference is crucial in read-intensive environments because write requests negatively affect the latency of read requests. In this paper, we propose a novel queue isolation scheme that efficiently eliminates the write interference and improves the read performance by isolating read and write requests. Our experimental results clearly show that our scheme improves the read performance by up to 33% in heavy read workloads, compared to Baseline.


title: Generating Descriptions of Entity Relationships

abstract: Large-scale knowledge graphs (KGs) store relationships between entities that are increasingly being used to improve the user experience in search applications. The structured nature of the data in KGs is typically not suitable to show to an end user and applications that utilize KGs therefore benefit from human-readable textual descriptions of KG relationships. We present a method that automatically generates textual descriptions of entity relationships by combining textual and KG information. Our method creates sentence templates for a particular relationship and then generates a textual description of a relationship instance by selecting the best template and filling it with appropriate entities. Experimental results show that a supervised variation of our method outperforms other variations as it best captures the semantic similarity between a relationship instance and a template, whilst providing more contextual information.


title: SAFE: Smart Authenticated Fast Exams for Student Evaluation in Classrooms

abstract: Considerable experimentation is happening in today's classrooms to handle large classes. In this paper, we present SAFE (Smart Authenticated Fast Exams), a tool that enables continuous assessment in the form of regular quizzes in classes. SAFE is based on a BYOD (bring your own device) model that leverages student smart-phones to conduct auto-graded, cheating-free exams in a proctored class room setting. SAFE has 3 components: a smart-phone app, a web server and WiFi infrastructure to enable app-server communication. SAFE support a rich set of features to handle various types of questions as well as instructor preferences. In the design of SAFE, we set to achieve 4 goals: easy setup, cheating-free operation, robustness and scale. Easy setup is achieved predominantly due to the BYOD model and online mode of exams. Cheating is prevented via locking the app for the intended purpose along with reporting to the server any user attempts at cheating. Robustness is handled via periodic syncing of messages and careful consideration of corner cases. Scale is handled by carefully tuning the WiFi as well as via application level scheduling. SAFE has been used so far to conduct 90+ in-class quizzes in 9 courses in the last year. It was also used to conduct a high stake admission test for a Master's program in Computer Science. The feedback from end-users has been very positive and we continue to get new requests for trials in other courses. This paper presents the design of SAFE and evaluation based on our experience thus far.


title: BLUESOUND: A New Resident Identification Sensor—Using Ultrasound Array and BLE Technology for Smart Home Platform

abstract: Activity recognition through ambient sensors in a smart home can efficiently monitor residents’ abnormal behaviors in daily life, while not adding extra burdens caused by wearable sensors. Monitoring and separating the activities of a specific individual in a multi-residential home is still, however, a significant challenge for these smart home applications. This paper proposes a new human identification sensor, which can efficiently differentiate multiple residents in a home environment to detect their height as a unique bio-feature. This sensor includes three sensing/communication modules: pyroelectric infrared (PIR) occupancy, ultrasound array, and bluetooth low energy (BLE) communication modules. The PIR occupancy module is used to detect the moving direction, while the ultrasound array module detects the moving residents’ height. The combination of these two sensing technologies can be used to then detect the moving velocity. The BLE advertising mode is then used to communicate these data to the data server. A new embedded algorithm increased the energy efficiency of this identification technology. A comprehensive modeling and experiments are done to assess the performance of this sensor and the results are provided.


title: Face recognition system using bag of features and multi-class SVM for robot applications

abstract: Face recognition system is used for the identification and verification of a face from a video or digital image. In the first phase, Viola Jones algorithm is used to detect and crop face region automatically from image/video frame. The second phase is to recognize the face of a person, in our proposed method Bag of Word technique used to extract features from an image which uses SURF for interest point selection, the proposed technique is tested on different state of the art face recognition databases and found more accurate and fast for face recognition. Our proposed method recognizes more than two person faces, multi-class support vector machine is used to classify the face image and assign a class label based on the learning of the classifier. The algorithm for the detection and recognition of faces is implemented in MATLAB application, and achieves a high accuracy rate of 99.21, which will be tested on the mobile robot.


title: Finding Influential Papers in Citation Networks

abstract: The citation network is the social network made up of papers and their citation relationships. A key task of the citation network is to find influential papers in the network. Traditional properties such as centralities can not reflect the influence of papers comprehensive, since it does not take the authority of the paper and the transfer effect into account. Other algorithms such as PageRank and HITS overcome those shortcomings. However, both of them involve matrix multiplication and repeated iterative process, which is less-effective. Comparing with another often mentioned network, the coauthor networks, we noticed that the citation network is a Directed Acyclic Graph(DAG) and it has a transitive relation. Making the most of properties of the citation network, we draw on the thought of topological sorting and design a more effective algorithm, whose time complexity is linear with the number of vertices and edges, which is O(V+E). In addition, we illustrate that the algorithm we proposed is stable and effective. We also apply our algorithm to another DAG task and results show that our algorithm has great scalability.


title: Safety at work: A meta-analytic investigation of the link between job demands, job resources, burnout, engagement, and safety outcomes.

abstract: In this article, we develop and meta-analytically test the relationship between job demands and resources and burnout, engagement, and safety outcomes in the workplace. In a meta-analysis of 203 independent samples (N 186,440), we found support for a health impairment process and for a motivational process as mechanisms through which job demands and resources relate to safety outcomes. In particular, we found that job demands such as risks and hazards and complexity impair employees’ health and positively relate to burnout. Likewise, we found support for job resources such as knowledge, autonomy, and a supportive environment motivating employees and positively relating to engagement. Job demands were found to hinder an employee with a negative relationship to engagement, whereas job resources were found to negatively relate to burnout. Finally, we found that burnout was negatively related to working safely but that engagement motivated employees and was positively related to working safely. Across industries, risks and hazards was the most consistent job demand and a supportive environment was the most consistent job resource in terms of explaining variance in burnout, engagement, and safety outcomes. The type of job demand that explained the most variance differed by industry, whereas a supportive environment remained consistent in explaining the most variance in all industries.


title: Actions ~ Transformations

abstract: What defines an action like "kicking ball"? We argue that the true meaning of an action lies in the change or transformation an action brings to the environment. In this paper, we propose a novel representation for actions by modeling an action as a transformation which changes the state of the environment before the action happens (precondition) to the state after the action (effect). Motivated by recent advancements of video representation using deep learning, we design a Siamese network which models the action as a transformation on a high-level feature space. We show that our model gives improvements on standard action recognition datasets including UCF101 and HMDB51. More importantly, our approach is able to generalize beyond learned action categories and shows significant performance improvement on cross-category generalization on our new ACT dataset.


title: Ibuprofen plasma concentration profile in deliberate ibuprofen overdose with circulatory depression treated with therapeutic plasma exchange: a case report

abstract: Inquiries relating to ibuprofen overdose have more than tripled in the last ten years in our poison control center. Although the vast majority of cases have a benign clinical course, there are few severe or even fatal cases present with refractory circulatory failure. We describe a case of a 48 year-old male with suicidal mono-ingestion of approximately 72 g ibuprofen. Despite an initial rapid spontaneous drop in the total ibuprofen plasma concentration (IPC) from 550 to 275 mcg/mL within the first 5 h after admission, the patient developed a circulatory failure, refractory to aggressive fluid resuscitation and high doses of vasopressors. Due to ibuprofen’s favorable pharmacokinetics (>95% bound to albumin, low volume of distribution) and in the absence of specific therapeutic alternatives thereby avoiding escalating vasopressor doses, therapeutic plasma exchange (TPE) for extracorporeal elimination of ibuprofen was considered as a therapeutic rescue option. An improvement of hemodynamics with a significant reduction of vasopressors was observed with TPE-initiation. However, neither the observed IPC-profile nor a pharmacokinetic (PK) simulation provided evidence for a quantitative effective elimination of ibuprofen by TPE. Based on PK-modeling we calculated an overall ibuprofen half-life of 17.2 h for the entire observation period over 5 days. To our knowledge this is the first report of a severe ibuprofen-mono intoxication treated with TPE and providing serial IPCs over a period of five days, indicating an estimated fivefold overall-elimination half-life of 17.2 h. Despite TPE clinically improved persistent hemodynamic instability, this procedure was neither consistent with the observed IPC-profile nor correlated with a meaningful quantitative elimination of ibuprofen.


title: Influenza epidemiology and characterization of influenza viruses in patients seeking treatment for acute fever in Cambodia

abstract: The epidemiology, symptomology, and viral aetiology of endemic influenza remain largely uncharacterized in Cambodia. In December 2006, we established passive hospital-based surveillance to identify the causes of acute undifferentiated fever in patients seeking healthcare. Fever was defined as tympanic membrane temperature >38 degrees C. From December 2006 to December 2008, 4233 patients were screened for influenza virus by real-time reverse-transcriptase polymerase chain reaction (rRT-PCR). Of these patients, 1151 (27.2%) were positive for influenza. Cough (68.8% vs. 50.5%, P < 0.0001) and sore throat (55.0% vs. 41.9%, P < 0.0001) were more often associated with laboratory-confirmed influenza-infected patients compared to influenza-negative enrollees. A clear influenza season was evident between July and December with a peak during the rainy season. Influenza A and B viruses were identified in 768 (66.3%) and 388 (33.7%) of the influenza-positive population (n = 1153), respectively. In December 2008, passive surveillance identified infection of the avian influenza virus H5N1 in a 19-year-old farmer from Kandal province who subsequently recovered. From a subset of diagnostic samples submitted in 2007, 15 A(H1N1), seven A(H3N2) and seven B viruses were isolated. The predominant subtype tested was influenza A(H1N1), with the majority antigenically related to the A/Solomon Island/03/2006 vaccine strain. The influenza A(H3N2) isolates and influenza B viruses analysed were closely related to A/Brisbane/10/2007 or B/Ohio/01/2005 (B/Victoria/2/87-lineage) vaccine strains, respectively. Phylogenetic analysis of the HA1 region of the HA gene of influenza A(H1N1) viruses demonstrated that the Cambodian isolates belonged to clade 2C along with representative H1N1 viruses circulating in SE Asia at the time. These viruses remained sensitive to oseltamivir. In total, our data suggest that viral influenza infections contribute to nearly one-fifth of acute febrile illnesses and demonstrate the importance of influenza surveillance in Cambodia.


title: Concept for an Intermodal Traveller Information System with Real-Time Data Using Complex Event Processing

abstract: As a result of increased urbanisation, people tend to choose their transport more flexible using different transportation modes during one trip. Thereby it is crucial to keep the traveler up-to-date with real-time information during the journey. As the amount of incoming data in intermodal route planning services is growing consistently and the reaction to unexpected events has to be fulfilled in real-time, the need for a solution which is able to deal with this requirements is rising. Complex Event Processing (CEP) is a main component of an Event-Driven Architecture (EDA), which is used to analyze and act on real-time streaming data and was designed to solve big data problems. We propose a conceptual architecture and a prototypical implementation of an EDA for a real-time intermodal route guide (RTiG) to assist traveler on their journey by providing travel assistance information in real-time and thus reacting on unexpected events. Our evaluation results show that our conceptual architecture was able to process real-time data from multiple thousand users in µs.


title: Persuasion, Influence, and Value: Perspectives from Communication and Social Neuroscience

abstract: Opportunities to persuade and be persuaded are ubiquitous. What determines whether influence spreads and takes hold? This review provides an overview of evidence for the central role of subjective valuation in persuasion and social influence for both propagators and receivers of influence. We first review evidence that decisions to communicate information are determined by the subjective value a communicator expects to gain from sharing. We next review evidence that the effects of social influence and persuasion on receivers, in turn, arise from changes in the receiver's subjective valuation of objects, ideas, and behaviors. We then review evidence that self-related and social considerations are two key inputs to the value calculation in both communicators and receivers. Finally, we highlight biological coupling between communicators and receivers as a mechanism through which perceptions of value can be transmitted.


Bluetooth , ZigBee , and Wibree : A Comparison of WPAN Technologies


title: [Hospital day-surgery: comparative evaluation of 3 general anesthesia techniques].

abstract: For the voluntary interruption of pregnancy, three anaesthetic techniques have been compared being a random assigned to three groups of 40 patients. Induction of anaesthesia was based on fentanyl 0.005 mg/kg+midazolam 0.2 mg/kg or fentanyl 0.005 mg/kg = propofol 2.5 mg/kg or ketamina 0.5 mg/kg+propofol 2.0 mg/kg. Anaesthesia was maintained delivering in spontaneous-assisted ventilation N2O 70% in O2. In addition to the intraoperative conditions, quality and rapidity of some neurofunctional aspects of the recovery have been evaluated using the Steward Score and the Coin Counting Test respectively. Our data suggest fentanyl-propofol association as the safest one as regards the needs of one-day surgery.


title: Dual Motion GAN for Future-Flow Embedded Video Prediction

abstract: Future frame prediction in videos is a promising avenue for unsupervised video representation learning. Video frames are naturally generated by the inherent pixel flows from preceding frames based on the appearance and motion dynamics in the video. However, existing methods focus on directly hallucinating pixel values, resulting in blurry predictions. In this paper, we develop a dual motion Generative Adversarial Net (GAN) architecture, which learns to explicitly enforce future-frame predictions to be consistent with the pixel-wise flows in the video through a dual-learning mechanism. The primal future-frame prediction and dual future-flow prediction form a closed loop, generating informative feedback signals to each other for better video prediction. To make both synthesized future frames and flows indistinguishable from reality, a dual adversarial training method is proposed to ensure that the future-flow prediction is able to help infer realistic future-frames, while the future-frame prediction in turn leads to realistic optical flows. Our dual motion GAN also handles natural motion uncertainty in different pixel locations with a new probabilistic motion encoder, which is based on variational autoencoders. Extensive experiments demonstrate that the proposed dual motion GAN significantly outperforms state-of-the-art approaches on synthesizing new video frames and predicting future flows. Our model generalizes well across diverse visual scenes and shows superiority in unsupervised video representation learning.


title: Emotion in criminal offenders with psychopathy and borderline personality disorder

abstract: Background Criminal offenders with a diagnosis of psychopathy or borderline personality disorder (BPD) share an impulsive nature but tend to differ in their style of emotional response. This study aims to use multiple psychophysiologic measures to compare emotional responses to unpleasant and pleasant stimuli. Methods Twenty-five psychopaths as defined by the Hare Psychopathy Checklist and 18 subjects with BPD from 2 high-security forensic treatment facilities were included in the study along with 24 control subjects. Electrodermal response was used as an indicator of emotional arousal, modulation of the startle reflex as a measure of valence, and electromyographic activity of the corrugator muscle as an index of emotional expression. Results Compared with controls, psychopaths were characterized by decreased electrodermal responsiveness, less facial expression, and the absence of affective startle modulation. A higher percentage of psychopaths showed no startle reflex. Subjects with BPD showed a response pattern very similar to that of controls, ie, they showed comparable autonomic arousal, and their startle responses were strongest to unpleasant slides and weakest to pleasant slides. However, corrugator electromyographic activity in subjects with BPD demonstrated little facial modulation when they viewed either pleasant or unpleasant slides. Conclusions The results support the theory that psychopaths are characterized by a pronounced lack of fear in response to aversive events. Furthermore, the results suggest a general deficit in processing affective information, regardless of whether stimuli are negative or positive. Emotional hyporesponsiveness was specific to psychopaths, since results for offenders with BPD indicate a widely adequate processing of emotional stimuli.


title: Organizational Chart Inference

abstract: Nowadays, to facilitate the communication and cooperation among employees, a new family of online social networks has been adopted in many companies, which are called the "enterprise social networks" (ESNs). ESNs can provide employees with various professional services to help them deal with daily work issues. Meanwhile, employees in companies are usually organized into different hierarchies according to the relative ranks of their positions. The company internal management structure can be outlined with the organizational chart visually, which is normally confidential to the public out of the privacy and security concerns. In this paper, we want to study the IOC (Inference of Organizational Chart) problem to identify company internal organizational chart based on the heterogeneous online ESN launched in it. IOC is very challenging to address as, to guarantee smooth operations, the internal organizational charts of companies need to meet certain structural requirements (about its depth and width). To solve the IOC problem, a novel unsupervised method Create (ChArT REcovEr) is proposed in this paper, which consists of 3 steps: (1) social stratification of ESN users into different social classes, (2) supervision link inference from managers to subordinates, and (3) consecutive social classes matching to prune the redundant supervision links. Extensive experiments conducted on real-world online ESN dataset demonstrate that Create can perform very well in addressing the IOC problem.


title: Towards Robust and Privacy-preserving Text Representations

abstract: Written text often provides sufficient clues to identify the author, their gender, age, and other important attributes. Consequently, the authorship of training and evaluation corpora can have unforeseen impacts, including differing model performance for different user groups, as well as privacy implications. In this paper, we propose an approach to explicitly obscure important author characteristics at training time, such that representations learned are invariant to these attributes. Evaluating on two tasks, we show that this leads to increased privacy in the learned representations, as well as more robust models to varying evaluation conditions, including out-of-domain corpora.


title: Analysis and Optimization of Switched-Capacitor DC–DC Converters

abstract: Analysis methods are developed that fully determine a switched-capacitor (SC) DC-DC converter's steady-state performance through evaluation of its output impedance. The simple formulation developed permits optimization of the capacitor sizes to meet a constraint such as a total capacitance or total energy storage limit, and also permits optimization of the switch sizes subject to constraints on total switch conductances or total switch volt-ampere (V-A) products. These optimizations then permit comparison among the switched-capacitor topologies, and comparisons of SC converters with conventional magnetic-based DC-DC converter circuits, in the context of various application settings. Significantly, the performance (based on conduction loss) of a ladder-type converter is found to be superior to that of a conventional boost converter for medium to high conversion ratios


title: Change blindness phenomena for stereoscopic projection systems

abstract: In visual perception, change blindness describes the phenomenon that persons viewing a visual scene may apparently fail to detect significant changes in that scene. These phenomena have been observed in both computer generated imagery and real-world scenes. Several studies have demonstrated that change blindness effects occur primarily during visual disruptions such as blinks or saccadic eye movements. However, until now the influence of stereoscopic vision on change blindness has not been studied thoroughly in the context of visual perception research. In this paper we introduce change blindness techniques for stereoscopic projection systems, providing the ability to substantially modify a virtual scene in a manner that is difficult for observers to perceive. We evaluate techniques for passive and active stereoscopic viewing and compare the results to those of monoscopic viewing conditions. For stereoscopic viewing conditions, we found that change blindness phenomena occur with the same magnitude as in monoscopic viewing conditions. Furthermore, we have evaluated the potential of the presented techniques for allowing abrupt, and yet significant, changes of a stereoscopically displayed virtual reality environment.


title: The 5G Effect on RF Filter Technologies

abstract: A great deal is being written about the next generation mobile standards, “5G.” As with the early stages of previous generations, it is hard to clearly see the direction forward. The intersection of technological obstacles, economic realities, and political forces produces a path that is not only difficult to predict but curiously interesting in retrospect. Even with this history we press on and predict—it is our way. Even with all the publications on 5G and its many technical challenges there is a dearth of information on the radio frequency (RF) filters required. This is somewhat surprising as filters have become a major part of the radio in a mobile phone. State of the art smartphones now contain more than 60 filters and command the largest share of the RF wallet. The key starting point is the proposed new RF bands that will be used for 5G. The FCC has recently proposed band sections between 3.5–6 GHz, 27–40 GHz, and 64–71 GHz. As anyone familiar with radios in these areas knows, each band commands its own set of issues and solutions. The span of filter solutions for 5G will be more diverse than in the current mobile technology bands.


title: Orthognathic surgery in the presence of temporomandibular dysfunction: what happens next?

abstract: One of the most well-known yet perhaps controversial conditions affecting temporomandibular dysfunction (TMD) and the signs and symptoms of facial pain and clinical outcomes after orthognathic surgery procedures is temporomandibular joint internal derangement. This article provides an overview of the mutual relationship between orthognathic surgery and TMD, with especial consideration to internal derangement. The existing literature is reviewed and analyzed and the pertinent findings are summarized. The objective is to guide oral and maxillofacial surgeons in their clinical decision making when contemplating orthognathic surgery in patients with preexisting TMD.


Optimization, Learning and Natural Algorithms


title: Death and Suicide in Universal Artificial Intelligence

abstract: Reinforcement learning (RL) is a general paradigm for studying intelligent behaviour, with applications ranging from artificial intelligence to psychology and economics. AIXI is a universal solution to the RL problem; it can learn any computable environment. A technical subtlety of AIXI is that it is defined using a mixture over semimeasures that need not sum to 1, rather than over proper probability measures. In this work we argue that the shortfall of a semimeasure can naturally be interpreted as the agent’s estimate of the probability of its death. We formally define death for generally intelligent agents like AIXI, and prove a number of related theorems about their behaviour. Notable discoveries include that agent behaviour can change radically under positive linear transformations of the reward signal (from suicidal to dogmatically self-preserving), and that the agent’s posterior belief that it will survive increases over time.


title: Theodor Lipps and the shift from ?sympathy? to ?empathy?

abstract: In the course of extensive philosophical debates on aesthetics in nineteenth-century Germany, Robert Vischer introduced the concept of Einfuhlung in relation to art. Theodor Lipps subsequently extended it from art to visual illusions and interpersonal understanding. While Lipps had regarded Einfuhlung as basically similar to the old notion of sympathy, Edward Titchener in America believed it had a different meaning. Hence, he coined the term empathy as its translation. This term came to be increasingly widely adopted, first in psychology and then more generally. But the lack of agreement about the supposed difference between these concepts suggests that Lipps had probably been right. © 2005 Wiley Periodicals, Inc.


title: Microplastic pollution in the surface waters of the Laurentian Great Lakes

abstract: Neuston samples were collected at 21 stations during an 700 nautical mile (1300 km) expedition in July 2012 in the Laurentian Great Lakes of the United States using a 333 lm mesh manta trawl and analyzed for plastic debris. Although the average abundance was approximately 43,000 microplastic particles/km 2 , station 20, downstream from two major cities, contained over 466,000 particles/km 2 , greater than all other stations combined. SEM analysis determined nearly 20% of particles less than 1 mm, which were initially identified as microplastic by visual observation, were aluminum silicate from coal ash. Many microplastic particles were multi-colored spheres, which were compared to, and are suspected to be, microbeads from consumer products containing microplastic particles of similar size, shape, texture and composition. The presence of microplastics and coal ash in these surface samples, which were most abundant where lake currents converge, are likely from nearby urban effluent and coal burning power plants.


title: Bacterial Cell Division

abstract: The process of cell division is necessary for the growth of all cells. The mechanism by which eubacteria divide has so far shown little homology to the mechanisms employed by eukaryotic cells. Although this lack of similarity may be a result of our lack of knowledge about these processes it may also be that different mechanisms are involved. If so, it would make cell division a selective target for antimicrobial agents.


title: Span, CRUNCH, and Beyond: Working Memory Capacity and the Aging Brain

abstract: Neuroimaging data emphasize that older adults often show greater extent of brain activation than younger adults for similar objective levels of difficulty. A possible interpretation of this finding is that older adults need to recruit neuronal resources at lower loads than younger adults, leaving no resources for higher loads, and thus leading to performance decrements [Compensation-Related Utilization of Neural Circuits Hypothesis; e.g., Reuter-Lorenz, P. A., & Cappell, K. A. Neurocognitive aging and the compensation hypothesis. Current Directions in Psychological Science,17, 177-182, 2008]. The Compensation-Related Utilization of Neural Circuits Hypothesis leads to the prediction that activation differences between younger and older adults should disappear when task difficulty is made subjectively comparable. In a Sternberg memory search task, this can be achieved by assessing brain activity as a function of load relative to the individual's memory span, which declines with age. Specifically, we hypothesized a nonlinear relationship between load and both performance and brain activity and predicted that asymptotes in the brain activation function should correlate with performance asymptotes (corresponding to working memory span). The results suggest that age differences in brain activation can be largely attributed to individual variations in working memory span. Interestingly, the brain activation data show a sigmoid relationship with load. Results are discussed in terms of Cowan's [Cowan, N. The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioraland Brain Sciences,24, 87-114, 2001] model of working memory and theories of impaired inhibitory processes in aging.


title: HIGH-LEVEL SCENE PERCEPTION

abstract: Three areas of high-level scene perception research are reviewed. The first concerns the role of eye movements in scene perception, focusing on the influence of ongoing cognitive processing on the position and duration of fixations in a scene. The second concerns the nature of the scene representation that is retained across a saccade and other brief time intervals during ongoing scene perception. Finally, we review research on the relationship between scene and object identification, focusing particularly on whether the meaning of a scene influences the identification of constituent objects.


title: Attitude, Inference, Association: On the Propositional Structure of Implicit Bias

abstract: The overwhelming majority of those who theorize about implicit biases posit that these biases are caused by some sort of association. However, what exactly this claim amounts to is rarely specified. In this paper, I distinguish between different understandings of association, and I argue that the crucial senses of association for elucidating implicit bias are the cognitive structure and mental process senses. A hypothesis is subsequently derived: if associations really underpin implicit biases, then implicit biases should be modulated by counterconditioning or extinction but should not be modulated by rational argumentation or logical interventions. This hypothesis is false; implicit biases are not predicated on any associative structures or associative processes but instead arise because of unconscious propositionally structured beliefs. I conclude by discussing how the case study of implicit bias illuminates problems with popular dual-process models of cognitive architecture.


title: Precision Farming: Sensor Analytics

abstract: The role of wireless sensor networks (WSNs) in agriculture has become prominent as part of the precision farming initiative. In the future, multiple WSN systems will be deployed on every farm. Accurate and timely analyses of the data collected by these systems will become paramount for increasing efficiency and sustainability of farming. Conventionally, analyzing monitoring data is considered to be beyond WSN capabilities, and, therefore, carried out remotely. Meanwhile, in recent years, complexity of the tasks performed by WSNs has been constantly increasing. Modern wireless sensors possess computational capacity sufficient for certain data-analytics functionality. This article overviews modern WSN functionality that incorporates a certain level of intelligence, while the authors explore challenges and potential benefits of WSN-based analytics in the scope of precision farming.


title: Orientation robust object detection in aerial images using deep convolutional neural network

abstract: Detectingobjects in aerial images is challenged by variance of object colors, aspect ratios, cluttered backgrounds, and in particular, undetermined orientations. In this paper, we propose to use Deep Convolutional Neural Network (DCNN) features from combined layers to perform orientation robust aerial object detection. We explore the inherent characteristics of DCNN as well as relate the extracted features to the principle of disentangling feature learning. An image segmentation based approach is used to localize ROIs of various aspect ratios, and ROIs are further classified into positives or negatives using an SVM classifier trained on DCNN features. With experiments on two datasets collected from Google Earth, we demonstrate that the proposed aerial object detection approach is simple but effective.


title: A 77-GHz active millimeter-wave reflector for FMCW radar

abstract: An 18-mW active millimeter-wave reflector fabricated in 45-nm SOI CMOS technology exhibits a peak gain of 20 dB at 77 GHz, a 3-dB bandwidth of 5 GHz from 75.5 to 80.5 GHz, and a 50-Ω noise figure of 7.5–8.5 dB over the same frequency band. It consists of an LNA, a BPSK modulator and two variable gain output stages each driving a separate transmit antenna. The chip occupies 570µm×880µm and is flip-chip mounted on a 7mm×7mm flexible interposer with two transmit and one receive antenna.


title: Catnap: exploiting high bandwidth wireless interfaces to save energy for mobile devices

abstract: Energy management is a critical issue for mobile devices, with network activity often consuming a significant portion of the total system energy. In this paper, we propose Catnap, a system that reduces energy consumption of mobile devices by allowing them to sleep during data transfers. Catnap exploits high bandwidth wireless interfaces -- which offer significantly higher bandwidth compared to available bandwidth across the Internet -- by combining small gaps between packets into meaningful sleep intervals, thereby allowing the NIC as well as the device to doze off. Catnap targets data oriented applications, such as web and file transfers, which can afford delay of individual packets as long as the overall transfer times do not increase. Our evaluation shows that for small transfers (128kB to 5MB), Catnap allows the NIC to sleep for up to 70% of the total transfer time and for larger transfers, it allows the whole device to sleep for a significant fraction of the total transfer time. This results in battery life improvement of up to 2-5x for real devices like Nokia N810 and Thinkpad T60.


The Psychology of Memory.


title: SignalLens: Focus+Context Applied to Electronic Time Series

abstract: Electronic test and measurement systems are becoming increasingly sophisticated in order to match the increased complexity and ultra-high speed of the devices under test. A key feature in many such instruments is a vastly increased capacity for storage of digital signals. Storage of 109 time points or more is now possible. At the same time, the typical screens on such measurement devices are relatively small. Therefore, these instruments can only render an extremely small fraction of the complete signal at any time. SignalLens uses a Focus+Context approach to provide a means of navigating to and inspecting low-level signal details in the context of the entire signal trace. This approach provides a compact visualization suitable for embedding into the small displays typically provided by electronic measurement instruments. We further augment this display with computed tracks which display time-aligned computed properties of the signal. By combining and filtering these computed tracks it is possible to easily and quickly find computationally detected features in the data which are often obscured by the visual compression required to render the large data sets on a small screen. Further, these tracks can be viewed in the context of the entire signal trace as well as visible high-level signal features. Several examples using real-world electronic measurement data are presented, which demonstrate typical use cases and the effectiveness of the design.