Query: The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox

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Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing <=SEP=> We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that allows programmers to perform in-memory computations on large clusters while retaining the fault tolerance of data flow models like MapReduce. RDDs are motivated by two types of applications that current data flow systems handle inefficiently: iterative algorithms, which are common in graph applications and machine learning, and interactive data mining tools. In both cases, keeping data in memory can improve performance by an order of magnitude. To achieve fault tolerance efficiently, RDDs provide a highly restricted form of shared memory: they are read-only datasets that can only be constructed through bulk operations on other RDDs. However, we show that RDDs are expressive enough to capture a wide class of computations, including MapReduce and specialized programming models for iterative jobs such as Pregel. Our implementation of RDDs can outperform Hadoop by 20× for iterative jobs and can be used interactively to search a 1 TB dataset with latencies of 5–7 seconds.

Applying Model Management to Classical Meta Data Problems <=SEP=> Model management is a new approach to meta data management that offers a higher level programming interface than current techniques. The main abstractions are models (e.g., schemas, interface definitions) and mappings between models. It treats these abstractions as bulk objects and offers such operators as Match, Merge, Diff, Compose, Apply, and ModelGen. This paper extends earlier treatments of these operators and applies them to three classical meta data management problems: schema integration, schema evolution, and round-trip engineering.

A Tutorial on Support Vector Machines for Pattern Recognition <=SEP=> The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.

Towards a unified architecture for in-RDBMS analytics <=SEP=> The increasing use of statistical data analysis in enterprise applications has created an arms race among database vendors to offer ever more sophisticated in-database analytics. One challenge in this race is that each new statistical technique must be implemented from scratch in the RDBMS, which leads to a lengthy and complex development process. We argue that the root cause for this overhead is the lack of a unified architecture for in-database analytics. Our main contribution in this work is to take a step towards such a unified architecture. A key benefit of our unified architecture is that performance optimizations for analytics techniques can be studied generically instead of an ad hoc, per-technique fashion. In particular, our technical contributions are theoretical and empirical studies of two key factors that we found impact performance: the order data is stored, and parallelization of computations on a single-node multicore RDBMS. We demonstrate the feasibility of our architecture by integrating several popular analytics techniques into two commercial and one open-source RDBMS. Our architecture requires changes to only a few dozen lines of code to integrate a new statistical technique. We then compare our approach with the native analytics tools offered by the commercial RDBMSes on various analytics tasks, and validate that our approach achieves competitive or higher performance, while still achieving the same quality.

Learning from delayed rewards <=SEP=> 

Sketch of Big Data Real-Time Analytics Model <=SEP=> Big Data has drawn huge attention from researchers in information sciences, decision makers in governments and enterprises. However, there is a lot of potential and highly useful value hidden in the huge volume of data. Data is the new oil, but unlike oil data can be refined further to create even more value. Therefore, a new scientific paradigm is born as data-intensive scientific discovery, also known as Big Data. The growth volume of real-time data requires new techniques and technologies to discover insight value. In this paper we introduce the Big Data real-time analytics model as a new technique. We discuss and compare several Big Data technologies for real-time processing along with various challenges and issues in adapting Big Data. Realtime Big Data analysis based on cloud computing approach is our future research direction.

Turning the Page : The Impact of Choice Closure on Satisfaction <=SEP=> This paper introduces the concept of choice closure, defined as the psychological process by which consumers come to perceive a decision to be resolved and complete, and focus on the decision outcome. Choice closure inhibits consumers who have already made a choice from reverting to the decision process and engaging in potentially unfavorable comparisons between the chosen and the forgone options. In four studies, we show that physical acts of closure—such as turning one’s back on, covering, and turning the page on the rejected alternatives—facilitate choice closure in the context of difficult decisions involving large choice sets, and that choice closure results in greater satisfaction.

Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding <=SEP=> There has been significant recent interest towards achievin g highly efficient deep neural network architectures. A promising paradigm for ach ieving this is the concept ofevolutionary deep intelligence , which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural n etworks over successive generations. An important aspect of evolutionary deep inte lligence is the genetic encoding scheme used to mimic heredity, which can have a sign ificant impact on the quality of offspring deep neural networks. Motivated by the neurobiological phenomenon of synaptic clustering, we introduce a new genet ic encoding scheme where synaptic probability is driven towards the formation of a highly sparse set of synaptic clusters. Experimental results for the task of ima ge classification demonstrated that the synthesized offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art perf ormance while having network architectures that are not only significantly more e ffici nt (with a∼125fold decrease in synapses for MNIST) compared to the origina l a cestor network, but also tailored for GPU-accelerated machine learning app lications.

Understanding and Predicting Graded Search Satisfaction <=SEP=> Understanding and estimating satisfaction with search engines is an important aspect of evaluating retrieval performance. Research to date has modeled and predicted search satisfaction on a binary scale, i.e., the searchers are either satisfied or dissatisfied with their search outcome. However, users' search experience is a complex construct and there are different degrees of satisfaction. As such, binary classification of satisfaction may be limiting. To the best of our knowledge, we are the first to study the problem of understanding and predicting graded (multi-level) search satisfaction. We ex-amine sessions mined from search engine logs, where searcher satisfaction was also assessed on multi-point scale by human annotators. Leveraging these search log data, we observe rich and non-monotonous changes in search behavior in sessions with different degrees of satisfaction. The findings suggest that we should predict finer-grained satisfaction levels. To address this issue, we model search satisfaction using features indicating search outcome, search effort, and changes in both outcome and effort during a session. We show that our approach can predict subtle changes in search satisfaction more accurately than state-of-the-art methods, affording greater insight into search satisfaction. The strong performance of our models has implications for search providers seeking to accu-rately measure satisfaction with their services.

A Comparative Analysis of Enterprise Architecture Frameworks Based on EA Quality Attributes <=SEP=> Many Enterprise Architecture Frameworks (EAFs) are in use today to guide or serve as a model for the enterprise architecture development. Researchers offer general comparative information about EAF. However, there has been little work on the quality of Enterprise Architecture (EA). This study provides the characteristics of the five EAFs using comparative analysis based on Enterprise Architecture Quality Attributes (EAQAs). The attribute of EA quality was extracted from the EAFs and the definition of EAQA was defined from EA user viewpoint. The criteria for each quality attribute were developed to compare EAFs using four dimensional concepts. This paper compared several frameworks using the criteria to provide guidance in the selection of an EAF that meets the quality requirement of EA

Speculations Concerning the First Ultraintelligent Machine <=SEP=> 

AR typing interface for mobile devices <=SEP=> We propose a new user interface system for mobile devices. By using augmented reality (AR) technology, the system overlays virtual objects on real images captured by a camera attached to the back of a mobile device, and the user can operate the mobile device by manipulating the virtual objects with his/her hand in the space behind the mobile device. This system allows the user to operate the device in a wide three-dimensional space and to select small objects easily. Also, the AR technology provides the user with a sense of reality in operating the device. We developed a typing application using our system and verified the effectiveness by user studies. The results showed that more than half of the subjects felt that the operation area of the proposed system is larger than that of a smartphone and that both AR and unfixed key-plane are effective for improving typing speed.

Comparison of Three Different CNN Architectures for Age Classification <=SEP=> As one of the powerful tools of machine learning, Convolutional Neural Network (CNN) architectures are used tosolve complex problems like image recognition, video analysisand natural language processing. In this paper, three differentCNN architectures for age classification using face images arecompared. The Morph dataset containing over 55k images isused in experiments and success of a 6-layer CNN and 2 variantsof ResNet with different depths are compared. The images in thedataset are divided into 6 different age classes. While 80% of theimages are used in training of the networks, the rest of the 20% isused for testing. The performance of the networks are comparedaccording to two different criteria namely, the ability to makethe estimation pointing the exact age classes of test images andthe ability to make the estimation pointing the exact age classesor at most neighboring classes of the images. According to theperformance results obtained, with 6-layer network, it is possibleto estimate the exact or neighboring classes of the images withless than 5% error. It is shown that for a 6 class age classificationproblem 6-layer network is more successful than the deeperResNet counterparts since 6-layer network is less susceptible tooverfitting for this problem.

Cardiac arrhythmia detection from ECG combining convolutional and long short-term memory networks <=SEP=> Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to initiate appropriate preventive measures and to improve the analysis of cardiac disease. To this end, this paper introduces a new approach to detect and classify automatically cardiac arrhythmias in electrocardiograms (ECG) recordings. Methods: The proposed approach used a combination of Convolution Neural Networks (CNNs) and a sequence of Long Short-Term Memory (LSTM) units, with pooling, dropout and normalization techniques to improve their accuracy. The network predicted a classification at every 18th input sample and we selected the final prediction for classification. Results were cross-validated on the Physionet Challenge 2017 training dataset, which contains 8,528 single lead ECG recordings lasting from 9s to just over 60s. Results: Using the proposed structure and no explicit feature selection, 10-fold stratified cross-validation gave an overall F-measure of 0.83.10±0.015 on the held-out test data (mean ± standard deviation over all folds) and 0.80 on the hidden dataset of the Challenge entry server.

Job Resources Boost Work Engagement , Particularly When Job Demands Are High <=SEP=> This study of 805 Finnish teachers working in elementary, secondary, and vocational schools tested 2 interaction hypotheses. On the basis of the job demands–resources model, the authors predicted that job resources act as buffers and diminish the negative relationship between pupil misbehavior and work engagement. In addition, using conservation of resources theory, the authors hypothesized that job resources particularly influence work engagement when teachers are confronted with high levels of pupil misconduct. In line with these hypotheses, moderated structural equation modeling analyses resulted in 14 out of 18 possible 2-way interaction effects. In particular, supervisor support, innovativeness, appreciation, and organizational climate were important job resources that helped teachers cope with demanding interactions with students.

NEVESIM: event-driven neural simulation framework with a Python interface <=SEP=> NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies.

Autonomous Positioning of Eye Surgical Robot Using the Tool Shadow and Kalman Filtering <=SEP=> Vitreoretinal surgery is one of the most difficult surgical operations, even for experienced surgeons. Thus, a master-slave eye surgical robot has been developed to assist the surgeon in safely performing vitreoretinal surgeries; however, in the master-slave control, the robotic positioning accuracy depends on the surgeon’s coordination skills. This paper proposes a new method of autonomous robotic positioning using the shadow of the surgical instrument. First, the microscope image is segmented into three regions—namely, a micropipette, its shadow, and the eye ground—using a Gaussian mixture model (GMM). The tips of the micropipette and its shadow are then extracted from the contour lines of the segmented regions. The micropipette is then autonomously moved down to the simulated eye ground until the distance between the tips of micropipette and its shadow in the microscopic image reaches a predefined threshold. To handle possible occlusions, the tip of the shadow is estimated using a Kalman filter. Experiments to evaluate the robotic positioning accuracy in the vertical direction were performed. The results show that the autonomous positioning using the Kalman filter enhanced the accuracy of robotic positioning.

Open circuit fault detection in PWM voltage source inverter for PMSM drive system <=SEP=> In this paper, a simple and low-cost open-circuit fault detection and identification method for a pulse-width modulated (PWM) voltage-source inverter (VSI) employing a permanent magnet synchronous motor is proposed. An open-circuit fault of a power switch in the PWM VSI changes the corresponding terminal voltage and introduces the voltage distortions to each phase voltage. The proposed open-circuit fault diagnosis method employs the model reference adaptive system techniques and requires no additional sensors or electrical devices to detect the fault condition and identify the faulty switch. The proposed method has the features of fast diagnosis time, simple structure, and being easily inserted to the existing control algorithms as a subroutine without major modifications. The simulations and experiments are carried out and the results show the effectiveness of the proposed method.

A Time Series Classification Method for Behaviour-Based Dropout Prediction <=SEP=> Students' dropout rate is a key metric in online and open distance learning courses. We propose a time-series classification method to construct data based on students' behaviour and activities on a number of online distance learning modules. Further, we propose a dropout prediction model based on the time series forest (TSF) classification algorithm. The proposed predictive model is based on interaction data and is independent of learning objectives and subject domains. The model enables prediction of dropout rates without the requirement for pedagogical experts. Results show that the prediction accuracy on two selected datasets increases as the portion of data used in the model grows. However, a reasonable prediction accuracy of 0.84 is possible with only 5% of the dataset processed. As a result, early prediction can help instructors design interventions to encourage course completion before a student falls too far behind.

Weakly-supervised text-to-speech alignment confidence measure <=SEP=> This work proposes a new confidence measure for evaluating text-to-speech alignment systems outputs, which is a key component for many applications, such as semi-automatic corpus anonymization, lips syncing, film dubbing, corpus preparation for speech synthesis and speech recognition acoustic models training. This confidence measure exploits deep neural networks that are trained on large corpora without direct supervision. It is evaluated on an open-source spontaneous speech corpus and outperforms a confidence score derived from a state-of-the-art text-to-speech aligner. We further show that this confidence measure can be used to fine-tune the output of this aligner and improve the quality of the resulting alignment.

A fast and efficient python library for interfacing with the Biological Magnetic Resonance Data Bank <=SEP=> The Biological Magnetic Resonance Data Bank (BMRB) is a public repository of Nuclear Magnetic Resonance (NMR) spectroscopic data of biological macromolecules. It is an important resource for many researchers using NMR to study structural, biophysical, and biochemical properties of biological macromolecules. It is primarily maintained and accessed in a flat file ASCII format known as NMR-STAR. While the format is human readable, the size of most BMRB entries makes computer readability and explicit representation a practical requirement for almost any rigorous systematic analysis. To aid in the use of this public resource, we have developed a package called nmrstarlib in the popular open-source programming language Python. The nmrstarlib’s implementation is very efficient, both in design and execution. The library has facilities for reading and writing both NMR-STAR version 2.1 and 3.1 formatted files, parsing them into usable Python dictionary- and list-based data structures, making access and manipulation of the experimental data very natural within Python programs (i.e. “saveframe” and “loop” records represented as individual Python dictionary data structures). Another major advantage of this design is that data stored in original NMR-STAR can be easily converted into its equivalent JavaScript Object Notation (JSON) format, a lightweight data interchange format, facilitating data access and manipulation using Python and any other programming language that implements a JSON parser/generator (i.e., all popular programming languages). We have also developed tools to visualize assigned chemical shift values and to convert between NMR-STAR and JSONized NMR-STAR formatted files. Full API Reference Documentation, User Guide and Tutorial with code examples are also available. We have tested this new library on all current BMRB entries: 100% of all entries are parsed without any errors for both NMR-STAR version 2.1 and version 3.1 formatted files. We also compared our software to three currently available Python libraries for parsing NMR-STAR formatted files: PyStarLib, NMRPyStar, and PyNMRSTAR. The nmrstarlib package is a simple, fast, and efficient library for accessing data from the BMRB. The library provides an intuitive dictionary-based interface with which Python programs can read, edit, and write NMR-STAR formatted files and their equivalent JSONized NMR-STAR files. The nmrstarlib package can be used as a library for accessing and manipulating data stored in NMR-STAR files and as a command-line tool to convert from NMR-STAR file format into its equivalent JSON file format and vice versa, and to visualize chemical shift values. Furthermore, the nmrstarlib implementation provides a guide for effectively JSONizing other older scientific formats, improving the FAIRness of data in these formats.

Designing an augmented reality board game with children: the battleboard 3D experience <=SEP=> This demo shows BattleBoard 3D which is an Augmented Reality (AR) based game prototype featuring the use of LEGO for the physical and digital pieces. Design concepts, the physical setting and, user interface for the game is illustrated and described. Based on qualitative studies of children playing the game we illustrate design issues for AR board games.

Research and implementation of RSA algorithm for encryption and decryption <=SEP=> Cryptographic technique is one of the principal means to protect information security. Not only has it to ensure the information confidential, but also provides digital signature, authentication, secret sub-storage, system security and other functions. Therefore, the encryption and decryption solution can ensure the confidentiality of the information, as well as the integrity of information and certainty, to prevent information from tampering, forgery and counterfeiting. Encryption and decryption algorithm's security depends on the algorithm while the internal structure of the rigor of mathematics, it also depends on the key confidentiality. Key in the encryption algorithm has a pivotal position, once the key was leaked, it means that anyone can be in the encryption system to encrypt and decrypt information, it means the encryption algorithm is useless. Therefore, what kind of data you choose to be a key, how to distribute the private key, and how to save both data transmission keys are very important issues in the encryption and decryption algorithm. This paper proposed an implementation of a complete and practical RSA encrypt/decrypt solution based on the study of RSA public key algorithm. In addition, the encrypt procedure and code implementation is provided in details.

Computer Self-Efficacy: Development of a Measure and Initial Test <=SEP=> This paper discusses the role of individuals’ beliefs about their abilities to competently use computers (computer self-efficacy) in the determination of computer use. A survey of Canadian managers and professionals was conducted to develop and validate a measure of computer self-efficacy and to assess both its impacts and antecedents. Computer selfefficacy was found to exert a significant influence on individuals’ expectations of the outcomes of using computers, their emotional reactions to computers (affect and anxiety), well as their actual computer use. An individual’s self-efficacy and outcome expectations were found to be positively influenced by the encouragement of others in their work group, as well as others’ use of computers. Thus, self-efficacy represents an important individual trait, which moderates organizational influences (such as encouragement and support) on an individual’s decision to use computers. Understanding self-efficacy, then, is important o the successful implementation of systems in Introduction Understanding the factors that influence an individual’s use of information technology has been a goal of MIS research since the mid-1970s, when organizations and researchers began to find that adoption of new technology was not living up to expectations. Lucas (1975, 1978) provides some of the earliest evidence of the individual or behavioral factors that influenced IT adoption. The first theoretical perspective to gain widespread acceptance in this research was the Theory of Reasoned Action (Fishbein and Ajzen, 1975). This theory maintains that individuals would use computers if they could see that there would be positive benefits (outcomes) associated with using them. This theory is still widely used today in the IS literature and has demonstrated validity. However, there is also a growing recognition that additional explanatory variables are needed (e.g., Thompson, et al., 1991; Webster and Martocchio, 1992). One such variable, examined in this research, comes from the writings of Albert Bandura and his work on Social Cognitive Theory (Bandura, 1986). Self-efficacy, the belief that one has the capability to perform a particular behavior, is an important construct in social psychology. Self-efficacy perceptions have been found to influence decisions about what behaviors to undertake (e.g., Bandura, et al., 1977; Betz and Hackett, 1981), the effort exerted and persistence in attempting those behaviors (e.g., Barling and Beattie, 1983; Brown and Inouye, 1978), the emotional responses (including stress and anxiety) of the individual performing the behaviors (e.g., Bandura, et al., 1977; Stumpf, et a1.,1987), and the actual performance attainments of the individual with respect to the behavior (e.g., Barling M/S Quarterly/June 1995 189 Computer Self-Efficacy---Measurement and Beattie, 1983; Collins, 1985; Locke, et al., 1984; Schunk, 1981; Wood and Bandura, 1989). These effects have been shown for a wide variety of behaviors in both clinical and managerial settings. Within a management context, self-efficacy has been found to be related to attendance (Frayne and Latham, 1987; Latham and Frayne, 1989), career choice and dev,elopment (Betz and Hackett, 1981; Jones, 1986), research productivity (Taylor, et al., 1984), and sales performance (Barling and Beattie, 1983). Several more recent studies (Burkhardt and Brass, 1990; Gist, et a1.,1989; Hill, et al., 1986; 1987; Webster and Martocohio, 1992; 1993) have examined the relationship between self-efficacy with respect to using computers and a variety of computer behaviors. These studies found evidence of a relationship between selfefficacy and registration in computer courses at universities (Hill, et al., 1987), adoption of high technology products (Hill, et al., 1986) and innovations (Burkhardt and Brass, 1990), as well performance in software training (Gist, et al., 1989; Webster and Martocchio, 1992; 1993). All of the studies argue the need for further research to explore fully the role of self-efficacy in computing behavior. This paper describes the first study in a program of research aimed at understanding the impact of self-efficacy on individual reactions to computing technology. The study involves the development of a measure for computer self-efficacy and a test of its reliability and validity. The measure was evaluated by examining its performance in a nomological network, through structural equations modeling. A research model for this purpose was developed with reference to literature from social psychology, as well as prior IS research. The paper is organized as follows. The next section presents the theoretical foundation for this research. The third section discusses the development of the self-efficacy measure. The research model is described and the hypotheses are presented in the following section. Then, the research methodology is outlined, and the results of the analyses are presented. The paper concludes with a discussion of the implications of these findings and the strengths and limitations of the research. Theoretical Background Social Cognitive Theory Social Cognitive Theory (Bandura, 1977; 1978; 1982; 1986) is a widely accepted, empirically validated model of individual behavior. It is based on the premise that environmental influences such as social pressures or unique situational characteristics, cognitive and other personal factors including personality as well as demographic haracteristics, and behavior are reciprocally determined. Thus, individuals choose the environments in which they exist in

Personalized machine learning for robot perception of affect and engagement in autism therapy <=SEP=> Robots have the potential to facilitate future therapies for children on the autism spectrum. However, existing robots are limited in their ability to automatically perceive and respond to human affect, which is necessary for establishing and maintaining engaging interactions. Their inference challenge is made even harder by the fact that many individuals with autism have atypical and unusually diverse styles of expressing their affective-cognitive states. To tackle the heterogeneity in children with autism, we used the latest advances in deep learning to formulate a personalized machine learning (ML) framework for automatic perception of the children’s affective states and engagement during robot-assisted autism therapy. Instead of using the traditional one-size-fits-all ML approach, we personalized our framework to each child using their contextual information (demographics and behavioral assessment scores) and individual characteristics. We evaluated this framework on a multimodal (audio, video, and autonomic physiology) data set of 35 children (ages 3 to 13) with autism, from two cultures (Asia and Europe), and achieved an average agreement (intraclass correlation) of ~60% with human experts in the estimation of affect and engagement, also outperforming nonpersonalized ML solutions. These results demonstrate the feasibility of robot perception of affect and engagement in children with autism and have implications for the design of future autism therapies.

Estimating alertness from the EEG power spectrum <=SEP=> In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, the authors show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEC; measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.

The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Resuming the discussion of AMSTAR: What can (should) be made better? <=SEP=> BACKGROUND
Evidence syntheses, and in particular systematic reviews (SRs), have become one of the cornerstones of evidence-based health care. The Assessment of Multiple Systematic Reviews (AMSTAR) tool has become the most widely used tool for investigating the methodological quality of SRs and is currently undergoing revision. The objective of this paper is to present insights, challenges and potential solutions from the point of view of a group of assessors, while referring to earlier methodological discussions and debates with respect to AMSTAR.


DISCUSSION
One major drawback of AMSTAR is that it relies heavily on reporting quality rather than on methodological quality. This can be found in several items. Furthermore, it should be acknowledged that there are now new methods and procedures that did not exist when AMSTAR was developed. For example, the note to item 1 should now refer to the International Prospective Register of Ongoing Systematic Reviews (PROSPERO). Furthermore, item 3 should consider the definition of hand-searching, as the process of reviewing conference proceedings using the search function (e.g. in Microsoft Word or in a PDF file) does not meet the definition set out by the Cochrane Collaboration. Moreover, methods for assessing the quality of the body of evidence have evolved since AMSTAR was developed and should be incorporated into a revised AMSTAR tool. Potential solutions are presented for each AMSTAR item with the aim of allowing a more thorough assessment of SRs. As the AMSTAR tool is currently undergoing further development, our paper hopes to add to preceding discussions and papers regarding this tool and stimulate further discussion.

Pregnancy-related gigantomastia. Case report <=SEP=> The term gigantomastia has been used to describe breast enlargement to extreme size, with sloughing, haemorrhage and infection. The condition is rare and a case of pregnancy-related gigantomastia is reported.

Disconnecting outcomes and evaluations: the role of negotiator focus. <=SEP=> Three experiments explored the role of negotiator focus in disconnecting negotiated outcomes and evaluations. Negotiators who focused on their target prices, the ideal outcome they could obtain, achieved objectively superior outcomes compared with negotiators who focused on their lower bound (e.g., reservation price). Those negotiators who focused on their targets, however, were less satisfied with their objectively superior outcomes. In the final experiment, when negotiators were reminded of their lower bound after the negotiation, the satisfaction of those negotiators who had focused on their target prices was increased, with outcomes and evaluations becoming connected rather than disconnected. The possible negative effects of setting high goals and the temporal dimensions of the disconnection and reconnection between outcomes and evaluations are discussed.