Single phase based on ups applied Z-source inverter by using matlab / simulink

Uninterruptible power supplies (UPSs) are widely used to supply critical loads, such as airline computers and life-support systems in hospitals, providing protection against power failure or anomalies of power-line voltage. In general, there are two types of traditional single phase UPSs. The first one couples a battery bank to a half or full-bridge inverter with a low-frequency transformer. In this type of UPSs, the ac output voltage is higher than that of the battery bank; thus, a step-up transformer is required to boost voltage. .The second one couples a battery bank to a dc/dc booster with a half or full bridge inverter. In this type of UPSs, the additional booster is needed, leading to high cost and low efficiency. The controlling of the switches in the booster also complicates the system. Due to the presence of the step-up transformer, the inverter current is much higher than the load current, causing high current stress on the switches of the inverter The dead time in the pulse width-modulation (PWM) signals to prevent the upper and lower switches at the same phase leg from shooting through has to be provided in the aforementioned two types of UPSs, and it distorts the voltage waveform of the ac output voltage. In order to overcome the above problems in the traditional UPSs, new topology of the UPS is proposed by using a voltage source inverter and Zsource inverter. With this new topology, the proposed UPS offers the advantages over the traditional UPSs such as the dc/dc booster and the inverter have been combined into one single-stage power conversion and the distortion of the ac output-voltage waveform is reduced in the absence of dead time in the PWM signals. The effectiveness of the proposed method is implemented by using MATLAB/SIMULINK software.


SAR automatic target recognition based on a visual cortical system

Human Vision system is the most complex and accurate system. In order to extract better features about Synthetic Aperture Radar (SAR) targets, a SAR automatic target recognition (ATR) algorithm based on human visual cortical system is proposed. This algorithm contains three stages: (1) Image preprocessing (we use a Kuan filter to do the enhancement and an adaptive Intersecting Cortical Model (ICM) to do the segmentation) (2) Feature extraction using a sparse autoencoder. (3) Classification using a softmax regression classifier. Experiment result of MSTAR public data shows a better performance of recognition.


Trust and mobile commerce in North America

Mobile Commerce (mCommerce) activities include the act of shopping and buying on mobile devices, along with the more recent emergence of mobile payment systems. Within North America, mCommerce activities have become increasingly popular and will likely continue on this upwards trend as mobile devices further proliferate markets. Historically, one common issue with the adoption and use of eCommerce systems (e.g., commerce activities on personal computers) is trust. Yet we know little of how trust and other social factors may affect mCommerce usageda new genre of commerce activities that explicitly occur on mobile devices. To help address this problem, we have conducted two studies that explore users' mCommerce activities. The first is a diary and interview study of mCommerce shoppers who have already adopted the technology and shop on their mobile devices regularly. Our study explores typical mCommerce routines and behaviors along with issues of trust, given its long-term concern for eCommerce. The second is a diary and interview study of new and existing users of mobile payment services in North America. Participants used a variety of services, including Google Wallet, Amazon Payments, LevelUp, Square and company apps geared towards payments (e.g., Starbucks). Our results show that when it comes to shopping on mobile devices, people have few trust concerns. Yet when mobile devices are used for payments within physical stores, trust issues emerge along with prepurchase anxiety and mental model challenges. We discuss these results and show the value in adapting and developing new trust mechanisms for mCommerce. © 2016 Elsevier Ltd. All rights reserved.


Implementing linearizability at large scale and low latency

Linearizability is the strongest form of consistency for concurrent systems, but most large-scale storage systems settle for weaker forms of consistency. RIFL provides a general-purpose mechanism for converting at-least-once RPC semantics to exactly-once semantics, thereby making it easy to turn non-linearizable operations into linearizable ones. RIFL is designed for large-scale systems and is lightweight enough to be used in low-latency environments. RIFL handles data migration by associating linearizability metadata with objects in the underlying store and migrating metadata with the corresponding objects. It uses a lease mechanism to implement garbage collection for metadata. We have implemented RIFL in the RAMCloud storage system and used it to make basic operations such as writes and atomic increments linearizable; RIFL adds only 530 ns to the 13.5 μs base latency for durable writes. We also used RIFL to construct a new multi-object transaction mechanism in RAMCloud; RIFL's facilities significantly simplified the transaction implementation. The transaction mechanism can commit simple distributed transactions in about 20 μs and it outperforms the H-Store main-memory database system for the TPC-C benchmark.


Penile septoplasty for congenital ventral penile curvature: results in 51 patients.

PURPOSE
The technique most widely used to correct congenital ventral penile curvature is still corporoplasty as originally described by Nesbit. We present results in patients treated with a variation of Nesbit corporoplasty used specifically for congenital ventral penile curvature.

MATERIALS AND METHODS
From June 2000 to June 2007 we treated 51 patients with congenital ventral penile curvature using modified corporoplasty (septoplasty), consisting of accessing the bed of the penile dorsal vein and excising 1 or more diamonds of tunica albuginea from it, extending in wedge-like formation 4 to 5 mm deep into the septum, until the penis is completely straightened. Patient history, clinical findings, self-photography results and the International Index of Erectile Function score were assessed. Curvature grade is expressed using the equation, 180 degrees - X, where X represents the deviation in degrees from the penis axis. Mean preoperative ventral curvature was 131.4 degrees (median 135, range 145 to 110). Of the patients 13 also had erectile dysfunction.

RESULTS
At followup postoperative mean ventral curvature was 178.3 degrees (median 179.1, range 180 to 175). A total of 49 stated that they were completely satisfied. Penile shortening was 5 to 15 mm. Compared to preoperative values there were marked improvements in the International Index of Erectile Function score in the various groups. No major postoperative complications developed. In 4 patients wound healing occurred by secondary intent.

CONCLUSIONS
This technique provides excellent straightening of the curved penis. By avoiding isolation of the whole dorsal neurovascular bundle there is no risk of neurovascular lesions. Suture perception is minimized.


Cross-Domain Metric and Multiple Kernel Learning Based on Information Theory

Learning an appropriate distance metric plays a substantial role in the success of many learning machines. Conventional metric learning algorithms have limited utility when the training and test samples are drawn from related but different domains (i.e., source domain and target domain). In this letter, we propose two novel metric learning algorithms for domain adaptation in an information-theoretic setting, allowing for discriminating power transfer and standard learning machine propagation across two domains. In the first one, a cross-domain Mahalanobis distance is learned by combining three goals: reducing the distribution difference between different domains, preserving the geometry of target domain data, and aligning the geometry of source domain data with label information. Furthermore, we devote our efforts to solving complex domain adaptation problems and go beyond linear cross-domain metric learning by extending the first method to a multiple kernel learning framework. A convex combination of multiple kernels and a linear transformation are adaptively learned in a single optimization, which greatly benefits the exploration of prior knowledge and the description of data characteristics. Comprehensive experiments in three real-world applications (face recognition, text classification, and object categorization) verify that the proposed methods outperform state-of-the-art metric learning and domain adaptation methods.


On multi-view feature learning

Sparse coding is a common approach to learning local features for object recognition. Recently, there has been an increasing interest in learning features from spatio-temporal, binocular, or other multi-observation data, where the goal is to encode the relationship between images rather than the content of a single image. We provide an analysis of multi-view feature learning, which shows that hidden variables encode transformations by detecting rotation angles in the eigenspaces shared among multiple image warps. Our analysis helps explain recent experimental results showing that transformation-specific features emerge when training complex cell models on videos. Our analysis also shows that transformation-invariant features can emerge as a by-product of learning representations of transformations.


Reducing the dimensionality of data with neural networks.

High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such "autoencoder" networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.


Learning Object Affordances: From Sensory--Motor Coordination to Imitation

Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy, and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We illustrate the benefits of the acquired knowledge in imitation games.


Deep learning via Hessian-free optimization

We develop a 2nd-order optimization method based on the “Hessian-free” approach, and apply it to training deep auto-encoders. Without using pre-training, we obtain results superior to those reported by Hinton & Salakhutdinov (2006) on the same tasks they considered. Our method is practical, easy to use, scales nicely to very large datasets, and isn’t limited in applicability to autoencoders, or any specific model class. We also discuss the issue of “pathological curvature” as a possible explanation for the difficulty of deeplearning and how 2nd-order optimization, and our method in particular, effectively deals with it.


Hierarchical Singular Value Decomposition of Tensors

We define the hierarchical singular value decomposition (SVD) for tensors of order d ≥ 2. This hierarchical SVD has properties like the matrix SVD (and collapses to the SVD in d = 2), and we prove these. In particular, one can find low rank (almost) best approximations in a hierarchical format (H-Tucker) which requires only O((d − 1)k3 + dnk) parameters, where d is the order of the tensor, n the size of the modes and k the (hierarchical) rank. The H-Tucker format is a specialization of the Tucker format and it contains as a special case all (canonical) rank k tensors. Based on this new concept of a hierarchical SVD we present algorithms for hierarchical tensor calculations allowing for a rigorous error analysis. The complexity of the truncation (finding lower rank approximations to hierarchical rank k tensors) is in O((d−1)k4 +dnk2) and the attainable accuracy is just 2–3 digits less than machine precision.


Contact-less Transfer of Energy by means of a Rotating Transformer

This paper examines the electrical properties of a rotating transformer used for contact-less transfer of energy to rotating equipment. Two winding layouts are analysed theoretically and experimentally. The reluctance modelling provides a deep understanding of how the geometry of the core and windings affect the electrical behaviour of the component. Theoretical calculations, measured results and finite element analysis are used to compare the proposed layouts. Basic design guidelines are given to adjust the leakage and magnetising inductances. The selection of a phaseshifted full bridge converter is suggested for applications at the power level of 1kW.


Anarchy, stability, and utopia: creating better matchings

Historically, the analysis of matching has centered on designing algorithms to produce stable matchings as well as on analyzing the incentive compatibility of matching mechanisms. Less attention has been paid to questions related to the social welfare of stable matchings in cardinal utility models. We examine the loss in social welfare that arises from requiring matchings to be stable, the natural equilibrium concept under individual rationality. While this loss can be arbitrarily bad under general preferences, when there is some structure to the underlying graph corresponding to natural conditions on preferences, we prove worst case bounds on the price of anarchy. Surprisingly, under simple distributions of utilities, the average case loss turns out to be significantly smaller than the worst-case analysis would suggest. Furthermore, we derive conditions for the existence of approximately stable matchings that are also close to socially optimal, demonstrating that adding small switching costs can make socially (near-)optimal matchings stable. Our analysis leads to several concomitant results of interest on the convergence of decentralized partner-switching algorithms, and on the impact of heterogeneity of tastes on social welfare.


Recovering human body configurations: combining segmentation and recognition

The goal of this work is to detect a human figure image and localize his joints and limbs along with their associated pixel masks. In this work we attempt to tackle this problem in a general setting. The dataset we use is a collection of sports news photographs of baseball players, varying dramatically in pose and clothing. The approach that we take is to use segmentation to guide our recognition algorithm to salient bits of the image. We use this segmentation approach to build limb and torso detectors, the outputs of which are assembled into human figures. We present quantitative results on torso localization, in addition to shortlisted full body configurations.


1 . 1 Definition of adjustable autonomy and human-centered autonomous

We expect a variety of autonomous systems, from rovers to life-support systems, to play a critical role in the success of manned Mars missions. The crew and ground support personnel will want to control and be informed by these systems at varying levels of detail depending on the situation. Moreover, these systems will need to operate safely in the presence of people and cooperate with them effectively. We call such autonomous systems human-centered in contrast with traditional Òblack-boxÓ autonomous systems. Our goal is to design a framework for human-centered autonomous systems that enables users to interact with these systems at whatever level of control is most appropriate whenever they so choose, but minimize the necessity for such interaction. This paper discusses on-going research at the NASA Ames Research Center and the Johnson Space Center in developing human-centered autonomous systems that can be used for a manned Mars mission.


Fat grafting in facial rejuvenation.

Patients with significant facial atrophy and age-related loss of facial fat generally achieve suboptimal improvement from both surface treatments of facial skin and surgical lifts. Restoring lost facial volume by fat grafting is a powerful technique that is now acknowledged by most plastic surgeons and other physicians engaged in treating the aging face as one of the most important advances in aesthetic surgery. Properly performed, the addition of fat to areas of the face that have atrophied because of age or disease can produce a significant and sustained improvement in appearance that is unobtainable by other means.


A UWB based Localization System for Indoor Robot Navigation

For robots to become more popular for domestic applications, the short comings of current indoor navigation technologies have to be overcome. In this paper, we propose the use of UWB-IR for indoor robot navigation. Various parts of an actual implementation of a UWB-IR based robot navigation system such as system architecture, RF sub-system design, antennas and localization algorithms are discussed. It is shown that by properly addressing the various issues, a localization error of less than 25 cm can be achieved at all points within a realistic indoor localization space.


Blockchain-Free Cryptocurrencies : A Framework for Truly Decentralised Fast Transactions

The “blockchain” distributed ledger pioneered by Bitcoin is effective at preventing double-spending, but inherently attracts (1) “user cartels” and (2) incompressible delays, as a result of linear verification and a winner-takes-all incentive lottery. We propose to forgo the “blocks” and “chain” entirely, and build a truly distributed ledger system based on a lean graph of cross-verifying transactions, which now become the main and only objects in the system. A fully distributed consensus mechanism, based on progressive proofs of work with predictable incentives, ensures rapid convergence even across a large network of unequal participants, who all get rewards working at their own pace. Graph-based affirmation fosters snappy response through automatic scaling, while application-agnostic design supports all modern cryptocurrency features such as multiple denominations, swaps, securitisation, scripting, smart contracts, etc. We prove theoretically, and experimentally verify, our proposal to show it achieves a crucial “convergence” property — meaning that any valid transaction entering the system will quickly become enshrined into the ancestry upon which all future transactions will rest.


Fuzzy Adaptive Sliding Mode Control of 6 DOF Parallel Manipulator with Electromechanical Actuators in Cartesian Space Coordinates

This paper proposes a fuzzy adaptive sliding mode controller in Cartesian space coordinates for trajectory tracking of the 6 DOF parallel manipulator with considering dynamics of electromechanical actuators. The 6-DOF sensors, may be very expensive or impossible to find at the desired accuracies; and also, especially at constant speeds, will contain errors; therefore, it is better to use LVDT position sensors and then, solving the forward kinematics problem using an iterative artificial neural network strategy, Instead of using the Numerical methods such as the Newton-Raphson method, that heavy computational load imposes to the system. This controller consists of a sliding mode control and adaptive learning algorithms to adjust uncertain parameters of system, that relax the requirement of bounding parameter values and not depending upon any parameter initialization conditions, and fuzzy approximators to estimate the plant’s unknown nonlinear functions, and robustifying control terms to compensate of approximation errors; so that closedloop stability and finite-time convergence of tracking errors can be guaranteed. Simulation results demonstrate that the proposed control strategy can achieve favorable control performance with regard to uncertainties, nonlinearities and external disturbances.


Spatial Planning: A Configuration Space Approach

This paper presents algorithms for computing constraints on the position of an object due to the presence of ther objects. This problem arises in applications that require choosing how to arrange or how to move objects without collisions. The approach presented here is based on characterizing the position and orientation of an object as a single point in a configuration space, in which each coordinate represents a degree of freedom in the position or orientation of the object. The configurations forbidden to this object, due to the presence of other objects, can then be characterized as regions in the configuration space, called configuration space obstacles. The paper presents algorithms for computing these configuration space obstacles when the objects are polygons or polyhedra.


Design and Describe REST API without Violating REST: A Petri Net Based Approach

As REST architectural style gains popularity in the web service community, there is a growing concern and debate on how to design Restful web services (REST API) in a proper way. We attribute this problem to lack of a standard model and language to describe a REST API that respects all the REST constraints. As a result, many web services that claim to be REST API are not hypermedia driven as prescribed by REST. This situation may lead to REST APIs that are not as scalable, extensible, and interoperable as promised by REST. To address this issue, this paper proposes REST Chart as a model and language to design and describe REST API without violating the REST constraints. REST Chart models a REST API as a special type of Colored Petri Net whose topology defines the REST API and whose token markings define the representational state space of user agents using that API. We demonstrate REST Chart with an example REST API. We also show how REST Chart can support efficient content negotiation and reuse hybrid representations to broaden design choices. Furthermore, we argue that the REST constraints, such as hypermedia driven and statelessness, can either be enforced naturally or checked automatically in REST Chart.


Power electronics for renewable energy systems: Wind turbine and photovoltaic systems

The use of renewable energy sources are increased because of the depletion of natural resources and the increasing pollution level from energy production. The wind energy and the solar energy are most widely used among the renewable energy sources. Power electronics is needed in almost all kinds of renewable energy system. It controls the renewable source and interfaces with the load effectively, which can be grid-connection or working in stand-alone mode. In this paper, overview of wind and photovoltaic energy systems are introduced. Next, the power electronic circuits behind the most common wind and photovoltaic configurations are discussed. Finally, their controls and important requirements for grid connection are explained.


The LightCycler: a microvolume multisample fluorimeter with rapid temperature control.

Experimental and commercial microvolume fluorimeters with rapid temperature control are described. Fluorescence optics adopted from flow cytometry were used to interrogate 1-10-microL samples in glass capillaries. Homogeneous temperature control and rapid change of sample temperatures (10 degrees C/s) were obtained by a circulating air vortex. A prototype 2-color, 32-sample version was constructed with a xenon arc for excitation, separate excitation and emission paths, and photomultiplier tubes for detection. The commercial LightCycler, a 3-color, 24-sample instrument, uses a blue light-emitting diode for excitation, paraxial epi-illumination through the capillary tip and photodiodes for detection. Applications include analyte quantification and nucleic acid melting curves with fluorescent dyes, enzyme assays with fluorescent substrates and techniques that use fluorescence resonance energy transfer. Microvolume capability allows analysis of very small or expensive samples. As an example of one application, rapid cycle DNA amplification was continuously monitored by three different fluorescence techniques, Which included using the double-stranded DNA dye SYBR Green I, a dual-labeled 5'-exonuclease hydrolysis probe, and adjacent fluorescein and Cy5z-labeled hybridization probes. Complete amplification and analysis requires only 10-15 min.


The Internet of things.

Keynote David Rose The New Vanguard for Business: Connectivity, Design, and the Internet of Things The Internet of Things is the hottest topic of the moment a shift predicted to be as momentous as the impact of the internet itself. The internet has allowed us to share ideas and data largely input by humans. What about a world where data from objects as diverse as umbrellas, fridges, and gas tanks all flows through the internet?


Kinetic Energy of Wind-Turbine Generators for System Frequency Support

As wind power penetration increases and fossil plants are retired, it is feared that there will be insufficient kinetic energy (KE) from the plants to support the system frequency. This paper shows the fear is groundless because the high inertias (H cong 4 seconds) of wind turbine-generators (WTGs) can be integrated to provide frequency support during generation outage.


Enhancing financial performance with social media: An impression management perspective

The growing plethora of social media outlets have sparked both opportunity and concern in how organizations manage their corporate image. While previous research has examined the various problems associated with negative, word-of-mouth transference of information occurring simultaneously throughout many networks in social media, this paper seeks to address social media usage in impression management (IM). Specifically, we seek to answer two questions: Do IM direct-assertive strategies in social media impact a firm’s financial performance? And which social media strategies impact a firm’s financial performance? To analyze these questions in depth, we use text mining to collect and analyze text from a variety of social network platforms, including blogs, forums, and corporate websites, to assess how such IM strategies impact financial performance. Our results provide text mining validation that social media have a 1 Corresponding author positive impact on IM. We also provide further understanding of how social media strengthens organizations’ communication with various internal and external stakeholders. Lastly, we provide future research ideas concerning social media’s usage in IM.


Low power/low voltage high speed CMOS differential track and latch comparator with rail-to-rail input

A new CMOS differential latched comparator suitable for low voltage, low-power application is presented. The circuit consists of a constant-gm rail-to-rail common-mode operational transconductance amplifier followed by a regenerative latch in a track and latch configuration to achieve a relatively constant delay. The use of a track and latch minimizes the total number of gain stages required for a given resolution. Potential offset from the constant-gm differential input stage, estimated as the main source of offset, can be minimized by proper choice of transistors sizes. Simulation results show that the circuit requires less than 86 μA with a supply voltage of 1.65 V in a standard CMOS 0.18 μm digital process. The average delay is less than 1 ns and is approximately independent of the common-mode input voltage.


From semantics to syntax and back again: Argument structure in the third year of life

An essential part of the human capacity for language is the ability to link conceptual or semantic representations with syntactic representations. On the basis of data from spontaneous production, suggested that young children acquire such links on a verb-by-verb basis, with little in the way of a general understanding of linguistic argument structure. Here, we suggest that a receptive understanding of argument structure--including principles linking syntax and conceptual/semantic structure--appears earlier. In a forced-choice pointing task we have shown that toddlers in the third year of life can map a single scene (involving a novel causative action paired with a novel verb) onto two distinct syntactic frames (transitive and intransitive). This suggests that even before toddlers begin generalizing argument structure in their own speech, they have some representation of conceptual/semantic categories, syntactic categories, and a system that links the two.


Physical Discipline and Behavior Problems in African American , European American , and Hispanic Children : Emotional Support as a Moderator

Using data collected over a 6-year period on a sample of 1,039 European American children, 550 African American children, and 401 Hispanic children from the children of the National Longitudinal Survey of Youth, this study assessed whether maternal emotional support of the child moderates the relation between spanking and behavior problems. Children were 4–5 years of age in the first of 4 waves of data used (1988, 1990, 1992, 1994). At each wave, mothers reported their use of spanking and rated their children’s behavior problems. Maternal emotional support of the child was based on interviewer observations conducted as part of the Home Observation for Measurement of the Environment. For each of the 3 racial-ethnic groups, spanking predicted an increase in the level of problem behavior over time, controlling for income-needs ratio and maternal emotional support. Maternal emotional support moderated the link between spanking and problem behavior. Spanking was associated with an increase in behavior problems over time in the context of low levels of emotional support, but not in the context of high levels of emotional support. This pattern held for all 3 racial-ethnic groups.


Depressive symptomatology among university students in Denizli, Turkey: prevalence and sociodemographic correlates.

AIM
To determine overall and subgroup prevalence of depressive symptomatology among university students in Denizli, Turkey during the 1999-2000 academic year, and to investigate whether sociodemographic factors were associated with depressive symptoms in university students.

METHODS
A stratified probability sample of 504 Turkish university students (296 male, 208 female) was used in a cross-sectional study. Data were obtained by self-administered questionnaire, including questions on sociodemographic characteristics and problem areas. The revised Beck Depression Inventory (BDI) was used to determine depressive symptoms of the participants. BDI scores 17 or higher were categorized as depressive for logistic regression analysis. Student t-test and linear regression were used for continuous data analysis.

RESULTS
Out of all participants, 26.2% had a BDI score 17 or higher. The prevalence of depressive symptoms increased to 32.1% among older students, 34.7% among students with low socioeconomic status, 31.2% among seniors, and 62.9% among students with poor school performance. The odds ratio of depressive symptoms was 1.84 (95% confidence interval [CI], 1.03-3.28) in students with low socioeconomic status and 7.34 (95% CI, 3.36-16.1) in students with poor school performance in the multivariate logistic model. The participants identified several problem areas: lack of social activities and shortage of facilities on the campus (69.0%), poor quality of the educational system (54.8%), economic problems (49.3%), disappointment with the university (43.2%), and friendship problems (25.9%).

CONCLUSIONS
Considering the high frequency of depressive symptoms among Turkish university students, a student counseling service offering mental health assistance is necessary. This service should especially find the way to reach out to poor students and students with poor school performance.


Momentum Investment Strategies of Mutual Funds , Performance Persistence , and Survivorship Bias

We show that the persistent use of momentum investment strategies by mutual funds has important implications for the performance persistence and survivorship bias controversies. Using mutual fund portfolio holdings from a database free of survivorship bias, we  ̄nd that the best performing funds during one year are the best performers during the following year, with the exception of 1981, 1983, 1988, and 1989. This pattern corresponds exactly to the pattern exhibited by the momentum e®ect in stock returns,  ̄rst documented by Jegadeesh and Titman (1993) and recently studied by Chan, Jegadeesh, and Lakonishok (1996). Our evidence points not only to the momentum e®ect in stock returns, but to the persistent, active use of momentum strategies by mutual funds as the reasons for performance persistence. Moreover, essentially no persistence remains after controlling for the one-year momentum e®ect in stock returns. We also explain why most recent studies have found that survivorship bias is a relatively small concern. Funds that were the best performers during one year are the worst performers during the following year whenever the momentum e®ect in stock returns is absent, and these funds tend to disappear with a frequency not appreciably lower than that of consistently poor performers. Therefore, the pool of non-surviving funds is more representative of the cross-section of all funds than previously thought. Speci ̄cally, we  ̄nd a di®erence of only 20 basis points per year in risk-adjusted pre-expense returns between the average fund and the average surviving fund.


Local Feature Based Multiple Object Instance Identification Using Scale and Rotation Invariant Implicit Shape Model

In this paper, we propose a Scale and Rotation Invariant Implicit Shape Model (SRIISM), and develop a local feature matching based system using the model to accurately locate and identify large numbers of object instances in an image. Due to repeated instances and cluttered background, conventional methods for multiple object instance identification suffer from poor identification results. In the proposed SRIISM, we model the joint distribution of object centers, scale, and orientation computed from local feature matches in Hough voting, which is not only invariant to scale changes and rotation of objects, but also robust to false feature matches. In the multiple object instance identification system using SRIISM, we apply a fast 4D bin search method in Hough space with complexity O(n), where n is the number of feature matches, in order to segment and locate each instance. Futhermore, we apply maximum likelihood estimation (MLE) for accurate object pose detection. In the evaluation, we created datasets simulating various industrial applications such as pick-and-place and inventory management. Experiment results on the datasets show that our method outperforms conventional methods in both accuracy (5%-30% gain) and speed (2x speed up).


Robust Object Detection with Interleaved Categorization and Segmentation

This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure-ground segmentation as two interleaved processes that closely collaborate towards a common goal. As shown in our work, the tight coupling between those two processes allows them to benefit from each other and improve the combined performance. The core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the Generalized Hough Transform. The resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result. This segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background. Moreover, the information from where in the image a hypothesis draws its support is employed in an MDL based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion. An extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories, including both rigid and articulated objects. In addition, its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems.


Real-time object detection and localization with SIFT-based clustering

a r t i c l e i n f o Keywords: Pick-and-place applications Machine vision for industrial applications SIFT This paper presents an innovative approach for detecting and localizing duplicate objects in pick-and-place applications under extreme conditions of occlusion, where standard appearance-based approaches are likely to be ineffective. The approach exploits SIFT keypoint extraction and mean shift clustering to partition the correspondences between the object model and the image onto different potential object instances with real-time performance. Then, the hypotheses of the object shape are validated by a projection with a fast Euclidean transform of some delimiting points onto the current image. Moreover, in order to improve the detection in the case of reflective or transparent objects, multiple object models (of both the same and different faces of the object) are used and fused together. Many measures of efficacy and efficiency are provided on random disposals of heavily-occluded objects, with a specific focus on real-time processing. Experimental results on different and challenging kinds of objects are reported. Information technologies have become in the last decades a fundamental aid for helping the automation of everyday people life and industrial processes. Among the many different disciplines contributing to this process, machine vision and pattern recognition have been widely used for industrial applications and especially for robot vision. A typical need is to automate the pick-and-place process of picking up objects, possibly performing some tasks, and then placing them down on a different location. Most of the pick-and-place systems are basically composed of robotic systems and sensors. The sensors are in charge of driving the robot arms to the right 3D location and possibly orientation of the next object to be picked up, according to the robot's degrees of freedom. Object picking can be very complicated if the scene is not well structured and constrained. The automation of object picking by using cameras, however, requires to detect and localize objects in the scene; they are crucial tasks for several other computer vision applications, such as image/ video retrieval [1,2], or automatic robot navigation [3]. This paper describes a new complete approach for pick-and-place processes with the following challenging requirements: 1. Different types of objects: the approach should work with every type of object of different dimension and complexity, with reflective surfaces or semi-transparent parts, such as in the case of pharmaceutical and cosmetic objects, often reflective or included in transparent flowpacks; 2. …


Extensibility, Safety and Performance in the SPIN Operating System

This paper describes the motivation architecture and performance of SPIN an extensible operating system SPIN provides an extension infrastructure together with a core set of extensible services that allow applica tions to safely change the operating system s interface and implementation Extensions allow an application to specialize the underlying operating system in order to achieve a particular level of performance and function ality SPIN uses language and link time mechanisms to inexpensively export ne grained interfaces to operat ing system services Extensions are written in a type safe language and are dynamically linked into the op erating system kernel This approach o ers extensions rapid access to system services while protecting the op erating system code executing within the kernel address space SPIN and its extensions are written in Modula and run on DEC Alpha workstations


Feature hashing for large scale multitask learning

Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation. In this paper we provide exponential tail bounds for feature hashing and show that the interaction between random subspaces is negligible with high probability. We demonstrate the feasibility of this approach with experimental results for a new use case --- multitask learning with hundreds of thousands of tasks.


A new approach to unwrap a 3-D fingerprint to a 2-D rolled equivalent fingerprint

For many years, fingerprints have been captured by pressing a finger against a paper or hard surface. This touch-based fingerprint acquisition introduces some problems such as distortions and deformations in the acquired images, which arise due to the contact of the fingerprint surface with the sensor platen, and degrades the recognition performance. A new touch-less fingerprint technology has been recently introduced to the market, which can address the problems with the contact-based fingerprint systems. In this paper, we propose a new algorithm for unwrapping the acquired 3-D scan of the subject's finger into a 2-D rolled equivalent image. Therefore, The resulting image can be matched with the conventional 2-D scans; it also can be used for matching unwrapped 3-D fingerprints among themselves with the 2-D fingerprint matching algorithms. The algorithm is based on curvature analysis of the 3-D surface. The quality of the resulting image is evaluated and analyzed using NIST fingerprint image software.


Functional Search-based Testing from State Machines

The application of metaheuristic search techniques in test data generation has been extensively investigated in recent years. Most studies, however, have concentrated on the application of such techniques in structural testing. The use of search-based techniques in functional testing is less frequent, the main cause being the implicit nature of the specification. This paper investigates the use of search-based techniques for functional testing, having the specification in form of a state machine. Its purpose is to generate input data for chosen paths in a state machine, so that the parameter values provided to the methods satisfy the corresponding guards and trigger the desired transitions. A general form of a fitness function for an individual path is presented and this approach is empirically evaluated using three search techniques: simulated annealing, genetic algorithms and particle swarm optimization.


Ensemble Pruning Using Reinforcement Learning

Multiple Classifier systems have been developed in order to improve classification accuracy using methodologies for effective classifier combination. Classical approaches use heuristics, statistical tests, or a meta-learning level in order to find out the optimal combination function. We study this problem from a Reinforcement Learning perspective. In our modeling, an agent tries to learn the best policy for selecting classifiers by exploring a state space and considering a future cumulative reward from the environment. We evaluate our approach by comparing with state-of-the-art combination methods and obtain very promising results.


Improved ID3 algorithm

as the classical algorithm of the decision tree classification algorithm, ID3 is famous for the merits of high classifying speed easy, strong learning ability and easy construction. But when use it to classify, there does exist the problem of inclining to chose attributions which has many values, which affects its practicality. This paper for solving the problem a decision tree algorithm based on attribute-importance is proposed. The improved algorithm uses attribute-importance to increase information gain of attribution which has fewer attributions and compares ID3 with improved ID3 by an example. The experimental analysis of the data show that the improved ID3 algorithm can get more reasonable and more effective rules.


Convolutional Neural Network Information Fusion based on Dempster-Shafer Theory for Urban Scene Understanding

Dempster-Shafer theory provides a sensor fusion framework that autonomously accounts for obstacle occlusion in dynamic, urban environments. However, to discern static and moving obstacles, the Dempster-Shafer approach requires manual tuning of parameters dependent on the situation and sensor types. The proposed methodology utilizes a deep fully convolutional neural network to improve the robust performance of the information fusion algorithm in distinguishing static and moving obstacles from navigable space. The image-like spatial structure of probabilistic occupancy allows a semantic segmentation framework to discern classes for individual grid cells. A subset of the KITTI LIDAR tracking dataset in combination with semantic map data was used for the information fusion task. The probabilistic occupancy grid output of the Dempster-Shafer information fusion algorithm was provided as input to the neural network. The network then learned an offset from the original DST result to improve semantic labeling performance. The proposed framework outperformed the baseline approach in the mean intersection over union metric reaching 0.546 and 0.531 in the validation and test sets respectively. However, little improvement was achieved in discerning moving and static cells due to the limited dataset size. To improve model performance in future work, the dataset will be expanded to facilitate more effective learning, and temporal data will be fed through individual convolutional networks prior to being merged in channels as input to the main network.


MARVEL: A Large-Scale Image Dataset for Maritime Vessels


Baseline Mechanisms for Enterprise Governance of IT in SMEs

Information Technology (IT) has become fundamental for most organizations since it is vital to their sustainability, development and success. This pervasive use led organizations to a critical dependency on IT. Despite the benefits, it exposes organizations to several risks. Hence, a significant focus on Enterprise Governance of IT (EGIT) is required. EGIT involve the definition and implementation of processes, structures and relational mechanisms to support the business/IT alignment and the creation of business value from IT investments. These mechanisms act as a mean to direct and operationalize IT-related decision-making. However, identifying the appropriate mechanisms is a complex task since there are internal and external contingency factors that influence the design and implementation of EGIT. Small and Medium Enterprises (SMEs) are considered key elements to promote economic growth, job creation, social integration and innovation. The use of IT in these organizations can imply severe consequences on survival and growth in highly competitive markets, becoming critical in this globalization era. Several studies were developed to investigate and identify EGIT mechanisms in different contingencies but very few focused on the organization' size criterion. However, SMEs have particular characteristics that require further investigation. Therefore, our goal in this research is to evaluate EGIT mechanisms in a SME's context and identify a baseline of mechanisms for SMEs using semi-structured interviews with IT experts that have experience in SMEs. The article ends by presenting contributions, limitations and future work.


Operability of Folded Microstrip Patch-Type Tag Antenna in the UHF RFID Bands Within 865-928 MHz

The emerging use of passive radio frequency identification (RFID) systems at ultra-high-frequency (UHF) spectrum requires application specific tag antenna designs for challenging applications. Many objects containing conductive material need novel tag antenna designs for reliable identification. The operability of folded microstrip patch-type tag antenna for objects containing conductive material is analyzed and tested within the UHF RFID bands used at the moment mainly in Europe and in North and South America. First the operability of the tag antenna design and the effects of conductive material are modeled with simulations based on finite element method (FEM). The performance of the tag antenna design affixed to a package containing metallic foil is verified with read range measurements. The results show that the antenna design is operable in both of the UHF RFID bands within 865-928 MHz


Lessons from the journey: a query log analysis of within-session learning

The Internet is the largest source of information in the world. Search engines help people navigate the huge space of available data in order to acquire new skills and knowledge. In this paper, we present an in-depth analysis of sessions in which people explicitly search for new knowledge on the Web based on the log files of a popular search engine. We investigate within-session and cross-session developments of expertise, focusing on how the language and search behavior of a user on a topic evolves over time. In this way, we identify those sessions and page visits that appear to significantly boost the learning process. Our experiments demonstrate a strong connection between clicks and several metrics related to expertise. Based on models of the user and their specific context, we present a method capable of automatically predicting, with good accuracy, which clicks will lead to enhanced learning. Our findings provide insight into how search engines might better help users learn as they search.


Dual-Band Multi-Pole Directional Filter for Microwave Multiplexing Applications

A novel microstrip directional filter for multiplexing applications is presented. This device uses composite right-left-handed transmission lines and resonators to achieve dual-band frequency response. In addition, by cascading two or more stages using dual frequency immitance inverters, multi-pole configurations can be obtained. Simulation and experimental results are presented with good agreement.


Multi-domain spoken language understanding with transfer learning

This paper addresses the problem of multi-domain spoken language understanding (SLU) where domain detection and domaindependent semantic tagging problems are combined. We present a transfer learning approach to the multi-domain SLU problem in which multiple domain-specific data sources can be incorporated. To implement multi-domain SLU with transfer learning, we introduce a triangular-chain structured model. This model effectively learns multiple domains in parallel, and allows use of domain-independent patterns among domains to create a better model for the target domain. We demonstrate that the proposed method outperforms baseline models on dialog data for multi-domain SLU problems. 2009 Elsevier B.V. All rights reserved.


Can Computers Overcome Humans? Consciousness Interaction and its Implications

Can computers overcome human capabilities? This is a paradoxical and controversial question, particularly because there are many hidden assumptions. This article focuses on that issue putting on evidence some misconception related to future generations of machines and the understanding of the brain. It will discuss to what extent computers might reach human capabilities, and how it would be possible only if the computer is a conscious machine. However, it will be shown that if the computer is conscious, an interference process due to consciousness would affect the information processing of the system. Therefore, it might be possible to make conscious machines to overcome human capabilities, which will have similar limitations than humans. In other words, trying to overcome human capabilities with computers implies the paradoxical conclusion that a computer will never overcome human capabilities at all, or if the computer does, it should not be considered as a computer anymore.


High-Speed Parallel Decimal Multiplication with Redundant Internal Encodings

The decimal multiplication is one of the most important decimal arithmetic operations which have a growing demand in the area of commercial, financial, and scientific computing. In this paper, we propose a parallel decimal multiplication algorithm with three components, which are a partial product generation, a partial product reduction, and a final digit-set conversion. First, a redundant number system is applied to recode not only the multiplier, but also multiples of the multiplicand in signed-digit (SD) numbers. Furthermore, we present a multioperand SD addition algorithm to reduce the partial product array. Finally, a digit-set conversion algorithm with a hybrid prefix network to decrease the number of the logic gates on the critical path is discussed. An analysis of the timing delay and an HDL model synthesized under 90 nm technology show that by considering the tradeoff of designs among three components, the overall delay of the proposed 16 × 16-digit multiplier takes about 11 percent less timing delay with 2 percent less area compared to the current fastest design.


Survey of Large-Scale MIMO Systems

The escalating teletraffic growth imposed by the proliferation of smartphones and tablet computers outstrips the capacity increase of wireless communications networks. Furthermore, it results in substantially increased carbon dioxide emissions. As a powerful countermeasure, in the case of full-rank channel matrices, MIMO techniques are potentially capable of linearly increasing the capacity or decreasing the transmit power upon commensurately increasing the number of antennas. Hence, the recent concept of large-scale MIMO (LS-MIMO) systems has attracted substantial research attention and been regarded as a promising technique for next-generation wireless communications networks. Therefore, this paper surveys the state of the art of LS-MIMO systems. First, we discuss the measurement and modeling of LS-MIMO channels. Then, some typical application scenarios are classified and analyzed. Key techniques of both the physical and network layers are also detailed. Finally, we conclude with a range of challenges and future research topics.


Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more

Designations used by companies to distinguish their products are often claimed as trademarks or registered trademarks. In all instances in which Morgan Kaufmann Publishers is aware of a claim, the product names appear in initial capital or all capital letters. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopying, scanning, or otherwise—without prior written permission of the publisher. may also complete your request on-line via the Elsevier homepage (http://elsevier.com), by selecting " Support & Contact " then " Copyright and Permission " and then " Obtaining Permissions. " Page 197: " Make a new plan Stan. .. and get yourself free. Physical database design : the database professional's guide to exploiting indexes, views, storage, and more / Sam Lightstone, Toby Teorey, and Tom Nadeau. p. cm.-(The Morgan Kaufmann series in database management systems) Includes bibliographical references and index.


Area and length minimizing flows for shape segmentation

A number of active contour models have been proposed that unify the curve evolution framework with classical energy minimization techniques for segmentation, such as snakes. The essential idea is to evolve a curve (in two dimensions) or a surface (in three dimensions) under constraints from image forces so that it clings to features of interest in an intensity image. The evolution equation has been derived from first principles as the gradient flow that minimizes a modified length functional, tailored to features such as edges. However, because the flow may be slow to converge in practice, a constant (hyperbolic) term is added to keep the curve/surface moving in the desired direction. We derive a modification of this term based on the gradient flow derived from a weighted area functional, with image dependent weighting factor. When combined with the earlier modified length gradient flow, we obtain a partial differential equation (PDE) that offers a number of advantages, as illustrated by several examples of shape segmentation on medical images. In many cases the weighted area flow may be used on its own, with significant computational savings.


Research Advances in Cloud Computing

Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services. It represents an evolution of cloud programmingmodels, abstractions, and platforms, and is a testament to the maturity and wide adoption of cloud technologies. In this chapter, we survey existing serverless platforms from industry, academia, and open-source projects, identify key characteristics and use cases, and describe technical challenges and open problems. I. Baldini · P. Castro (B) · K. Chang · P. Cheng · S. Fink · N. Mitchell · V. Muthusamy · R. Rabbah · A. Slominski IBM Research, New York, USA e-mail: ioana@us.ibm.com P. Castro e-mail: castrop@us.ibm.com K. Chang e-mail: Kerry.Chang@ibm.com P. Cheng e-mail: perry@us.ibm.com S. Fink e-mail: sjfink@us.ibm.com N. Mitchell e-mail: nickm@us.ibm.com V. Muthusamy (B) e-mail: vmuthus@us.ibm.com R. Rabbah e-mail: rabbah@us.ibm.com A. Slominski (B) e-mail: aslom@us.ibm.com V. Ishakian (B) Bentley University, Waltham, USA e-mail: vishakian@bentley.edu P. Suter Two Sigma, New York, USA © Springer Nature Singapore Pte Ltd. 2017 S. Chaudhary et al. (eds.), Research Advances in Cloud Computing, DOI 10.1007/978-981-10-5026-8_1 1


Primed for Violence : The Role of Gender Inequality in Predicting Internal Conflict

We know, most notably through Ted Gurr’s research, that ethnic discrimination can lead to ethnopolitical rebellion–intrastate conflict. I seek to discover what impact, if any, gender inequality has on intrastate conflict. Although democratic peace scholars and others highlight the role of peaceful domestic behavior in predicting state behavior, many scholars have argued that a domestic environment of inequality and violenceFstructural and cultural violenceFresults in a greater likelihood of violence at the state and the international level. This project contributes to this line of inquiry and further tests the grievance theory of intrastate conflict by examining the norms of violence that facilitate a call to arms. And in many ways, I provide an alternative explanation for the significance of some of the typical economic measuresFthe greed theoryFbased on the link between discrimination, inequality, and violence. I test whether states characterized by higher levels of gender inequality are more likely to experience intrastate conflict. Ultimately, the basic link between gender inequality and intrastate conflict is confirmedFstates characterized by gender inequality are more likely to experience intrastate conflict, 1960–2001.


Sales Taxes and Internet Commerce∗

We estimate the sensitivity of Internet retail purchasing to sales taxes using data from the eBay marketplace. Our first approach exploits the fact that seller locations are revealed only after buyers have expressed interest in an item by clicking on its listing. We use millions of location “surprises”to estimate price elasticities with respect to the charged sales tax. We then use aggregated data to estimate cross-state substitution parameters, and substitution between offl ine and online purchases, relying both on cross-sectional variation in state and local sales taxes, and on changes in these rates over time. We find substantial sensitivity to sales taxes. Using our item-level approach, we find an average price elasticity of around -2 for interested buyers. Using our aggregate approach, we find that a one percentage point increase in a state’s sales tax increases online purchases by state residents by just under two percent, but decreases their online purchases from home-state retailers by 3-4 percent. (JEL: D12, H20, H71, L81) ∗We are grateful to Glenn Ellison and many seminar participants for useful comments. We appreciate support from the Stanford Institute for Economic Policy Research and the Toulouse Network on Information Technology. Data access for this study was obtained under a consulting agreement between the Stanford authors (Einav, Knoepfle and Levin) and eBay Research. †Department of Economics, Stanford University, Stanford, CA 94305-6072 (Einav, Knoepfle, Levin), NBER (Einav and Levin), and eBay Labs (Sundaresan). Email: leinav@stanford.edu, knoepfle@stanford.edu, jdlevin@stanford.edu, nsundaresan@ebay.com.


Internet Web servers: workload characterization and performance implications

This paper presents a workload characterization study for Internet Web servers. Six different data sets are used in the study: three from academic environments, two from scientific research organizations, and one from a commercial Internet provider. These data sets represent three different orders of magnitude in server activity, and two different orders of magnitude in time duration, ranging from one week of activity to one year. The workload characterization focuses on the document type distribution, the document size distribution, the document referencing behavior, and the geographic distribution of server requests. Throughout the study, emphasis is placed on finding workload characteristics that are common to all the data sets studied. Ten such characteristics are identified. The paper concludes with a discussion of caching and performance issues, using the observed workload characteristics to suggest performance enhancements that seem promising for Internet Web servers.


TIARA: a visual exploratory text analytic system

In this paper, we present a novel exploratory visual analytic system called TIARA (Text Insight via Automated Responsive Analytics), which combines text analytics and interactive visualization to help users explore and analyze large collections of text. Given a collection of documents, TIARA first uses topic analysis techniques to summarize the documents into a set of topics, each of which is represented by a set of keywords. In addition to extracting topics, TIARA derives time-sensitive keywords to depict the content evolution of each topic over time. To help users understand the topic-based summarization results, TIARA employs several interactive text visualization techniques to explain the summarization results and seamlessly link such results to the original text. We have applied TIARA to several real-world applications, including email summarization and patient record analysis. To measure the effectiveness of TIARA, we have conducted several experiments. Our experimental results and initial user feedback suggest that TIARA is effective in aiding users in their exploratory text analytic tasks.


Dynamic topic models

A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics. Variational approximations based on Kalman filters and nonparametric wavelet regression are developed to carry out approximate posterior inference over the latent topics. In addition to giving quantitative, predictive models of a sequential corpus, dynamic topic models provide a qualitative window into the contents of a large document collection. The models are demonstrated by analyzing the OCR'ed archives of the journal Science from 1880 through 2000.


EventRiver: Visually Exploring Text Collections with Temporal References

Many text collections with temporal references, such as news corpora and weblogs, are generated to report and discuss real life events. Thus, event-related tasks, such as detecting real life events that drive the generation of the text documents, tracking event evolutions, and investigating reports and commentaries about events of interest, are important when exploring such text collections. To incorporate and leverage human efforts in conducting such tasks, we propose a novel visual analytics approach named EventRiver. EventRiver integrates event-based automated text analysis and visualization to reveal the events motivating the text generation and the long term stories they construct. On the visualization, users can interactively conduct tasks such as event browsing, tracking, association, and investigation. A working prototype of EventRiver has been implemented for exploring news corpora. A set of case studies, experiments, and a preliminary user test have been conducted to evaluate its effectiveness and efficiency.


Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora

Mining cluster evolution from multiple correlated time-varying text corpora is important in exploratory text analytics. In this paper, we propose an approach called evolutionary hierarchical Dirichlet processes (EvoHDP) to discover interesting cluster evolution patterns from such text data. We formulate the EvoHDP as a series of hierarchical Dirichlet processes~(HDP) by adding time dependencies to the adjacent epochs, and propose a cascaded Gibbs sampling scheme to infer the model. This approach can discover different evolving patterns of clusters, including emergence, disappearance, evolution within a corpus and across different corpora. Experiments over synthetic and real-world multiple correlated time-varying data sets illustrate the effectiveness of EvoHDP on discovering cluster evolution patterns.


The Word Tree, an Interactive Visual Concordance

We introduce the Word Tree, a new visualization and information-retrieval technique aimed at text documents. A Word Tree is a graphical version of the traditional "keyword-in-context" method, and enables rapid querying and exploration of bodies of text. In this paper we describe the design of the technique, along with some of the technical issues that arise in its implementation. In addition, we discuss the results of several months of public deployment of word trees on Many Eyes, which provides a window onto the ways in which users obtain value from the visualization.


Total variation regularization of local-global optical flow

More data fidelity terms in variational optical flow methods improve the estimation's robustness. A robust and anisotropic smoother enhances the specific fill-in process. This work presents a combined local-global (CLG) approach with total variation regularization. The combination of bilateral filtering and anisotropic (image driven) regularization is used to control the propagation phenomena. The resulted method, CLG-TV, is able to compute larger displacements in a reasonable time. The numerical scheme is highly parallelizable and runs in real-time on current generation graphic processing units.


Learning intermediate object affordances: Towards the development of a tool concept

Inspired by the extraordinary ability of young infants to learn how to grasp and manipulate objects, many works in robotics have proposed developmental approaches to allow robots to learn the effects of their own motor actions on objects, i.e., the objects affordances. While holding an object, infants also promote its contact with other objects, resulting in object-object interactions that may afford effects not possible otherwise. Depending on the characteristics of both the held object (intermediate) and the acted object (primary), systematic outcomes may occur, leading to the emergence of a primitive concept of tool. In this paper we describe experiments with a humanoid robot exploring object-object interactions in a playground scenario and learning a probabilistic causal model of the effects of actions as functions of the characteristics of both objects. The model directly links the objects' 2D shape visual cues to the effects of actions. Because no object recognition skills are required, generalization to novel objects is possible by exploiting the correlations between the shape descriptors. We show experiments where an affordance model is learned in a simulated environment, and is then used on the real robotic platform, showing generalization abilities in effect prediction. We argue that, despite the fact that during exploration no concept of tool is given to the system, this very concept may emerge from the knowledge that intermediate objects lead to significant effects when acting on other objects.


Stereovision-Based Object Segmentation for Automotive Applications

Obstacle detection and classification in a complex urban area are highly demanding, but desirable for pedestrian protection, stop & go, and enhanced parking aids. The most difficult task for the system is to segment objects from varied and complicated background. In this paper, a novel position-based object segmentationmethod has been proposed to solve this problem. According to the method proposed, object segmentation is performed in two steps: in depth map (X-Z plane) and in layered images (X-Y planes). The stereovision technique is used to reconstruct image points and generate the depth map. Objects are detected in the depth map. Afterwards, the original edge image is separated into different layers based on the distance of detected objects. Segmentation performed in these layered images can be easier and more reliable. It has been proved that the proposed method offers robust detection of potential obstacles and accurate measurement of their location and size.


Attacks on Copyright Marking Systems

In the last few years, a large number of schemes have been proposed for hiding copyright marks and other information in digital pictures, video, audio and other multimedia objects. We describe some contenders that have appeared in the research literature and in the field; we then present a number of attacks that enable the information hidden by them to be removed or otherwise rendered unusable. 1 Information Hiding Applications The last few years have seen rapidly growing interest in ways to hide information in other information. A number of factors contributed to this. Fears that copyright would be eroded by the ease with which digital media could be copied led people to study ways of embedding hidden copyright marks and serial numbers in audio and video; concern that privacy would be eroded led to work on electronic cash, anonymous remailers, digital elections and techniques for making mobile computer users harder for third parties to trace; and there remain the traditional ‘military’ concerns about hiding one’s own traffic while making it hard for the opponent to do likewise. The first international workshop on information hiding [2] brought these communities together and a number of hiding schemes were presented there; more have been presented elsewhere. We formed the view that useful progress in steganography and copyright marking might come from trying to attack all these first generation schemes. In the related field of cryptology, progress was iterative: cryptographic algorithms were proposed, attacks on them were found, more algorithms were proposed, and so on. Eventually, theory emerged: fast correlation attacks on stream ciphers and differential and linear attacks on block ciphers, now help us understand the strength of cryptographic algorithms in much more detail than before. Similarly, many cryptographic protocols were proposed and almost all the early candidates were broken, leading to concepts of protocol robustness and techniques for formal verification [6]. So in this paper, we first describe the copyright protection context in which most recent schemes have been developed; we then describe a selection of these ? The first author is grateful to Intel Corporation for financial support under the grant ‘Robustness of Information Hiding Systems’ ?? The third author is supported by a European Commission Marie-Curie grant David Aucsmith (Ed.): Information Hiding 1998, LNCS 1525, pp. 218–238, 1998. c © Springer-Verlag Berlin Heidelberg 1998 Attacks on Copyright Marking Systems 219 schemes and present a number of attacks, which break most of them. We finally make some remarks on the meaning of robustness in the context of steganography in general and copyright marking in particular. 1.1 Copyright Protection Issues Digital recording media offer many new possibilities but their uptake has been hindered by widespread fears among intellectual property owners such as Hollywood and the rock music industry that their livelihoods would be threatened if users could make unlimited perfect copies of videos, music and multimedia works. One of the first copy protection mechanisms for digital media was the serial copy management system (SCMS) introduced by Sony and Phillips for digital audio tapes in the eighties [31]. The idea was to allow consumers to make a digital audio tape of a CD they owned in order to use it (say) in their car, but not to make a tape of somebody else’s tape; thus copies would be limited to first generation only. The implementation was to include a Boolean marker in the header of each audio object. Unfortunately this failed because the hardware produced by some manufacturers did not enforce it. More recently the Digital Video Disk, also known as Digital Versatile Disk (DVD) consortium called for proposals for a copyright marking scheme to enforce serial copy management. The idea is that the DVD players sold to consumers will allow unlimited copying of home videos and time-shifted viewing of TV programmes, but cannot easily be abused for commercial piracy [19, 44]. The proposed implementation is that videos will be unmarked, or marked ‘never copy’, or ‘copy once only’; compliant players would not record a video marked ‘never copy’ and when recording one marked ‘copy once only’ would change its mark to ‘never copy’. Commercially sold videos would be marked ‘never copy’, while TV broadcasts and similar material would be marked ‘copy once only’ and home videos would be unmarked. Electronic copyright management schemes have also been proposed by European projects such as Imprimatur and CITED [45, 66, 67], and American projects such as the proposed by the Working Group on Intellectual Property Rights [69].


DPIL@FIRE2016: Overview of the Shared task on Detecting Paraphrases in Indian language

This paper explains the overview of the shared task "Detecting Paraphrases in Indian Languages" (DPIL) conducted at FIRE 2016. Given a pair of sentences in the same language, participants are asked to detect the semantic equivalence between the sentences. The shared task is proposed for four Indian languages namely Tamil, Malayalam, Hindi and Punjabi. The dataset created for the shared task has been made available online and it is the first open-source paraphrase detection corpora for Indian languages.


A comparison of trading algorithms based on machine learning classifiers : application on the S & P 500

In this paper we analyse whether different machine learning classifiers can be useful when it comes to financial data prediction. We first measure the accuracy and the volatility of our algorithm on the stocks included in the S&P 500 stock index. We then back-test it against the same benchmark for two different periods. Overall, our results show that an algorithm that trades just according to the predictions of the classifiers, is not profitable even if the accuracy obtained by the algorithm is higher than the accuracy that should be obtained according to the random walk theory. However, it can boost the results obtained by other strategies, maintaining the same level of volatility. JEL classification: C4, C8, G14.


Visual Perception of Parallel Coordinate Visualizations

Parallel coordinates is a visualization technique that provides an unbiased representation of high-dimensional data. The parallel configuration of axes treats data dimensions uniformly and is well suited for exploratory visualization. However, first-time users of parallel coordinate visualizations can find the representation confusing and difficult to understand.We used eye tracking to study how parallel coordinate visualizations are perceived, and compared the results to the optimal visual scan path required to complete the tasks. The results indicate that even first-time users quickly learn how to use parallel coordinate visualizations, pay attention to the correct task-specific areas in the visualization, and become rapidly proficient with it.


Attentive Explanations: Justifying Decisions and Pointing to the Evidence

Deep models are the defacto standard in visual decision problems due to their impressive performance on a wide array of visual tasks. However, they are frequently seen as opaque and are unable to explain their decisions. In contrast, humans can justify their decisions with natural language and point to the evidence in the visual world which supports their decisions. We propose a method which incorporates a novel explanation attention mechanism; our model is trained using textual rationals, and infers latent attention to visually ground explanations. We collect two novel datasets in domains where it is interesting and challenging to explain decisions. First, we extend the visual question answering task to not only provide an answer but also visual and natural language explanations for the answer. Second, we focus on explaining human activities in a contemporary activity recognition dataset. We extensively evaluate our model, both on the justification and pointing tasks, by comparing it to prior models and ablations using both automatic and human evaluations.


A chaincode based scheme for fingerprint feature extraction

A feature extraction method using the chaincode representation of fingerprint ridge contours is presented for use by Automatic Fingerprint Identification Systems. The representation allows efficient image quality enhancement and detection of fine minutiae feature points. For image enhancement a given fingerprint image is first binarized after a quick averaging to generate its chaincode representation. The direction field is estimated from a set of selected chaincodes. The original gray scale image is then enhanced using connected component analysis and a dynamic filtering scheme that takes advantage of the knowledge gained from the estimated direction flow of the contours. For feature extraction, the enhanced fingerprint image is carefully binarized using a local-global binarization algorithm for generating the chaincode representation. The minutiae are generated using a sophisticated ridge contour following procedure. Visual inspection of the experiment images shows that the method is very effective.


Model-Based Stabilisation of Deep Reinforcement Learning

Though successful in high-dimensional domains, deep reinforcement learning exhibits high sample complexity and suffers from stability issues as reported by researchers and practitioners in the field. These problems hinder the application of such algorithms in real-world and safety-critical scenarios. In this paper, we take steps towards stable and efficient reinforcement learning by following a model-based approach that is known to reduce agent-environment interactions. Namely, our method augments deep Q-networks (DQNs) with model predictions for transitions, rewards, and termination flags. Having the model at hand, we then conduct a rigorous theoretical study of our algorithm and show, for the first time, convergence to a stationary point. En route, we provide a counterexample showing that ’vanilla’ DQNs can diverge confirming practitioners’ and researchers’ experiences. Our proof is novel in its own right and can be extended to other forms of deep reinforcement learning. In particular, we believe exploiting the relation between reinforcement (with deep function approximators) and online learning can serve as a recipe for future proofs in the domain. Finally, we validate our theoretical results in 20 games from the Atari benchmark. Our results show that following the proposed model-based learning approach not only ensures convergence but leads to a reduction in sample complexity and superior performance. Introduction Model-free deep reinforcement learning methods have recently demonstrated impressive performance on a range of complex learning tasks (Hessel et al. 2018; Lillicrap et al. 2017; Jaderberg et al. 2017). Deep Q-Networks (DQNs) (Mnih et al. 2015), in particular, stand out as a versatile and effective tool for a wide range of applications. DQNs offer an end-to-end solution for many reinforcement learning problems by generalizing tabular Q-learning to highdimensional input spaces. Unfortunately, DQNs still suffer from a number of important drawbacks that hinder wide-spread adoption. There is a general lack of theoretical understanding to guide the use of deep Q-learning algorithms. Combining non-linear function approximation with off-policy reinforcement learning is known to lead to possible divergence issues, even in very simple tasks (Boyan and Preliminary work. Moore 1995). Despite improvements introduced by deep Qnetworks (Mnih et al. 2015), Q-learning approaches based on deep neural networks still exhibit occasional instability during the learning process, see for example (van Hasselt, Guez, and Silver 2016). In this paper, we aim to address the instability of current deep RL approaches by augmenting model-free value learning with a predictive model. We propose a hybrid model and value learning objective, which stabilises the learning process and reduces sample complexity when compared to model-free algorithms. We attribute this reduction to the richer feedback signal provided by our method compared to standard deep Q-networks. In particular, our model provides feedback at every time step based on the current prediction error, which in turn eliminates one source of sample inefficiency caused by sparse rewards. These conclusions are also in accordance with previous research conducted on linear function approximation in reinforcement learning (Parr et al. 2008; Sutton et al. 2012; Song et al. 2016). While linear function approximation results provide a motivation for model-based stabilisation, such theories fail to generalise to deep non-linear architectures (Song et al. 2016). To close this gap in the literature and demonstrate stability of our method, we prove convergence in the general deep RL setting with deep value function approximators. Theoretically analysing deep RL algorithms, however, is challenging due to nonlinear functional dependencies and objective non-convexities prohibiting convergence to globally optimal policies. As such, the best one would hope for is a stationary point (e.g., a first-order one) that guarantees vanishing gradients. Even understanding gradient behavior for deep networks in RL can be difficult due to explorationbased policies and replay memory considerations. To alleviate some of these problems, we map deep reinforcement learning to a mathematical framework explicitly designed for understanding the exploration/exploitation trade-off. Namely, we formalise the problem as an online learning game the agent plays against an adversary (i.e., the environment). Such a link allows us to study a more general problem combining notions of regret, reinforcement learning, and optimisation. Given such a link, we prove that for any > 0, regret vanishes as O ( T 1−2 lnT ) with T being the total number of rounds. This, in turn, guarantees convergence to a stationary point. ar X iv :1 80 9. 01 90 6v 1 [ cs .L G ] 6 S ep 2 01 8 Guided by the above theoretical results, we validate that our algorithm leads to faster learning by conducting experiments on 20 Atari games. Due to the high computational and budgeting demands, we chose a benchmark that is closest to our setting. Concretely, we picked DQNs as our template algorithm to improve on both theoretically and empirically. It is worth noting that extending our theoretical results to more complex RL algorithms is an orthogonal future direction. Background: Reinforcement Learning We consider a discrete time, infinite horizon, discounted Markov Decision Process (MDP) 〈 S,A, P , P , γ 〉 . Here S denotes the state space, A the set of possible actions, P (R) : S × A × R → [0, 1] is the reward function, P (S) : S × A × S → [0, 1] is the state transition function, and γ ∈ (0, 1) is a discount factor. An agent, being in state St ∈ S at time step t, executes an action at ∈ A sampled from its policy π(at|St), a conditional probability distribution where π : S × A → [0, 1]. The agent’s action elicits from the environment a reward signal rt ∈ R indicating instantaneous reward, a terminal flag ft ∈ {0, 1} indicating a terminal event that restarts the environment, and a transition to a successor state St+1 ∈ S. We assume that the sets S, A, and R are discrete. The reward rt is sampled from the conditional probability distribution P (rt|St,at). Similarly, ft ∼ P (F (ft|St,at) with P (F ) : S × A × {0, 1} → [0, 1], where a terminal event (ft = 1) restarts the environment according to some initial state distribution S0 ∼ P (S) 0 (S0). The state transition to a successor state is determined by a stochastic state transition function according to St+1 ∼ P (St+1|St,at). The agent’s goal is to maximise future cumulative reward E P (S) 0 ,π,P (R),P (F ),P (S) [ ∞ ∑ t=0 ( t ∏ t′=0 (1− ft′−1) ) γrt ] with respect to the policy π. An important quantity in RL are Q-values Q(St,at), which are defined as the expected future cumulative reward value when executing action at in state St and subsequently following policy π. Qvalues enable us to conveniently phrase the RL problem as maxπ ∑ S p (S) ∑ a π(a|S)Q(S,a), where p(S) is the stationary state distribution obtained when executing π in the environment (starting from S0 ∼ P (S) 0 (S0)). Deep Q-Networks: Value-based reinforcement learning approaches identify optimal Q-values directly using parametric function approximators Qθ(S,a), where θ ∈ Θ represents the parameters (Watkins 1989; Busoniu et al. 2010). Optimal Q-value estimates then correspond to an optimal policy π(a|S) = δa,argmaxa′Qθ(S,a′). Deep Q-networks (Mnih et al. 2015) learn a deep neural network based Qvalue approximation by performing stochastic gradient descent on the following training objective: L(θ) = ES,a,r,f,S′ [( r + (1− f)γmax a′ Qθ−(S ′,a′) −Qθ(S,a) )2] . (1) The expectation ranges over transitions S,a, r, f,S′ sampled from an experience replay memory (S′ denotes the state at the next time step). Use of this replay memory, together with the use of a separate target network Qθ− (with different parameters θ− ∈ Θ) for calculating the bootstrap values maxa′ Qθ−(S ′,a′), helps stabilise the learning process. Background: Online Learning In this paper, we employ a special form of regret minimisation games that we briefly review here. A regret minimisation game is a triple 〈Θ,F , T 〉, where Θ is a non-empty decision set, F is the set of moves of the adversary which contains bounded convex functions from R to R and T is the total number of rounds. The game commences in rounds, where at round j = 1, . . . , T , the agent chooses a prediction θj ∈ Θ and the adversary a loss function Lj ∈ F . At the end of the round, the adversary reveals its choice and the agent suffers a loss Lj(θj). In this paper, we are concerned with the full-information case where the agent can observe the complete loss function Lj at the end of each round. The goal of the game is for the agent to make successive predictions to minimise cumulative regret defined as:


Membrane Bioreactor (MBR) Technology for Wastewater Treatment and Reclamation: Membrane Fouling

The membrane bioreactor (MBR) has emerged as an efficient compact technology for municipal and industrial wastewater treatment. The major drawback impeding wider application of MBRs is membrane fouling, which significantly reduces membrane performance and lifespan, resulting in a significant increase in maintenance and operating costs. Finding sustainable membrane fouling mitigation strategies in MBRs has been one of the main concerns over the last two decades. This paper provides an overview of membrane fouling and studies conducted to identify mitigating strategies for fouling in MBRs. Classes of foulants, including biofoulants, organic foulants and inorganic foulants, as well as factors influencing membrane fouling are outlined. Recent research attempts on fouling control, including addition of coagulants and adsorbents, combination of aerobic granulation with MBRs, introduction of granular materials with air scouring in the MBR tank, and quorum quenching are presented. The addition of coagulants and adsorbents shows a significant membrane fouling reduction, but further research is needed to establish optimum dosages of the various coagulants/adsorbents. Similarly, the integration of aerobic granulation with MBRs, which targets biofoulants and organic foulants, shows outstanding filtration performance and a significant reduction in fouling rate, as well as excellent nutrients removal. However, further research is needed on the enhancement of long-term granule integrity. Quorum quenching also offers a strong potential for fouling control, but pilot-scale testing is required to explore the feasibility of full-scale application.


BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

We introduce BinaryNet, a method which trains DNNs with binary weights and activations when computing parameters’ gradient. We show that it is possible to train a Multi Layer Perceptron (MLP) on MNIST and ConvNets on CIFAR-10 and SVHN with BinaryNet and achieve nearly state-of-the-art results. At run-time, BinaryNet drastically reduces memory usage and replaces most multiplications by 1-bit exclusive-not-or (XNOR) operations, which might have a big impact on both general-purpose and dedicated Deep Learning hardware. We wrote a binary matrix multiplication GPU kernel with which it is possible to run our MNIST MLP 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The code for BinaryNet is available.


Lagrangian Texture Advection: Preserving both Spectrum and Velocity Field

Texturing an animated fluid is a useful way to augment the visual complexity of pictures without increasing the simulation time. But texturing flowing fluids is a complex issue, as it creates conflicting requirements: we want to keep the key texture properties (features, spectrum) while advecting the texture with the underlying flow-which distorts it. In this paper, we present a new, Lagrangian, method for advecting textures: the advected texture is computed only locally and follows the velocity field at each pixel. The texture retains its local properties, including its Fourier spectrum, even though it is accurately advected. Due to its Lagrangian nature, our algorithm can perform on very large, potentially infinite scenes in real time. Our experiments show that it is well suited for a wide range of input textures, including, but not limited to, noise textures.


Producing radiologist-quality reports for interpretable artificial intelligence

Current approaches to explaining the decisions of deep learning systems for medical tasks have focused on visualising the elements that have contributed to each decision. We argue that such approaches are not enough to “open the black box” of medical decision making systems because they are missing a key component that has been used as a standard communication tool between doctors for centuries: language. We propose a model-agnostic interpretability method that involves training a simple recurrent neural network model to produce descriptive sentences to clarify the decision of deep learning classifiers. We test our method on the task of detecting hip fractures from frontal pelvic x-rays. This process requires minimal additional labelling despite producing text containing elements that the original deep learning classification model was not specifically trained to detect. The experimental results show that: 1) the sentences produced by our method consistently contain the desired information, 2) the generated sentences are preferred by doctors compared to current tools that create saliency maps, and 3) the combination of visualisations and generated text is better than either alone.


On Divergences and Informations in Statistics and Information Theory

The paper deals with the f-divergences of Csiszar generalizing the discrimination information of Kullback, the total variation distance, the Hellinger divergence, and the Pearson divergence. All basic properties of f-divergences including relations to the decision errors are proved in a new manner replacing the classical Jensen inequality by a new generalized Taylor expansion of convex functions. Some new properties are proved too, e.g., relations to the statistical sufficiency and deficiency. The generalized Taylor expansion also shows very easily that all f-divergences are average statistical informations (differences between prior and posterior Bayes errors) mutually differing only in the weights imposed on various prior distributions. The statistical information introduced by De Groot and the classical information of Shannon are shown to be extremal cases corresponding to alpha=0 and alpha=1 in the class of the so-called Arimoto alpha-informations introduced in this paper for 0<alpha<1 by means of the Arimoto alpha-entropies. Some new examples of f-divergences are introduced as well, namely, the Shannon divergences and the Arimoto alpha-divergences leading for alphauarr1 to the Shannon divergences. Square roots of all these divergences are shown to be metrics satisfying the triangle inequality. The last section introduces statistical tests and estimators based on the minimal f-divergence with the empirical distribution achieved in the families of hypothetic distributions. For the Kullback divergence this leads to the classical likelihood ratio test and estimator


A column store engine for real-time streaming analytics

This paper describes novel aspects of the column store implemented in the MemSQL database engine and describes the design choices made to support real-time streaming workloads. Column stores have traditionally been restricted to data warehouse scenarios where low latency queries are a secondary goal, and where restricting data ingestion to be offline, batched, append-only, or some combination thereof is acceptable. In contrast, the MemSQL column store implementation treats low latency queries and ongoing writes as first class citizens, with a focus on avoiding interference between read, ingest, update, and storage optimization workloads through the use of fragmented snapshot transactions and optimistic storage reordering. This implementation broadens the range of serviceable column store workloads to include those with more stringent demands on query and data latency, such as those backing operational systems used by adtech, financial services, fraud detection and other real-time or data streaming applications.


An Improved Approach for Text-Independent Speaker Recognition

This paper presents new Speaker Identification and Speaker Verification systems based on the use of new feature vectors extracted from the speech signal. The proposed structure combine between the most successful Mel Frequency Cepstral Coefficients and new features which are the Short Time Zero Crossing Rate of the signal. A comparison between speaker recognition systems based on Gaussian mixture models using the well known Mel Frequency Cepstral Coefficients and the novel systems based on the use of a combination between both reduced Mel Frequency Cepstral Coefficients features vectors and Short Time Zero Crossing Rate features is given. This comparison proves that the use of the new reduced feature vectors help to improve the system’s performance and also help to reduce the time and memory complexity of the system which is required for realistic applications that suffer from computational resource limitation. The experiments were performed on speakers from TIMIT database for different training durations. The suggested systems performances are evaluated against the baseline systems. The increase of the proposed systems performances are well observed for identification experiments and the decrease of Equal Error Rates are also remarkable for verification experiments. Experimental results demonstrate the effectiveness of the new approach which avoids the use of more complex algorithms or the combination of different approaches requiring lengthy calculation. Keywords—GMM; speaker verification; speaker recognition; speaker identification


Simple topologies of PWM AC-AC converters

This letter proposes a new family of simple topologies of PWM AC-AC converters with minimal switches. With extension from the basic DC-DC converters, a series of AC-AC converters such as buck, boost, buck-boost, Cuk, and isolated converters are obtained. By PWM duty ratio control, they become a "solid-state transformer" with a continuously variable turns ratio. All the proposed AC-AC converters in this paper employ only two switches. Compared to the existing circuits that use six switches or more, they can reduce cost and improve reliability. The operating principle and control method of the proposed topologies are presented. Analysis and simulation results are given using the Cuk AC-AC converter as an example. The analysis can be easily extended to other converters of the proposed family.