Query: Communication Efficient Distributed Machine Learning with the Parameter Server

Relevant passages:
Parallel Coordinate Descent for L1-Regularized Loss Minimization <=SEP=> We propose Shotgun, a parallel coordinate descent algorithm for minimizing L1regularized losses. Though coordinate descent seems inherently sequential, we prove convergence bounds for Shotgun which predict near-linear speedups, up to a problemdependent limit. We present a comprehensive empirical study of Shotgun for Lasso and sparse logistic regression. Our theoretical predictions on the potential for parallelism closely match behavior on real data. Shotgun outperforms other published solvers on a range of large problems, proving to be one of the most scalable algorithms for L1.

Distributed large-scale natural graph factorization <=SEP=> Natural graphs, such as social networks, email graphs, or instant messaging patterns, have become pervasive through the internet. These graphs are massive, often containing hundreds of millions of nodes and billions of edges. While some theoretical models have been proposed to study such graphs, their analysis is still difficult due to the scale and nature of the data.
 We propose a framework for large-scale graph decomposition and inference. To resolve the scale, our framework is distributed so that the data are partitioned over a shared-nothing set of machines. We propose a novel factorization technique that relies on partitioning a graph so as to minimize the number of neighboring vertices rather than edges across partitions. Our decomposition is based on a streaming algorithm. It is network-aware as it adapts to the network topology of the underlying computational hardware. We use local copies of the variables and an efficient asynchronous communication protocol to synchronize the replicated values in order to perform most of the computation without having to incur the cost of network communication. On a graph of 200 million vertices and 10 billion edges, derived from an email communication network, our algorithm retains convergence properties while allowing for almost linear scalability in the number of computers.

The Matrix Cookbook <=SEP=> Acknowledgements: We would like to thank the following for contributions and suggestions: Bill Baxter, Brian Templeton, Christian Rishøj, Christian Schröppel Douglas L. Theobald, Esben Hoegh-Rasmussen, Glynne Casteel, Jan Larsen, Jun Bin Gao, Jürgen Struckmeier, Kamil Dedecius, Korbinian Strimmer, Lars Christiansen, Lars Kai Hansen, Leland Wilkinson, Liguo He, Loic Thibaut, Miguel Barão, Ole Winther, Pavel Sakov, Stephan Hattinger, Vasile Sima, Vincent Rabaud, Zhaoshui He. We would also like thank The Oticon Foundation for funding our PhD studies.

ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning <=SEP=> F . Here,Cx denotes the covariance matrix of the data. E is the matrix whose columns are eigenvectors of Cx; andD is a diagonal matrix of eigenvalues of Cx. This result also means that when the data is not whitened, the reconstruction cost becomes a weighted linear regression in the space rotated by eigenvec tors and scaled by eigenvalues. The cost is therefore weighted heavily in the principal vector d irections and less so in the other directions. Also, note that D 1 2E is the well-known PCA whitening matrix. The optimization th erefore builds in an inverse whitening matrix such that W will have to learn the whitening matrix to cancel outED 1 2 .

Chain Replication for Supporting High Throughput and Availability <=SEP=> Chain replication is a new approach to coordinating clusters of fail-stop storage servers. The approach is intended for supporting large-scale storage services that exhibit high throughput and availability without sacrificing strong consistency guarantees. Besides outlining the chain replication protocols themselves, simulation experiments explore the performance characteristics of a prototype implementation. Throughput, availability, and several objectplacement strategies (including schemes based on distributed hash table routing) are discussed.

Modeling IoT-Based Solutions Using Human-Centric Wireless Sensor Networks <=SEP=> The Internet of Things (IoT) has inspired solutions that are already available for addressing problems in various application scenarios, such as healthcare, security, emergency support and tourism. However, there is no clear approach to modeling these systems and envisioning their capabilities at the design time. Therefore, the process of designing these systems is ad hoc and its real impact is evaluated once the solution is already implemented, which is risky and expensive. This paper proposes a modeling approach that uses human-centric wireless sensor networks to specify and evaluate models of IoT-based systems at the time of design, avoiding the need to spend time and effort on early implementations of immature designs. It allows designers to focus on the system design, leaving the implementation decisions for a next phase. The article illustrates the usefulness of this proposal through a running example, showing the design of an IoT-based solution to support the first responses during medium-sized or large urban incidents. The case study used in the proposal evaluation is based on a real train crash. The proposed modeling approach can be used to design IoT-based systems for other application scenarios, e.g., to support security operatives or monitor chronic patients in their homes.

pVM: persistent virtual memory for efficient capacity scaling and object storage <=SEP=> Next-generation byte-addressable nonvolatile memories (NVMs), such as phase change memory (PCM) and Memristors, promise fast data storage, and more importantly, address DRAM scalability issues. State-of-the-art OS mechanisms for NVMs have focused on improving the block-based virtual file system (VFS) to manage both persistence and the memory capacity scaling needs of applications. However, using the VFS for capacity scaling has several limitations, such as the lack of automatic memory capacity scaling across DRAM and NVM, inefficient use of the processor cache and TLB, and high page access costs. These limitations reduce application performance and also impact applications that use NVM for persistent object storage with flat namespaces, such as photo stores, NoSQL databases, and others.
 To address such limitations, we propose persistent virtual memory (pVM), a system software abstraction that provides applications with (1) automatic OS-level memory capacity scaling, (2) flexible memory placement policies across NVM, and (3) fast object storage. pVM extends the OS virtual memory (VM) instead of building on the VFS and abstracts NVM as a NUMA node with support for NVM-based memory placement mechanisms. pVM inherits benefits from the cache and TLB-efficient VM subsystem and augments these further by distinguishing between persistent and nonpersistent capacity use of NVM. Additionally, pVM achieves fast persistent storage by further extending the VM subsystem with consistent and durable OS-level persistent metadata. Our evaluation of pVM with memory capacity-intensive applications shows a 2.5x speedup and up to 80% lower TLB and cache misses compared to VFS-based systems. pVM's object store provides 2x higher throughput compared to the block-based approach of the state-of-the art solution and up to a 4x reduction in the time spent in the OS.

Long-Term Visual Localization Using Semantically Segmented Images <=SEP=> Robust cross-seasonal localization is one of the major challenges in long-term visual navigation of autonomous vehicles. In this paper, we exploit recent advances in semantic segmentation of images, i.e., where each pixel is assigned a label related to the type of object it represents, to attack the problem of long-term visual localization. We show that semantically labeled 3D point maps of the environment, together with semantically segmented images, can be efficiently used for vehicle localization without the need for detailed feature descriptors (SIFT, SURF, etc.), Thus, instead of depending on hand-crafted feature descriptors, we rely on the training of an image segmenter. The resulting map takes up much less storage space compared to a traditional descriptor based map. A particle filter based semantic localization solution is compared to one based on SIFT-features, and even with large seasonal variations over the year we perform on par with the larger and more descriptive SIFT-features, and are able to localize with an error below 1 m most of the time.

Sexual Conflict in Human Mating <=SEP=> Despite interdependent reproductive fates that favor cooperation, males and females exhibit many psychological and behavioral footprints of sexually antagonistic coevolution. These include strategies of deception, sexual exploitation, and sexual infidelity as well as anti-exploitation defenses such as commitment skepticism and emotions such as sexual regret and jealousy. Sexual conflict pervades the mating arena prior to sexual consummation, after a mating relationship has formed, and in the aftermath of a breakup. It also permeates many other social relationships in forms such as daughter-guarding, conflict in opposite-sex friendships, and workplace sexual harassment. As such, sexual conflict constitutes not a narrow or occasional flashpoint but rather persistent threads that run through our intensely group-living species.

Evaluating the relationship between white matter integrity, cognition, and varieties of video game learning. <=SEP=> BACKGROUND
Many studies are currently researching the effects of video games, particularly in the domain of cognitive training. Great variability exists among video games however, and few studies have attempted to compare different types of video games. Little is known, for instance, about the cognitive processes or brain structures that underlie learning of different genres of video games.


OBJECTIVE
To examine the cognitive and neural underpinnings of two different types of game learning in order to evaluate their common and separate correlates, with the hopes of informing future intervention research.


METHODS
Participants (31 younger adults and 31 older adults) completed an extensive cognitive battery and played two different genres of video games, one action game and one strategy game, for 1.5 hours each. DTI scans were acquired for each participant, and regional fractional anisotropy (FA) values were extracted using the JHU atlas.


RESULTS
Behavioral results indicated that better performance on tasks of working memory and perceptual discrimination was related to enhanced learning in both games, even after controlling for age, whereas better performance on a perceptual speed task was uniquely related with enhanced learning of the strategy game. DTI results indicated that white matter FA in the right fornix/stria terminalis was correlated with action game learning, whereas white matter FA in the left cingulum/hippocampus was correlated with strategy game learning, even after controlling for age.


CONCLUSION
Although cognition, to a large extent, was a common predictor of both types of game learning, regional white matter FA could separately predict action and strategy game learning. Given the neural and cognitive correlates of strategy game learning, strategy games may provide a more beneficial training tool for adults suffering from memory-related disorders or declines in processing speed, particularly older adults.

Empirical vulnerability analysis of automated smart contracts security testing on blockchains <=SEP=> e emerging blockchain technology supports decentralized computing paradigm shi and is a rapidly approaching phenomenon. While blockchain is thought primarily as the basis of Bitcoin, its application has grown far beyond cryptocurrencies due to the introduction of smart contracts. Smart contracts are self-enforcing pieces of soware, which reside and run over a hosting blockchain. Using blockchain-based smart contracts for secure and transparent management to govern interactions (authentication, connection, and transaction) in Internet-enabled environments, mostly IoT, is a niche area of research and practice. However, writing trustworthy and safe smart contracts can be tremendously challenging because of the complicated semantics of underlying domain-specic languages and its testability. ere have been high-prole incidents that indicate blockchain smart contracts could contain various code-security vulnerabilities, instigating nancial harms. When it involves security of smart contracts, developers embracing the ability to write the contracts should be capable of testing their code, for diagnosing security vulnerabilities, before deploying them to the immutable environments on blockchains. However, there are only a handful of security testing tools for smart contracts. is implies that the existing research on automatic smart contracts security testing is not adequate and remains in a very stage of infancy. With a specic goal to more readily realize the application of blockchain smart contracts in security and privacy, we should rst understand their vulnerabilities before widespread implementation. Accordingly, the goal of this paper is to carry out a far-reaching experimental assessment of current static smart contracts security testing tools, for the most widely used blockchain, the Ethereum and its domain-specic programming language, Solidity, to provide the rst body of knowledge for creating more secure blockchainbased soware. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for prot or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

CONVOLUTIONAL SEQUENCE LEARNING <=SEP=> We present Deep Voice 3, a fully-convolutional attention-based neural textto-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training an order of magnitude faster. We scale Deep Voice 3 to dataset sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, we identify common error modes of attention-based speech synthesis networks, demonstrate how to mitigate them, and compare several different waveform synthesis methods. We also describe how to scale inference to ten million queries per day on a single GPU server.

Performance analysis of two open source intrusion detection systems <=SEP=> Several studies have been conducted where authors compared the performance of open source Intrusion detection systems, namely Snort and Suricata. However, most studies were limited to either security indicators or performance measurements under the same operating system. The objective of this study is to give a comprehensive analysis of both products in terms of several security related and performance related indicators. In addition, we tested the products under two different operating systems. Several experiments were run to evaluate the effects of open source intrusion detection and prevention systems Snort and Suricata, operating systems Windows, Linux and various attack types on system resource usage, dropped packets rate and ability to detect intrusions. The results show that Suricata has a higher CPU and RAM utilization than Snort in all cases on both operating systems, but lower percentage of dropped packets when evaluated during five of six simulated attacks. Both products had the same number of correctly identified intrusions. The results show that Linux-based solutions consume more system resources, but Windows-based systems had a higher rate of dropped packets. This indicates that these two intrusion detection and prevention systems should be run on Linux. However, both systems are inappropriate for high volumes of traffic in single-server setting.

Parenting cognitions → parenting practices → child adjustment? The standard model. <=SEP=> In a large-scale (N = 317) prospective 8-year longitudinal multiage, multidomain, multivariate, multisource study, we tested a conservative three-term model linking parenting cognitions in toddlerhood to parenting practices in preschool to classroom externalizing behavior in middle childhood, controlling for earlier parenting practices and child externalizing behavior. Mothers who were more knowledgeable, satisfied, and attributed successes in their parenting to themselves when their toddlers were 20 months of age engaged in increased supportive parenting during joint activity tasks 2 years later when their children were 4 years of age, and 6 years after that their 10-year-olds were rated by teachers as having fewer classroom externalizing behavior problems. This developmental cascade of a "standard model" of parenting applied equally to families with girls and boys, and the cascade from parenting attributions to supportive parenting to child externalizing behavior obtained independent of 12 child, parent, and family covariates. Conceptualizing socialization in terms of cascades helps to identify points of effective intervention.

Attribute-Based Access Control Scheme in Federated IoT Platforms <=SEP=> The Internet of Things (IoT) introduced the possibility to connect electronic things from everyday life to the Internet, while making them ubiquitously available. With advanced IoT services, based on a trusted federation among heterogeneous IoT platforms, new security problems (including authentication and authorization) emerge. This contribution aims at describing the main facets of the preliminary security architecture envisaged in the context of the symbIoTe project, recently launched by European Commission under the Horizon 2020 EU program. Our approach features distributed and decoupled mechanisms for authentication and authorization services in complex scenarios embracing heterogeneous and federated IoT platforms, by leveraging Attribute Based Access Control and token-based authorization techniques.

On the privacy of anonymized networks <=SEP=> The proliferation of online social networks, and the concomitant accumulation of user data, give rise to hotly debated issues of privacy, security, and control. One specific challenge is the sharing or public release of anonymized data without accidentally leaking personally identifiable information (PII). Unfortunately, it is often difficult to ascertain that sophisticated statistical techniques, potentially employing additional external data sources, are unable to break anonymity. In this paper, we consider an instance of this problem, where the object of interest is the structure of a social network, i.e., a graph describing users and their links. Recent work demonstrates that anonymizing node identities may not be sufficient to keep the network private: the availability of node and link data from another domain, which is correlated with the anonymized network, has been used to re-identify the anonymized nodes. This paper is about conditions under which such a de-anonymization process is possible.
 We attempt to shed light on the following question: can we assume that a sufficiently sparse network is inherently anonymous, in the sense that even with unlimited computational power, de-anonymization is impossible? Our approach is to introduce a random graph model for a version of the de-anonymization problem, which is parameterized by the expected node degree and a similarity parameter that controls the correlation between two graphs over the same vertex set. We find simple conditions on these parameters delineating the boundary of privacy, and show that the mean node degree need only grow slightly faster than log n with network size n for nodes to be identifiable. Our results have policy implications for sharing of anonymized network information.

New technology and health care costs--the case of robot-assisted surgery. <=SEP=> n engl j med 363;8 nejm.org august 19, 2010 701 stood. These technologies can lead to increases in costs, either because they are simply more expensive than previous treatments or because their introduction leads to an expansion in the types and numbers of patients treated. We examined these patterns as they apply to the case of robot-assisted surgery. Robotic surgical devices allow a surgeon at a console to operate remote-controlled robotic arms, which may facilitate the performance of laparoscopic procedures. Laparoscopic surgery, in turn, is associated with shorter hospital stays than open surgery, as well as with less postoperative pain and scarring and lower risks of infection and need for blood transfusion. Robotic technology has been adopted rapidly over the past 4 years in both the United States and Europe. The number of robot-assisted procedures that are performed worldwide has nearly tripled since 2007, from 80,000 to 205,000. Between 2007 and 2009, the number of da Vinci systems, the leading robotic technology, that were installed in U.S. hospitals grew by approximately 75%, from almost 800 to around 1400, and the number that were installed in other countries doubled, from 200 to nearly 400, according to Intuitive Surgical, da Vinci’s manufacturer. A wide range of procedures are now performed by means of robot-assisted surgery. Some of these procedures were already being performed laparoscopically before robots were introduced; the introduction of robotic technology affects expenditures associated with such procedures primarily by increasing the cost per procedure. For procedures that were more often performed as open surgeries, the introduction of robots may affect both the cost and the volume of surgeries performed. Robotic surgical systems have high fixed costs, with prices ranging from $1 million to $2.5 million for each unit. Surgeons must perform 150 to 250 procedures to become adept in their use. The systems also require costly maintenance and demand the use of additional consumables (singleuse robotic appliances). The use of robotic systems may also require more operating time than alternatives. In the case of procedures that had previously been performed as open surgery, however, some of the new costs will New Technology and Health Care Costs — The Case of Robot-Assisted Surgery

Minimally supervised question classification on fine-grained taxonomies <=SEP=> This article presents a minimally supervised approach to question classification on fine-grained taxonomies. We have defined an algorithm that automatically obtains lists of weighted terms for each class in the taxonomy, thus identifying which terms are highly related to the classes and are highly discriminative between them. These lists have then been applied to the task of question classification. Our approach is based on the divergence of probability distributions of terms in plain text retrieved from the Web. A corpus of questions with which to train the classifier is not therefore necessary. As the system is based purely on statistical information, it does not require additional linguistic resources or tools. The experiments were performed on English questions and their Spanish translations. The results reveal that our system surpasses current supervised approaches in this task, obtaining a significant improvement in the experiments carried out.

Crowdsourcing Information Systems – A Systems Theory Perspective <=SEP=> Crowdsourcing harnesses the potential of large and open networks of people. It is a relatively new phenomenon and attracted substantial interest in practice. Related research, however, lacks a theoretical foundation. We propose a systemtheoretical perspective on crowdsourcing systems to address this gap and illustrate its applicability by using it to classify crowdsourcing systems. By deriving two principal dimensions from theory, we identify four fundamental types of crowdsourcing systems that help to distinguish important features of such systems. We analyse their respective characteristics and discuss implications and requirements for various aspects related to the design of such systems. Our results demonstrate that systems theory can inform the study of crowdsourcing systems. The identified system types and the implications on their design may prove useful for researchers to frame future studies and for practitioners to identify the right crowdsourcing systems for a particular purpose.

Methods of mapping from phase to sine amplitude in direct digital synthesis <=SEP=> There are many methods for performing functional mapping from phase to sine amplitude (e.g., ROM look-up, coarse/fine segmentation into multiple ROM's, Taylor series, CORDIC algorithm). The spectral purity of the conventional direct digital synthesizer (DDS) is also determined by the resolution of the values stored in the sine table ROM. Therefore, it is desirable to increase the resolution of the ROM. Unfortunately, larger ROM storage means higher power consumption, lower reliability, lower speed, and greatly increased costs. Different memory compression and algorithmic techniques and their effect on distortion and trade-offs are investigated in detail. A computer program has been created to simulate the effects of the memory compression and algorithmic techniques on the output spectrum of the DDS. For each memory compression and algorithmic technique, the worst case spurious response is calculated using the computer program.

Why does deep and cheap learning work so well? <=SEP=> We show how the success of deep learning could depend not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can frequently be approximated through “cheap learning” with exponentially fewer parameters than generic ones. We explore how properties frequently encountered in physics such as symmetry, locality, compositionality, and polynomial log-probability translate into exceptionally simple neural networks. We further argue that when the statistical process generating the data is of a certain hierarchical form prevalent in physics and machine-learning, a deep neural network can be more efficient than a shallow one. We formalize these claims using information theory and discuss the relation to the renormalization group. We prove various “no-flattening theorems” showing when efficient linear deep networks cannot be accurately approximated by shallow ones without efficiency loss; for example, we show that n variables cannot be multiplied using fewer than 2 neurons in a single hidden layer.

Auto-Calibration Around-View Monitoring System <=SEP=> This work is to establish an auto-calibration around-view monitoring system to avoid the inconsistent performance from deviations of camera installation. First, a vehicle is parked at a specific area which has a special pattern on the ground. Second, the captured images from four embedded cameras are compared with the reference pattern images to obtain the deviations in angle, X-axis and Y-axis. Finally, these deviations are used to compensate the coefficients of transformation matrices associated with view-point conversion and image stitching. Particularly, the adaptive edge recovery scheme is developed to help on precise alignments. The experimental results show that the proposed system implemented at the TI DaVinci DSP platform can accomplish auto-calibration of a vehicle within 60 seconds and have deviation rates smaller than 38% at stitched regions and 25% at the other regions. Additionally, ±2° deviation errors at angles, X-axis and Y-axis are allowed. Particularly, the proposed system can generate precise 2-meter around-view pictures at a vehicle speed of 30km/h. Therefore, the auto-calibration system proposed herein can be widely applied to vehicle manufacture, verification and repair.

Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations <=SEP=> Registration of range sensor measurements is an important task in mobile robotics and has received a lot of attention. Several iterative optimization schemes have been proposed in order to align three-dimensional (3D) point scans. With the more widespread use of high-frame-rate 3D sensors and increasingly more challenging application scenarios for mobile robots, there is a need for fast and accurate registration methods that current state-of-the-art algorithms cannot always meet. This work proposes a novel algorithm that achieves accurate point cloud registration an order of a magnitude faster than the current state of the art. The speedup is achieved through the use of a compact spatial representation: the Three-Dimensional Normal Distributions Transform (3D-NDT). In addition, a fast, global-descriptor based on the 3D-NDT is defined and used to achieve reliable initial poses for the iterative algorithm. Finally, a closed-form expression for the covariance of the proposed method is also derived. The proposed algorithms are evaluated on two standard point cloud data sets, resulting in stable performance on a par with or better than the state of the art. The implementation is available as an open-source package for the Robot Operating System (ROS).

Incivility in nursing education: An intervention. <=SEP=> Incivility in nursing education is an unfortunate phenomenon affecting nursing students in all aspects of their educational experience. Students and their instructors are often ill equipped to deal with academic incivility and their lack of ability to handle such behaviors has proven detrimental to the future of the nursing profession. Nursing instructors need tools to help educate nursing students on how to recognize uncivil behaviors within themselves as well as others and ways to combat it. This research project addressed these aspects of academic incivility and implemented an e-learning module that was developed to educate students on incivility. The data was collected through a pre-test, post-test model with resulting statistical analysis using the McNemar's test. Results showed the nursing students obtained increased self-efficacy in regards to their ability to define, detect, and combat academic incivility after viewing the e-learning module. In conclusion, the successful implementation of the e-learning module provides further incentive for schools of nursing to consider implementing incivility education in their curriculums.

Single phase based on ups applied Z-source inverter by using matlab / simulink <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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. <=SEP=> 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 <=SEP=> 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.