title: Effects of Display Size and Resolution on User Behavior and Insight Acquisition in Visual Exploration

abstract: Large high-resolution displays are becoming increasingly common in research settings, providing data scientists with visual interfaces for the analysis of large datasets. Numerous studies have demonstrated unique perceptual and cognitive benefits afforded by these displays in visual analytics and information visualization tasks. However, the effects of these displays on knowledge discovery in exploratory visual analysis are still poorly understood. We present the results of a small-scale study to better understand how display size and resolution affect insight. Analyzing participants' verbal statements, we find preliminary evidence that larger displays with more pixels can significantly increase the number of discoveries reported during visual exploration, while yielding broader, more integrative insights. Furthermore, we find important differences in how participants performed the same visual exploration task using displays of varying sizes. We tie these results to extant work and propose explanations by considering the cognitive and interaction costs associated with visual exploration.


title: Asynchronous Complex Analytics in a Distributed Dataflow Architecture

abstract: Scalable distributed dataflow systems have recently experienced widespread adoption, with commodity dataflow engines such as Hadoop and Spark, and even commodity SQL engines routinely supporting increasingly sophisticated analytics tasks (e.g., support vector machines, logistic regression, collaborative filtering). However, these systems' synchronous (often Bulk Synchronous Parallel) dataflow execution model is at odds with an increasingly important trend in the machine learning community: the use of asynchrony via shared, mutable state (i.e., data races) in convex programming tasks, which has---in a single-node context---delivered noteworthy empirical performance gains and inspired new research into asynchronous algorithms. In this work, we attempt to bridge this gap by evaluating the use of lightweight, asynchronous state transfer within a commodity dataflow engine. Specifically, we investigate the use of asynchronous sideways information passing (ASIP) that presents single-stage parallel iterators with a Volcano-like intra-operator iterator that can be used for asynchronous information passing. We port two synchronous convex programming algorithms, stochastic gradient descent and the alternating direction method of multipliers (ADMM), to use ASIPs. We evaluate an implementation of ASIPs within on Apache Spark that exhibits considerable speedups as well as a rich set of performance trade-offs in the use of these asynchronous algorithms.


title: Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality

abstract: Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised deep neural network, i.e., stacked convolutional autoencoders (SCAEs). The deep features are subsequently fused with the hand-crafted features for more accurate representation. Considering the exhaustive search is usually very time-consuming in large-scale databases, we employ a novel binary coding method to compress feature vectors into short binary codes. Our framework is validated on a public data set including 58,000 neurons, showing promising retrieval precision and efficiency compared with state-of-the-art methods. In addition, we develop a novel neuron visualization program based on the techniques of augmented reality (AR), which can help users take a deep exploration of neuron morphologies in an interactive and immersive manner.


title: A Spherical Inverted-F Antenna (SIFA)

abstract: This letter proposes a spherical inverted-F antenna (SIFA). The radiating element of this antenna is conformally integrated onto one quadrant of a metal-coated foam sphere. The substrate has been recessed into the spherical volume to remain conformal to the spherical host chassis. Design variables are provided in spherical coordinates to describe the structure; this includes the ground, patch, and a spherical foam shell that physically supports the patch and acts as its substrate. Measured and simulated results are presented for a design operating around 400 MHz.


title: Mobile robot navigation using potential functions

abstract: A method is presented in order to construct a smooth obstacle-free path amidst a finite number of disjoint disk-shaped obstacles for mobile robot navigation. The first step is to assign artificial potential functions to each obstacle and the goal. Then, the gradient flow associated with the weighted sum of the above functions generates the desired path. The procedure does not require complete prior knowledge of the positions of the obstacles, as long as they can be detected and approximated by disjoint disks. The algorithm presented represents a computationally simple way to construct paths and a systematic method of encoding the geometric data about the environment into a smooth vector field, which is used for mobile robot navigation. >


Breeding for disease resistance in farm animals


title: Demonstration of 100-Gb/s/λ-Based Coherent WDM-PON System Using New AGC EDFA Based Upstream Preamplifier and Optically Superimposed AMCC Function

abstract: In this paper, we present the first 100-Gb/s/ λ -based coherent wavelength division multiplexing passive optical network (WDM-PON) prototype system, highlighting a real-time 100-Gb/s coherent transceiver with a simplified digital signal processing suitable for access spans, and a new automatic-gain-controlled erbium-doped fiber amplifier based preamplifier with an amplified spontaneous emission compensation function for the upstream to improve the minimum receiver sensitivity for coherent detection, especially at very low received signal power. Thanks to our proposed technologies, this first demonstration achieved an increased bidirectional loss budget of more than 39.1 dB, which supports 80 km transmission with eight optical network unit splits, with an improved upstream receiver sensitivity of –38.1 dBm. In addition, to show the feasibility of the proposed 100-Gb/s/ λ -based coherent WDM-PON as a promising candidate for forthcoming 5G mobile fronthaul networks, we further investigate experimentally a 128-kb/s auxiliary management and control channel (AMCC) superimposed on the 100-Gb/s dual polarization quadrature phase-shift keying (DP-QPSK) signals employed by the 100-Gb/s/ λ -based coherent WDM-PON system. By setting the modulation index of the AMCC signals between 5% and 40%, we also successfully demonstrated 100-Gb/s DP-QPSK signal transmission with only a small power penalty of 0.2 dB due to the introduction of the AMCC signal.


title: Altered functional properties of tritordeum by transformation with HMW glutenin subunit genes

abstract: The high-molecular-weight (HMW) subunits of wheat glutenin are the major determinants of the gluten visco-elasticity that allows wheat doughs to be used to make bread, pasta and other food products. In order to increase the proportions of the HMW subunits, and hence improve breadmaking performance, particle bombardment was used to transform tritordeum, a fertile amphiploid between wild barley and pasta wheat, with genes encoding two HMW glutenin subunits (1Ax1 and 1Dx5). Of the 13 independent transgenic lines recovered (a transformation frequency of 1.4%) six express the novel HMW subunits at levels similar to, or higher than, those of the endogenous subunits encoded on chromosome 1B. Small-scale mixograph analysis of T2 seeds from a line expressing the transgene for 1Dx5 indicated that the addition of novel HMW subunits can result in significant improvements in dough strength and stability, thus demonstrating that transformation can be used to modify the functional properties of tritordeum for improved breadmaking.


title: Design tradeoffs for tiled CMP on-chip networks

abstract: We develop detailed area and energy models for on-chip interconnection networks and describe tradeoffs in the design of efficient networks for tiled chip multiprocessors. Using these detailed models we investigate how aspects of the network architecture including topology, channel width, routing strategy, and buffer size affect performance and impact area and energy efficiency. We simulate the performance of a variety of on-chip networks designed for tiled chip multiprocessors implemented in an advanced VLSI process and compare area and energy efficiencies estimated from our models. We demonstrate that the introduction of a second parallel network can increase performance while improving efficiency, and evaluate different strategies for distributing traffic over the subnetworks. Drawing on insights from our analysis, we present a concentrated mesh topology with replicated subnetworks and express channels which provides a 24% improvement in area efficiency and a 48% improvement in energy efficiency over other networks evaluated in this study.


title: Essential Communication Practices for Extreme Programming in a Global Software Development Team

abstract: Abstract We conducted an industrial case study of a distributed team in the USA and the Czech Republic that used Extreme Programming. Our goal was to understand how this globally-distributed team created a successful project in a new problem domain using a methodology that is dependent on informal, face-to-face communication. We collected quantitative and qualitative data and used grounded theory to identify four key factors for communication in globally-distributed XP teams working within a new problem domain. Our study suggests that, if these critical enabling factors are addressed, methodologies dependent on informal communication can be used on global software development projects.


title: A novel multi-drive electric vehicle system control based on multi-input multi-output PID controller

abstract: In-wheel-motor drive electric vehicle (EV) is an innovative confi- guration of the modern EV, in which each wheel is driven individually by an electric motor. The classical traction motor control called the Independent Machine Control Structure (IMCS) using a PID speed controller presents major inconveniences in modern EV safety, when the proposed control can not ensure stability of the EV with differing road topology and variations of speed. A new approach is proposed for a control of a two-in-wheel-motor drive EV, called the Maximum Control Structure MCS. This is based on a multivariable PID (MIMO-PID) strategy, which is employed to estimate the linear speed error of each of the two back driving wheels, when the error of each wheel is taken into account in the other speed control computations. Simulation results show that the new control system presents increased safety for the EVs compared with the IMCS strategy and can maintain the error slip rate within the optimal range, ensuring the stability of the vehicle either in a straight or a curved line.


title: 5G ultra-reliable and low-latency systems design

abstract: 5G New Radio (NR) is envisioned to support three broad categories of services: evolved mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC), and massive machine-type communications (mMTC). The URLLC services refer to future applications that require secure data communications from one end to another with ultra-high reliability and deadline-based low latency requirements. This type of quality-of-service is vastly different from that of traditional mobile broadband applications. In this paper, we discuss the systems design principles to enable the URLLC services in 5G. Theoretical queueing analysis and system-level simulations are provided to support these systems design choices, many of which have been considered as work items in the 3GPP Release 15 standards, which will be the first release for 5G NR.


title: Calibration of an elasto-plastic constitutive model by a constrained optimisation procedure

abstract: The presented paper deals with a constrained optimisation technique for the calibration of elasto-plastic model parameters in a rational and objective manner. The procedure consists in finding a set of model parameters which minimise the difference between the experimental data and the numerical simulations defined by an objective function. For this purpose, an optimisation routine, termed ParaID, has been developed which combines the quasi-Newton and stochastic methods. The optimisation technique was employed to calibrate a multi-mechanism elasto-plastic constitutive model. Using the results of three isotropically consolidated drained triaxial compression tests, a comparison between numerical and experimental results clearly shows the capability of the optimisation procedure to determine the model parameters correctly.


title: Adoption of business continuity planning processes in IT service management

abstract: For any fault of the same severity level, traditional fault discovery and notification tools provide equal weighting from business points of view. To improve the fault correlation from business perspectives, we proposed a framework to automate network and system alerts with respect to its business service impact for proactive notification to IT operations management. This paper outlines the value of business continuity planning (BCP) during the course of service impact analysis, placing particular emphasis on the business perspective in the processes of IT service management. The framework explicitly employs BCP relevant processes in order to identify the relationships between business services and IT resources A practical case in IT operations to illustrate the concept was then conducted.


Feature-based prediction of trajectories for socially compliant navigation


Determining the Number of Factors to Retain in EFA: An easy-to-use computer program for carrying out Parallel Analysis


title: Integrating K-means clustering with a relational DBMS using SQL

abstract: Integrating data mining algorithms with a relational DBMS is an important problem for database programmers. We introduce three SQL implementations of the popular K-means clustering algorithm to integrate it with a relational DBMS: 1) a straightforward translation of K-means computations into SQL, 2) an optimized version based on improved data organization, efficient indexing, sufficient statistics, and rewritten queries, and 3) an incremental version that uses the optimized version as a building block with fast convergence and automated reseeding. We experimentally show the proposed K-means implementations work correctly and can cluster large data sets. We identify which K-means computations are more critical for performance. The optimized and incremental K-means implementations exhibit linear scalability. We compare K-means implementations in SQL and C++ with respect to speed and scalability and we also study the time to export data sets outside of the DBMS. Experiments show that SQL overhead is significant for small data sets, but relatively low for large data sets, whereas export times become a bottleneck for C++.


title: Imaging of Single and Double Scatterers in Urban Areas via SAR Tomography

abstract: Microwave scattering is a rather complex mechanism, especially in urban areas. Three-dimensional (3-D) synthetic aperture radar (SAR) tomography is a technique that uses multiple views to map the scattering power at different heights, thus extending the capability of SAR sensors to fully image the scene in the 3-D space. This paper presents a first validation of spaceborne long-term SAR tomography by demonstrating the capability to resolve a simple layover case, i.e., to separate single- and double-scattering mechanisms within imaged pixels. Results obtained with real data acquired by the European Remote Sensing 1 and 2 (ERS-1 and ERS-2) satellites over the urban area of Napoli are presented. As an additional contribution, an innovative algorithm estimating residual topography and surface deformation, called the spatial-differencing technique, is also discussed in detail at the data calibration stage


title: Real-time human collision detection for industrial robot cells

abstract: A collision detection system triggering on human motion was developed using the Robot Operating System (ROS) and the Point Cloud Library (PCL). ROS was used as the core of the programs and for the communication with an industrial robot. Combining the depths fields from the 3D cameras was accomplished by the use of PCL. The library was also the underlying tool for segmenting the human from the registrated point clouds. Benchmarking of several collision algorithms was done in order to compare the solution. The registration process gave satisfactory results when testing the repetitiveness and the accuracy of the implementation. The segmentation algorithm was able to segment a person represented by 4–6000 points in real-time successfully.


title: A Double-Blind, Randomized Study of Minocycline for the Treatment of Negative and Cognitive Symptoms in Early-Phase Schizophrenia

abstract: Background: Current antipsychotics have only a limited effect on 2 core aspects of schizophrenia: negative symptoms and cognitive deficits. Minocycline is a second-generation tetracycline that has a beneficial effect in various neurologic disorders. Recent findings in animal models and human case reports suggest its potential for the treatment of schizophrenia. These findings may be linked to the effect of minocycline on the glutamatergic system, through inhibition of nitric oxide synthase and blocking of nitric oxide-induced neurotoxicity. Other proposed mechanisms of action include effects of minocycline on the dopaminergic system and its inhibition of microglial activation. Objective: To examine the efficacy of minocycline as an add-on treatment for alleviating negative and cognitive symptoms in early-phase schizophrenia. Method: A longitudinal double-blind, randomized, placebo-controlled design was used, and patients were followed for 6 months from August 2003 to March 2007. Seventy early-phase schizophrenia patients (according to DSM-IV) were recruited and 54 were randomly allocated in a 2:1 ratio to minocycline 200 mg/d. All patients had been initiated on treatment with an atypical antipsychotic ≤14 days prior to study entry (risperidone, olanzapine, quetiapine, or clozapine; 200-600 mg/d chlorpromazine-equivalent doses). Clinical, cognitive, and functional assessments were conducted, with the Scale for the Assessment of Negative Symptoms (SANS) as the primary outcome measure. Results: Minocycline was well tolerated, with few adverse events. It showed a beneficial effect on negative symptoms and general outcome (evident in SANS, Clinical Global Impressions scale). A similar pattern was found for cognitive functioning, mainly in executive functions (working memory, cognitive shifting, and cognitive planning). Conclusions: Minocycline treatment was associated with improvement in negative symptoms and executive functioning, both related to frontal-lobe activity. Overall, the findings support the beneficial effect of minocycline add-on therapy in early-phase schizophrenia.


title: CBCT-based bone quality assessment: are Hounsfield units applicable?

abstract: CBCT is a widely applied imaging modality in dentistry. It enables the visualization of high-contrast structures of the oral region (bone, teeth, air cavities) at a high resolution. CBCT is now commonly used for the assessment of bone quality, primarily for pre-operative implant planning. Traditionally, bone quality parameters and classifications were primarily based on bone density, which could be estimated through the use of Hounsfield units derived from multidetector CT (MDCT) data sets. However, there are crucial differences between MDCT and CBCT, which complicates the use of quantitative gray values (GVs) for the latter. From experimental as well as clinical research, it can be seen that great variability of GVs can exist on CBCT images owing to various reasons that are inherently associated with this technique (i.e. the limited field size, relatively high amount of scattered radiation and limitations of currently applied reconstruction algorithms). Although attempts have been made to correct for GV variability, it can be postulated that the quantitative use of GVs in CBCT should be generally avoided at this time. In addition, recent research and clinical findings have shifted the paradigm of bone quality from a density-based analysis to a structural evaluation of the bone. The ever-improving image quality of CBCT allows it to display trabecular bone patterns, indicating that it may be possible to apply structural analysis methods that are commonly used in micro-CT and histology.


title: Brain MRI super-resolution using deep 3D convolutional networks

abstract: Example-based single image super-resolution (SR) has recently shown outcomes with high reconstruction performance. Several methods based on neural networks have successfully introduced techniques into SR problem. In this paper, we propose a three-dimensional (3D) convolutional neural network to generate high-resolution (HR) brain image from its input low-resolution (LR) with the help of patches of other HR brain images. Our work demonstrates the need of fitting data and network parameters for 3D brain MRI.


title: China CEO: Voices of Experience from 20 International Business Leaders

abstract: Foreword: China: A Must-win Market. Introduction: Opportunity and Challenge. Chapter 1. Qualities of a Successful International Manager in China. Chapter 2. Managing Chinese Employees. Chapter 3. Working with Business Partners. Chapter 4. Communicating with Headquarters. Chapter 5. Facing Competitors. Chapter 6. Battling Intellectual Property Rights Infringers. Chapter 7. Winning Over Chinese Consumers. Chapter 8. Negotiating with the Chinese Government. Chapter 9. Living in China. Chapter 10. Conclusion. Appendix: Research Methodolgy. Index.


Should Farmers Use Futures and Options? A Pseudo-Experimental Analysis


title: Corneal Endothelial Alterations in Chronic Renal Failure.

abstract: PURPOSE ::: To evaluate the corneal endothelial changes in patients with chronic renal failure. ::: ::: ::: METHODS ::: A total of 128 corneas of 128 subjects were studied, and 3 groups were formed. The first, the dialyzed group, composed of 32 corneas of 32 patients; the second, the nondialyzed group, composed of 34 corneas of 34 patients; and the third, the age-matched control group, composed of 64 corneas of 64 healthy subjects were examined by a specular microscope and the endothelial parameters were compared. The dialyzed group (enhanced level of toxins in the blood) was further analyzed to assess the influence of blood urea, serum creatinine, serum calcium, and serum phosphorus including the duration of dialysis on corneal endothelium. ::: ::: ::: RESULTS ::: On comparing the 3 groups using analysis of variance and posthoc tests, a significant difference was found in the central corneal thickness (CCT) and endothelial cell density (CD) between the control (CCT: 506 ± 29 μm, CD: 2760 ± 304 cells/mm) and dialyzed groups (CCT: 549 ± 30 μm, CD: 2337 ± 324 cells/mm) [P < 0.001 (CCT); P < 0.001 (CD)]; control and nondialyzed groups (CCT: 524 ± 27 μm, CD: 2574 ± 260 cells/mm) [P = 0.023 (CCT); P = 0.016 (CD)]; and dialyzed and nondialyzed groups [P = 0.002 (CCT); P = 0.007 (CD)]. Using the linear generalized model, a significant correlation was found between the endothelial parameters and blood urea only [P = 0.006 (CCT), 0.002 (coefficient of variation), 0.022 (CD), and 0.026 (percentage of hexagonality)], although the correlation was poorly positive for CCT but poorly negative for the remaining endothelial parameters. ::: ::: ::: CONCLUSIONS ::: Corneal endothelial alteration is present in patients with chronic renal failure, more marked in patients undergoing hemodialysis and with raised blood urea level.


title: An ultra low-power, high-PSRR CMOS voltage reference

abstract: A CMOS voltage reference based on the difference between the gate-source voltages of pMOS and nMOS transistors operating in subthreshold region is presented. Power consumption is optimized by subthreshold design and minimizing the number of passive elements in any circuit branches from supply to the ground. A reference voltage of 912 mV with a temperature coefficient of 68 ppm/°C from −40 °C to 120 °C is achieved. Proposed voltage reference circuit can operate at supply voltages ranging from 1.5 V to 3.5 V while the current drawn from supply is at most 47 nA. Power supply rejection ratio without any filtering capacitor at 100 Hz and 10 MHz frequencies are about −68 dB and −70 dB respectively.


Ranking of fuzzy numbers based on alpha-distance


title: The successor representation in human reinforcement learning

abstract: Theories of reward learning in neuroscience have focused on two families of algorithms thought to capture deliberative versus habitual choice. ‘Model-based’ algorithms compute the value of candidate actions from scratch, whereas ‘model-free’ algorithms make choice more efficient but less flexible by storing pre-computed action values. We examine an intermediate algorithmic family, the successor representation, which balances flexibility and efficiency by storing partially computed action values: predictions about future events. These pre-computation strategies differ in how they update their choices following changes in a task. The successor representation’s reliance on stored predictions about future states predicts a unique signature of insensitivity to changes in the task’s sequence of events, but flexible adjustment following changes to rewards. We provide evidence for such differential sensitivity in two behavioural studies with humans. These results suggest that the successor representation is a computational substrate for semi-flexible choice in humans, introducing a subtler, more cognitive notion of habit. Momennejad et al. formulate and provide evidence for the successor representation, a computational learning mechanism intermediate between the two dominant models (a fast but inflexible ‘model-free’ system and a flexible but slow ‘model-based’ one).


title: 40-W CW broad-band spatial power combiner using dense finline arrays

abstract: This paper presents a broad-band spatial power-combining system based on tapered-slot antenna arrays integrated in a standard WR-90 waveguide environment. The system is designed using a modular tray architecture, providing full waveguide-band frequency coverage and an excellent thermal environment for a set of monolithic-microwave integrated-circuit (MMIC) amplifiers. The shape of the tapered-slot or finline structures was optimized to minimize return loss and provide a broad-band impedance transformation from the waveguide mode to the MMIC amplifiers. A prototype eight-element array using commercial GaAs MMIC power amplifiers yielded a maximum of 41 W output power (continuous wave) with a gain variation less than /spl plusmn/1.2 dB within the entire band of interest. The average combining efficiency over the operating band was estimated at 73%. The results suggest the efficacy of the design and a strong potential for higher powers by moving toward a greater number of MMIC's per tray and a larger number of trays. Should the 100 W system be realized in the near future, our combiner system will become a promising candidate to challenge the dominant position currently claimed by the traveling-wave tube amplifiers.


title: Greater Internet use is not associated with faster growth in political polarization among US demographic groups

abstract: Abstract We combine eight previously proposed measures to construct an index of political polarization among US adults. We find that polarization has increased the most among the demographic groups least likely to use the Internet and social media. Our overall index and all but one of the individual measures show greater increases for those older than 65 than for those aged 18–39. A linear model estimated at the age-group level implies that the Internet explains a small share of the recent growth in polarization.


title: Prefrontal Engagement and Reduced Default Network Suppression Co-occur and Are Dynamically Coupled in Older Adults: The Default–Executive Coupling Hypothesis of Aging

abstract: Reduced executive control is a hallmark of neurocognitive aging. Poor modulation of lateral pFC activity in the context of increasing task challenge in old adults and a "failure to deactivate" the default network during cognitive control tasks have been observed. Whether these two patterns represent discrete mechanisms of neurocognitive aging or interact into older adulthood remains unknown. We examined whether altered pFC and default network dynamics co-occur during goal-directed planning over increasing levels of difficulty during performance on the Tower of London task. We used fMRI to investigate task-and age-related changes in brain activation and functional connectivity across four levels of task challenge. Frontoparietal executive control regions were activated and default network regions were suppressed during planning relative to counting performance in both groups. Older adults, unlike young, failed to modulate brain activity in executive control and default regions as planning demands increased. Critically, functional connectivity analyses revealed bilateral dorsolateral pFC coupling in young adults and dorsolateral pFC to default coupling in older adults with increased planning complexity. We propose a default-executive coupling hypothesis of aging. First, this hypothesis suggests that failure to modulate control and default network activity in response to increasing task challenge are linked in older adulthood. Second, functional brain changes involve greater coupling of lateral pFC and the default network as cognitive control demands increase in older adults. We speculate that these changes reflect an adaptive shift in cognitive approach as older adults come to rely more upon stored representations to support goal-directed task performance.


title: Running or gaming

abstract: We developed Exertainer, a sensor-enabled, interactive running entertainment system to support advanced exercise applications. We designed Exertainer to be used in urban environments where outdoor running is often not convenient or practical; as such, Exertainer and Exertainer running applications represent an attractive alternative to traditional treadmill running. Exertainer effectively creates a robust design space around treadmill running. Developers can leverage Exertainer's components, an advanced treadmill called Interactive Treadmill, Sensor Bracelet and the PSD game platform, to design interactive and immersive running games and other advanced running applications. We also developed Swan Boat, a multiplayer team racing game making the treadmill running an exciting social activity, and conducted a user study.


Subreddit Recommendations within Reddit Communities


General Principles and Overview


Annotating documents with relevant Wikipedia concepts


title: Commitment Without Regrets: Online Learning in Stackelberg Security Games

abstract: In a Stackelberg Security Game, a defender commits to a randomized deployment of security resources, and an attacker best-responds by attacking a target that maximizes his utility. While algorithms for computing an optimal strategy for the defender to commit to have had a striking real-world impact, deployed applications require significant information about potential attackers, leading to inefficiencies. We address this problem via an online learning approach. We are interested in algorithms that prescribe a randomized strategy for the defender at each step against an adversarially chosen sequence of attackers, and obtain feedback on their choices (observing either the current attacker type or merely which target was attacked). We design no-regret algorithms whose regret (when compared to the best fixed strategy in hindsight) is polynomial in the parameters of the game, and sublinear in the number of times steps.


title: Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study

abstract: Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU), have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.


title: Bubble sort: an archaeological algorithmic analysis

abstract: Text books, including books for general audiences, invariably mention bubble sort in discussions of elementary sorting algorithms. We trace the history of bubble sort, its popularity, and its endurance in the face of pedagogical assertions that code and algorithmic examples used in early courses should be of high quality and adhere to established best practices. This paper is more an historical analysis than a philosophical treatise for the exclusion of bubble sort from books and courses. However, sentiments for exclusion are supported by Knuth [17], "In short, the bubble sort seems to have nothing to recommend it, except a catchy name and the fact that it leads to some interesting theoretical problems." Although bubble sort may not be a best practice sort, perhaps the weight of history is more than enough to compensate and provide for its longevity.


title: Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies

abstract: We present NEWSROOM, a summarization dataset of 1.3 million articles and summaries written by authors and editors in newsrooms of 38 major news publications. Extracted from search and social media metadata between 1998 and 2017, these high-quality summaries demonstrate high diversity of summarization styles. In particular, the summaries combine abstractive and extractive strategies, borrowing words and phrases from articles at varying rates. We analyze the extraction strategies used in NEWSROOM summaries against other datasets to quantify the diversity and difficulty of our new data, and train existing methods on the data to evaluate its utility and challenges. The dataset is available online at summari.es.


title: A compact and selective low-pass filter with reduced spurious responses, based on CPW tapered periodic structures

abstract: This paper describes a new low-pass filter topology based on tapered periodic structures. These filters exhibit interesting characteristics in terms of compactness, return loss, insertion loss, selectivity, and the suppression of spurious frequency bands. Hybrid prototypes with a 1-GHz cutoff frequency, based on a coplanar-waveguide technology, and using both low-cost and high-performance substrates, have been fabricated and measured. Spurious frequency bands can be suppressed to below -22 dB at frequencies up to 20 GHz. Passband ripples are negligible, and the return loss is better than 20 dB. A two-section filter has a length of 0.2 lambda and exhibits a -120-dB/dec selectivity, while a six-section filter is 0.51 lambda long and has a -560-dB/dec selectivity. A design procedure has been established. These filters are compatible with monolithic microwave integrated circuit technologies in which the capacitors can be realized as metal-insulator-metal structures


title: Learning and Transferring Social and Item Visibilities for Personalized Recommendation

abstract: User feedback in the form of movie-watching history, item ratings, or product consumption is very helpful in training recommender systems. However, relatively few interactions between items and users can be observed. Instances of missing user--item entries are caused by the user not seeing the item (although the actual preference to the item could still be positive) or the user seeing the item but not liking it. Separating these two cases enables missing interactions to be modeled with finer granularity, and thus reflects user preferences more accurately. However, most previous studies on the modeling of missing instances have not fully considered the case where the user has not seen the item. Social connections are known to be helpful for modeling users' potential preferences more extensively, although a similar visibility problem exists in accurately identifying social relationships. That is, when two users are unaware of each other's existence, they have no opportunity to connect. In this paper, we propose a novel user preference model for recommender systems that considers the visibility of both items and social relationships. Furthermore, the two kinds of information are coordinated in a unified model inspired by the idea of transfer learning. Extensive experiments have been conducted on three real-world datasets in comparison with five state-of-the-art approaches. The encouraging performance of the proposed system verifies the effectiveness of social knowledge transfer and the modeling of both item and social visibilities.


title: Adversarial Geometry and Lighting using a Differentiable Renderer

abstract: Many machine learning classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification. Modern adversarial methods either directly alter pixel colors, or"paint"colors onto a 3D shapes. We propose novel adversarial attacks that directly alter the geometry of 3D objects and/or manipulate the lighting in a virtual scene. We leverage a novel differentiable renderer that is efficient to evaluate and analytically differentiate. Our renderer generates images realistic enough for correct classification by common pre-trained models, and we use it to design physical adversarial examples that consistently fool these models. We conduct qualitative and quantitate experiments to validate our adversarial geometry and adversarial lighting attack capabilities.


title: Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

abstract: Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification prob...


title: Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?

abstract: Current-generation Deep Neural Networks (DNNs), such as AlexNet and VGG, rely heavily on dense floating-point matrix multiplication (GEMM), which maps well to GPUs (regular parallelism, high TFLOP/s). Because of this, GPUs are widely used for accelerating DNNs. Current FPGAs offer superior energy efficiency (Ops/Watt), but they do not offer the performance of today's GPUs on DNNs. In this paper, we look at upcoming FPGA technology advances, the rapid pace of innovation in DNN algorithms, and consider whether future high-performance FPGAs will outperform GPUs for next-generation DNNs. The upcoming Intel® 14-nm Stratix? 10 FPGAs will have thousands of hard floating-point units (DSPs) and on-chip RAMs (M20K memory blocks). They will also have high bandwidth memories (HBMs) and improved frequency (HyperFlex? core architecture). This combination of features brings FPGA raw floating point performance within striking distance of GPUs. Meanwhile, DNNs are quickly evolving. For example, recent innovations that exploit sparsity (e.g., pruning) and compact data types (e.g., 1-2 bit) result in major leaps in algorithmic efficiency. However, these innovations introduce irregular parallelism on custom data types, which are difficult for GPUs to handle but would be a great fit for FPGA's extreme customizability. This paper evaluates a selection of emerging DNN algorithms on two generations of Intel FPGAs (Arria'10, Stratix'10) against the latest highest performance Titan X Pascal GPU. We created a customizable DNN accelerator template for FPGAs and used it in our evaluations. First, we study various GEMM operations for next-generation DNNs. Our results show that Stratix 10 FPGA is 10%, 50%, and 5.4x better in performance (TOP/sec) than Titan X Pascal GPU on GEMM operations for pruned, Int6, and binarized DNNs, respectively. Then, we present a detailed case study on accelerating Ternary ResNet which relies on sparse GEMM on 2-bit weights (i.e., weights constrained to 0,+1,-1) and full-precision neurons. The Ternary ResNet accuracy is within ~1% of the full-precision ResNet which won the 2015 ImageNet competition. On Ternary-ResNet, the Stratix 10 FPGA can deliver 60% better performance over Titan X Pascal GPU, while being 2.3x better in performance/watt. Our results indicate that FPGAs may become the platform of choice for accelerating next-generation DNNs.


title: Growing the Blockchain Information Infrastructure

abstract: In this paper, we present ethnographic data that unpacks the everyday work of some of the many infrastructuring agents who contribute to creating, sustaining and growing the Blockchain information infrastructure. We argue that this infrastructuring work takes the form of entrepreneurial actions, which are self-initiated and primarily directed at sustaining or increasing the initiator's stake in the emerging information infrastructure. These entrepreneurial actions wrestle against the affordances of the installed base of the Blockchain infrastructure, and take the shape of engaging or circumventing activities. These activities purposefully aim at either influencing or working around the enablers and constraints afforded by the Blockchain information infrastructure, as its installed base is gaining inertia. This study contributes to our understanding of the purpose of infrastructuring, seen from the perspective of heterogeneous entrepreneurial agents. It supplements existing accounts of the "when" and "how" of infrastructure, with a lens for examining the "why" of infrastructure.


title: Device-to-Device Communications Underlaying Cellular Networks

abstract: In cellular networks, proximity users may communicate directly without going through the base station, which is called Device-to-device (D2D) communications and it can improve spectral efficiency. However, D2D communications may generate interference to the existing cellular networks if not designed properly. In this paper, we study a resource allocation problem to maximize the overall network throughput while guaranteeing the quality-of-service (QoS) requirements for both D2D users and regular cellular users (CUs). A three-step scheme is proposed. It first performs admission control and then allocates powers for each admissible D2D pair and its potential CU partners. Next, a maximum weight bipartite matching based scheme is developed to select a suitable CU partner for each admissible D2D pair to maximize the overall network throughput. Numerical results show that the proposed scheme can significantly improve the performance of the hybrid system in terms of D2D access rate and the overall network throughput. The performance of D2D communications depends on D2D user locations, cell radius, the numbers of active CUs and D2D pairs, and the maximum power constraint for the D2D pairs.


title: Adversarial Extreme Multi-label Classification

abstract: The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels. Datasets in extreme classification exhibit a long tail of labels which have small number of positive training instances. In this work, we pose the learning task in extreme classification with large number of tail-labels as learning in the presence of adversarial perturbations. This view motivates a robust optimization framework and equivalence to a corresponding regularized objective. Under the proposed robustness framework, we demonstrate efficacy of Hamming loss for tail-label detection in extreme classification. The equivalent regularized objective, in combination with proximal gradient based optimization, performs better than state-of-the-art methods on propensity scored versions of precision@k and nDCG@k(upto 20% relative improvement over PFastreXML - a leading tree-based approach and 60% relative improvement over SLEEC - a leading label-embedding approach). Furthermore, we also highlight the sub-optimality of a sparse solver in a widely used package for large-scale linear classification, which is interesting in its own right. We also investigate the spectral properties of label graphs for providing novel insights towards understanding the conditions governing the performance of Hamming loss based one-vs-rest scheme vis-\`a-vis label embedding methods.


title: Jersey Number Recognition with Semi-Supervised Spatial Transformer Network

abstract: It is still a challenging task to recognize the jersey number of players on the court in soccer match videos, as the jersey numbers are very small in the object detection task and annotated data are not easy to collect. Based on the object detection results of all the players on the court, a CNN model is first introduced to classify these numbers on the deteced players' images. To localize the jersey number more precisely without involving another digit detector and extra consumption, we then improve the former network to an end-to-end framework by fusing with the spatial transformer network (STN). To further improve the accuracy, we bring extra supervision to STN and upgrade the model to a semi-supervised multi-task learning system, by labeling a small portion of the number areas in the dataset by quadrangle. Extensive experiments illustrate the effectiveness of the proposed framework.


title: Information overload and virtual institutions

abstract: The internet puts at our disposal an unprecedented wealth of information. Unfortunately much of this information is unreliable and its very quantity exceeds our cognitive capacity. To deal with the resulting information overload requires knowledge evaluation procedures that have traditionally been performed by social institutions, such as the press or universities. But the internet has also given rise to a new type of social institution operating online, such as Wikipedia. We will analyze these virtual institutions to understand how they function, and to determine to what extent they can help manage the information overload. Their distributed and collaborative nature, their agility and low cost make them a very interesting social model, but also a rather fragile one. To be durable, virtual institutions probably need strong rules and norms, as well as an appropriate social framework.


title: PopCache: Cache more or less based on content popularity for information-centric networking

abstract: Due to a mismatch between downloading and caching content, the network may not gain significant benefit from the sophisticated in-network caching of information-centric networking (ICN) architectures by using a basic caching mechanism. This paper aims to seek an effective caching decision policy to improve the content dissemination in ICN. We propose PopCache-a caching decision policy with respect to the content popularity-that allows an individual ICN router to cache content more or less in accordance with the popularity characteristic of the content. We propose an analytical model to evaluate the performance of different caching decision policies in terms of the server-hit rate and expected round-trip time. The analysis confirmed by simulation results shows that PopCache yields the lowest expected round-trip time compared with three benchmark caching decision policies, i.e., the always, fixed probability and path-capacity-based probability, and PopCache provides the server-hit rate comparable to the lowest ones.


title: A Context-Aware Recurrent Encoder for Neural Machine Translation

abstract: Neural machine translation (NMT) heavily relies on its encoder to capture the underlying meaning of a source sentence so as to generate a faithful translation. However, most NMT encoders are built upon either unidirectional or bidirectional recurrent neural networks, which either do not deal with future context or simply concatenate the history and future context to form context-dependent word representations, implicitly assuming the independence of the two types of contextual information. In this paper, we propose a novel context-aware recurrent encoder (CAEncoder), as an alternative to the widely-used bidirectional encoder, such that the future and history contexts can be fully incorporated into the learned source representations. Our CAEncoder involves a two-level hierarchy: The bottom level summarizes the history information, whereas the upper level assembles the summarized history and future context into source representations. Additionally, CAEncoder is as efficient as the bidirectional RNN encoder in terms of both training and decoding. Experiments on both Chinese–English and English–German translation tasks show that CAEncoder achieves significant improvements over the bidirectional RNN encoder on a widely-used NMT system. 1


title: Temporal-Logic-Based Reactive Mission and Motion Planning

abstract: This paper provides a framework to automatically generate a hybrid controller that guarantees that the robot can achieve its task when a robot model, a class of admissible environments, and a high-level task or behavior for the robot are provided. The desired task specifications, which are expressed in a fragment of linear temporal logic (LTL), can capture complex robot behaviors such as search and rescue, coverage, and collision avoidance. In addition, our framework explicitly captures sensor specifications that depend on the environment with which the robot is interacting, which results in a novel paradigm for sensor-based temporal-logic-motion planning. As one robot is part of the environment of another robot, our sensor-based framework very naturally captures multirobot specifications in a decentralized manner. Our computational approach is based on first creating discrete controllers satisfying specific LTL formulas. If feasible, the discrete controller is then used to guide the sensor-based composition of continuous controllers, which results in a hybrid controller satisfying the high-level specification but only if the environment is admissible.


title: RDMA over Commodity Ethernet at Scale

abstract: Over the past one and half years, we have been using RDMA over commodity Ethernet (RoCEv2) to support some of Microsoft's highly-reliable, latency-sensitive services. This paper describes the challenges we encountered during the process and the solutions we devised to address them. In order to scale RoCEv2 beyond VLAN, we have designed a DSCP-based priority flow-control (PFC) mechanism to ensure large-scale deployment. We have addressed the safety challenges brought by PFC-induced deadlock (yes, it happened!), RDMA transport livelock, and the NIC PFC pause frame storm problem. We have also built the monitoring and management systems to make sure RDMA works as expected. Our experiences show that the safety and scalability issues of running RoCEv2 at scale can all be addressed, and RDMA can replace TCP for intra data center communications and achieve low latency, low CPU overhead, and high throughput.


title: Color image database for evaluation of image quality metrics

abstract: In this contribution, a new image database for testing full-reference image quality assessment metrics is presented. It is based on 1700 test images (25 reference images, 17 types of distortions for each reference image, 4 levels for each type of distortion). Using this image database, 654 observers from three different countries (Finland, Italy, and Ukraine) have carried out about 400000 individual human quality judgments (more than 200 judgments for each distorted image). The obtained mean opinion scores for the considered images can be used for evaluating the performances of visual quality metrics as well as for comparison and for the design of new metrics. The database, with testing results, is freely available.


title: Diversity of sharp-wave–ripple LFP signatures reveals differentiated brain-wide dynamical events

abstract: Sharp-wave–ripple (SPW-R) complexes are believed to mediate memory reactivation, transfer, and consolidation. However, their underlying neuronal dynamics at multiple scales remains poorly understood. Using concurrent hippocampal local field potential (LFP) recordings and functional MRI (fMRI), we study local changes in neuronal activity during SPW-R episodes and their brain-wide correlates. Analysis of the temporal alignment between SPW and ripple components reveals well-differentiated SPW-R subtypes in the CA1 LFP. SPW-R–triggered fMRI maps show that ripples aligned to the positive peak of their SPWs have enhanced neocortical metabolic up-regulation. In contrast, ripples occurring at the trough of their SPWs relate to weaker neocortical up-regulation and absent subcortical down-regulation, indicating differentiated involvement of neuromodulatory pathways in the ripple phenomenon mediated by long-range interactions. To our knowledge, this study provides the first evidence for the existence of SPW-R subtypes with differentiated CA1 activity and metabolic correlates in related brain areas, possibly serving different memory functions.


title: Omnidirectional Circularly Polarized Antennas – a Small Antenna Perspective

abstract: 1 Abstract—The paper discusses recent developments and challenges in the design of small omnidirectional circularly-polarized (CP) antennas. Although omnidirectional CP coverage is easily achievable using antenna arrays, it is just recently that small and low-cost antennas delivered this functionality. The paper addresses practical design problems for these antennas, not reported in previous publications. This includes selection of the omnidirectional plane relative to the ground plane and measurement challenges. Future perspectives on how these antennas can provide electronically steerable beams are investigated using simulated results.


title: No Metaphorical Timeline in Gesture and Cognition Among Yucatec Mayas

abstract: In numerous languages, space provides a productive domain for the expression of time. This paper examines how time-to-space mapping is realized in Yucatec Maya. At the linguistic level, Yucatec Maya has numerous resources to express deictic time, whereas expression of sequential time is highly constrained. Specifically, in gesture, we do not find any metaphorical oriented timeline, but only an opposition between ‘current time’ (mapped on the ‘here’ space) and ‘remote time’ (mapped on the ‘remote/distant space’). Additionally, past and future are not contrasted. Sequential or deictic time in language and gesture are not conceived as unfolding along a metaphorical oriented line (e.g. left-right or front-back) but as a succession of completed events not spatially organized. Interestingly, although Yucatec Maya speakers preferentially use a geocentric spatial frame of reference, especially visible in their use of gesture, time is not mapped onto a geocentric axis (e.g. east-west). We argue that, instead of providing a source for time mapping, the use of a spatial geocentric frame of reference in Yucatec Maya seems to inhibit it. The Yucatec Maya expression of time in language and gesture fits the more general cultural conception of time as cyclic. Experimental results confirmed, to some extent, this non-linear, non-directional conception of time in Yucatec Maya.


title: A solution to the simultaneous localization and map building (SLAM) problem

abstract: The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from estimation-theoretic foundations of this problem, the paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. The paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeter-wave radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are cross-compared with absolute locations of the map landmarks obtained by surveying. In conclusion, the paper discusses a number of key issues raised by the solution to the SLAM problem including suboptimal map-building algorithms and map management.


title: Automatic Design and Manufacture of Soft Robots

abstract: We present the automated design and manufacture of static and locomotion objects in which functionality is obtained purely by the unconstrained 3-D distribution of materials. Recent advances in multimaterial fabrication techniques enable continuous shapes to be fabricated with unprecedented fidelity unhindered by spatial constraints and homogeneous materials. We address the challenges of exploitation of the freedom of this vast new design space using evolutionary algorithms. We first show a set of cantilever beams automatically designed to deflect in arbitrary static profiles using hard and soft materials. These beams were automatically fabricated, and their physical performance was confirmed within 0.5-7.6% accuracy. We then demonstrate the automatic design of freeform soft robots for forward locomotion using soft volumetrically expanding actuator materials. One robot was fabricated automatically and assembled, and its performance was confirmed with 15% error. We suggest that this approach to design automation opens the door to leveraging the full potential of the freeform multimaterial design space to generate novel mechanisms and deformable robots.


title: Seminar Childhood obesity: public-health crisis, common sense cure

abstract: Historically, a fat child meant a healthy child, one who was likely to survive the rigors of undernourishment and infection. In the past decade, however, excessive fatness has arguably become the primary childhood health problem in developed nations and, to some degree, in other parts of the world. Here we review the scope of the problem and discuss developments in establishment of cause, prevention, and treatment of obesity. We argue that fundamental changes in the social environment will be needed to combat this emerging public-health crisis.


title: LoRaWAN in the Wild: Measurements from The Things Network

abstract: The Long-Range Wide-Area Network (LoRaWAN) specification was released in 2015, primarily to support the Internet-of-Things by facilitating wireless communication over long distances. Since 2015, the role-out and adoption of LoRaWAN has seen a steep growth. To the best of our knowledge, we are the first to have extensively measured, analyzed, and modeled the performance, features, and use cases of an operational LoRaWAN, namely The Things Network. Our measurement data, as presented in this paper, cover the early stages up to the production-level deployment of LoRaWAN. In particular, we analyze packet payloads, radio-signal quality, and spatio-temporal aspects, to model and estimate the performance of LoRaWAN. We also use our empirical findings in simulations to estimate the packet-loss.


title: Detecting web attacks with end-to-end deep learning

abstract: Web applications are popular targets for cyber-attacks because they are network-accessible and often contain vulnerabilities. An intrusion detection system monitors web applications and issues alerts when an attack attempt is detected. Existing implementations of intrusion detection systems usually extract features from network packets or string characteristics of input that are manually selected as relevant to attack analysis. Manually selecting features, however, is time-consuming and requires in-depth security domain knowledge. Moreover, large amounts of labeled legitimate and attack request data are needed by supervised learning algorithms to classify normal and abnormal behaviors, which is often expensive and impractical to obtain for production web applications. This paper provides three contributions to the study of autonomic intrusion detection systems. First, we evaluate the feasibility of an unsupervised/semi-supervised approach for web attack detection based on the Robust Software Modeling Tool (RSMT), which autonomically monitors and characterizes the runtime behavior of web applications. Second, we describe how RSMT trains a stacked denoising autoencoder to encode and reconstruct the call graph for end-to-end deep learning, where a low-dimensional representation of the raw features with unlabeled request data is used to recognize anomalies by computing the reconstruction error of the request data. Third, we analyze the results of empirically testing RSMT on both synthetic datasets and production applications with intentional vulnerabilities. Our results show that the proposed approach can efficiently and accurately detect attacks, including SQL injection, cross-site scripting, and deserialization, with minimal domain knowledge and little labeled training data.


title: Breaking and Fixing Origin-Based Access Control in Hybrid Web/Mobile Application Frameworks.

abstract: Hybrid mobile applications (apps) combine the features of Web applications and “native” mobile apps. Like Web applications, they are implemented in portable, platform-independent languages such as HTML and JavaScript. Like native apps, they have direct access to local device resources—file system, location, camera, contacts, etc. ::: ::: Hybrid apps are typically developed using hybrid application frameworks such as PhoneGap. The purpose of the framework is twofold. First, it provides an embedded Web browser (for example, WebView on Android) that executes the app's Web code. Second, it supplies “bridges” that allow Web code to escape the browser and access local resources on the device. ::: ::: We analyze the software stack created by hybrid frameworks and demonstrate that it does not properly compose the access-control policies governing Web code and local code, respectively. Web code is governed by the same origin policy, whereas local code is governed by the access-control policy of the operating system (for example, user-granted permissions in Android). The bridges added by the framework to the browser have the same local access rights as the entire application, but are not correctly protected by the same origin policy. This opens the door to fracking attacks, which allow foreign-origin Web content included into a hybrid app (e.g., ads confined in iframes) to drill through the layers and directly access device resources. Fracking vulnerabilities are generic: they affect all hybrid frameworks, all embedded Web browsers, all bridge mechanisms, and all platforms on which these frameworks are deployed. ::: ::: We study the prevalence of fracking vulnerabilities in free Android apps based on the PhoneGap framework. Each vulnerability exposes sensitive local resources—the ability to read and write contacts list, local files, etc.—to dozens of potentially malicious Web domains. We also analyze the defenses deployed by hybrid frameworks to prevent resource access by foreign-origin Web content and explain why they are ineffectual. ::: ::: We then present NoFrak, a capability-based defense against fracking attacks. NoFrak is platform-independent, compatible with any framework and embedded browser, requires no changes to the code of the existing hybrid apps, and does not break their advertising-supported business model.


title: A Survey on Deep Transfer Learning

abstract: As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation, which limits its development. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data. This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications. We defined deep transfer learning, category and review the recent research works based on the techniques used in deep transfer learning.


title: Robust sliding mode control of three-phase voltage source PWM rectifier with uncertainties and disturbances

abstract: This paper investigates the sliding mode control of three-phase voltage source PWM rectifier (VSR). When the three-phase VSR system is applied in different environmental conditions, external disturbances are inevitable and stochastic, so these conditions should be taken into consideration when designing the controllers. To have a good performance and a fast response, the whole closed loop control is designed in two parts: outer voltage loop control and inner current loop control, to achieve the aim of well controlled output dc-bus voltage, sinusoidal input currents at AC mains, controlled power factor and bidirectional power flow, etc. Simulation results are given to demonstrate the effectiveness of the proposed control method and it is in good agreement with the desired control performance. Therefore, the proposed sliding mode control in this paper presents new insights concerning the control of three-phase VSR in the presence of uncertainties and external disturbances and hence has a wide range of applications with good robustness.


title: Stitching and Surface Reconstruction From Endoscopic Image Sequences: A Review of Applications and Methods

abstract: Endoscopic procedures form part of routine clinical practice for minimally invasive examinations and interventions. While they are beneficial for the patient, reducing surgical trauma and making convalescence times shorter, they make orientation and manipulation more challenging for the physician, due to the limited field of view through the endoscope. However, this drawback can be reduced by means of medical image processing and computer vision, using image stitching and surface reconstruction methods to expand the field of view. This paper provides a comprehensive overview of the current state of the art in endoscopic image stitching and surface reconstruction. The literature in the relevant fields of application and algorithmic approaches is surveyed. The technological maturity of the methods and current challenges and trends are analyzed.


title: Cloud-Centric Assured Information Sharing

abstract: In this paper we describe the design and implementation of cloud-based assured information sharing systems. In particular, we will describe our current implementation of a centralized cloud-based assured information sharing system and the design of a decentralized hybrid cloud-based assured information sharing system of the future. Our goal is for coalition organizations to share information stored in multiple clouds and enforce appropriate policies.


title: Adaptive Nonlinear Finite Elements for Deformable Body Simulation Using Dynamic Progressive Meshes

abstract: Realistic behavior of deformable objects is essential for many applications such as simulation for surgical training. Existing techniques of deformable modeling for real time simulation have either used approximate methods that are not physically accurate or linear methods that do not produce reasonable global behavior. Nonlinear finite element methods (FEM) are globally accurate, but conventional FEM is not real time. In this paper, we apply nonlinear FEM using mass lumping to produce a diagonal mass matrix that allows real time computation. Adaptive meshing is necessary to provide sufficient detail where required while minimizing unnecessary computation. We propose a scheme for mesh adaptation based on an extension of the progressive mesh concept, which we call dynamic progressive meshes.


title: College Students' Social Networking Experiences on Facebook

abstract: Millions of contemporary young adults use social networking sites. However, little is known about how much, why, and how they use these sites. In this study, 92 undergraduates completed a diary-like measure each day for a week, reporting daily time use and responding to an activities checklist to assess their use of the popular social networking site, Facebook. At the end of the week, they also completed a follow-up survey. Results indicated that students use Facebook approximately 30 min throughout the day as part of their daily routine. Students communicated on Facebook using a one-to-many style, in which they were the creators disseminating content to their friends. Even so, they spent more time observing content on Facebook than actually posting content. Facebook was used most often for social interaction, primarily with friends with whom the students had a pre-established relationship offline. In addition to classic identity markers of emerging adulthood, such as religion, political ideology, and work, young adults also used media preferences to express their identity. Implications of social networking site use for the development of identity and peer relationships are discussed.


title: Author manuscript, published in "Journal of Parallel and Distributed Computing (2011)" A Parallel Bi-objective Hybrid Metaheuristic for Energy-aware Scheduling for Cloud Computing Systems

abstract: In this paper, we investigate the problem of scheduling precedence-constrained parallel applications on heterogeneous computing systems (HCSs) like cloud computing infrastructures. This kind of application was studied and used in many research works. Most of these works propose algorithms to minimize the completion time (makespan) without paying much attention to energy consumption. We propose a new parallel bi-objective hybrid genetic algorithm that takes into account, not only makespan, but also energy consumption. We particularly focus on the island parallel model and the multi-start parallel model. Our new method is based on dynamic voltage scaling (DVS) to minimize energy consumption. In terms of energy consumption, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of completion time, the obtained schedules are also shorter than those of other algorithms. Furthermore, our study demonstrates the potential of DVS.


title: Core curricula for postdoctoral dental students: recent problems, potential solutions, and a model for the future.

abstract: Development of common core curricula for the graduate advanced education/specialty programs in dental schools presents significant challenges. Similarities in graduate education accreditation standards justify such an approach, yet a core curriculum is difficult to achieve for a variety of reasons including scheduling constraints and the capacity of a common, single pathway curriculum to address the specific educational needs of postgraduate students in different disciplines. Additionally, many dental schools are experiencing severe shortages of qualified faculty to provide graduate program instruction. There are no previous reports regarding graduate core curricula and the definition/delivery of such core curricula in advanced education programs in dentistry although there are several reports in the medical literature that support the educational value of a unified core curriculum implemented in a modular format. Graduate curricula are typically designed to provide residents with advanced education/training beyond what is acquired during their predoctoral dental school experience. Advanced education programs must emphasize knowledge and skills that are discipline-specific; however, there is a large amount of common foundational material within the early phases of these programs. Dental schools have attempted to identify and present this common material within the context of an organized shared set of courses/seminars where residents from each advanced education program are scheduled simultaneously. However, there have been problems with the implementation of a shared core curricula including the following: 1) dissimilar educational backgrounds/abilities among residents; 2) relevance of material to all residents; 3) lack of central management; 4) scheduling conflicts; and 5) lack of adequate and consistent program evaluation. In an attempt to resolve these problems, a new comprehensive graduate core curriculum was implemented at the Marquette University School of Dentistry in 2000. This core curriculum was designed to 1) be user-friendly; 2) allow flexibility; 3) meet specific programmatic/accreditation needs for each advanced education program; and 4) provide assessment tools for continuous resident feedback and curriculum improvement. Assessment data obtained from residents, faculty, and program directors indicate that this approach has been successful and has transformed graduate education at Marquette. Thus, this model may prove useful for other institutions seeking to refine or develop graduate core curricula.


SNODGRASS Procedure A Versatile Technique for Various Types of Hypospadias Repair


title: How Far Can You Get By Combining Change Detection Algorithms?

abstract: Given the existence of many change detection algorithms, each with its own peculiarities and strengths, we propose a combination strategy, that we termed IUTIS (In Unity There Is Strength), based on a genetic Programming framework. This combination strategy is aimed at leveraging the strengths of the algorithms and compensate for their weakness. In this paper we show our findings in applying the proposed strategy in two different scenarios. The first scenario is purely performance-based. The second scenario performance and efficiency must be balanced. Results demonstrate that starting from simple algorithms we can achieve comparable results with respect to more complex state-of-the-art change detection algorithms, while keeping the computational complexity affordable for real-time applications.


title: Low-loss ultra-wideband transition between conductor-backed coplanar waveguide and substrate integrated waveguide

abstract: A novel transition between conductor-backed coplanar waveguide (CBCPW) and substrate integrated waveguide (SIW) is presented for microwave and millimeter-wave integrated circuit design. The proposed integrated transition that can provide simultaneous field and impedance matching, exhibits outstanding low-loss performances over an ultra-wideband range (entire Ka-band in our case). In this work, a generalized impedance inverter whose parameters are accurately extracted by the use of a numerical Thru-Reflection-Line (TRL) calibration technique is utilized to design the transition. Measured results for the fabricated transition in back-to-back configuration show that the insertion loss is better than 0.4 dB while the return loss is better than 20 dB over the entire Ka-band.


title: Quantitative analysis of perchlorate in extracts of whole fish homogenates by ion chromatography: comparison of suppressed conductivity detection and electrospray ionization mass spectrometry.

abstract: The perchlorate anion (ClO4-) is an anthropogenic contaminant of increasing concern in water supplies, and has been shown to disrupt thyroid activity. Most perchlorate analyses are currently carried out by ion chromatography (IC) with suppressed conductivity detection (SCD). While this procedure has been demonstrated to provide acceptable performance for analysis of water samples, the determination of perchlorate in high-conductivity aqueous extracts of plant or animal material is not readily accomplished by IC-SCD unless lengthy cleanup protocols are applied. With the addition of electrospray ionization mass spectrometry (ESI-MS) to IC, it was hypothesized that the interference imposed by various ionic species could be significantly reduced without the need for purification; however, the analysis of perchlorate in relatively unpurified extracts of biologically derived homogenates by IC-ESI-MS has not previously been described in the literature. The research presented here represents a comparison of the capabilities of IC-SCD and IC-ESI-MS to detect perchlorate in reagent water and in crude extracts of perchlorate-exposed fish (threespine stickleback, Gasterosteus aculeatus). ESI-MS was found to compare favorably to SCD for the detection of perchlorate in deionized water, and to exceed SCD performance in perchlorate analysis of fish-derived extracts.


title: Performance evaluation of ADV DSR & GOD in VANET for city & highway scenario

abstract: VANET has grasp the attention of various researchers in this field due to its wide range of applications in different fields i.e comfort, safety and entertainment. It is very expensive to test every network protocol or any network algorithm in real network by connecting a number of routers, computers and data links. Thus, in this paper QOS parameters like throughput, packet drop and collision rate for different routing protocols performed on simulator to prevent from damage or unpredictable result and without spending money.


title: Fish sedation, analgesia, anesthesia, and euthanasia: considerations, methods, and types of drugs.

abstract: Fish display robust neuroendocrine and physiologic stress responses to noxious stimuli. Many anesthetic, sedative, or analgesic drugs used in other vertebrates reduce stress in fish, decrease handling trauma, minimize movement and physiologic changes in response to nociceptive stimuli, and can be used for euthanasia. But extrapolating from limited published anesthetic and sedative data to all fish species is potentially harmful because of marked anatomic, physiologic, and behavioral variations; instead, a stepwise approach to anesthetizing or sedating unfamiliar species or using unproven drugs for familiar species is advisable. Additionally, knowledge of how water quality influences anesthesia or sedation helps limit complications. The most common method of drug administration is through immersion, a technique analogous to gaseous inhalant anesthesia in terrestrial animals, but the use of injectable anesthetic and sedative agents (primarily intramuscularly, but also intravenously) is increasing. Regardless of the route of administration, routine preprocedural preparation is appropriate, to stage both the animals and the supplies for induction, maintenance, and recovery. Anesthetic and sedation monitoring and resuscitation are similar to those for other vertebrates. Euthanasia is most commonly performed using an overdose of an immersion drug but injectable agents are also effective. Analgesia is an area in need of significant research as only a few studies exist and they provide some contrasting results. However, fish have mu and kappa opiate receptors throughout the brain, making it reasonable to expect some effect of at least opioid treatments in fish experiencing noxious stimuli.


title: On partial contraction analysis for coupled nonlinear oscillators

abstract: We describe a simple yet general method to analyze networks of coupled identical nonlinear oscillators and study applications to fast synchronization, locomotion, and schooling. Specifically, we use nonlinear contraction theory to derive exact and global (rather than linearized) results on synchronization, antisynchronization, and oscillator death. The method can be applied to coupled networks of various structures and arbitrary size. For oscillators with positive definite diffusion coupling, it can be shown that synchronization always occurs globally for strong enough coupling strengths, and an explicit upper bound on the corresponding threshold can be computed through eigenvalue analysis. The discussion also extends to the case when network structure varies abruptly and asynchronously, as in "flocks" of oscillators or dynamic elements.


title: Clustering Algorithms in Biomedical Research: A Review

abstract: Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples including gene expression data analysis, genomic sequence analysis, biomedical document mining, and MRI image analysis. However, due to the diversity of cluster analysis, the differing terminologies, goals, and assumptions underlying different clustering algorithms can be daunting. Thus, determining the right match between clustering algorithms and biomedical applications has become particularly important. This paper is presented to provide biomedical researchers with an overview of the status quo of clustering algorithms, to illustrate examples of biomedical applications based on cluster analysis, and to help biomedical researchers select the most suitable clustering algorithms for their own applications.


Why is Speech Recognition Difficult?


title: Curve-Skeleton Extraction Using Iterative Least Squares Optimization

abstract: A curve skeleton is a compact representation of 3D objects and has numerous applications. It can be used to describe an object's geometry and topology. In this paper, we introduce a novel approach for computing curve skeletons for volumetric representations of the input models. Our algorithm consists of three major steps: 1) using iterative least squares optimization to shrink models and, at the same time, preserving their geometries and topologies, 2) extracting curve skeletons through the thinning algorithm, and 3) pruning unnecessary branches based on shrinking ratios. The proposed method is less sensitive to noise on the surface of models and can generate smoother skeletons. In addition, our shrinking algorithm requires little computation, since the optimization system can be factorized and stored in the precomputational step. We demonstrate several extracted skeletons that help evaluate our algorithm. We also experimentally compare the proposed method with other well-known methods. Experimental results show advantages when using our method over other techniques.


title: Proofs of retrievability: theory and implementation

abstract: A proof of retrievability (POR) is a compact proof by a file system (prover) to a client (verifier) that a target file F is intact, in the sense that the client can fully recover it. As PORs incur lower communication complexity than transmission of F itself, they are an attractive building block for high-assurance remote storage systems. In this paper, we propose a theoretical framework for the design of PORs. Our framework improves the previously proposed POR constructions of Juels-Kaliski and Shacham-Waters, and also sheds light on the conceptual limitations of previous theoretical models for PORs. It supports a fully Byzantine adversarial model, carrying only the restriction---fundamental to all PORs---that the adversary's error rate be bounded when the client seeks to extract F. We propose a new variant on the Juels-Kaliski protocol and describe a prototype implementation. We demonstrate practical encoding even for files F whose size exceeds that of client main memory.


title: Brain ventricle volume correlates with effortful control in healthy young males

abstract: The neuroanatomical correlates of temperament in human have been examined extensively but not comprehensively. Most studies have focused on certain brain regions of gray and white matter, such as prefrontal cortex, amygdala, anterior cingulate cortex, etc, however, less is known about the contribution of other areas to human temperament [1-4]. Previous studies have shown that brain ventricle is related to psychiatric diseases, which exemplify problems in self-control, here we sought to explore whether the size of brain ventricle is associated with individual differences in temperament in healthy college students. ::: ::: Thirty-eight subjects (21 males, 17 females, age 18-23 years) were selected randomly, and scanned using a three-dimensional T1-weighted magnetization-prepared gradient echo (MPRAGE) sequence on a Siemens 3T Allegra scanner to acquire anatomic data, which were then segmented by FreeSufer and calculated for volume. Individual temperament was assessed using the Adult Temperament Questionnaire (ATQ)[5], and scores of each scale were then correlated with the ventricular volume. ::: ::: We found a positive correlation between the volume of fourth ventricle and effortful control (one of the four core temperament dimensions in ATQ) in males (r=0.485, p=0.026), suggesting that males with larger fourth ventricle volume, have great capacity to focus and direct attention, and inhibit inappropriate behavior. However, no significant correlation was detected in females.


title: Low cost low loss waveguide-fed patch antenna array for automotive radar system

abstract: This paper presents a low cost high gain patch antenna array for automotive radar applications. A compact wide band (9.6GHz) low loss (0.5dB at 79GHz) micro-strip line to waveguide transition is also proposed for the antenna testing. The proposed antenna has a measured gain of 14.5dBi at 79GHz. The proposed antenna (including the transition) has been fabricated using a single dielectric layer PCB process with a total length of 35mm.


title: Liquid chromatographic–mass spectrometric determination of post-harvest fungicides in citrus fruits

abstract: Liquid chromatography (LC)–atmospheric pressure ionisation (API)–mass spectrometry (MS) has been used to determine residues of five fungicides in oranges with a minimum sample cleanup. Atmospheric pressure chemical ionisation (APCI) and electrospray (ES) were compared and both gave similar results in terms of sensitivity and structural information. The main ions were [M+H]+for carbendazim, imazalil, thiophanate methyl and thiabendazole, and [M+H–C4H9NHCO]+ for benomyl. Samples were extracted with sodium sulphate and ethyl acetate. Although benomyl and thiophanate methyl were transformed through the extraction procedure to carbendazim, the method showed good precision ( 70%), except for thiophanate methyl (50%), whilst also yielding limits of detection (<0.03 mg kg−1) that are adequate for the determination of the studied fungicides in oranges.


ABSTRACT Nanotechnology and business opportunities: scenarios as awareness instrument


title: Ultra-Low Power, Process-Tolerant 10T (PT10T) SRAM with Improved Read/Write Ability for Internet of Things (IoT) Applications

abstract: In this paper, an ultra-low power (ULP) 10T static random access memory (SRAM) is presented for Internet of Things (IoT) applications, which operates at sub-threshold voltage. The proposed SRAM has the tendency to operate at low supply voltages with high static and dynamic noise margins. The IoT application requires battery-enabled low leakage memory architecture in a subthreshold regime. Therefore, to improve leakage power consumption and provide better cell stability, a power-gated robust 10T SRAM is presented in this paper. The proposed cell uses a power-gated p-MOS transistor to reduce the leakage power or static power in standby mode. Moreover, due to the stacking of n-MOS transistors in 10T SRAM latch and by separating the read path from the 10T SRAM latch, the static and dynamic noise margins in read and write operations has shown significant tolerance w.r.t. the variations in device process, voltage, and temperature (PVT) values. The proposed SRAM shows significantly improved performance in terms of leakage power, read static noise margin (RSNM), write static noise margin (WSNM), write ability or write trip point (WTP), read–write energy, and dynamic read margin (DRM). Furthermore, these parameters of the proposed cell are observed at 8-Kilo bit (Kb) SRAM and compared with existing SRAM architectures. From the Monte Carlo simulation results, it is observed that the leakage power of a proposed low threshold voltage-LVT 10T SRAM is reduced by 98.76%, 98.6%, 6.7%, and 98.2% as compared to the LVT C6T, RD8T, LP9T, and ST10T SRAM, respectively, at 0.3V VDD. Additionally, in the proposed 10T SRAM, parameters such as RSNM, WSNM, WTP, and DRM are improved by 3×, 2×, 1.11×, and 1.32×, respectively, as compared to C6T SRAM. Similarly, the proposed 10T SRAM shows an improvement of 1.48×, 1.25×, and 1.1× in RSNM, WSNM, and WTP, respectively, in the parameters as compared to RD8T SRAM at 0.3 V VDD.


title: MOSFET failure modes in the zero-voltage-switched full-bridge switching mode power supply applications

abstract: Phase-shifted zero-voltage-switching (ZVS) full bridge topologies are gaining popularity due to their extremely low switching losses in the power devices even at higher switching frequency. However the intrinsic body diode is required to conduct in order to create the ZVS turn-on rendition for the power MOSFET. Due to the extremely low reverse voltage, the reverse recovery charges might not be swept out before turning off the MOSFET. Therefore, the body diode might be subjected to the dv/dt stress when it is not yet capable of blocking reverse voltage. Also, not able to maintain the ZVS operation at low load will force the on-state MOSFET to turn off at hard-switching condition. Like in the hard-switched full bridge topology, the cdv/dt shoot-through current might produce a voltage spike at the gate of the off-state MOSFET on the same leg and cause devices failure. Several silicon technologies are presented to resolve the above-mentioned failure modes in the ZVS topology. Fast reverse recovery time and better dv/dt ruggedness make this new MOSFET technology suitable for higher frequency ZVS full-bridge applications. Inherent with extremely high silicon density and low gate charge, these new MOSFETs can reduce the component count with the same or better performance and will enable much higher power density for the next generation telecom/server SMPS designs.


title: Accelerating Viola-Jones face detection for embedded and SoC environments

abstract: In this communication a speed optimized implementation of Viola-Jones Face Detection Algorithm based on the baseline OpenCV face detection application is presented. The baseline OpenCV face detection application is analyzed. Then the necessary modifications and improvements are described in order to accelerate the execution speed in an embedded or SoC (System-on-Chip) environments.


title: Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method

abstract: Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System (ITS) and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is in combination with the graph convolutional network (GCN) and gated recurrent unit (GRU). Specifically, the graph convolutional network is used to learn complex topological structures to capture the spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic flow to capture the temporal dependence. Then, the T-GCN model is employed to forecast traffic based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatiotemporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of T-GCN is available at https://github.com/lehaifeng/T-GCN.


title: Importance of Search and Evaluation Strategies in Neural Dialogue Modeling

abstract: Search strategies for generating a response from a neural dialogue model have received relatively little attention compared to improving network architectures and learning algorithms in recent years. In this paper, we consider a standard neural dialogue model based on recurrent networks with an attention mechanism, and focus on evaluating the impact of the search strategy. We compare four search strategies: greedy search, beam search, iterative beam search and iterative beam search followed by selection scoring. We evaluate these strategies using human evaluation of full conversations and compare them using automatic metrics including log-probabilities, scores and diversity metrics. We observe a significant gap between greedy search and the proposed iterative beam search augmented with selection scoring, demonstrating the importance of the search algorithm in neural dialogue generation.


title: Flexible Design Method for Multi-Repeater Wireless Power Transfer System Based on Coupled Resonator Bandpass Filter Model

abstract: It has been verified that wireless power transfer (WPT) system with multiple relay resonators (repeaters) inserted properly between the transmitter and the receiver can increase the transmission distance significantly. Based on the coupled resonator bandpass filter model, a flexible design method for wireless power transfer system with arbitrary number of repeaters is proposed in detail. The transmission efficiency and the transferred power of the proposed wireless power transfer system are derived under the design theory of filters. It is illuminated the reason that Butterworth type is preferred type for designing WPT with multi-repeater. Moreover, the relation between transmission distance and bandwidth is examined and a technique is further introduced to increase the transmission distance. An RS=50 Ω WPT system with three repeaters is tested and another RS=0 Ω WPT system with one repeater is simulated, and the effectiveness of the proposed method is confirmed as well.


title: Nanomaterials formulations for photothermal and photodynamic therapy of cancer

abstract: Abstract Nanomaterials with well-defined size, shape, composition, and surface functionalities offer multimodal and multifunctional platforms for various bioanalytical, bioimaging, and therapeutic applications. In this review, we focus on the different theranostic formulations of nanomaterials based on gold, silver, silica, semiconductor quantum dots, upconversion lanthanides, oxide magnets, polymers, liposomes, carbon nanotubes, graphene and carbon nanohorns, and their applications in photothermal and photodynamic therapy of cancer.


title: Aging-related changes in the expression of apoptosis-associated molecules in neurosecretory cells of the mouse hypothalamus

abstract: The aim of the present work was to identify the characteristics of the control of apoptosis in neurosecretory centers of aged (18 months) mice on exposure to the immunomodulator interferon-alpha (IA) in comparison with young (two months) mice. Age-related activation of apoptosis in the supraoptic (SON) and paraventricular (PVN) nuclei was found to be mediated by different pathways and could result from changes in the ratio of synthesis of pro-(Bax) and antiapoptotic (Mcl-1, Bcl-2) molecules. In addition, the apoptosis signal cascade in young mice treated with IA was identical in both nuclei, while in aged mice there were differences between the SON and the PVN.


How to Evaluate Educational Games: a Systematic Literature Review.