title: Biometric Based Network Security Using MIPS Cryptography Processor

abstract: The empowerment in network on chip (NOC) and System on chip (SOC) in Microelectronics and Sensors have developed the various wireless communication Network technologies. In the past few years, many researchers have been focusing on building system architecture of network monitoring to improve the technical requirement specially designed for network security. Less research was found in providing the strong biometric based network security system to provide bulletproof security. The popular MIPS based cryptography processor is used for hardware and software products and standards require big cryptography keys length for higher security level. The major weakness of Normal cryptography system based on asymmetric algorithms need the storage of secret keys. Stored keys are often protected by poorly selected user passwords that can either be guessed or obtained through brute force attacks. Combining biometric with MIPS cryptography processor is as a possible solution. In this paper I propose a new approach to network security using MIPS based crypto processor based on contactless palm vein biometric system. This approach takes into account NOC constraints and its topology. It provides more security with less key length and there is no need to store any private key anywhere.


title: Accelerated Hypothesis Generation for Multistructure Data via Preference Analysis

abstract: Random hypothesis generation is integral to many robust geometric model fitting techniques. Unfortunately, it is also computationally expensive, especially for higher order geometric models and heavily contaminated data. We propose a fundamentally new approach to accelerate hypothesis sampling by guiding it with information derived from residual sorting. We show that residual sorting innately encodes the probability of two points having arisen from the same model, and is obtained without recourse to domain knowledge (e.g., keypoint matching scores) typically used in previous sampling enhancement methods. More crucially, our approach encourages sampling within coherent structures and thus can very rapidly generate all-inlier minimal subsets that maximize the robust criterion. Sampling within coherent structures also affords a natural ability to handle multistructure data, a condition that is usually detrimental to other methods. The result is a sampling scheme that offers substantial speed-ups on common computer vision tasks such as homography and fundamental matrix estimation. We show on many computer vision data, especially those with multiple structures, that ours is the only method capable of retrieving satisfactory results within realistic time budgets.


title: Local collaborative ranking

abstract: Personalized recommendation systems are used in a wide variety of applications such as electronic commerce, social networks, web search, and more. Collaborative filtering approaches to recommendation systems typically assume that the rating matrix (e.g., movie ratings by viewers) is low-rank. In this paper, we examine an alternative approach in which the rating matrix is locally low-rank. Concretely, we assume that the rating matrix is low-rank within certain neighborhoods of the metric space defined by (user, item) pairs. We combine a recent approach for local low-rank approximation based on the Frobenius norm with a general empirical risk minimization for ranking losses. Our experiments indicate that the combination of a mixture of local low-rank matrices each of which was trained to minimize a ranking loss outperforms many of the currently used state-of-the-art recommendation systems. Moreover, our method is easy to parallelize, making it a viable approach for large scale real-world rank-based recommendation systems.


title: Hashing Over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning

abstract: In reinforcement learning (RL) tasks, an efficient exploration mechanism should be able to encourage an agent to take actions that lead to less frequent states which may yield higher accumulative future return. However, both knowing about the future and evaluating the frequentness of states are non-trivial tasks, especially for deep RL domains, where a state is represented by high-dimensional image frames. In this paper, we propose a novel informed exploration framework for deep RL tasks, where we build the capability for a RL agent to predict over the future transitions and evaluate the frequentness for the predicted future frames in a meaningful manner. To this end, we train a deep prediction model to generate future frames given a state-action pair, and a convolutional autoencoder model to generate deep features for conducting hashing over the seen frames. In addition, to utilize the counts derived from the seen frames to evaluate the frequentness for the predicted frames, we tackle the challenge of making the hash codes for the predicted future frames to match with their corresponding seen frames. In this way, we could derive a reliable metric for evaluating the novelty of the future direction pointed by each action, and hence inform the agent to explore the least frequent one. We use Atari 2600 games as the testing environment and demonstrate that the proposed framework achieves significant performance gain over a state-of-the-art informed exploration approach in most of the domains.


title: SQL databases v. NoSQL databases

abstract: Michael Stonebraker considers several performance arguments in favorofNoSQL databases—andfinds them insufficient.


title: The Impact of Menu Label Design on Visual Attention, Food Choice and Recognition: An Eye Tracking Study

abstract: Nutritional labeling on menus has been found to promote informed food choices and reduce information asymmetry between manufacturers and consumers. However, lack of attention to nutritional labels limits their effectiveness. This study manipulated the way in which nutritional information was provided on menus in aim of enhancing visual attention to the most health relevant information. A between-subject design was implemented with three experimental conditions (nondirective label; directive label; semidirective label). A total of 84 participants chose meals off a starter, main and desert menu whilst their eye movements were tracked using Tobii eye tracking software. Results showed that the menu labels did not significantly differ in their attentional gaining properties, however, the use of color and health logos led participants to choose meals containing significantly less calories compared to when nutritional information was presented in black text alone. These findings indicate that nutritional information should be provided in color or as health logos as this has the largest impact on food choice.


title: Student Academic Performance: The Role of Motivation, Strategies, and Perceived Factors Hindering Liberian Junior and Senior High School Students Learning

abstract: The nature of motivation and learning strategy use is vital to improving student learning outcomes. This study was intended to explore the motivational beliefs and learning strategy use by Liberian junior and senior high school students in connection with their academic performance. It also solicited students’ self-reports about presumed factors hindering their learning. Utilizing a cross-sectional quantitative research design, 323 participants took part in the study from 2 counties. Motivated Strategies for Learning Questionnaire (MSLQ) was adapted and 12 potential learning hindrances were identified and used as instruments. Data analyses were conducted using SPSS 17.0. The results showed the motivational belief component of extrinsic goal orientation as the most preferred belief and test anxiety was the least possessed belief. Rehearsal strategies were found to be the most frequently used, while help seeking was reported to be the least strategy considered. The result also showed significant relationships between the two constructs. In addition, the study found some learning hindrances. A number of conclusions as well as some practical recommendations for action relative to the improvement of student performance have been advanced.


title: Resonant optical transmission through hole-arrays in metal films: physics and applications

abstract: Extraordinary optical transmission through an array of holes in a metal film was reported by Ebbesen and coworkers in 1998. Since that work there has been abundant research activ- ity aimed at understanding the physics and at the development of the many applications associated with this phenomenon, hence the topic of this review. The study of hole-arrays in a metal is not new - theoretical contributions on a small-hole array date back to Lord Rayleigh's description of Wood's anomaly in 1907 and there has been considerable research on metal meshes and hole- arrays since 1962. Bethe's theory, adapted to treat hole-arrays, is the simplest theoretical description of the transmission reso- nance. Following a description of this basic theory, we present the research on the additional effects from variations in real metal properties at different wavelengths, film thickness, hole- shape and lattice configuration. The many promising applica- tions being developed using hole-arrays are examined, including polarization control, filtering, switching, nonlinear optics, sur- face plasmon resonance sensing, surface-enhanced fluorescence, surface-enhanced Raman scattering, absorption spectroscopy, and quantum interactions. Finally, the various approaches, devel- opments in hole-array fabrication, and integration of hole-arrays into devices are described. (top left) Schematic of resonant transmission through nanohole array using scanning electron microscope image of as-fabricated sample. (top right) Microfluidic chip incorporating nanohole ar- rays. (bottom) Composite image of array of nanohole arrays used as sensors in a immunoassay-like microfluidic device, showing (left to right) schematic of microfluidic layout, microscope im- age of arrays in microfluidic channels, and laser transmission modified by adsorbed molecules.


Real-time fur simulation and rendering


title: Doubly Label Consistent Autoencoder: Accounting User and Item Metadata in Recommender Systems

abstract: Recent studies have experimentally shown that autoencoder based formulations for collaborative filtering can outperform other approaches. However, prior studies in this area, were either based on only ratings information; and at most accounted for either user or item metadata but not both. The metadata was appended to the ratings and just passed on as inputs. This is the most that can be done in the standard neural network based autoencoder formulation. However, collaborative filtering is a highly under-determined problem, therefore being able to utilize maximum information will naturally boost results. Previous autoencoder based formulations either had to leave out user metadata or item metadata. This is the first work, that proposes to modify the autoencoder to account for both user and item metadata. Results using explicit metadata (e.g. age, gender, occupation for users and genre for movies) and implicit metadata (neighborhood information) on our proposed formulation improves upon the state-of -the-art techniques considerably.


title: Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks

abstract: Recent explorations of Deep Learning in the physical layer (PHY) of wireless communication have shown the capabilities of Deep Neuron Networks in tasks like channel coding, modulation, and parametric estimation. However, it is unclear if Deep Neuron Networks could also learn the advanced waveforms of current and next-generation wireless networks, and potentially create new ones. In this paper, a Deep Complex Convolutional Network (DCCN) without explicit Discrete Fourier Transform (DFT) is developed as an Orthogonal Frequency-Division Multiplexing (OFDM) receiver. Compared to existing deep neuron network receivers composed of fully-connected layers followed by non-linear activations, the developed DCCN not only contains convolutional layers but is also almost (and could be fully) linear. Moreover, the developed DCCN not only learns to convert OFDM waveform with Quadrature Amplitude Modulation (QAM) into bits under noisy and Rayleigh channels, but also outperforms expert OFDM receiver based on Linear Minimum Mean Square Error channel estimator with prior channel knowledge in the low to middle Signal-to-Noise Ratios of Rayleigh channels. It shows that linear Deep Neuron Networks could learn transformations in signal processing, thus master advanced waveforms and wireless channels.


title: The Effects of Culture of Adoption of Telemedicine in Medically Underserved Communities

abstract: Within the information systems discipline, three streams have emerged that address the issue of information technology adoption, diffusion and use. The first examines the factors influencing an individual's decision to accept a new technology. The second stream deals with the impact of culture on the development and use of information technology; and the third stream is directed toward the transfer of information technology from one country or context into another. While these three streams have attempted to theorize and empirically explain the factors influencing information technology adoption within a new environment, they have largely been used separately and tested within the context of advanced economies. In this paper we attempt to integrate all three in examining the introduction of telemedicine technology in medically underserved communities. The results suggest that the interaction effects of the factors derived from all theories provide a better explanation of technology introduction in medically underserved communities.


title: E-government adoption: A cultural comparison

abstract: E-government diffusion is an international phenomenon. This study compares e-government adoption in the U.K. to adoption in the U.S. In particular, this study seeks to determine if the same factors are salient in both countries. Several studies have explored citizen acceptance of e-government services in the U.S. However, few studies have explored this phenomenon in the U.K. To identify the similarities and differences between the U.K. and the U.S. a survey is conducted in the U.K. and the findings are compared to the literature that investigates diffusion in the U.S. This study proposes a model of e-government adoption in the U.K. based on salient factors in the U.S. A survey is administered to 260 citizens in London to assess the importance of relative advantage, trust and the digital divide on intention to use e-government. The results of binary logistic regression indicate that there are cultural differences in e-government adoption in the U.K. and the U.S. The results indicate that of the prevailing adoption constructs, relative advantage and trust are pertinent in both the U.S. and the U.K., while ICT adoption barriers such as access and skill may vary by culture. Implications for research and practice are discussed.


title: Gluteal muscle activation during common therapeutic exercises.

abstract: STUDY DESIGN ::: Experimental laboratory study. ::: ::: ::: OBJECTIVES ::: To quantify and compare electromyographic signal amplitude of the gluteus maximus and gluteus medius muscles during exercises of varying difficulty to determine which exercise most effectively recruits these muscles. ::: ::: ::: BACKGROUND ::: Gluteal muscle weakness has been proposed to be associated with lower extremity injury. Exercises to strengthen the gluteal muscles are frequently used in rehabilitation and injury prevention programs without scientific evidence regarding their ability to activate the targeted muscles. ::: ::: ::: METHODS ::: Surface electromyography was used to quantify the activity level of the gluteal muscles in 21 healthy, physically active subjects while performing 12 exercises. Repeated-measures analyses of variance were used to compare normalized mean signal amplitude levels, expressed as a percent of a maximum voluntary isometric contraction (MVIC), across exercises. ::: ::: ::: RESULTS ::: Significant differences in signal amplitude among exercises were noted for the gluteus medius (F5,90 = 7.9, P<.0001) and gluteus maximus (F5,95 = 8.1, P<.0001). Gluteus medius activity was significantly greater during side-lying hip abduction (mean +/- SD, 81% +/- 42% MVIC) compared to the 2 types of hip clam (40% +/- 38% MVIC, 38% +/- 29% MVIC), lunges (48% +/- 21% MVIC), and hop (48% +/- 25% MVIC) exercises. The single-limb squat and single-limb deadlift activated the gluteus medius (single-limb squat, 64% +/- 25% MVIC; single-limb deadlift, 59% +/- 25% MVIC) and maximus (single-limb squat, 59% +/- 27% MVIC; single-limb deadlift, 59% +/- 28% MVIC) similarly. The gluteus maximus activation during the single-limb squat and single-limb deadlift was significantly greater than during the lateral band walk (27% +/- 16% MVIC), hip clam (34% +/- 27% MVIC), and hop (forward, 35% +/- 22% MVIC; transverse, 35% +/- 16% MVIC) exercises. ::: ::: ::: CONCLUSION ::: The best exercise for the gluteus medius was side-lying hip abduction, while the single-limb squat and single-limb deadlift exercises led to the greatest activation of the gluteus maximus. These results provide information to the clinician about relative activation of the gluteal muscles during specific therapeutic exercises that can influence exercise progression and prescription. J Orthop Sports Phys Ther 2009;39(7):532-540, Epub 24 February 2009. doi:10.2519/jospt.2009.2796.


title: Effective Crop Productivity and Nutrient Level Monitoring in Agriculture Soil Using IOT

abstract: In today's world, there is an increase in population growth. Hence there is a need in increase in food production. Fertilizers are being used to increase crop productivity but what is ideal to produce more crop, nutrient level of soil and crop monitoring is more important. Hence we introduce Internet of Things for monitoring the soil parameters including pH level, soil moisture, temperature and humidity. Soil moisture and pH value are the important parameters for crop growth. The soil parameters are detected and monitored using various sensors. Infrared sensors are also used in order to detect disease in plants. The values detected are stored and updated in cloud server for future use. Also an application is developed for farmers in order to know the pH level of nutrients in soil, soil moisture and temperature for efficient productivity of crops.


title: Person Re-identification in the Wild

abstract: This paper presents a novel large-scale dataset and comprehensive baselines for end-to-end pedestrian detection and person recognition in raw video frames. Our baselines address three issues: the performance of various combinations of detectors and recognizers, mechanisms for pedestrian detection to help improve overall re-identification (re-ID) accuracy and assessing the effectiveness of different detectors for re-ID. We make three distinct contributions. First, a new dataset, PRW, is introduced to evaluate Person Re-identification in the Wild, using videos acquired through six synchronized cameras. It contains 932 identities and 11,816 frames in which pedestrians are annotated with their bounding box positions and identities. Extensive benchmarking results are presented on this dataset. Second, we show that pedestrian detection aids re-ID through two simple yet effective improvements: a cascaded fine-tuning strategy that trains a detection model first and then the classification model, and a Confidence Weighted Similarity (CWS) metric that incorporates detection scores into similarity measurement. Third, we derive insights in evaluating detector performance for the particular scenario of accurate person re-ID.


title: A Review on the Use of Wearable Functional Near‐Infrared Spectroscopy in Naturalistic Environments

abstract: The development of novel miniaturized wireless and wearable functional near‐infrared spectroscopy (fNIRS) devices has paved the way for new functional brain imaging that could revolutionize the cognitive research fields. Over the past few decades, several studies have been conducted with conventional fNIRS systems that have demonstrated the suitability of this technology for a wide variety of populations and applications, to investigate both the healthy brain and the diseased brain. However, what makes wearable fNIRS even more appealing is its capability to allow measurements in everyday‐life scenarios that are not possible with other gold‐standard neuroimaging modalities, such as functional magnetic resonance imaging. This could have a huge impact on the way we explore the neural bases and mechanisms underpinning human brain functioning. The aim of this review is to provide an overview of studies conducted with wearable fNIRS in naturalistic settings in the field of cognitive neuroscience. In addition, we present the challenges associated with the use of wearable fNIRS in unrestrained contexts, discussing solutions that will allow accurate inference of functional brain activity. Finally, we provide an overview of the future perspectives in cognitive neuroscience that we believe would benefit the most from the study of wearable fNIRS devices.


title: Influence of Some Parameters on the Effectiveness of Induction Heating

abstract: We have investigated the effectiveness of heating conductive plates by induction in low frequencies analytically and numerically, in relation to the shape of the plate, the area of the region exposed to the magnetic flux, and the position of the exposed region with respect to the center of the plate. We considered both uniform and nonuniform magnetic fields. For plates with equivalent area exposed to the same uniform magnetic flux, the one with the most symmetrical shape is heated most effectively. If a coil is employed for the excitation, the results depend on the shape of the plate, the shape of the coil section, and the coil lift-off. When only the central region of a plate is exposed to a variable magnetic flux, there is a specific value of the exposed area for which the power dissipated in the plate material reaches a maximum. The most effective heating of a plate partially exposed occurs when the axis of the exciting coil is at the plate center.


title: Artificial intelligence in nonlinear process control based on fuzzy logic

abstract: Artificial Intelligence (AI) techniques are becoming useful as alternative approaches to conventional techniques or as components of integrated systems, coping with highly nonlinear processes. Fuzzy Logic (FL) is a major branch of AI based on human-like reasoning. In this paper a nonlinear system control design using a Hybrid of Fuzzy Logic and PI controller is presented. The proposed control scheme enables the controller to yield better control performance for highly nonlinear processes, compared to a regular PI controller.


title: X-ray Polarimetry: a new window on the high energy sky

abstract: Abstract Polarimetry is widely considered a powerful observational technique in X-ray astronomy, useful to enhance our understanding of the emission mechanisms, geometry and magnetic field arrangement of many compact objects. However, the lack of suitable sensitive instrumentation in the X-ray energy band has been the limiting factor for its development in the last three decades. Up to now, polarization measurements have been made exclusively with Bragg diffraction at 45 ∘ or Compton scattering at 90 ∘ and the only unambiguous detection of X-ray polarization has been obtained for one of the brightest object in the X-ray sky, the Crab Nebula. Only recently, with the development of a new class of high sensitivity imaging detectors, the possibility to exploit the photoemission process to measure the photon polarization has become a reality. We will report on the performance of an imaging X-ray polarimeter based on photoelectric effect. The device derives the polarization information from the track of the photoelectrons imaged by a finely subdivided Gas Pixel Detector. It has a great sensitivity even with telescopes of modest area and can perform simultaneously good imaging, moderate spectroscopy and high rate timing. Being truly 2D it is non-dispersive and does not require any rotation. This device is included in the scientific payload of many proposals of satellite mission which have the potential to unveil polarimetry also in X-rays in a few years.


title: Ontologies Improve Text Document Clustering

abstract: Text clustering typically involves clustering in a high dimensional space, which appears difficult with regard to virtually all practical settings. In addition, given a particular clustering result it is typically very hard to come up with a good explanation of why the text clusters have been constructed the way they are. In this paper, we propose a new approach for applying background knowledge during preprocessing in order to improve clustering results and allow for selection between results. We preprocess our input data applying an ontology-based heuristics for feature selection and feature aggregation. Thus, we construct a number of alternative text representations. Based on these representations, we compute multiple clustering results using KMeans. The results may be distinguished and explained by the corresponding selection of concepts in the ontology. Our results compare favourably with a sophisticated baseline preprocessing strategy.


title: Incremental Graph Pattern Based Node Matching

abstract: Graph Pattern based Node Matching (GPNM) is to find all the matches of the nodes in a data graph GD based on a given pattern graph GP. GPNM has become increasingly important in many applications, e.g., group finding and expert recommendation. In real scenarios, both GP and GD are updated frequently. However, the existing GPNM methods need to perform a new GPNM procedure from scratch to deliver the node matching results based on the updated GP and updated GD, which consumes much time. Therefore, there is a pressing need for a novel method to efficiently deliver the node matching results. In this paper, we propose a novel INCremental GPNM method called INC-GPNM, where we first build up indices to incrementally maintain the shortest path length range between different label types in GD, and then identify the affected parts of GD in GPNM including nodes and edges w.r.t. the updates of GP and GD. Moreover, based on the index structure and our novel search strategies, INC-GPNM can efficiently deliver node matching results taking the updates of GP and GD as input, and can greatly save the query processing time with improved time complexity. The extensive experiments on five real-world social graphs demonstrate that our method greatly outperforms the state-of-the-art GPNM method in efficiency.


title: A Light-Field Journey to Virtual Reality

abstract: Producing ultra-high-quality content is the next grand challenge for the VR industry. Creating a virtual reality that even the human eye cannot distinguish from the real world will require light-field technology--3D imaging technology that captures the light rays people perceive from different locations and directions. When combined with computer vision and machine learning, light-field technology provides a viable path for producing low-cost, high-quality VR content.


title: Antecedents of information systems user behaviour - extended expectation-confirmation model

abstract: The study examines antecedents that affect information systems IS users' behaviour and influence their decision to either continue or discontinue with IS use. Two models were used: the expectation-confirmation model of IS continuance ECM-IS and the extended expectation-confirmation model of IS continuance EECM-IS – the ECM-IS model extended by the additional construct of conditions of support. Confirmatory factor analysis has shown that both models demonstrated good factor, convergent and discriminant validity based on data collected from questionnaires filled out by users of the integrated accounting and budgeting software IABS Finova. ECM-IS explained 49% of IS continuance intention, and EECM-IS 59%. Users' perceived conditions of support, satisfaction and perceived usefulness determine their IS continuance intention, contributing to 48.5%, 33.9% and 17.6% of the R 2, respectively. Confirmation confirmed user expectation has a positive impact on perceived usefulness, conditions of support and satisfaction. Conditions of support, perceived usefulness and confirmation are significant predictors of satisfaction, contributing to 61.3%, 20.5% and 18.2% of the R 2, respectively.


title: Towards a core ontology for robotics and automation

abstract: In this paper, we present the current results of the newly formed IEEE-RAS Working Group, named Ontologies for Robotics and Automation. In particular, we introduce a core ontology that encompasses a set of terms commonly used in Robotics and Automation along with the methodology we have adopted. Our work uses ISO/FDIS 8373 standard developed by the ISO/TC184/SC2 Working Group as a reference. This standard defines, in natural language, some generic terms which are common in Robotics and Automation such as robot, robotic device, etc. Furthermore, we discuss the ontology development process employed along with the problems and decisions taken.


title: The Quantification of Surgical Changes in Nasal Tip Support

abstract: Objectives To quantify the changes in the strength of nasal tip support associated with various surgical modifications and to identify the procedures that best maintain or augment tip support. Design Case study in 2 phases. Phase 1 included 10 patients undergoing primary rhinoplasty, 5 undergoing secondary rhinoplasty, and 5 control patients. Fresh cadavers were used in phase 2. A purpose-built instrument (Beaty Tensegrometer; G. M. Tooling, Chamblee, Ga) was used to measure nasal tip support before and after surgical modifications. Results In the patients who underwent primary rhinoplasty, there was a 25% decrease in tip support when the ligamentous attachments between the lateral crura were divided. Reconstruction of these attachments increased tip support over baseline by 35%. With a columellar strut and ligament reconstruction, tip support was increased by 44%. In the secondary rhinoplasty group, reconstruction with the dynamic adjustable rotation tip-tensioning technique increased nasal tip support over baseline by 70%. In cadavers, intercartilaginous incisions and delivery of the lower lateral cartilages caused a loss of tip support, while raising the skin–soft tissue envelope with the open technique did not. Extensive resection of the lower lateral cartilages caused a loss of tip support. Conclusions This study demonstrates that nasal tip support can be reliably quantified in a reproducible manner. Use of the open approach, reconstruction of the attachments between the lateral crura, conservative resection of the lower lateral cartilages, and the dynamic adjustable rotation tip-tensioning technique for secondary rhinoplasty best preserve nasal tip support.


title: 3D measurement by estimating Homogeneous Light Transport (HLT) Matrix

abstract: We introduce Homogeneous Light Transport (HLT) Matrix for efficient 3D measurements. Which is an extension of the Light Transport (LT) matrix. The LT Matrix is a model of light transportation between a projector and a camera. It describes weights for all combinations of the projector pixels and the camera pixels. The HLT Matrix includes background lights information with LT Matrix. By using the HLT Matrix, we can estimate LT Matrix without backgrounds measurement. In the traditional methods, it is assumed that the light ray reflects only once in the scene. However, in the actual scenes, it may reflect multiple times due to specular reflections or subsurface scatterings; thus 3D measurement may become difficult in some cases. The LT Matrix can express multiple times reflection. In this paper, we demonstrate that 3D points can be measured by using “1-pixel projection” and considering the epipolar geometry, for measurement targets which are difficult to measure by traditional methods. This measurement is the same as the measurement using the LT Matrix approach. We use a compressive sensing method for estimation to reduce the number of projection and capture.


title: A Neural Networks Committee for the Contextual Bandit Problem

abstract: This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-experts approach, are proposed to choose online the parameters of multi-layer perceptrons. The proposed algorithms are successfully tested on a large dataset with and without stationarity of rewards.


title: WCET analysis for a Java processor

abstract: In this paper we propose a solution for a worst-case execution time (WCET) analyzable Java system: a combination of a time predictable Java processor and a tool that performs WCET analysis of Java bytecode. We present a Java processor, called JOP, designed for time-predictable execution of real-time tasks. JOP is an implementation of the Java virtual machine (JVM) in hardware. The execution time of bytecodes, the instructions of the JVM, is known cycle accurate for JOP. Therefore, JOP simplifies the low-level WCET analysis. A method cache, that fills whole Java methods into the cache, is analyzable with respect to the WCET. The WCET analysis tool is based on integer linear programming. The tool performs the low-level analysis at the bytecode level and integrates the method cache analysis for a two block cache.


title: Beyond the rainbow: self-adaptive failure avoidance in configurable systems

abstract: Self-adaptive software systems monitor their state and then adapt when certain conditions are met, guided by a global utility function. In prior work we developed algorithms and conducted a post-hoc analysis demonstrating the possibility of adapting to software failures by judiciously changing configurations. In this paper we present the REFRACT framework that realizes this idea in practice by building on the self-adaptive Rainbow architecture. REFRACT extends Rainbow with new components and algorithms targeting failure avoidance. We use REFRACT in a case study running four independently executing Firefox clients with 36 passing test cases and 7 seeded faults. The study show that workarounds for all but one of the seeded faults are found and the one that is not found never fails -- it is guarded from failing by a related workaround. Moreover, REFRACT finds workarounds for eight configuration-related unseeded failures from tests that were expected to pass (and did under the default configuration). Finally, the data show that when a failure and its workaround are found, configuration guards prevent the failure from appearing again. In a simulation lasting 24 hours we see over 150 guard activations and no failures with workarounds remaining beyond 16 hours.


title: Anthropomorphism and the social robot

abstract: This paper discusses the issues pertinent to the development of a meaningful social interaction between robots and people through employing degrees of anthropomorphism in a robot’s physical design and behaviour. As robots enter our social space, we will inherently project/impose our interpretation on their actions similar to the techniques we employ in rationalising, for example, a pet’s behaviour. This propensity to anthropomorphise is not seen as a hindrance to social robot development, but rather a useful mechanism that requires judicious examination and employment in social robot research. © 2003 Elsevier Science B.V. All rights reserved.


title: Design of Power Supply for Driving High Power Piezoelectric Actuators

abstract: Subject of this contribution is a novel PWM controlled inverter of the kW power range, feeding a multi- mass ultrasonic motor (MM-USM) via a LLCC-type filter. The system is designed in a way to reduce the total harmonic distortion of the motor voltage and to locally compensate for the reactive power of the motor. Operating a three- level inverter with PWM pattern of eliminated low-order harmonics allows the use of a small and lightweight output filter showing besides improved dynamic behavior.


title: Visiting theories that predict college students' self-disclosure on Facebook

abstract: This study explores factors that may explain information disclosure behavior on Facebook and provides understanding of each factor's contribution in explaining such behavior. Factors tested in this study are drawn from theories (e.g., social contract theory and uses and gratification theory) and constructs (e.g., trust/self-disclosure relationships, time spent on Facebook, number of Facebook friends, and gender difference). Findings suggest the potential of all the factors examined in this study as frameworks to explain self-disclosure behavior on Facebook. This social media-specific study offers evidence that these theories may have implications that are different from the current e-commerce literature on self-disclosure. Both theoretical and practical implications are discussed.


title: Cell-Cycle-Dependent Recovery from Heavy-Ion Damage in G 1 -Phase Cells

abstract: The cell-cycle-dependent capacity of synchronized G1-phase human T-1 cells to repair damage from either 425 MeV/u Bragg peak neon ions or 225 kVp X rays has been compared. The dose-survival respons...


title: On the GDOP and Accuracy for Indoor Positioning

abstract: Position accuracy and geometric dilution of precision (GDOP) analysis are presented for indoor mesh positioning systems in multipath and non-line-of-sight (NLOS) propagation environments. Concise analytical formulas are derived for the position error statistics in the presence of biased ranging errors and uniform random spatial anchor node deployment. Simulation and experimental verification are conducted and the results demonstrate that the analytical formulas match the simulated and experimental results well. It is shown that the system can achieve accuracy of around half a meter.


title: Vertically stacked folded dipole antenna using multi-layer for mm-Wave mobile terminals

abstract: Novel low-profile antenna is proposed for mm-Wave 5G mobile terminals at 28 GHz band. The presented antenna topology is a modified folded dipole antenna which has vertically stacked structure using multi-layer for minimizing the antenna width maintaining the characteristics of planar folded dipole. Through simulation and measurement results, the proposed antenna is determined to be applicable for 5G mobile terminals.


title: Personalized Hotel Recommendation Using Text Mining and Mobile Browsing Tracking

abstract: With the prevalence of mobile devices such as smartphones and tablets, the ways people access to the Internet have changed enormously. In addition to the information that can be recorded by traditional Web-based e-commerce like frequent online shopping stores and browsing histories, mobile devices are capable of tracking sophisticated browsing behavior. The aim of this study is to utilize users' browsing behavior of reading hotel reviews on mobile devices and subsequently apply text-mining techniques to construct user interest profiles to make personalized hotel recommendations. Specifically, we design and implement an app where the user can search hotels and browse hotel reviews, and every gesture the user has performed on the touch screen when reading the hotel reviews is recorded. We then identify the paragraphs of hotel reviews that a user has shown interests based on the gestures the user has performed. Text mining techniques are applied to construct the interest profile of the user according to the review content the user has seriously read. We collect more than 5,000 reviews of hotels in Taipei, the largest metropolitan area of Taiwan, and recruit 18 users to participate in the experiment. Experimental results demonstrate that the recommendations made by our system better match the user's hotel selections than previous approaches.


title: High school students' time management skills in relation to research anxiety

abstract: This study aimed to determine the opinions of high school students relating to time management and present a correlation of their time management skills with demographic variables, as well as examining the relation between their level of research anxiety and time management skills. The study group composed 270 12th-grade students (127 males and 143 females) from the same socio-economic level attending seven state high schools in Ardahan province, Turkey. Pearson coefficient of correlation, t-test, and weighted mean and standard deviation values were used. Results showed a meaningful relation in the negative correlation (r=-.188**, p<0.01) between time management skills and research anxiety level, and there was a meaningful relation between time attitudes, the second factor on the time management scale, and research anxiety in the negative direction (r=-.210**, p<0.01). We conclude that as students’ time management skills improve, their research anxiety level decreases. ::: ::: Key words: Time management, research anxiety, high school.


title: Causal Reasoning for Algorithmic Fairness

abstract: In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making. We give a review of existing approaches to fairness, describe work in causality necessary for the understanding of causal approaches, argue why causality is necessary for any approach that wishes to be fair, and give a detailed analysis of the many recent approaches to causality-based fairness.


Abstract Lexical Pragmatics


title: Adaptive Online Learning

abstract: We propose a general framework for studying adaptive regret bounds in the online learning setting, subsuming model selection and data-dependent bounds. Given a data- or model-dependent bound we ask, "Does there exist some algorithm achieving this bound?" We show that modifications to recently introduced sequential complexity measures can be used to answer this question by providing sufficient conditions under which adaptive rates can be achieved. In particular each adaptive rate induces a set of so-called offset complexity measures, and obtaining small upper bounds on these quantities is sufficient to demonstrate achievability. A cornerstone of our analysis technique is the use of one-sided tail inequalities to bound suprema of offset random processes. ::: ::: Our framework recovers and improves a wide variety of adaptive bounds including quantile bounds, second order data-dependent bounds, and small loss bounds. In addition we derive a new type of adaptive bound for online linear optimization based on the spectral norm, as well as a new online PAC-Bayes theorem.


TRUST BREAKTHROUGH IN THE SHARING ECONOMY: AN EMPIRICAL STUDY OF AIRBNB1


title: Gated Residual Networks With Dilated Convolutions for Monaural Speech Enhancement

abstract: For supervised speech enhancement, contextual information is important for accurate mask estimation or spectral mapping. However, commonly used deep neural networks DNNs are limited in capturing temporal contexts. To leverage long-term contexts for tracking a target speaker, we treat speech enhancement as a sequence-to-sequence mapping, and present a novel convolutional neural network CNN architecture for monaural speech enhancement. The key idea is to systematically aggregate contexts through dilated convolutions, which significantly expand receptive fields. The CNN model additionally incorporates gating mechanisms and residual learning. Our experimental results suggest that the proposed model generalizes well to untrained noises and untrained speakers. It consistently outperforms a DNN, a unidirectional long short-term memory LSTM model, and a bidirectional LSTM model in terms of objective speech intelligibility and quality metrics. Moreover, the proposed model has far fewer parameters than DNN and LSTM models.


title: The role of moderating factors in user technology acceptance

abstract: Along with increasing investments in new technologies, user technology acceptance becomes a frequently studied topic in the information systems discipline. The last two decades have seen user acceptance models being proposed, tested, refined, extended and unified. These models have contributed to our understanding of user technology acceptance factors and their relationships. Yet they have also presented two limitations: the relatively low explanatory power and inconsistent influences of the factors across studies. Several researchers have recently started to examine the potential moderating effects that may overcome these limitations. However, studies in this direction are far from being conclusive. This study attempts to provide a systematic analysis of the explanatory and situational limitations of existing technology acceptance studies. Ten moderating factors are identified and categorized into three groups: organizational factors, technological factors and individual factors. An integrative model is subsequently established, followed by corresponding propositions pertaining to the moderating factors.


title: Environmental causes of violence

abstract: Violent and anti-social behavior is usually attributed to social factors, including poverty, poor education, and family instability. There is also evidence that many forms of violent behavior are more frequent in individuals of lower IQ. The role of exposure to environmental contaminants has received little attention as a factor predisposing to violent behavior. However a number of environmental exposures are documented to result in a common pattern of neurobehavioral effects, including lowered IQ, shortened attention span, and increased frequency of antisocial behavior. This pattern is best described for children exposed to lead early in life, but a similar pattern is seen upon exposure to polychlorinated biphenyls and methyl mercury. Although not as extensively studied, similar decrements in IQ are seen upon exposure to arsenic and secondhand smoke (SHS) exposure. Prenatal and postnatal SHS exposure is also associated with increased rates of conduct disorder and attention deficit hyperactivity. Recent evidence suggests that temporal trends in rates of violent crime in many nations are consistent with earlier preschool blood lead trends, with a lag of about 20 years. These ecologic correlations are consistent with many controlled studies suggesting that lead-exposed children suffer irreversible brain alterations that make them more likely to commit violent crimes as young adults. If this pattern is true for lead and other contaminants, the most effective way to fight crime may be to prevent exposure to these contaminants.


title: Malignant neoplasms and parathyroid adenoma

abstract: Isolated publications note the association of thyroid carcinoma, thymoma, and parathyroid adenoma with malignancies of other organs or systems. Of 166 patients with histologically proven parathyroid adenomas, 42% of those found at autopsy and 16% of such patients with parathyroidectomy and no autopsy had such malignancies. Seventy-five similar patients were added to these from literature survey. Gastrointestinal, breast, thyroid, genitourinary, and respiratory systems were the primary sites of malignancy, in decreasing order. These were significantly higher than that observed in control autopsies of comparable age and sex. In contrast, thyroid carcinoma is associated with respiratory, gastrointestinal, breast, and genital malignancy; thymoma with dermatologic, respiratory, thyroid, gastrointestinal, and breast malignancies, both groups in decreasing order of frequency. The pathogenesis and significance of such observations remain obscure in some instances.


title: A reference ontology for biomedical informatics: the foundational model of anatomy

abstract: The Foundational Model of Anatomy (FMA), initially developed as an enhancement of the anatomical content of UMLS, is a domain ontology of the concepts and relationships that pertain to the structural organization of the human body. It encompasses the material objects from the molecular to the macroscopic levels that constitute the body and associates with them nonmaterial entities (spaces, surfaces, lines, and points) required for describing structural relationships. The disciplined modeling approach employed for the development of the FMA relies on a set of declared principles, high level schemes, Aristotelian definitions and a frame-based authoring environment. We propose the FMA as a reference ontology in biomedical informatics for correlating different views of anatomy, aligning existing and emerging ontologies in bioinformatics ontologies and providing a structure-based template for representing biological functions.


title: Reversible Arithmetic Logic Unit

abstract: A function is reversible if each input vector produces a unique output vector. Reversible logic is of growing importance to many future computer technologies. In this paper, the design of a reversible Arithmetic Logic Unit (ALU) is presented making use of multiplexer unit as well as control signals. ALU is one of the most important components of CPU that can be part of a programmable reversible computing device such as a quantum computer. In multiplexer based ALU the operations are performed depending on the selection line. The control unit based ALU is developed with 9« elementary reversible gates for four basic arithmetic logical operations on two w-bit operands. The series of operations are performed on the same line depending on control signals, instead of selecting the desired result by a multiplexer. The later design is found to be advantageous over the former in terms of number of garbage outputs and constant inputs produced.


title: Unsupervised Representation Learning by Sorting Sequences

abstract: We present an unsupervised representation learning approach using videos without semantic labels. We leverage the temporal coherence as a supervisory signal by formulating representation learning as a sequence sorting task. We take temporally shuffled frames (i.e., in non-chronological order) as inputs and train a convolutional neural network to sort the shuffled sequences. Similar to comparison-based sorting algorithms, we propose to extract features from all frame pairs and aggregate them to predict the correct order. As sorting shuffled image sequence requires an understanding of the statistical temporal structure of images, training with such a proxy task allows us to learn rich and generalizable visual representation. We validate the effectiveness of the learned representation using our method as pre-training on high-level recognition problems. The experimental results show that our method compares favorably against state-of-the-art methods on action recognition, image classification and object detection tasks.


title: CAVE and fishtank virtual-reality displays: a qualitative and quantitative comparison

abstract: We present the results from a qualitative and quantitative user study comparing fishtank virtual-reality (VR) and CAVE displays. The results of the qualitative study show that users preferred the fishtank VR display to the CAVE system for our scientific visualization application because of perceived higher resolution, brightness and crispness of imagery, and comfort of use. The results of the quantitative study show that users performed an abstract visual search task significantly more quickly and more accurately on the fishtank VR display system than in the CAVE. The same study also showed that visual context had no significant effect on task performance for either of the platforms. We suggest that fishtank VR displays are more effective than CAVEs for applications in which the task occurs outside the user's reference frame, the user views and manipulates the virtual world from the outside in, and the size of the virtual object that the user interacts with is smaller than the user's body and fits into the fishtank VR display. The results of both studies support this proposition


title: An Improved Micro-Expression Recognition Method Based on Necessary Morphological Patches

abstract: Micro-expression is a spontaneous emotional representation that is not controlled by logic. A micro-expression is both transitory (short duration) and subtle (small intensity), so it is difficult to detect in people. Micro-expression detection is widely used in the fields of psychological analysis, criminal justice and human-computer interaction. Additionally, like traditional facial expressions, micro-expressions also have local muscle movement. Psychologists have shown micro-expressions have necessary morphological patches (NMPs), which are triggered by emotion. Furthermore, the objective of this paper is to sort and filter these NMPs and extract features from NMPs to train classifiers to recognize micro-expressions. Firstly, we use the optical flow method to compare the on-set frame and the apex frame of the micro-expression sequences. By doing this, we could find facial active patches. Secondly, to find the NMPs of micro-expressions, this study calculates the local binary pattern from three orthogonal planes (LBP-TOP) operators and cascades them with optical flow histograms to form the fusion features of the active patches. Finally, a random forest feature selection (RFFS) algorithm is used to identify the NMPs and to characterize them via support vector machine (SVM) classifier. We evaluated the proposed method on two popular publicly available databases: CASME II and SMIC. Results show that NMPs are statistically determined and contribute to significant discriminant ability instead of holistic utilization of all facial regions.


Head retraction and respiratory disorders in infancy.


title: Pathophysiological distortions in time perception and timed performance

abstract: Distortions in time perception and timed performance are presented by a number of different neurological and psychiatric conditions (e.g. Parkinson's disease, schizophrenia, attention deficit hyperactivity disorder and autism). As a consequence, the primary focus of this review is on factors that define or produce systematic changes in the attention, clock, memory and decision stages of temporal processing as originally defined by Scalar Expectancy Theory. These findings are used to evaluate the Striatal Beat Frequency Theory, which is a neurobiological model of interval timing based upon the coincidence detection of oscillatory processes in corticostriatal circuits that can be mapped onto the stages of information processing proposed by Scalar Timing Theory.


title: iQIYI-VID: A Large Dataset for Multi-modal Person Identification

abstract: Person identification in the wild is very challenging due to great variation in poses, face quality, clothes, makeup and so on. Traditional research, such as face recognition, person re-identification, and speaker recognition, often focuses on a single modal of information, which is inadequate to handle all the situations in practice. Multi-modal person identification is a more promising way that we can jointly utilize face, head, body, audio features, and so on. In this paper, we introduce iQIYI-VID, the largest video dataset for multi-modal person identification. It is composed of 600K video clips of 5,000 celebrities. These video clips are extracted from 400K hours of online videos of various types, ranging from movies, variety shows, TV series, to news broadcasting. All video clips pass through a careful human annotation process, and the error rate of labels is lower than 0.2%. We evaluated the state-of-art models of face recognition, person re-identification, and speaker recognition on the iQIYI-VID dataset. Experimental results show that these models are still far from being perfect for task of person identification in the wild. We further demonstrate that a simple fusion of multi-modal features can improve person identification considerably. We have released the dataset online to promote multi-modal person identification research.


title: Structured Pruning of Neural Networks With Budget-Aware Regularization

abstract: Pruning methods have shown to be effective at reducing the size of deep neural networks while keeping accuracy almost intact. Among the most effective methods are those that prune a network while training it with a sparsity prior loss and learnable dropout parameters. A shortcoming of these approaches however is that neither the size nor the inference speed of the pruned network can be controlled directly; yet this is a key feature for targeting deployment of CNNs on low-power hardware. To overcome this, we introduce a budgeted regularized pruning framework for deep CNNs. Our approach naturally fits into traditional neural network training as it consists of a learnable masking layer, a novel budget-aware objective function, and the use of knowledge distillation. We also provide insights on how to prune a residual network and how this can lead to new architectures. Experimental results reveal that CNNs pruned with our method are more accurate and less compute-hungry than state-of-the-art methods. Also, our approach is more effective at preventing accuracy collapse in case of severe pruning; this allows pruning factors of up to 16x without significant accuracy drop.


Aerobic treatment of textile mill wastes.


title: The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations

abstract: In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps.


Implementing a Functional Spreadsheet in Clean


title: Tensor network theory of the metaorganization of functional geometries in the central nervous system

abstract: Here we present an elaboration and a quantitative example for a hypothetical neuronal process, implementing what we refer to as the metaorganization principle. This process allows the internalization of external (body) geometries into the central nervous system (CNS) and a reciprocal and equally important action of the CNS geometry on the external (body) geometry. The hypothesis is based on the distinction, within the CNS, between covariant sensory and contravariant motor vectorial expressions of the extrinsic geometry. These sensory and motor expressions, given in natural co-ordinate systems, are transformed from one to the other by a neuronal network which acts as a metric tensor. The metric tensor determines the relationship of these two expressions and thus comprises the functional geometry of the system. The emergence through metaorganization of networks that implement such metric function is viewed as the result of interactions between the covariant motor execution which generates a physical action on the external world (via the musculoskeletal system) and the covariant sensory proprioception which measures the effect of such motor output. In this transformation of contravariants to covariants by the physical geometry of the motor system, a covariant metric tensor is expressed implicitly. However, co-ordinated motor action requires its dual tensor (the contravariant metric) which is assembled in the CNS based on the metaorganization principle, i.e. the ability of CNS and external geometries to mold one another. The two metric transformations acting on each other detect error signals whenever the match of the physical and functional geometries is imperfect. Such error signals are utilized by the metaorganization process to improve the match between the two metrics, so that with use the internal representation becomes increasingly homeometric with the geometry of the external world. The proposed physical process by which the metaorganization principle is implemented is based on oscillatory reverberation. If covariant proprioception is used as a recurrent signal to the motor apparatus, as if it were a contravariant motor expression, then reverberations at their steady-state yield the eigenvectors and eigenvalues of the system. The stored eigenvectors and eigenvalues can serve, respectively, as a means for the genesis of a metric (in the form of its spectral representation) with the given eigenvectors and as a means of comparing the eigenvalues that are implicit in the external body geometry and those of the internal metric.(ABSTRACT TRUNCATED AT 400 WORDS)


title: Culture in business process management: a literature review

abstract: Purpose – Business process management (BPM) is a management approach that developed with a strong focus on the adoption of information technology (IT). However, there is a growing awareness that BPM requires a holistic organizational perspective especially since culture is often considered a key element in BPM practice. Therefore, the purpose of this paper is to provide an overview of existing research on culture in BPM.Design/methodology/approach – This literature review builds on major sources of the BPM community including the BPM Journal, the BPM Conference and central journal/conference databases. Forward and backward searches additionally deepen the analysis. Based on the results, a model of culture's role in BPM is developed.Findings – The results of the literature review provide evidence that culture is still a widely under‐researched topic in BPM. Furthermore, a framework on culture's role in BPM is developed and areas for future research are revealed.Research limitations/implications – The analy...


title: Waveform Contenders for 5G - Suitability for Short Packet and Low Latency Transmissions

abstract: In this paper we compare three candidate multicarrier waveforms for the air interface of 5G: filtered CP-OFDM - the choice for 4G, FBMC - heavily discussed in recent years, and Universal Filtered Multi-Carrier (UFMC) - a new contender making its appearance recently. We judge their time-frequency efficiency when transmitting very small bursts (e.g. for machine to machine communications) and under very tight response time requirements (e.g. for vehicle to vehicle communications). While FBMC is very efficient when transmitting long sequences, it suffers when having to transmit short bursts/frames. Due to the cyclic prefix and wide frequency guards, OFDM is rather inefficient. UFMC proofs to be the best choice, here, outperforming OFDM by about 10% in any case and FBMC in case of very short packets while performing similar for long sequences.


title: Analysis of the Co-purchase Network of Products to Predict Amazon Sales-Rank

abstract: Amazon sales-rank gives a relative estimate of a product item’s popularity among other items in the same category. An early prediction of the Amazon sales-rank of a product would imply an early guess of its sales-popularity relative to the other products on Amazon, which is one of the largest e-commerce hub across the globe. Traditional methods suggest use of product review related features, e.g., volume of reviews, text content of the reviews etc. for the purpose of prediction. In contrast, we propose in this paper for the first time a network-assisted approach to construct suitable features for prediction. In particular, we build a co-purchase network treating the individual products as nodes, with edges in between if two products are bought with one another. The way a product is positioned in this network (e.g., its centrality, clustering coefficient etc.) turns out to be a strong indicator of its sales-rank. This network-assisted approach has two distinct advantages over the traditional baseline method based on review analysis – (i) it works even if the product has no reviews (relevant especially in the early stages of the product launch) and (ii) it is notably more discriminative in classifying a popular (i.e., low sales-rank) product from an unpopular (i.e., high sales-rank) one. Based on this observation, we build a supervised model to early classify a popular product from an unpopular one. We report our results on two different product categories (CDs and cell phones) and obtain remarkably better classification accuracy compared to the baseline scheme. When the top 100 (700) products based on sales-rank are labelled as popular and the bottom 100 (700) are labelled as unpopular, the classification accuracy of our method is 89.85% (82.1%) for CDs and 84.11% (84.8%) for cell phones compared to 46.37% (68.75%) and 83.17% (71.95%) respectively from the baseline method.


title: A 2.6GHz band 537W peak power GaN HEMT asymmetric Doherty amplifier with 48% drain efficiency at 7dB

abstract: A 537W saturation output power (Psat) asymmetric Doherty amplifier for 2.6GHz band was successfully developed. The main and peak amplifiers were implemented with Psat of 210W and 320W GaN HEMTs. The newly developed 320W GaN HEMT consists of a single GaN die, both input and output partial match networks and a compact package. Its output matching network was tuned to inverse class F and a single-ended 320W GaN HEMT achieved higher than 61.8% drain efficiency from 2.4GHz to 2.7GHz. The 210W and 320W GaN HEMTs asymmetric Doherty amplifier exhibited 57.3dBm (537W) Psat and 48% drain efficiency with −50.6dBc ACLR at 50.3dBm (107W) average output power using a 4-carrier W-CDMA signal and commercially available digital pre-distortion system. These excellent performances show the good suitability for 2.6GHz band basestations.


title: The neural basis of the central executive system of working memory

abstract: Working memory refers to a system for temporary storage and manipulation of information in the brain, a function critical for a wide range of cognitive operations. It has been proposed that working memory includes a central executive system (CES) to control attention and information flow to and from verbal and spatial short-term memory buffers. Although the prefrontal cortex is activated during both verbal and spatial passive working memory tasks, the brain regions involved in the CES component of working memory have not been identified. We have used functional magnetic resonance imaging (fMRI) to examine brain activation during the concurrent performance of two tasks, which is expected to engage the CES. Activation of the prefrontal cortex was observed when both tasks are performed together, but not when they are performed separately. These results support the view that the prefrontal cortex is involved in human working memory.


title: Oral Hairy Leukoplakia in Patients With No Evidence of Immunosuppression: A Case Series and Review of the Literature.

abstract: OBJECTIVES ::: Oral hairy leukoplakia (OHL) is caused by Epstein-Barr virus (EBV) and is often associated with HIV and other immunosuppressive conditions. It is rare in HIV-negative patients, but has been reported in patients who use immune-modulating medications (e.g., cyclosporine). The objectives of this study were to determine the occurrence of OHL in HIV-negative patients and report Langerhans cell counts in these lesions. ::: ::: ::: STUDY DESIGN ::: A series of 7 new cases of OHL among HIV-negative patients is described. Langerhans cells were counted using an immunoperoxidase stain for CD1a and light microscopy. ::: ::: ::: RESULTS ::: The 7 patients were male, ranging in age from 26 to 69 years. Clinically, all lesions were diagnosed as leukoplakia on the lateral border of the tongue. Microscopic examination revealed hyperparakeratosis and candidiasis in some cases, acanthosis and a band-like zone with clearing of cells in the upper spinous layer, which were EBV-positive by in-situ hybridization. There was a significant decrease in Langerhans cell counts in OHL patients. ::: ::: ::: CONCLUSION ::: OHL can occur in HIV-negative patients.


title: Ambiguity Tolerance, Learner Beliefs, Learning Styles, and Listening Comprehension of Senior EFL Students

abstract: The current study investigated the relationship between learner beliefs, learning styles, ambiguity tolerance and listening comprehension of Iranian senior students majoring in English as a Foreign Language (EFL). Four hundred and eighty-seven participants participated in the study by completing the Beliefs about Language Learning Inventory (BALLI) developed and validated by Horwitz (1987), the Perceptual Learning Style Preference Questionnaire (PLSPQ) designed by Reid (1987), the Second Language Tolerance of Ambiguity Scale (SLTAS) developed by Ely (1995), and a listening comprehension test of IELTS. The results of Pearson Correlations indicated that there was a significant positive correlation between a) learner beliefs and listening comprehension, b) learning styles and listening comprehension and, c) ambiguity tolerance and listening comprehension of the participants. Moreover, the results of multiple regression analyses showed that ambiguity tolerance was a stronger predictor of listening comprehension of EFL learners. The results of the study might be of benefit to (foreign language) education policy makers, syllabus designers and teachers to enhance EFL learners’ ambiguity tolerance level, change their written-in-the-stone beliefs, and help them choose and use the type of learning styles which suit them most in order to boost their foreign language acquisition in general and their listening comprehension in particular.


Change and Its Leadership: The Role of Positive Emotions


title: Electrochemical properties of fuel cell catalysts loaded on carbon nanomaterials with different geometries

abstract: Abstract PtRu or Pt catalysts were supported on four types of carbon nanomaterials with different shapes, sizes, and graphitic and electrical properties, and their resulting catalytic activities were evaluated by electrochemical methods. The carbon nanomaterials used included two types of particles: Arc Black (AcB) and Vulcan XC-72R (Vulcan), and two types of nanofibers: carbon nanocoils (CNC) and VGCF-X. Pt and Ru were loaded onto the nanomaterials by a reduction method using sodium borohydride. Transmission electron microscopy and X-ray diffraction (XRD) revealed the PtRu catalyst particles to be 4–6 nm in diameters. The shifts in the Pt(1 1 1) XRD peak of the catalysts on CNC and VGCF-X were larger than those on AcB and Vulcan, indicating a higher degree of alloying between Pt and Ru. The diameters of the CNC-supported Pt and PtRu catalyst particles had the narrowest distributions and were constant within the range of catalyst loadings investigated. Electrochemical studies of the catalysts during methanol oxidation were carried out using cyclic voltammetry. The catalyst particles supported on CNC and VGCF-X exhibited higher catalytic activity than those on AcB and Vulcan. The effect of the surface area of the carbon nanomaterials on the catalytic activity is discussed.


title: Eyewitness: identifying local events via space-time signals in twitter feeds

abstract: We present a methodology for automatically extracting and summarizing reports of significant local events from large-scale Twitter feeds. While previous work has relied on an analysis of tweet text to identify local events, we show how to reliably detect events using only time series analysis of geotagged tweet volumes from localized regions. The algorithm sweeps through different spatial and temporal resolutions and finds events as anomalous spikes in the rate of geotagged tweets. We applied the approach to a corpus of over 733 million geotagged tweets. Using a panel of 103 crowdsourced judges who tagged 2400 detected events, we achieved a local event detection precision of 70%. Using these judged events as ground truth, a decision tree classifier was able to raise the detection precision to 93%.


title: Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation

abstract: CNN architectures have terrific recognition performance but rely on spatial pooling which makes it difficult to adapt them to tasks that require dense, pixel-accurate labeling. This paper makes two contributions: (1) We demonstrate that while the apparent spatial resolution of convolutional feature maps is low, the high-dimensional feature representation contains significant sub-pixel localization information. (2) We describe a multi-resolution reconstruction architecture based on a Laplacian pyramid that uses skip connections from higher resolution feature maps and multiplicative gating to successively refine segment boundaries reconstructed from lower-resolution maps. This approach yields state-of-the-art semantic segmentation results on the PASCAL VOC and Cityscapes segmentation benchmarks without resorting to more complex random-field inference or instance detection driven architectures.


A Technique for Data Deduplication using Q-Gram Concept with Support Vector Machine


Distributed rational decision making


title: Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding

abstract: One of the tasks for which empirical mode decomposition (EMD) is potentially useful is nonparametric signal denoising, an area for which wavelet thresholding has been the dominant technique for many years. In this paper, the wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. We show that although a direct application of this principle is not feasible in the EMD case, it can be appropriately adapted by exploiting the special characteristics of the EMD decomposition modes. In the same manner, inspired by the translation invariant wavelet thresholding, a similar technique adapted to EMD is developed, leading to enhanced denoising performance.


The Study on Embedded Memory Circuit Design in Low-power SoC Chips for Mobile Systems


Summer 6-15-2016 VALUE OF DECENTRALIZED CONSENSUS SYSTEMS – EVALUATION FRAMEWORK


title: Patterns and functional implications of rare germline variants across 12 cancer types

abstract: Large-scale cancer sequencing data enable discovery of rare germline cancer susceptibility variants. Here we systematically analyse 4,034 cases from The Cancer Genome Atlas cancer cases representing 12 cancer types. We find that the frequency of rare germline truncations in 114 cancer-susceptibility-associated genes varies widely, from 4% (acute myeloid leukaemia (AML)) to 19% (ovarian cancer), with a notably high frequency of 11% in stomach cancer. Burden testing identifies 13 cancer genes with significant enrichment of rare truncations, some associated with specific cancers (for example, RAD51C, PALB2 and MSH6 in AML, stomach and endometrial cancers, respectively). Significant, tumour-specific loss of heterozygosity occurs in nine genes (ATM, BAP1, BRCA1/2, BRIP1, FANCM, PALB2 and RAD51C/D). Moreover, our homology-directed repair assay of 68 BRCA1 rare missense variants supports the utility of allelic enrichment analysis for characterizing variants of unknown significance. The scale of this analysis and the somatic-germline integration enable the detection of rare variants that may affect individual susceptibility to tumour development, a critical step toward precision medicine.


title: Semantic Visual Localization

abstract: Robust visual localization under a wide range of viewing conditions is a fundamental problem in computer vision. Handling the difficult cases of this problem is not only very challenging but also of high practical relevance, e.g., in the context of life-long localization for augmented reality or autonomous robots. In this paper, we propose a novel approach based on a joint geometric and semantic understanding of the world, enabling it to succeed under conditions where previous approaches failed. Our method leverages a novel generative model for descriptor learning, trained on semantic scene completion as an auxiliary task. The resulting descriptors are robust to missing observations by encoding high-level geometric and semantic information. Experiments on several challenging large-scale localization datasets demonstrate reliable localization under extreme viewpoint, illumination, and geometry changes.


Learning multiple layers of features from tiny images


A Bag of Useful Tricks for Practical Neural Machine Translation: Embedding Layer Initialization and Large Batch Size


title: Communication-avoiding parallel strassen: implementation and performance

abstract: Matrix multiplication is a fundamental kernel of many high performance and scientific computing applications. Most parallel implementations use classical O(n3) matrix multiplication, even though there exist algorithms with lower arithmetic complexity. We recently presented a new Communication-Avoiding Parallel Strassen algorithm (CAPS), based on Strassen's fast matrix multiplication, that minimizes communication (SPAA '12). It communicates asymptotically less than all classical and all previous Strassen-based algorithms, and it attains theoretical lower bounds. In this paper we show that CAPS is also faster in practice. We benchmark and compare its performance to previous algorithms on Hopper (Cray XE6), Intrepid (IBM BG/P), and Franklin (Cray XT4). We demonstrate significant speedups over previous algorithms both for large matrices and for small matrices on large numbers of processors. We model and analyze the performance of CAPS and predict its performance on future exascale platforms.


title: Deep stochastic radar models

abstract: Accurate simulation and validation of advanced driver assistance systems requires accurate sensor models. Modeling automotive radar is complicated by effects such as multipath reflections, interference, reflective surfaces, discrete cells, and attenuation. Detailed radar simulations based on physical principles exist but are computationally intractable for realistic automotive scenes. This paper describes a methodology for the construction of stochastic automotive radar models based on deep learning with adversarial loss connected to real-world data. The resulting model exhibits fundamental radar effects while remaining real-time capable.


title: IOHMM for location prediction with missing data

abstract: In recent years, the widespread adoption of GPS enabled vehicles brings the Location Based Services new opportunities. It benefits many related fields such as urban planning, city traffic modeling, personalized recommendations and driving suggestions. The service providers can understand their users better by modeling the mobility pattern and provide more personalized services by predicting the destination of users' travels. In this paper, we propose to model both the temporal and spatial mobility patterns of human movements and predict the user's travel destination from certain origin place at certain time with specific IOHMM. In order to account for data missing, we introduce a dummy state in the process of constructing the IOHMM data sequence. We also demonstrate the possibility to represent individual mobility preference by building the user mobility profiles with the learnt IOHMM. We evaluate the prediction accuracy of our method with two datasets, and the experimental results show that our method outperforms several state-of-the-art works on both datasets.


Real-time Control of Animated Broad Agents


title: Native-NMOS-triggered SCR (NANSCR) for ESD protection in 0.13-/spl mu/m CMOS integrated circuits

abstract: A novel native-NMOS-triggered SCR (NANSCR) is proposed for efficient ESD protection design in a 0.13-/spl mu/m CMOS process. As compared with the traditional LVTSCR, the trigger voltage, turn-on resistance, turn-on speed, and CDM ESD level of NAN SCR have been greatly improved to protect the ultra-thin gate oxide against ESD stresses. The proposed NANSCR can be designed for the input, output, and power-rail ESD protection circuits without latchup danger in a 0.13-/spl mu/m CMOS process with VDD of 1.2 V. A new whole-chip ESD protection scheme realized with the NANSCR devices has been also demonstrated with the consideration of pin-to-pin ESD zapping.


title: Improving Use Case Point (UCP) Based on Function Point (FP) Mechanism

abstract: The cost of error correction has been increasing exponentially with the advancement of software industry. To minimize software errors, it is necessary to extract accurate requirements in the early stage of software development. In the previous study, we extracted the priorities of requirements based on the Use Case Point (UCP), which however revealed the issues inherent to the existing UCP as follows. (i) The UCP failed to specify the structure of use cases or the method of write the use cases, and (ii) the number of transactions determined the use case weight in the UCP. Yet, efforts taken for implementation depend on the types and number of operations performed in each transaction. To address these issues, the present paper proposes an improved UCP and applies it to the prioritization. The proposed method enables more accurate measurement than the existing UCP-based prioritization.


title: The Duchenne smile : emotional expression and brain physiology. II

abstract: Facial expression, EEG, and self-report of subjective emotional experience were recorded while subjects individually watched both pleasant and unpleasant films. Smiling in which the muscle that orbits the eye is active in addition to the muscle that pulls the lip corners up (the Duchenne smile) was compared with other smiling in which the muscle orbiting the eye was not active


title: Multiobjective Pareto-Efficient Approaches for Recommender Systems

abstract: Recommender systems are quickly becoming ubiquitous in applications such as e-commerce, social media channels, and content providers, among others, acting as an enabling mechanism designed to overcome the information overload problem by improving browsing and consumption experience. A typical task in many recommender systems is to output a ranked list of items, so that items placed higher in the rank are more likely to be interesting to the users. Interestingness measures include how accurate, novel, and diverse are the suggested items, and the objective is usually to produce ranked lists optimizing one of these measures. Suggesting items that are simultaneously accurate, novel, and diverse is much more challenging, since this may lead to a conflicting-objective problem, in which the attempt to improve a measure further may result in worsening other measures. In this article, we propose new approaches for multiobjective recommender systems based on the concept of Pareto efficiency—a state achieved when the system is devised in the most efficient manner in the sense that there is no way to improve one of the objectives without making any other objective worse off. Given that existing multiobjective recommendation algorithms differ in their level of accuracy, diversity, and novelty, we exploit the Pareto-efficiency concept in two distinct manners: (i) the aggregation of ranked lists produced by existing algorithms into a single one, which we call Pareto-efficient ranking, and (ii) the weighted combination of existing algorithms resulting in a hybrid one, which we call Pareto-efficient hybridization. Our evaluation involves two real application scenarios: music recommendation with implicit feedback (i.e., Last.fm) and movie recommendation with explicit feedback (i.e., MovieLens). We show that the proposed Pareto-efficient approaches are effective in suggesting items that are likely to be simultaneously accurate, diverse, and novel. We discuss scenarios where the system achieves high levels of diversity and novelty without compromising its accuracy. Further, comparison against multiobjective baselines reveals improvements in terms of accuracy (from 10.4p to 10.9p), novelty (from 5.7p to 7.5p), and diversity (from 1.6p to 4.2p).


title: Form-Focused Instruction: Isolated or Integrated?

abstract: There is increasing consensus that form-focused instruction helps learners in communicative or content-based instruction to learn features of the target language that they may not acquire without guidance. The subject of this article is the role of instruction that is provided in separate (isolated) activities or within the context of communicative activities (integrated). Research suggests that both types of instruction can be beneficial, depending on the language feature to be learned, as well as characteristics of the learner and the learning conditions. For example, isolated lessons may be necessary to help learners who share the same first language (L1) overcome problems related to L1 influence on their interlanguage; integrated instruction may be best for helping learners develop the kind of fluency and automaticity that are needed for communication outside the classroom. The evidence suggests that teachers and students see the benefits of both types of instruction. Explanations for the effectiveness of each type of instruction are drawn from theoretical work in second language acquisition and cognitive psychology as well as from empirical research.


Reinforcement Learning for Coreference Resolution


Challenges with Variational Autoencoders for Text


title: Hidradenitis suppurativa

abstract: Hidradenitis suppurativa is a chronic disease characterized by recurrent, painful, deep-seated, rounded nodules and abscesses of apocrine gland-bearing skin. Subsequent suppuration, sinus tracts and hypertrophic scarring are its main features. Onset is usually after puberty, although it is most common during the third decade and may persist in old age. The disease tends to be chronic and may develop to subcutaneous extension leading to indurations, sinus, and fistula having a profound impact on the quality of life. The prevalence is 1% in several studies. Axillary and inguinal involvement is more common in females; peri-anal and buttocks localizations are prevalent in males. The exact aetiology remains unknown. The primary event is a follicular occlusion with secondary inflammation, infection and destruction of the pilo-sebaceo-apocrine apparatus and extension to the adjacent sub-cutaneous tissue. Infection is common. Smoking may be a triggering factor. Obesity aggravates the discomfort. Differential diagnostic includes Crohn's disease, nodular acne and furonculosis. The main complications are arthropathy, carcinoma. Treatment depends upon the stage of the disease. Early nodular lesions may be treated by antibiotics for acute stage; long-term antibiotics, zinc salts may be useful as maintenance treatment; anti-TNF drugs have been used in severe cases; systemic steroids, estrogens, anti-androgens, retinoids have been used as options with limited success. Surgical treatment includes incision with or without drainage for limited abscesses; limited excisions are used for locally recurring draining sinuses. Total wide excision and healing with secondary intention or flaps and grafts is the only curative procedure in case of advanced disease.


title: A 1V 11fJ/conversion-step 10bit 10MS/s asynchronous SAR ADC in 0.18µm CMOS

abstract: This paper presents a 10-bit SAR ADC using a variable window function to reduce the unnecessary switching in DAC network. At 10-MS/s and 1-V supply, the ADC consumes only 98 µW and achieves an SNDR of 60.97 dB, resulting in an FOM of 11 fJ/Conversion-step. The prototype is fabricated in a 0.18µm CMOS technology.


TV Viewing and Advertising Targeting Yiting


Artificial Diversity as Maneuvers in a Control Theoretic Moving Target Defense


title: Metamorphic slice: An application in spectrum-based fault localization

abstract: Context: Because of its simplicity and effectiveness, Spectrum-Based Fault Localization (SBFL) has been one of the popular approaches towards fault localization. It utilizes the execution result of failure or pass, and the corresponding coverage information (such as program slice) to estimate the risk of being faulty for each program entity (such as statement). However, all existing SBFL techniques assume the existence of a test oracle to determine the execution result of a test case. But, it is common that test oracles do not exist, and hence the applicability of SBFL has been severely restricted. Objective: We aim at developing a framework that can extend the application of SBFL to the common situations where test oracles do not exist. Method: Our approach uses a new concept of metamorphic slice resulting from the integration of metamorphic testing and program slicing. In SBFL, instead of using the program slice and the result of failure or pass for an individual test case, a metamorphic slice and the result of violation or non-violation of a metamorphic relation are used. Since we need not know the execution result for an individual test case, the existence of a test oracle is no longer a requirement to apply SBFL. Results: An experimental study involving nine programs and three risk evaluation formulas was conducted. The results show that our proposed solution delivers a performance comparable to the performance observed by existing SBFL techniques for the situations where test oracles exist. Conclusion: With respect to the problem that SBFL is only applicable to programs with test oracles, we propose an innovative solution. Our solution is not only intuitively appealing and conceptually feasible, but also practically effective. Consequently, test oracles are no longer mandatory for SBFL, and hence the applicability of SBFL is significantly extended.


title: Skin diseases associated with Malassezia species.

abstract: The yeasts of the genus Malassezia have been associated with a number of diseases affecting the human skin, such as pityriasis versicolor, Malassezia (Pityrosporum) folliculitis, seborrheic dermatitis and dandruff, atopic dermatitis, psoriasis, and--less commonly--with other dermatologic disorders such as confluent and reticulated papillomatosis, onychomycosis, and transient acantholytic dermatosis. Although Malassezia yeasts are a part of the normal microflora, under certain conditions they can cause superficial skin infection. The study of the clinical role of Malassezia species has been surrounded by controversy because of their fastidious nature in vitro, and relative difficulty in isolation, cultivation, and identification. Many studies have been published in the past few years after the taxonomic revision carried out in 1996 in which 7 species were recognized. Two new species have been recently described, one of which has been isolated from patients with atopic dermatitis. This review focuses on the clinical, mycologic, and immunologic aspects of the various skin diseases associated with Malassezia. It also highlights the importance of individual Malassezia species in the different dermatologic disorders related to these yeasts.


title: Collapse Mechanisms for High Aspect Ratio Structures with Application to Clean Processing

abstract: Introduction The fabrication of state of the art semiconductor devices includes the generation of high aspect ratio structures, i.e. the vertical dimension exceeds the lateral dimension multiple times. Such geometries are found e.g. in Shallow Trench Insulation (STI), Capacitor over Bitline (CoB) and gate stacks. In the final device, the gaps between the structures are filled with e.g. dielectric materials for insulation and the filler acts also as a mechanical support. During production, however, there are several process steps where such support has not been established yet. Consequently, these structures may be damaged if the lateral mechanical stability is exceeded during processing. A typical example is the wet cleaning step which can produce such forces when ill adjusted. The most extreme forces during a wet process are introduced by physical force assisted cleaning (Megasonic, Aerosol and Shock wave systems) and capillary force in the subsequent drying step. A good understanding of the mechanical stability of nanostructures is therefore essential to tune force balance between particle removal and damage during the cleaning process or improve the stability of the structures. Mechanical fracture tests were performed with an AFM on polysilicon line structures to gain experimental data about the stability of such nanostructures. In this paper a comparison between the simulation of a lateral load on a beam and an experimental force on a gate stack is presented. Therefore the lateral forces on three line widths (nominally 25 nm, 40 nm and 65 nm) have been studied.


title: Genome-Wide Protein Function Prediction through Multi-Instance Multi-Label Learning

abstract: Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the vast majority of proteins can only be annotated computationally. Nature often brings several domains together to form multidomain and multi-functional proteins with a vast number of possibilities, and each domain may fulfill its own function independently or in a concerted manner with its neighbors. Thus, it is evident that the protein function prediction problem is naturally and inherently Multi-Instance Multi-Label (MIML) learning tasks. Based on the state-of-the-art MIML algorithm MIMLNN, we propose a novel ensemble MIML learning framework EnMIMLNN and design three algorithms for this task by combining the advantage of three kinds of Hausdorff distance metrics. Experiments on seven real-world organisms covering the biological three-domain system, i.e., archaea, bacteria, and eukaryote, show that the EnMIMLNN algorithms are superior to most state-of-the-art MIML and Multi-Label learning algorithms.


title: Learning trajectory patterns by clustering: Experimental studies and comparative evaluation

abstract: Recently a large amount of research has been devoted to automatic activity analysis. Typically, activities have been defined by their motion characteristics and represented by trajectories. These trajectories are collected and clustered to determine typical behaviors. This paper evaluates different similarity measures and clustering methodologies to catalog their strengths and weaknesses when utilized for the trajectory learning problem. The clustering performance is measured by evaluating the correct clustering rate on different datasets with varying characteristics.


title: Three-dimensional alpha shapes

abstract: Frequently, data in scientific computing is in its abstract form a finite point set in space, and it is sometimes useful or required to compute what one might call the ``shape'' of the set. For that purpose, this paper introduces the formal notion of the family of $\alpha$-shapes of a finite point set in $\Real^3$. Each shape is a well-defined polytope, derived from the Delaunay triangulation of the point set, with a parameter $\alpha \in \Real$ controlling the desired level of detail. An algorithm is presented that constructs the entire family of shapes for a given set of size $n$ in time $O(n^2)$, worst case. A robust implementation of the algorithm is discussed and several applications in the area of scientific computing are mentioned.


title: PyCP: An Open-Source Conformal Predictions Toolkit

abstract: The Conformal Predictions framework is a new game-theoretic approach to reliable machine learning, which provides a methodology to obtain error calibration under classification and regression settings. The framework combines principles of transductive inference, algorithmic randomness and hypothesis testing to provide guaranteed error calibration in online settings (and calibration in offline settings supported by empirical studies). As the framework is being increasingly used in a variety of machine learning settings such as active learning, anomaly detection, feature selection, and change detection, there is a need to develop algorithmic implementations of the framework that can be used and further improved by researchers and practitioners. In this paper, we introduce PyCP, an open-source implementation of the Conformal Predictions framework that currently provides support for classification problems within transductive and Mondrian settings. PyCP is modular, extensible and intended for community sharing and development.


title: Biomedical imaging modalities: a tutorial.

abstract: The introduction of advanced imaging technologies has improved significantly the quality of medical care available to patients. Non-invasive imaging modalities allow a physician to make increasingly accurate diagnoses and render precise and measured modes of treatment. Current uses of imaging technologies include laboratory medicine, surgery, radiation therapy, nuclear medicine, and diagnostic radiology. This paper provides an overview of most of the popular imaging modalities currently in clinical use. It is hoped that a general understanding of the modality from which an image is derived will help researchers in the subsequent analysis of the image data.


title: Models For The Semantic Classification Of Noun Phrases

abstract: This paper presents an approach for detecting semantic relations in noun phrases. A learning algorithm, called semantic scattering, is used to automatically label complex nominals, genitives and adjectival noun phrases with the corresponding semantic relation.