Visual Event Recognition in Videos by Learning from Web Data

We propose a visual event recognition framework for consumer videos by leveraging a large amount of loosely labeled web videos (e.g., from YouTube). Observing that consumer videos generally contain large intraclass variations within the same type of events, we first propose a new method, called Aligned Space-Time Pyramid Matching (ASTPM), to measure the distance between any two video clips. Second, we propose a new transfer learning method, referred to as Adaptive Multiple Kernel Learning (A-MKL), in order to 1) fuse the information from multiple pyramid levels and features (i.e., space-time features and static SIFT features) and 2) cope with the considerable variation in feature distributions between videos from two domains (i.e., web video domain and consumer video domain). For each pyramid level and each type of local features, we first train a set of SVM classifiers based on the combined training set from two domains by using multiple base kernels from different kernel types and parameters, which are then fused with equal weights to obtain a prelearned average classifier. In A-MKL, for each event class we learn an adapted target classifier based on multiple base kernels and the prelearned average classifiers from this event class or all the event classes by minimizing both the structural risk functional and the mismatch between data distributions of two domains. Extensive experiments demonstrate the effectiveness of our proposed framework that requires only a small number of labeled consumer videos by leveraging web data. We also conduct an in-depth investigation on various aspects of the proposed method A-MKL, such as the analysis on the combination coefficients on the prelearned classifiers, the convergence of the learning algorithm, and the performance variation by using different proportions of labeled consumer videos. Moreover, we show that A-MKL using the prelearned classifiers from all the event classes leads to better performance when compared with A-MKL using the prelearned classifiers only from each individual event class.


Averaged Collapsed Variational Bayes Inference

This paper presents the Averaged CVB (ACVB) inference and offers convergence-guaranteed and practically useful fast Collapsed Variational Bayes (CVB) inferences. CVB inferences yield more precise inferences of Bayesian probabilistic models than Variational Bayes (VB) inferences. However, their convergence aspect is fairly unknown and has not been scrutinized. To make CVB more useful, we study their convergence behaviors in a empirical and practical approach. We develop a convergence-guaranteed algorithm for any CVB-based inference called ACVB, which enables automatic convergence detection and frees non-expert practitioners from the difficult and costly manual monitoring of inference processes. In experiments, ACVB inferences are comparable to or better than those of existing inference methods and deterministic, fast, and provide easier convergence detection. These features are especially convenient for practitioners who want precise Bayesian inference with assured convergence.


Measuring Machine Intelligence Through Visual Question Answering

As machines have become more intelligent, there has been a renewed interest in methods for measuring their intelligence. A common approach is to propose tasks for which a human excels, but one which machines find difficult. However, an ideal task should also be easy to evaluate and not be easily gameable. We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine’s ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.


Issues in User Authentication 1

CHI 2007 Workshop: Security user studies: methodol gies and best practices, April 2007. ABSTRACT In this brief paper we outline our recent work on u sability studies of user authentication systems. In particul ar, we have conducted studies on password managers and graphical password systems, using in-lab experiment s, interviews, and broader field studies. We then disc us some of the important issues arising, including mental m odels of security, the role of persuasion, and the nature of identify theft.


Network Traffic Management

The purposes of this paper have to discuss issues related to Network Traffic Management. A relatively new category of network management is fast becoming a necessity in converged business Networks. Mid-sized and large organizations are finding they must control network traffic behavior to assure that their strategic applications always get the resources they need to perform optimally. Controlling network traffic requires limiting bandwidth to certain applications, guaranteeing minimum bandwidth to others, and marking traffic with high or low priorities. This exercise is called Network Traffic Management.


Internal Uniplanar Antenna for LTE/GSM/UMTS Operation in a Tablet Computer

With the use of an internal printed loop matching circuit, a small-size uniplanar antenna with eight-band LTE/GSM/UMTS operation in the tablet computer is proposed. The obtained impedance bandwidth across the operating bands can reach about 262/1610 MHz for the LTE/GSM/UMTS bands, respectively. Only with the antenna size of $40\times 15\times 0.8\ {\rm mm}^{3}$, the proposed planar antenna has the compact operation with more than 30% antenna size reduction. Furthermore, the measured peak gains and antenna efficiencies are about 2.81/4.97 dBi and 79/87% for the LTE/GSM/UMTS bands, respectively.


Novice Programmers and the Problem Description Effect

It is often debated whether a problem presented in a straightforward minimalist fashion is better, or worse, for learning than the same problem presented with a "real-life" or "concrete" context. The presentation, contextualization, or "problem description" has been well studied over several decades in disciplines such as mathematics education and psychology; however, little has been published in the field of computing education. In psychology it has been found that not only the presence of context, but the type of context can have dramatic results on problem success. In mathematics education it has been demonstrated that there are non-mathematical factors in problem presentation that can affect success in solving the problem and learning. The contextual background of a problem can also impact cognitive load, which should be considered when evaluating the effects of context. Further, it has been found that regarding cognitive load, computer science has unique characteristics compared to other disciplines, with the consequence that results from other disciplines may not apply to computer science, thus requiring investigation within computer science.
 This paper presents a multi-national, multi-institutional study of the effects of problem contextualization on novice programmer success in a typical CS1 exercise.


A PVT-insensitive CMOS output driver with constant slew rate

In this paper, we propose an output driver which has constant slew rate over PVT variation. To make output driver's slew rate to be constant, rising and failing time of pre-driving node of output driver is kept constant by predriver, which has constant pull-up and pull-down path resistance. To make output voltage swing level to be constant, another replica bias circuit was used with CMOS push-pull output driver. HSPICE simulations show that the slew rate is 3.21 V/nS on fast condition and 3.12V/nS on slow condition with 1.4K pre-driver's path resistance. This circuit was implemented with 0.18 /spl mu/m CMOS process.


Sensorless vector control of asynchronous machine based on reduced order Kalman filter

The article describes sensorless vector control of the asynchronous machine based on Kalman filter. A control system block diagram is presented along with a functional scheme of the original speed observer using dynamic system state vector filter estimator. The synthesis of current regulator using machine parameters determined by inverse r model is shown. The method of a control matrix dimension reduction is proposed by removal the flux linkage calculations out of Kalman filter algorithms. Current and speed regulators, as well as a flux estimator, were implemented in the Mexbios Development Studio simulation environment. The robustness of a speed observer and the efficiency of the asynchronous machine sensorless control system were confirmed by the simulation results.


Rapid solution of problems by nuclear-magnetic resonance quantum computation

We offer an improved method for using a nuclear-magnetic-resonance quantum computer (NMRQC) to solve the Deutsch-Jozsa problem. Two known obstacles to the application of the NMRQC are exponential diminishment of density-matrix elements with the number of bits, threatening weak signal levels, and the high cost of preparing a suitable starting state. A third obstacle is a heretofore unnoticed restriction on measurement operators available for use by an NMRQC. Variations on the function classes of the Deutsch-Jozsa problem are introduced, both to extend the range of problems advantageous for quantum computation and to escape all three obstacles to use of an NMRQC. By adapting it to one such function class, the Deutsch-Jozsa problem is made solvable without exponential loss of signal. The method involves an extra work bit and a polynomially more involved Oracle; it uses the thermal-equilibrium density matrix systematically for an arbitrary number of spins, thereby avoiding both the preparation of a pseudopure state and temporal averaging.


3D Face Recognition using Kinect

2D Face recognition systems bound to fail on images with varying pose angles and occlusions. Many pose invariant methods are proposed in recent years but they are still not able to achieve very good accuracies. So in order to achieve a better accuracy we need to extend algorithms over 3D faces. Due to the high cost involved in acquisition of 3D faces we developed our approach for low-cost and low-quality Microsoft Kinect Sensor and propose an algorithm to produce better results than existing 2D Face recognition techniques even after compromising on the quality of the images from the sensor. Our proposed algorithm is based on modified SURF descriptors on RGB images combined with various enhancements on automatically generated training images using Depth and Color images. We compare our results obtained with State Of The Art Techniques obtained on publicly available RGB-D Face databases. Our System obtained recognition rate of 98.07% on 30° CurtinFace Database, 89.28% on EURECOM Database, 98.00% on 15° Internal Database and 81.00% on 30° Internal Database.


Metaheuristics in combinatorial optimization: Overview and conceptual comparison

The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.


AoA and ToA Accuracy for Antenna Arrays in Dense Multipath Channels

The accuracy that can be achieved in time of arrival (ToA) estimation strongly depends on the utilized signal bandwidth. In an indoor environment multipath propagation usually causes a degradation of the achievable accuracy due to the overlapping signals. A similar effect can be observed for the angle of arrival (AoA) estimation using arrays. This paper derives a closed-form equation for the Cramer-Rae lower bound (CRLB) of the achievable AoA and the ToA error variances, considering the presence of dense multipath. The Fisher information expressions for both parameters allows an evaluation of the influence of channel parameters and system parameters such as the array geometry. Our results demonstrate that the AoA estimation accuracy is strongly related to the signal bandwidth, due to the multipath influence. The theoretical results are evaluated with experimental data.


MODEL DIFFERENCES IN THE ECLIPSE MODELING FRAMEWORK

Increasingly, recording the various kinds of design-level structural evolution that a system undergoes throughout its entire life-cycle is gaining a fundamental importance and cannot be neglected in software modeling and development. In this respect, an interesting and useful operation between the designs of subsequent system versions is the difference management consisting in calculation, representation, and visualization. This work presents EMF Compare, an approach to model difference calculation and representation for the Eclipse modeling framework. Apart from leveraging the rank of model differences to that of first-class artifacts according to the “everything is a model” principle, the approach presents several properties which are discussed according to a conceptual framework.


A Blended Deep Learning Approach for Predicting User Intended Actions

User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on predicting attrition, which is one of typical user intended actions. Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. It integrates user activity logs, dynamic and static user profiles based on multi-path learning. It exploits historical user records by establishing a decaying multi-snapshot technique. And finally it employs the precedent user intentions via guiding them to the subsequent learning procedure. As a result, it addresses all disadvantages of conventional methods. We evaluate our methodology on two public data repositories and one private user usage dataset provided by Adobe Creative Cloud. The extensive experiments demonstrate that it can offer the appealing performance in comparison with several existing approaches as rated by different popular metrics. Furthermore, we introduce an advanced interpretation and visualization strategy to effectively characterize the periodicity of user activity logs. It can help to pinpoint important factors that are critical to user attrition and retention and thus suggests actionable improvement targets for business practice. Our work will provide useful insights into the prediction and elucidation of other user intended actions as well.


OINT C LOUDS

Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep autoencoder (AE) network with state-of-the-art reconstruction quality and generalization ability. The learned representations outperform existing methods on 3D recognition tasks and enable basic shape editing via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation. We perform a thorough study of different generative models including: GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space of our AEs and, Gaussian mixture models (GMM). For our quantitative evaluation we propose measures of sample fidelity and diversity based on matchings between sets of point clouds. Interestingly, our careful evaluation of generalization, fidelity and diversity reveals that GMMs trained in the latent space of our AEs produce the best results.


Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization

Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.


Enterprise systems implementation and accounting benefits

Purpose – The aim of this paper is to examine the accounting benefits involved in adopting enterprise systems, but also to comprehend the underlying causes that motivate their adoption. Design/methodology/approach – This paper presents evidence from a survey of 73 companies which implemented ES on the related benefits to their accounting information provision and practices. Findings – Empirical evidence confirms a number of benefits derived from the application of ES that focus on the following dimensions: organisation, operations, management and IT infrastructure. These benefits are related both to the reasons leading to the implementation of ES and to the relevant selection of ES modules. Originality/value – The corresponding results from this survey can then be used as a basis for establishing the best approach in order to fully exploit the potential of future ES applications in accounting practice.


Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction

In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is proposed for the detection and localization of myocardial infarction (MI) from multilead electrocardiogram (ECG). Wavelet decomposition of multilead ECG signals grossly segments the clinical components at different subbands. In MI, pathological characteristics such as hypercute T-wave, inversion of T-wave, changes in ST elevation, or pathological Q-wave are seen in ECG signals. This pathological information alters the covariance structures of multiscale multivariate matrices at different scales and the corresponding eigenvalues. The clinically relevant components can be captured by eigenvalues. In this study, multiscale wavelet energies and eigenvalues of multiscale covariance matrices are used as diagnostic features. Support vector machines (SVMs) with both linear and radial basis function (RBF) kernel and K-nearest neighbor are used as classifiers. Datasets, which include healthy control, and various types of MI, such as anterior, anteriolateral, anterioseptal, inferior, inferiolateral, and inferioposterio-lateral, from the PTB diagnostic ECG database are used for evaluation. The results show that the proposed technique can successfully detect the MI pathologies. The MEES approach also helps localize different types of MIs. For MI detection, the accuracy, the sensitivity, and the specificity values are 96%, 93%, and 99% respectively. The localization accuracy is 99.58%, using a multiclass SVM classifier with RBF kernel.


Secure Logging as a Service—Delegating Log Management to the Cloud

Securely maintaining log records over extended periods of time is very important to the proper functioning of any organization. Integrity of the log files and that of the logging process need to be ensured at all times. In addition, as log files often contain sensitive information, confidentiality and privacy of log records are equally important. However, deploying a secure logging infrastructure involves substantial capital expenses that many organizations may find overwhelming. Delegating log management to the cloud appears to be a viable cost saving measure. In this paper, we identify the challenges for a secure cloud-based log management service and propose a framework for doing the same.


Revenue : 229 B Revenue : ? Birth Year : ? Height : ? Weight : 70

Knowledge bases (KB) are often represented as a collection of facts in the form (head, predicate, tail), where head and tail are entities while predicate is a binary relationship that links the two. It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction. However, though frequently ignored, these repositories also contain numerical facts. Numerical facts link entities to numerical values via numerical predicates; e.g., (Paris, latitude, 48.8). Likewise, numerical facts also suffer from the incompleteness problem. To address this issue, we introduce the numerical attribute prediction problem. This problem involves a new type of query where the relationship is a numerical predicate. Consequently, and contrary to link prediction, the answer to this query is a numerical value. We argue that the numerical values associated with entities explain, to some extent, the relational structure of the knowledge base. Therefore, we leverage knowledge base embedding methods to learn representations that are useful predictors for the numerical attributes. An extensive set of experiments on benchmark versions of Freebase and Yago show that our approaches largely outperform sensible baselines. We make the datasets available under a permissive BSD-3 license.


Severely suppressed bone turnover: a potential complication of alendronate therapy.

Alendronate, an inhibitor of bone resorption, is widely used in osteoporosis treatment. However, concerns have been raised about potential oversuppression of bone turnover during long-term use. We report on nine patients who sustained spontaneous nonspinal fractures while on alendronate therapy, six of whom displayed either delayed or absent fracture healing for 3 months to 2 yr during therapy. Histomorphometric analysis of the cancellous bone showed markedly suppressed bone formation, with reduced or absent osteoblastic surface in most patients. Osteoclastic surface was low or low-normal in eight patients, and eroded surface was decreased in four. Matrix synthesis was markedly diminished, with absence of double-tetracycline label and absent or reduced single-tetracycline label in all patients. The same trend was seen in the intracortical and endocortical surfaces. Our findings raise the possibility that severe suppression of bone turnover may develop during long-term alendronate therapy, resulting in increased susceptibility to, and delayed healing of, nonspinal fractures. Although coadministration of estrogen or glucocorticoids appears to be a predisposing factor, this apparent complication can also occur with monotherapy. Our observations emphasize the need for increased awareness and monitoring for the potential development of excessive suppression of bone turnover during long-term alendronate therapy.


Risk factors for shoulder and elbow injuries in adolescent baseball pitchers.

BACKGROUND
There is little evidence supporting current safety recommendations for adolescent pitchers.

HYPOTHESIS
Pitching practices of adolescent pitchers without history of arm injury will be significantly different from those of adolescent pitchers who required shoulder or elbow surgery.

STUDY DESIGN
Case control study; Level of evidence, 3.

METHODS
Ninety-five adolescent pitchers who had shoulder or elbow surgery and 45 adolescent pitchers who never had a significant pitching-related injury completed a survey. Responses were compared between the 2 groups using t tests and chi(2) analyses. Multivariable logistic regression models were developed to identify the risk factors.

RESULTS
The injured group pitched significantly more months per year, games per year, innings per game, pitches per game, pitches per year, and warm-up pitches before a game. These pitchers were more frequently starting pitchers, pitched in more showcases, pitched with higher velocity, and pitched more often with arm pain and fatigue. They also used anti-inflammatory drugs and ice more frequently to prevent an injury. Although the groups were age matched, the injured group was taller and heavier. There were no significant differences regarding private pitching instruction, coach's chief concern, pitcher's self-rating, exercise programs, stretching practices, relieving frequency, pitch type frequency, or age at which pitch types were first thrown.

CONCLUSION
Pitching practices were significantly different between the groups. The factors with the strongest associations with injury were overuse and fatigue. High pitch velocity and participation in showcases were also associated with increased risk for injury.

CLINICAL RELEVANCE
New recommendations were made based on these results. Adherence to the recommendations may reduce the incidence of significant injury to adolescent pitchers.


Christopher Alexander's fifteen properties: Toward developing evaluation metrics for security visualizations

The objective of security data visualizations is to help cyber analysts to perceive trends and patterns, and gain insights into security data. Sound and systematic evaluations of security data visualizations are rarely performed, in part due to the lack of proper quantitative and qualitative measures. In this paper, we present a novel evaluation approach for security visualization based on Christopher Alexander's fifteen properties of living structures. Alexander's fifteen properties are derived from various visual patterns that appear in nature. Each property represents the guidelines for good design. We believe that using these fundamental properties have the potential for building a more robust evaluation. Each property offers essential qualities that enable better analytical reasoning. We demonstrate how to use Alexander's properties to evaluate security related visualizations. We derive a set of visualization-specific properties for evaluation, developed based on Alexander's original properties.


Influence Maximization for Complementary Goods: Why Parties Fail to Cooperate?

We consider the problem where companies provide different types of products and want to promote their products through viral marketing simultaneously. Most previous works assume products are purely competitive. Different from them, our work considers that each product has a pairwise relationship which can be from strongly competitive to strongly complementary to each other's product. The problem is to maximize the spread size with the presence of different opponents with different relationships on the network. We propose Interacting Influence Maximization (IIM) game to model such problems by extending the model of the Competitive Influence Maximization (CIM) game studied by previous works, which considers purely competitive relationship. As for the theoretical approach, we prove that the Nash equilibrium of highly complementary products of different companies may still be very inefficient due to the selfishness of companies. We do so by introducing a well-known concept in game theory, called Price of Stability (PoS) of the extensive-form game. We prove that in any k selfish players symmetric complementary IIM game, the overall spread of the products can be reduced to as less as 1/k of the optimal spread. Since companies may fail to cooperate with one another, we propose different competitive objective functions that companies may consider and deal with separately. We propose a scalable strategy for maximizing influence differences, called TOPBOSS that is guaranteed to beat the first player in a single-round two-player second-move game. In the experiment, we first propose a learning method to learn the ILT model, which we propose for IIM game, from both synthetic and real data to validate the effectiveness of ILT. We then exhibit that the performance of several heuristic strategies in the traditional influence maximization problem can be improved by acquiring the knowledge of the existence of competitive/complementary products in the network. Finally, we compare the TOPBOSS with different heuristic algorithms in real data and demonstrate the merits of TOPBOSS.


The Polar Parallel Coordinates Method for Time-Series Data Visualization

Parallel coordinates is a very important visualization method, but the dimensions it can express are limited by the length or width of the screen. In this paper, we present the polar parallel coordinates method. First, we define the polar parallel coordinates as the coordinate whose axes exist between the same degree and show the expression of the axis. We give the algorithms for the polar parallel coordinates generation and data shown in the polar parallel coordinates. Then we point out two feathers of the polar parallel coordinates. It can express more axes than parallel coordinates and reflect the characteristics of time-series data. Based on this, we compare the parallel coordinates with the polar parallel coordinates by the time-series data visualization.


Empirically Evaluating the Quality of Automatically Generated and Manually Written Test Suites

The creation, execution, and maintenance of tests are some of the most expensive tasks in software development. To help reduce the cost, automated test generation tools can be used to assist and guide developers in creating test cases. Yet, the tests that automated tools produce range from simple skeletons to fully executable test suites, hence their complexity and quality vary. This paper compares the complexity and quality of test suites created by sophisticated automated test generation tools to that of developer-written test suites. The empirical study in this paper examines ten real-world programs with existing test suites and applies two state-of-the-art automated test generation tools. The study measures the resulting test suite quality in terms of code coverage and fault-finding capability. On average, manual tests covered 31.5% of the branches while the automated tools covered 31.8% of the branches. In terms of mutation score, the tests generated by automated tools had an average mutation score of 39.8% compared to the average mutation score of 42.1% for manually written tests. Even though automatically created tests often contain more lines of source code than those written by developers, this paper's empirical results reveal that test generation tools can provide value by creating high quality test suites while reducing the cost and effort needed for testing.


Ranking scientific publications: the effect of nonlinearity

Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.


GradNets: Dynamic Interpolation Between Neural Architectures

In machine learning, there is a fundamental trade-off between ease of optimization and expressive power. Neural Networks, in particular, have enormous expressive power and yet are notoriously challenging to train. The nature of that optimization challenge changes over the course of learning. Traditionally in deep learning, one makes a static trade-off between the needs of early and late optimization. In this paper, we investigate a novel framework, GradNets, for dynamically adapting architectures during training to get the benefits of both. For example, we can gradually transition from linear to non-linear networks, deterministic to stochastic computation, shallow to deep architectures, or even simple downsampling to fully differentiable attention mechanisms. Benefits include increased accuracy, easier convergence with more complex architectures, solutions to test-time execution of batch normalization, and the ability to train networks of up to 200 layers.


A Survey on NoSQL Stores

Recent demands for storing and querying big data have revealed various shortcomings of traditional relational database systems. This, in turn, has led to the emergence of a new kind of complementary nonrelational data store, named as NoSQL. This survey mainly aims at elucidating the design decisions of NoSQL stores with regard to the four nonorthogonal design principles of distributed database systems: data model, consistency model, data partitioning, and the CAP theorem. For each principle, its available strategies and corresponding features, strengths, and drawbacks are explained. Furthermore, various implementations of each strategy are exemplified and crystallized through a collection of representative academic and industrial NoSQL technologies. Finally, we disclose some existing challenges in developing effective NoSQL stores, which need attention of the research community, application designers, and architects.


Distributed Manufacturing Scheduling Using Intelligent Agents

reflect the temporal relationships between manufacturing activities and the capacity limitations of a set of shared resources. The allocation also affects a schedule's opti-mality with respect to criteria such as cost, lateness, or throughput. The globalization of manufacturing makes such optimization increasingly important. To survive in this competitive market, manufacturing enterprises must increase their productivity and profitability through greater shop floor agility. Agent-based manufacturing scheduling systems are a promising way to provide this optimization. Scheduling problems are not exclusive to manufacturing systems. Similar situations happen routinely in publishing houses, universities, hospitals, airports, transportation companies, and so on. Scheduling problems are typically NP-hard; that is, finding an optimal solution is impossible without using an essentially enumerative algorithm , and the computation time increases exponentially with the problem size. Manufacturing scheduling is one of the most difficult scheduling problems. A well-known manufacturing scheduling problem is classical job shop scheduling, which involves a set of jobs and a set of machines. Each machine can handle at most one job at a time. Each job consists of a chain of operations , each of which must be processed during an uninterrupted time period of given length on a given machine. The purpose is to find a best schedule—that is, an allocation of the operations to time intervals on the machines that has the minimum duration required to complete all jobs. The total possible solutions for this problem with n jobs and m machines is (n!) m. The problem becomes even more complex when it includes other variables. Additional resources In a real manufacturing enterprise, production managers or human schedulers must consider all kinds of manufacturing resources, not just jobs and machines. For example, a classical job shop scheduling problem with n jobs, m machines, and k operators could have ((n!) m) k possible solutions. Simultaneous planning and scheduling Traditional approaches separating process planning and scheduling can obtain suboptimal solutions at two separate phases. Global optimization of a manufacturing system is only possible when process planning and scheduling are integrated. However, this makes the scheduling problem much more difficult to solve. Unforeseen dynamic situations In a job shop manufacturing environment, things rarely go as expected. Scheduled jobs get canceled and new jobs are inserted. Certain resources become unavailable and additional resources are introduced. A scheduled task takes more or less time than anticipated, and tasks arrive early or late. Other uncertainties include power system failures, …


A Compact Wideband Dual-Polarized Slot Antenna With Five Resonances

This letter presents the design and analysis of a compact dual-polarized slot antenna using a triple-mode stepped slot-line resonator and a dual-mode cross-shaped monopole. The orthogonal polarization is achieved by feeding the two ports symmetrically at the opposite positions of the slot. All the five resonance frequencies can be flexibly adjusted by tuning different dimension parameters, so by increasing the frequency difference between the five resonance modes, a very wide common operating band (1.63–2.93 GHz) is obtained and also with a high port isolation (< $-$32 dB). Finally, by loading two grounded lumped capacitors on the cusp sections of the monopole, its second resonance frequency can be independently reconfigured.


Online social networking addiction among college students in Singapore: Comorbidity with behavioral addiction and affective disorder.

PURPOSE OF THE RESEARCH
This study aimed to determine the prevalence of addiction to social networking sites/platforms (SNS) and its comorbidity with other behavioral addiction and affective disorder among college students in Singapore. 1110 college students (age: M=21.46, SD=1.80) in Singapore completed measures assessing online social networking, unhealthy food intake and shopping addiction as well as depression, anxiety and mania. Descriptive analyses were conducted to investigate the prevalence and comorbidity of behavioral addiction and affective disorder. Chi-square tests were used to examine gender differences.

THE PRINCIPAL RESULTS
The prevalence rates of SNS, food and shopping addiction were 29.5%, 4.7% and 9.3% respectively for the total sample. SNS addiction was found to co-occur with food addiction (3%), shopping addiction (5%), and both food and shopping addiction (1%). The comorbidity rates of SNS addiction and affective disorder were 21% for depression, 27.7% for anxiety, and 26.1% for mania. Compared with the total sample, students with SNS addiction reported higher comorbidity rates with other behavioral addiction and affective disorder. In general, females as compared to males reported higher comorbidity rates of SNS addiction and affective disorder.

MAJOR CONCLUSIONS
SNS addiction has a high prevalence rate among college students in Singapore. Students with SNS addiction were vulnerable to experience other behavior addiction as well as affective disorder, especially among females.


Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.


Cerberus: a context-aware security scheme for smart spaces

Ubiquitous computing has fueled the idea of constructing sentient, information-rich "smart spaces" that extend the boundaries of traditional computing to encompass physical spaces, embedded devices, sensors, and other machinery. To achieve this, smart spaces need to capture situational information so that they can detect changes in context and adapt themselves accordingly. However, without considering basic security issues ubiquitous computing environments could be rife with vulnerabilities. Ubiquitous computing environments impose new requirements on security. Security services, like authentication and access control, have to be non-intrusive, intelligent, and able to adapt to the rapidly changing contexts of the spaces. We present a ubiquitous security mechanism that integrates context-awareness with automated reasoning to perform authentication and access control in ubiquitous computing environments.


Managing Context Information for Adaptive Security in IoT Environments

Pervasive computing is becoming a reality due to the rise of the so-called Internet of Things (IoT). In this paradigm, everyday and physical objects are being equipped with capabilities to detect and communicate information they receive from their environment, turning them into smart objects. However, such entities are usually deployed on environments with changing and dynamic conditions, which can be used by them to modify their operation or behavior. Under the foundations of EU FP7 SocIoTal project, this work provides an overview about how contextual information can be taken into account by smart objects when making security decisions, by considering such information as a first-class component, in order to realize the so-called context-aware security on IoT scenarios.


A Context-Aware Security Architecture for Emerging Applications

We describe an approach to building security services for context-aware environments. Specifically, we focus on the design of security services that incorporate the use of security-relevant “context” to provide flexible access control and policy enforcement. We previously presented a generalized access control model that makes significant use of contextual information in policy definition. This document provides a concrete realization of such a model by presenting a system-level service architecture, as well as early implementation experience with the framework. Through our context-aware security services, our system architecture offers enhanced authentication services, more flexible access control and a security subsystem that can adapt itself based on current conditions in the environment. We discuss our architecture and implementation and show how it can be used to secure several sample applications.


Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift

Integration of sensors into various kinds of products and machines provides access to in-depth usage information as basis for product optimization. Presently, this large potential for more user-friendly and efficient products is not being realized because (a) sensor integration and thus usage information is not available on a large scale and (b) product optimization requires considerable efforts in terms of manpower and adaptation of production equipment. However, with the advent of cloud-based services and highly flexible additive manufacturing techniques, these obstacles are currently crumbling away at rapid pace. The present study explores the state of the art in gathering and evaluating product usage and life cycle data, additive manufacturing and sensor integration, automated design and cloud-based services in manufacturing. By joining and extrapolating development trends in these areas, it delimits the foundations of a manufacturing concept that will allow continuous and economically viable product optimization on a general, user group or individual user level. This projection is checked against three different application scenarios, each of which stresses different aspects of the underlying holistic concept. The following discussion identifies critical issues and research needs by adopting the relevant stakeholder perspectives.


Message-Locked Encryption for Lock-Dependent Messages

Motivated by the problem of avoiding duplication in storage systems, Bellare, Keelveedhi, and Ristenpart have recently put forward the notion of Message-Locked Encryption (MLE) schemes which subsumes convergent encryption and its variants. Such schemes do not rely on permanent secret keys, but rather encrypt messages using keys derived from the messages themselves. We strengthen the notions of security proposed by Bellare et al. by considering plaintext distributions that may depend on the public parameters of the schemes. We refer to such inputs as lock-dependent messages. We construct two schemes that satisfy our new notions of security for message-locked encryption with lock-dependent messages. Our main construction deviates from the approach of Bellare et al. by avoiding the use of ciphertext components derived deterministically from the messages. We design a fully randomized scheme that supports an equality-testing algorithm defined on the ciphertexts. Our second construction has a deterministic ciphertext component that enables more efficient equality testing. Security for lock-dependent messages still holds under computational assumptions on the message distributions produced by the attacker. In both of our schemes the overhead in the length of the ciphertext is only additive and independent of the message length.


Miller Decoding Algorithm Using Sample Recovery and Correlation Method for UHF Gen2 RFID System

In this paper, by analyzing the characteristics of the received signal in both practical and theory environment, a Miller decoding algorithm basing on a sample recovery method and correlation concept is designed. The sample recovery method is designed to apply on the samples of the received signal which often include some degree of distortions caused by noise or timing jitter. Basing on the concept of matched filter, which is obtained by correlating the received signal with a known reference signal to detect the presence of the data symbol in the received signal, a Miller decoding algorithm utilizing correlation is designed. Together with the signal recovery method, the Miller decoding algorithm using correlation shows a good decoding performance under condition of timing jitter and high signal corruption rate caused by noise.


Weakly Supervised Cyberbullying Detection Using Co-Trained Ensembles of Embedding Models

Social media has become an inevitable part of individuals personal and business lives. Its benefits come with various negative consequences. One major concern is the prevalence of detrimental online behavior on social media, such as online harassment and cyberbullying. In this study, we aim to address the computational challenges associated with harassment detection in social media by developing a machine learning framework with three distinguishing characteristics. (1) It uses minimal supervision in the form of expert-provided key phrases that are indicative of bullying or non-bullying. (2) It detects harassment with an ensemble of two learners that co-train one another; one learner examines the language content in the message, and the other learner considers the social structure. (3) It incorporates distributed word and graph-node representations by training nonlinear deep models. The model is trained by optimizing an objective function that balances a co-training loss with a weak-supervision loss. We evaluate the effectiveness of our approach using post-hoc, crowdsourced annotation of Twitter, Ask.fm, and Instagram data, finding that our deep ensembles outperform previous non-deep methods for weakly supervised harassment detection.


A Generative Entity-Mention Model for Linking Entities with Knowledge Base

Linking entities with knowledge base (entity linking) is a key issue in bridging the textual data with the structural knowledge base. Due to the name variation problem and the name ambiguity problem, the entity linking decisions are critically depending on the heterogenous knowledge of entities. In this paper, we propose a generative probabilistic model, called entitymention model, which can leverage heterogenous entity knowledge (including popularity knowledge, name knowledge and context knowledge) for the entity linking task. In our model, each name mention to be linked is modeled as a sample generated through a three-step generative story, and the entity knowledge is encoded in the distribution of entities in document P(e), the distribution of possible names of a specific entity P(s|e), and the distribution of possible contexts of a specific entity P(c|e). To find the referent entity of a name mention, our method combines the evidences from all the three distributions P(e), P(s|e) and P(c|e). Experimental results show that our method can significantly outperform the traditional methods.


Toward understanding characteristics of dedicated short range communications (DSRC) from a perspective of vehicular network engineers

IEEE 802.11p-based Dedicated Short Range Communications (DSRC) is considered a promising wireless technology for enhancing transportation safety and improving highway efficiency. Here, using a large set of empirical measurement data taken in a rich variety of realistic driving environments, we attempt to characterize communication properties of DSRC as well as to analyze the causes of communication loss. Specifically, from a perspective of vehicular network engineers, the fundamental characteristic of DSRC communications is Packet Delivery Ratio (PDR). We investigate the impact of both uncontrollable environmental factors and controllable radio parameters on DSRC characteristics. Moreover, we also examine temporal correlation, spatial correlation and symmetric correlation of DSRC characteristics under realistic vehicular environments.


Learning Common Representation from RGB and Depth Images

We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGBD images. We revise the state of art based on the RGB and depth feature fusion, where both modalities are assumed to be available at train and test time. We propose a new architecture where the feature fusion is replaced with a common deep representation. Combined with an encoder-decoder type of the network, the architecture can jointly learn models for semantic segmentation and depth estimation based on their common representation. This representation, inspired by multi-view learning, offers several important advantages, such as using one modality available at test time to reconstruct the missing modality. In the RGB-D case, this enables the cross-modality scenarios, such as using depth data for semantically segmentation and the RGB images for depth estimation. We demonstrate the effectiveness of the proposed network on two publicly available RGB-D datasets. The experimental results show that the proposed method works well in both semantic segmentation and depth estimation tasks.


Brain Activity during Episodic Retrieval of Autobiographical and Laboratory Events: An fMRI Study using a Novel Photo Paradigm

Functional neuroimaging studies of episodic memory retrieval generally measure brain activity while participants remember items encountered in the laboratory (controlled laboratory condition) or events from their own life (open autobiographical condition). Differences in activation between these conditions may reflect differences in retrieval processes, memory remoteness, emotional content, retrieval success, self-referential processing, visual/spatial memory, and recollection. To clarify the nature of these differences, a functional MRI study was conducted using a novel photo paradigm, which allows greater control over the autobiographical condition, including a measure of retrieval accuracy. Undergraduate students took photos in specified campus locations (controlled autobiographical condition), viewed in the laboratory similar photos taken by other participants (controlled laboratory condition), and were then scanned while recognizing the two kinds of photos. Both conditions activated a common episodic memory network that included medial temporal and prefrontal regions. Compared with the controlled laboratory condition, the controlled autobiographical condition elicited greater activity in regions associated with self-referential processing (medial prefrontal cortex), visual/ spatial memory (visual and parahippocampal regions), and recollection (hippocampus). The photo paradigm provides a way of investigating the functional neuroanatomy of real-life episodic memory under rigorous experimental control.


Ambient RF Energy-Harvesting Technologies for Self-Sustainable Standalone Wireless Sensor Platforms

In this paper, various ambient energy-harvesting technologies (solar, thermal, wireless, and piezoelectric) are reviewed in detail and their applicability in the development of self-sustaining wireless platforms is discussed. Specifically, far-field low-power-density energy-harvesting technology is thoroughly investigated and a benchmarking prototype of an embedded microcontroller-enabled sensor platform has been successfully powered by an ambient ultrahigh-frequency (UHF) digital TV signal (512-566 MHz) where a broadcasting antenna is 6.3 km away from the proposed wireless energy-harvesting device. A high-efficiency dual-band ambient energy harvester at 915 MHz and 2.45 GHz and an energy harvester for on-body application at 460 MHz are also presented to verify the capabilities of ambient UHF/RF energy harvesting as an enabling technology for Internet of Things and smart skins applications.


Advanced Voltage Support and Active Power Flow Control in Grid-Connected Converters Under Unbalanced Conditions

Supporting the grid and improving its reliability have recently become major requirements for large distributed generation units. Under most grid faults, the accuracy of the traditional voltage support schemes (VSSs) is dramatically affected due to the existence of the zero-sequence voltage. Also, the traditional VSSs have been used only in the STATCOM applications, where the active power is zero. This paper proposes an advanced VSS in the converter-interfaced units, called zero-sequence compensated voltage support (ZCVS), to accurately regulate the three-phase voltages of the connection point within the pre-set safety limits. The proposed scheme not only compensates the zero-sequence component but also considers the active power injection. Unlike the traditional methods, the proposed VSS is adapted even in resistive distribution systems. The contribution of this paper is, however, ternate. As the second contribution, the limited active power oscillation (LAPO) is proposed to be augmented to the ZCVS. This feature limits the oscillation to a specified value which provides an adjustable dc-link voltage oscillation setting while simultaneously supporting the ac host grid, even under severe unbalanced faults. Third, the maximum active power delivery (MAPD) to the ac grid is also formulated for the ZCVS. The successful results of the proposed support scheme and complementary strategies are verified using selected simulation and experimental test cases.


A critical examination of 3D stackable NAND Flash memory architectures by simulation study of the scaling capability

Various 3D NAND Flash array architectures including P-BiCS, TCAT, VSAT, and VG are critically examined in this work by extensive 3D TCAD simulations. All structures have X,Y lateral scaling limitation since the minimal ONO thickness (∼20 nm) and poly channel thickness (∼10nm) can not be scaled further. Among them VG may have the best X-direction scalability to F∼2X nm node, and no penalty of increasing Z layer number since the channel current flows horizontally. We propose a buried-channel junction-free NAND to improve the read current for all 3D NAND arrays and our simulation results well support this structure. For the first time, “Z-interference” in 3D NAND Flash is examined and it indicates a new Z-direction scaling limitation. The present work is of crucial importance in understanding various 3D NAND Flash approaches.


ICIET ’ 14 Image Steganography Method Using Integer Wavelet Transform

Digital Steganography explains the art and science of writing hidden messages in such a way that, apart from the sender and intended recipient, no one suspects the existence of the message, a form of security through the state of being unknown. The main two famous schemes used for image steganography are spatial domain embedding and transform domain embedding. The main aim of Wavelet Transform is used to transform original image (cover image) from spatial domain to frequency domain. But here in our proposed work, Integer Wavelet Transform is performed on a gray level cover image and in turn embeds the message bitstream into the LSB's of the integer wavelet coefficients of a the image . The main purpose of the proposed work is to focus on improving embedding capacity and bring down the distortion occurring to the stego image. The refinement of the algorithm plays an important role for accomplishing higher embedding capacity and low distortion rate. The experimental results prove that the assessment metric such as PSNR is improved in a high manner. The experimental results show that the algorithm has a high capacity and a good invisibility.


Point Cloud Segmentation and Denoising via Constrained Nonlinear Least Squares Normal Estimates


Business ecosystem as the new approach to complex adaptive business environments

This paper discusses the concept of business ecosystem. Business ecosystem is a relatively new concept in the field of business research, and there is still a lot of work to be done to establish it. First the subject is approached by examining a biological ecosystem, especially how biological ecosystems are defined, how they evolve and how they are classified and structured. Second, different analogies of biological ecosystem are reviewed, including industrial ecosystem, economy as an ecosystem, digital business ecosystem and social ecosystem. Third, business ecosystem concept is outlined by discussing views of main contributors and then bringing authors’ own definition out. Fourth, the emerging research field of complexity in social sciences is brought out due to authors’ attitude to consider ecosystems and business ecosystems as complex, adaptive systems. The focal complexity aspects appearing in business ecosystems are presented; they are self-organization, emergence, co-evolution and adaptation. By connecting business ecosystem concept to complexity research, it is possible to bring new insights to changing business environments.


Framing the Salmonidae Family Phylogenetic Portrait: A More Complete Picture from Increased Taxon Sampling

Considerable research efforts have focused on elucidating the systematic relationships among salmonid fishes; an understanding of these patterns of relatedness will inform conservation- and fisheries-related issues, as well as provide a framework for investigating evolutionary mechanisms in the group. However, uncertainties persist in current Salmonidae phylogenies due to biological and methodological factors, and a comprehensive phylogeny including most representatives of the family could provide insight into the causes of these difficulties. Here we increase taxon sampling by including nearly all described salmonid species (n = 63) to present a time-calibrated and more complete portrait of Salmonidae using a combination of molecular markers and analytical techniques. This strategy improved resolution by increasing the signal-to-noise ratio and helped discriminate methodological and systematic errors from sources of difficulty associated with biological processes. Our results highlight novel aspects of salmonid evolution. First, we call into question the widely-accepted evolutionary relationships among sub-families and suggest that Thymallinae, rather than Coregoninae, is the sister group to the remainder of Salmonidae. Second, we find that some groups in Salmonidae are older than previously thought and that the mitochondrial rate of molecular divergence varies markedly among genes and clades. We estimate the age of the family to be 59.1 MY (CI: 63.2-58.1 MY) old, which likely corresponds to the timing of whole genome duplication in salmonids. The average, albeit highly variable, mitochondrial rate of molecular divergence was estimated as ~0.31%/MY (CI: 0.27-0.36%/MY). Finally, we suggest that some species require taxonomic revision, including two monotypic genera, Stenodus and Salvethymus. In addition, we resolve some relationships that have been notoriously difficult to discern and present a clearer picture of the evolution of the group. Our findings represent an important contribution to the systematics of Salmonidae, and provide a useful tool for addressing questions related to fundamental and applied evolutionary issues.


Metrics for Polyphonic Sound Event Detection

This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources active simultaneously. The system output in this case contains overlapping events, marked as multiple sounds detected as being active at the same time. The polyphonic system output requires a suitable procedure for evaluation against a reference. Metrics from neighboring fields such as speech recognition and speaker diarization can be used, but they need to be partially redefined to deal with the overlapping events. We present a review of the most common metrics in the field and the way they are adapted and interpreted in the polyphonic case. We discuss segment-based and event-based definitions of each metric and explain the consequences of instance-based and class-based averaging using a case study. In parallel, we provide a toolbox containing implementations of presented metrics.


Towards Haskell in the cloud

We present Cloud Haskell, a domain-specific language for developing programs for a distributed computing environment. Implemented as a shallow embedding in Haskell, it provides a message-passing communication model, inspired by Erlang, without introducing incompatibility with Haskell's established shared-memory concurrency. A key contribution is a method for serializing function closures for transmission across the network. Cloud Haskell has been implemented; we present example code and some preliminary performance measurements.


Aphasia after stroke: type, severity and prognosis. The Copenhagen aphasia study.

AIM
To determine the types, severity and evolution of aphasia in unselected, acute stroke patients and evaluate potential predictors for language outcome 1 year after stroke.

METHODS
270 acute stroke patients with aphasia (203 with first-ever strokes) were included consecutively and prospectively from three hospitals in Copenhagen, Denmark, and assessed with the Western Aphasia Battery. The assessment was repeated 1 year after stroke.

RESULTS
The frequencies of the different types of aphasia in acute first-ever stroke were: global 32%, Broca's 12%, isolation 2%, transcortical motor 2%, Wernicke's 16%, transcortical sensory 7%, conduction 5% and anomic 25%. These figures are not substantially different from what has been found in previous studies of more or less selected populations. The type of aphasia always changed to a less severe form during the first year. Nonfluent aphasia could evolve into fluent aphasia (e.g., global to Wernicke's and Broca's to anomic), whereas a fluent aphasia never evolved into a nonfluent aphasia. One year after stroke, the following frequencies were found: global 7%, Broca's 13%, isolation 0%, transcortical motor 1%, Wernicke's 5%, transcortical sensory 0%, conduction 6% and anomic 29%. The distribution of aphasia types in acute and chronic aphasia is, thus, quite different. The outcome for language function was predicted by initial severity of the aphasia and by the initial stroke severity (assessed by the Scandinavian Stroke Scale), but not by age, sex or type of aphasia. Thus, a scoring of general stroke severity helps to improve the accuracy of the prognosis for the language function. One year after stroke, fluent aphasics were older than nonfluent aphasics, whereas such a difference was not found in the acute phase.


Measuring semantic similarity between Gene Ontology terms

Many bioinformatics applications would benefit from comparing proteins based on their biological role rather than their sequence. This manuscript adds two new contributions. First, a study of the correlation between Gene Ontology (GO) terms and family similarity demonstrates that protein families constitute an appropriate baseline for validating GO similarity. Secondly, we introduce GraSM, a novel method that uses all the information in the graph structure of the Gene Ontology, instead of considering it as a hierarchical tree. GraSM gives a consistently higher family similarity correlation on all aspects of GO than the original semantic similarity measures.


Hierarchical organization in complex networks.

Many real networks in nature and society share two generic properties: they are scale-free and they display a high degree of clustering. We show that these two features are the consequence of a hierarchical organization, implying that small groups of nodes organize in a hierarchical manner into increasingly large groups, while maintaining a scale-free topology. In hierarchical networks, the degree of clustering characterizing the different groups follows a strict scaling law, which can be used to identify the presence of a hierarchical organization in real networks. We find that several real networks, such as the Worldwideweb, actor network, the Internet at the domain level, and the semantic web obey this scaling law, indicating that hierarchy is a fundamental characteristic of many complex systems.


Lest We Remember: Cold Boot Attacks on Encryption Keys

Contrary to widespread assumption, dynamic RAM (DRAM), the main memory in most modern computers, retains its contents for several seconds after power is lost, even at room temperature and even if removed from a motherboard. Although DRAM becomes less reliable when it is not refreshed, it is not immediately erased, and its contents persist sufficiently for malicious (or forensic) acquisition of usable full-system memory images. We show that this phenomenon limits the ability of an operating system to protect cryptographic key material from an attacker with physical access to a machine. It poses a particular threat to laptop users who rely on disk encryption: we demonstrate that it could be used to compromise several popular disk encryption products without the need for any special devices or materials. We experimentally characterize the extent and predictability of memory retention and report that remanence times can be increased dramatically with simple cooling techniques. We offer new algorithms for finding cryptographic keys in memory images and for correcting errors caused by bit decay. Though we discuss several strategies for mitigating these risks, we know of no simple remedy that would eliminate them.


Object Detection with Discriminatively Trained Part-Based Models

• Lighting. When an object is in bright light, it looks brighter than when it’s in shadow, so a program can’t just look at image intensity values. • Within-class variation. Different instances of the same kind of object can look quite different to one another. For example, a green station wagon and a red convertible are both cars, so a program can’t simply compare a picture to one example. • Aspect. The same object can look very different when viewed at from different directions—pick up a book and compare its cover and its spine to see this effect. Again, this means that a program might need to have many examples of each type of object. • Deformation. Many objects can change their appearance significantly without their identity changing. For example, you can move your limbs around, change clothes, paint your face, or have your hair cut. You will look very different indeed, but you will still be a person.


Simultaneous Detection and Segmentation

We aim to detect all instances of a category in an image and, for each instance, mark the pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS). Unlike classical bounding box detection, SDS requires a segmentation and not just a box. Unlike classical semantic segmentation, we require individual object instances. We build on recent work that uses convolutional neural networks to classify category-independent region proposals (R-CNN [16]), introducing a novel architecture tailored for SDS. We then use category-specific, topdown figure-ground predictions to refine our bottom-up proposals. We show a 7 point boost (16% relative) over our baselines on SDS, a 5 point boost (10% relative) over state-of-the-art on semantic segmentation, and state-of-the-art performance in object detection. Finally, we provide diagnostic tools that unpack performance and provide directions for future work.


Applications of soft union sets to hemirings via SU-h-ideals

The aim of this article is to lay a foundation for providing a soft algebraic tool in considering many problems that contain uncertainties. In order to provide these soft algebraic structures, we introduce the concepts of soft union hemirings (soft union h-ideals) of hemirings by soft intersection-union product and obtain some related results. Finally, we investigate some characterizations of h-hemiregular hemirings by soft union h-ideals.


Effects of an intervention to increase bed alarm use to prevent falls in hospitalized patients: a cluster randomized trial.

BACKGROUND
Bed alarm systems intended to prevent hospital falls have not been formally evaluated.

OBJECTIVE
To investigate whether an intervention aimed at increasing bed alarm use decreases hospital falls and related events.

DESIGN
Pair-matched, cluster randomized trial over 18 months. Nursing units were allocated by computer-generated randomization on the basis of baseline fall rates. Patients and outcome assessors were blinded to unit assignment; outcome assessors may have become unblinded. (ClinicalTrials.gov registration number: NCT00183053)

SETTING
16 nursing units in an urban community hospital.

PATIENTS
27 672 inpatients in general medical, surgical, and specialty units.

INTERVENTION
Education, training, and technical support to promote use of a standard bed alarm system (intervention units); bed alarms available but not formally promoted or supported (control units).

MEASUREMENTS
Pre-post difference in change in falls per 1000 patient-days (primary end point); number of patients who fell, fall-related injuries, and number of patients restrained (secondary end points).

RESULTS
Prevalence of alarm use was 64.41 days per 1000 patient-days on intervention units and 1.79 days per 1000 patient-days on control units (P = 0.004). There was no difference in change in fall rates per 1000 patient-days (risk ratio, 1.09 [95% CI, 0.85 to 1.53]; difference, 0.41 [CI, -1.05 to 2.47], which corresponds to a greater difference in falls in control vs. intervention units) or in the number of patients who fell, injurious fall rates, or the number of patients physically restrained on intervention units compared with control units.

LIMITATION
The study was conducted at a single site and was slightly underpowered compared with the initial design.

CONCLUSION
An intervention designed to increase bed alarm use in an urban hospital increased alarm use but had no statistically or clinically significant effect on fall-related events or physical restraint use.

PRIMARY FUNDING SOURCE
National Institute on Aging.


Design and control of a GaN-based, 13-level, flying capacitor multilevel inverter

Multilevel topologies are an appealing method to achieve higher power density inverters for both mobile and stationary systems. This work discusses the design and development of a 13-level, flying capacitor multilevel (FCML) inverter. Operating from an 800 V bus, this inverter requires switches with a voltage blocking capability of less than 80 V. A 120 kHz switching frequency is enabled through the use of GaN FETs and the development of custom integrated switching cells, which reduce commutation loop inductance and allow for a modular design. Additionally, the frequency multiplication effect of FCML inverters allows the output inductor of the inverter to be made exceptionally small (4.7 μH) while maintaining a 0.7 % THD due to the 1.44 MHz effective inductor ripple frequency.


An Improved Control Strategy for Grid-Connected Voltage Source Inverters With an LCL Filter

A novel current control strategy based on a new current feedback for grid-connected voltage source inverters with an LCL-filter is proposed in this paper. By splitting the capacitor of the LCL-filter into two parts, each with the proportional division of the capacitance, the current flowing between these two parts is measured and used as the feedback to a current regulator to stabilize and improve the system performances. Consequently, the V-1 transfer function of the grid-connected inverter system with the LCL-filter is degraded from a third-order function to a first-order one, therefore the closed-loop current feedback control system can be optimized easily for minimum steady-state error and current harmonic distortion, as well as the system stability. The characteristics of the inverter system with the proposed controller are investigated and compared with those using traditional control strategies. Experimental results are provided, and the new current control strategy has been verified on a 5 kW fuel cell inverter.


A Light Rule-based Approach to English Subject-Verb Agreement Errors on the Third Person Singular Forms

Verb errors are one of the most common grammar errors made by non-native writers of English. This work especially focus on an important type of verb usage errors, subject-verb agreement for the third person singular forms, which has a high proportion in errors made by non-native English learners. Existing work has not given a satisfied solution for this task, in which those using supervised learning method usually fail to output good enough performance, and rule-based methods depend on advanced linguistic resources such as syntactic parsers. In this paper, we propose a rule-based method to detect and correct the concerned errors. The proposed method relies on a series of rules to automatically locate subject and predicate in four types of sentences. The evaluation shows that the proposed method gives state-of-the-art performance with quite limited linguistic resources. ∗ Correspondence author † This work was partially supported by the National Natural Science Foundation of China (No. 61170114, and No. 61272248), the National Basic Research Program of China (No. 2013CB329401), the Science and Technology Commission of Shanghai Municipality (No. 13511500200), the European Union Seventh Framework Program (No. 247619), the Cai Yuanpei Program (CSC fund 201304490199 and 201304490171), and the art and science interdiscipline funds of Shanghai Jiao Tong University, No. 14X190040031, and the Key Project of National Society Science Foundation of China, No. 15-ZDA041.


Eyes-Free Barcode Localization and Decoding for Visually Impaired Mobile Phone Users

An eyes-free barcode localization and decoding method is presented that enables visually impaired (VI) mobile phone users to decode MSI (Modified Plessy) barcodes on shelves and UPC barcodes on individual boxes, cans, and bottles. Simple and efficient barcode localization and decoding techniques are augmented with an interactive haptic feedback loop that allows the VI user to align the phone’s camera with a fixed surface in the pitch and yaw planes. The method is implemented on a Google Nexus One smart phone running Android 2.1. A laboratory study is presented in which the method was evaluated by one VI and four blindfolded sighted participants.


Text Detection and Character Extraction in Natural Scene Images

Detecting text regions in natural scene images has become an important area due to its varies applications. Text Information Extraction (TIE) System involves detecting text regions in a given image, localizing it, extracting the text part and recognizing text using OCR. This work basically concentrates on the detection and extraction of text in natural scene images. In this work, the test image will be preprocessed using RGB to Gray conversion, binarization, Edge Detection method and Geometric based Noise removal method. The features from the pre-processed image are extracted. The extracted features are used by the trained SVM classifier to detect the text regions. After detecting text regions, characters are extracted and finally displayed. Keywords— Text Information Extraction (TIE); Natural Scene Images; Text Detection; Text Extraction; Support Vector Machine (SVM).


Prosody-Based Automatic Segmentation of Speech into Sentences and Topics

A crucial step in processing speech audio data for informati onextraction, topicdetection, or browsing/playback is to segment the input into sentence and topic units. Speech segm entation is challenging, since the cues typically present for segmenting text (headers, paragraphs, punctuation) ar e absent in spoken language. We investigate the use of prosody (informationgleaned from the timing and melody of s peech) for these tasks. Using decision tree and hidden Markov modeling techniques, we combine prosodic cues with w ord-based approaches, and evaluate performance on two speech corpora, Broadcast News and Switchboard. Resu lts show that the prosodic model alone performs on par with, or better than, word-based statistical languag e models—for both true and automatically recognized words in news speech. The prosodic model achieves comparabl e performance with significantly less training data, and requires no hand-labeling of prosodic events. Across ta sks nd corpora, we obtain a significant improvement over word-only models using a probabilistic combination of prosodic and lexical information. Inspection reveals that the prosodic models capture language-independent bou dary indicators described in the literature. Finally, cue usage is task and corpus dependent. For example, pause and pi tch features are highly informative for segmenting news speech, whereas pause, duration and word-based cues do minate for natural conversation.


Scalable and Crash-Tolerant Load Balancing Based on Switch Migration for Multiple Open Flow Controllers

As the size of networks continues to increase, the scalability of the centralized controller becomes increasingly issues in Software Defined Networking. Distributed controllers have been proposed to solve the problem that the centralized controllers such as NOX and Floodlight suffer from. That logically centralized, but physically distributed architecture divide the network into zones with separate multiple controllers to achieve a better scalability control plane. However, Such distributed architecture introduces a new challenge to the load rebalancing of controllers when uneven load distribution among the controllers due to the statically configured mapping between a switch and a controller. Therefore, under variable traffic conditions in real networks, keeping load balance dynamically among the controller clusters is essential for realizing a high performance and scalability control plane. To address these issues, we propose a dynamic load rebalancing method based on switch migration mechanism for clustered controllers. The multiple controllers use Groups to coordinate actions for switch migration. The whole network is divided into several groups and each group is set up one controller cluster. Our proposed method can dynamically shift the load across the multiple controllers through switch migration. The mechanism support controller failover without switch disconnection avoiding the single point of failure problem. We also implemented a prototype system based on Open Day light Hydrogen controller to evaluated the performance of our design. Our preliminary result shows that the method enables controllers to relieve the overload via switch migration and can improve throughput and reduce the response time of the control plane.


Nature Inspired Methods and Their Industry Applications—Swarm Intelligence Algorithms

In this paper, we present the swarm intelligence (SI) concept and mention some metaheuristics belonging to the SI. We present the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) method as the representatives of the SI approach. In recent years, researchers are eager to develop and apply a variety of these two methods, despite the development of many other newer methods as Bat or FireFly algorithms. Presenting the PSO and ACO we put their pseudocode, their properties, and intuition lying behind them. Next, we focus on their real-life applications, indicating many papers presented varieties of basic algorithms and the areas of their applications.


LITE Kernel RDMA Support for Datacenter Applications

Recently, there is an increasing interest in building data-center applications with RDMA because of its low-latency, high-throughput, and low-CPU-utilization benefits. However, RDMA is not readily suitable for datacenter applications. It lacks a flexible, high-level abstraction; its performance does not scale; and it does not provide resource sharing or flexible protection. Because of these issues, it is difficult to build RDMA-based applications and to exploit RDMA's performance benefits.
 To solve these issues, we built LITE, a Local Indirection TiEr for RDMA in the Linux kernel that virtualizes native RDMA into a flexible, high-level, easy-to-use abstraction and allows applications to safely share resources. Despite the widely-held belief that kernel bypassing is essential to RDMA's low-latency performance, we show that using a kernel-level indirection can achieve both flexibility and low-latency, scalable performance at the same time. To demonstrate the benefits of LITE, we developed several popular datacenter applications on LITE, including a graph engine, a MapReduce system, a Distributed Shared Memory system, and a distributed atomic logging system. These systems are easy to build and deliver good performance. For example, our implementation of PowerGraph uses only 20 lines of LITE code, while outperforming PowerGraph by 3.5x to 5.6x.


Security Enhancements for Mobile Ad Hoc Networks With Trust Management Using Uncertain Reasoning

The distinctive features of mobile ad hoc networks (MANETs), including dynamic topology and open wireless medium, may lead to MANETs suffering from many security vulnerabilities. In this paper, using recent advances in uncertain reasoning that originated from the artificial intelligence community, we propose a unified trust management scheme that enhances the security in MANETs. In the proposed trust management scheme, the trust model has two components: trust from direct observation and trust from indirect observation. With direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain reasoning when the full probability model can be defined. On the other hand, with indirect observation, which is also called secondhand information that is obtained from neighbor nodes of the observer node, the trust value is derived using the Dempster-Shafer theory (DST), which is another type of uncertain reasoning when the proposition of interest can be derived by an indirect method. By combining these two components in the trust model, we can obtain more accurate trust values of the observed nodes in MANETs. We then evaluate our scheme under the scenario of MANET routing. Extensive simulation results show the effectiveness of the proposed scheme. Specifically, throughput and packet delivery ratio (PDR) can be improved significantly with slightly increased average end-to-end delay and overhead of messages.


Action Observation and Motor Imagery: Innovative Cognitive Tools in the Rehabilitation of Parkinson's Disease

Parkinson's disease (PD) is characterized by a progressive impairment of motor skills with deterioration of autonomy in daily living activities. Physiotherapy is regarded as an adjuvant to pharmacological and neurosurgical treatment and may provide small and short-lasting clinical benefits in PD patients. However, the development of innovative rehabilitation approaches with greater long-term efficacy is a major unmet need. Motor imagery (MI) and action observation (AO) have been recently proposed as a promising rehabilitation tool. MI is the ability to imagine a movement without actual performance (or muscle activation). The same cortical-subcortical network active during motor execution is engaged in MI. The physiological basis of AO is represented by the activation of the "mirror neuron system." Both MI and AO are involved in motor learning and can induce improvements of motor performance, possibly mediated by the development of plastic changes in the motor cortex. The review of available evidences indicated that MI ability and AO feasibility are substantially preserved in PD subjects. A few preliminary studies suggested the possibility of using MI and AO as parts of rehabilitation protocols for PD patients.


A Characterization of the Utility of Using Artificial Intelligence to Test Two Artificial Intelligence Systems

An artificial intelligence system, designed for operations in a real-world environment faces a nearly infinite set of possible performance scenarios. Designers and developers, thus, face the challenge of validating proper performance across both foreseen and unforeseen conditions, particularly when the artificial intelligence is controlling a robot that will be operating in close proximity, or may represent a danger, to humans. While the manual creation of test cases allows limited testing (perhaps ensuring that a set of foreseeable conditions trigger an appropriate response), this may be insufficient to fully characterize and validate safe system performance. An approach to validating the performance of an artificial intelligence system using a simple artificial intelligence test case producer (AITCP) is presented. The AITCP allows the creation and simulation of prospective operating scenarios at a rate far exceeding that possible by human testers. Four scenarios for testing an autonomous navigation control system are presented: single actor in two-dimensional space, multiple actors in two-dimensional space, single actor in three-dimensional space, and multiple actors in three-dimensional space. The utility of using the AITCP is compared to that of human testers in each of these scenarios.


The game, the player, the world: looking for a heart of gameness

This paper proposes a definition of games. I describe the classic game model, a list of six features that are necessary and sufficient for something to be a game. The definition shows games to be transmedial: There is no single game medium, but rather a number of game media, each with its own strengths. The computer is simply the latest game medium to emerge. While computer games 1 are therefore part of the broader area of games, they have in many cases evolved beyond the classic game model.


Homotopic path validation using non-holonomic differential drive robot model

In this paper the tracking of collision-free path obtained by using Homotopic Path Planning Method (HPPM) with automatic value assignation of repulsion parameter is presented. This work is center on show the utility of this method to obtain a collision free path for maps with narrow corridors on indoor environments. First, the basis of homotopic path planning method with automatic value assignation of repulsion parameter are explained. Second, the Pure-Pursuit controller is brief explained. Third, the capability of the HPPM is shown by using two experiments that which contain narrow passages and several circular obstacles. Finally, in order to validate the solution paths, a tracking on Robot Operating System by using 3P-DX robot model and on MATLAB using a Pure-Pursuit controller from the Robotics Toolbox is performed.


Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion

A 3 AIS-Impute (proposed algorithm). Require: partially observed matrix O, parameter λ, decay parameter ν ∈ (0, 1), threshold ; 1: [U0, λ0, V0] = rank-1 SVD(PΩ(O)); 2: c = 1, ̃0 = ‖PΩ(O)‖F , X0 = X1 = λ0U0V > 0 ; 3: for t = 1, 2, . . . do 4: ̃t = ν̃0; θt = (c− 1)/(c+ 2); 5: λt = ν(λ0 − λ) + λ; 6: Yt = Xt + θt(Xt −Xt−1); 7: Z̃t = Yt + PΩ(O − Yt); 8: Vt−1 = Vt−1 − Vt(VtVt−1), remove zero columns; 9: Rt = QR([Vt, Vt−1]); 10: [Ut+1,Σt+1, Vt+1] = approx-SVT(Z̃t, Rt, λt, ̃t); 11: if F (Ut+1Σt+1V > t+1) > F (UtΣtV > t ); c = 1 else c = c+ 1; 12: end for 13: return Xt+1 = Ut+1Σt+1V > t+1.


How Popular is Your Paper ? An Empirical Study of the Citation Distribution

Numerical data for the distribution of citations are examined for: (i) papers published in 1981 in journals which are catalogued by the Institute for Scientific Information (783,339 papers) and (ii) 20 years of publications in Physical Review D, vols. 11-50 (24,296 papers). A Zipf plot of the number of citations to a given paper versus its citation rank appears to be consistent with a power-law dependence for leading rank papers, with exponent close to −1/2. This, in turn, suggests that the number of papers with x citations, N(x), has a large-x power law decay N(x) ∼ x, with α ≈ 3. PACS. 02.50.-r Probability theory, stochastic processes, and statistics – 01.75.+m Science and society – 89.90.+n Other areas of general interest to physicists In this article, I consider a question which is of relevance to those for whom scientific publication is a primary means of scholarly communication. Namely, how often is a paper cited? While the average or total number of citations are often quoted anecdotally and tabulations of highly-cited papers exist [1,2], the focus of this work is on the more fundamental distribution of citations, namely, the number of papers which have been cited a total of x times,N(x). In spite of the fact that many academics are obliged to document their citations for merit-based considerations, there have been only a few scientific investigations on quantifying citations or related measures of scientific productivity. In a 1957 study based on the publication record of the scientific research staff at Brookhaven National Laboratory, Shockley [3] claimed that the scientific publication rate is described by a log-normal distribution. Much more recently, Laherrere and Sornette [4] have presented numerical evidence, based on data of the 1120 most-cited physicists from 1981 through June 1997, that the citation distribution of individual authors has a stretched exponential form, N(x) ∝ exp[−(x/x0) ] with β ≈ 0.3. Both papers give qualitative justifications for their assertions which are based on plausible general principles; however, these arguments do not provide specific numerical predictions. Here, the citation distribution of scientific publications based on two relatively large data sets is investigated [5]. One (ISI) is the citation distribution of 783,339 papers (with 6,716,198 citations) published in 1981 and cited bea redner@sid.bu.edu tween 1981 – June 1997 that have been cataloged by the Institute for Scientific Information. The second (PRD) is the citation distribution, as of June 1997, of the 24,296 papers cited at least once (with 351,872 citations) which were published in volumes 11 through 50 of Physical Review D, 1975–1994. Unlike reference [4], the focus here is on citations of publications rather than citations of specific authors. A primary reason for this emphasis is that the publication citation count reflects on the publication itself, while the author citation count reflects ancillary features, such as the total number of author publications, the quality of each of these publications, and co-author attributes. Additionally, only most-cited author data is currently available; this permits reconstruction of just the large-citation tail of the citation distribution. The main result of this study is that the asymptotic tail of the citation distribution appears to be described by a power law, N(x) ∼ x−α, with α ≈ 3. This conclusion is reached indirectly by means of a Zipf plot (to be defined and discussed below), however, because Figure 1 indicates that the citation distribution is not described by a single function over the whole range of x. Since the distribution curves downward on a double logarithmic scale and upward on a semi-logarithmic scale (Figs. 1a and b respectively), a natural first hypothesis is that this distribution is a stretched exponential, N(x) ∝ exp[−(x/x0) ]. Visually, the numerical data fit this form fairly well for x ≤ 200 (PRD) and x ≤ 500 (ISI) as indicated in Figure 1b, with best fit values β ≈ 0.39 (PRD) and β ≈ 0.44 (ISI). However, the stretched exponential is unsuitable to describe the large-x data. Here, 132 The European Physical Journal B 10 0 10 1 10 2 10 3 10 4 x 10 0 10 1 10 2 10 3 10 4 10 5


Multi-Instance Multi-Label Learning with Weak Label

Multi-Instance Multi-Label learning (MIML) deals with data objects that are represented by a bag of instances and associated with a set of class labels simultaneously. Previous studies typically assume that for every training example, all positive labels are tagged whereas the untagged labels are all negative. In many real applications such as image annotation, however, the learning problem often suffers from weak label; that is, users usually tag only a part of positive labels, and the untagged labels are not necessarily negative. In this paper, we propose the MIMLwel approach which works by assuming that highly relevant labels share some common instances, and the underlying class means of bags for each label are with a large margin. Experiments validate the effectiveness of MIMLwel in handling the weak label problem.


Why the sunny side is up: association between affect and vertical position.

Metaphors linking spatial location and affect (e.g., feeling up or down) may have subtle, but pervasive, effects on evaluation. In three studies, participants evaluated words presented on a computer. In Study 1, evaluations of positive words were faster when words were in the up rather than the down position, whereas evaluations of negative words were faster when words were in the down rather than the up position. In Study 2, positive evaluations activated higher areas of visual space, whereas negative evaluations activated lower areas of visual space. Study 3 revealed that, although evaluations activate areas of visual space, spatial positions do not activate evaluations. The studies suggest that affect has a surprisingly physical basis.


A high-precision time-to-digital converter for pulsed time-of-flight laser radar applications

A time-to-digital converter (TDC) has been designed, and six units have been constructed and tested. It consists of integrated digital time-interval measurement electronics with a 10-ns resolution that can be improved to 10 ps by an analog interpolation method. Identical construction of the interpolation electronics leads to stable performance of the TDC. The drifts of all six TDC units remain within 10 ps over a temperature range from 10 C to+50 C. The stability can be improved further by a real-time calibration procedure developed here. The single-shot precision of the TDC is better than 15 ps (standard deviation), but precision can be improved to below 0.1 ps by averaging about 10 000 measurements at the maximum measurement speed of 100 kHz. The time range of the TDC is currently 2.55 s, but this can be increased by adding an external digital counter. The TDC suffers from a periodic nonlinearity over the measured time range from 0 to 1.3 s, the amplitude and period of the nonlinearity being 20 ps and 40 ns, respectively. The reason lies in the integrated digital part of the electronics. The linearity of interpolation in the 10-ns time range improves, however, as more results are averaged.