title: Data pricing strategy based on data quality

abstract: Abstract This paper presents a bi-level mathematical programming model for the data-pricing problem that considers both data quality and data versioning strategies. Data products and data-related services differ from information products or services in terms of quality assessment methods. For this problem, we consider two aspects of data quality: (1) its multidimensionality and (2) the interaction between the dimensions. We designed a multi-version data strategy and propose a data-pricing bi-level programming model based on the data quality to maximize the profit by the owner of the data platform and the utility to consumers. A genetic algorithm was used to solve the model. The numerical solutions for the data-pricing model indicate that the multi-version strategy achieves a better market segmentation and is more profitable and feasible when the multiple dimensions of data quality are considered. These results also provide managerial guidance on data provision and data pricing for platform owners.


title: MMR-Based Active Machine Learning For Bio Named Entity Recognition

abstract: This paper presents a new active learning paradigm which considers not only the uncertainty of the classifier but also the diversity of the corpus. The two measures for uncertainty and diversity were combined using the MMR (Maximal Marginal Relevance) method to give the sampling scores in our active learning strategy. We incorporated MMR-based active machine-learning idea into the biomedical named-entity recognition system. Our experimental results indicated that our strategies for active-learning based sample selection could significantly reduce the human effort.


title: Context-dependent Semantic Parsing for Time Expressions

abstract: We present an approach for learning context-dependent semantic parsers to identify and interpret time expressions. We use a Combinatory Categorial Grammar to construct compositional meaning representations, while considering contextual cues, such as the document creation time and the tense of the governing verb, to compute the final time values. Experiments on benchmark datasets show that our approach outperforms previous stateof-the-art systems, with error reductions of 13% to 21% in end-to-end performance.


title: Deterministic Dependency Parsing Of English Text

abstract: This paper presents a deterministic dependency parser based on memory-based learning, which parses English text in linear time. When trained and evaluated on the Wall Street Journal section of the Penn Treebank, the parser achieves a maximum attachment score of 87.1%. Unlike most previous systems, the parser produces labeled dependency graphs, using as arc labels a combination of bracket labels and grammatical role labels taken from the Penn Treebank II annotation scheme. The best overall accuracy obtained for identifying both the correct head and the correct arc label is 86.0%, when restricted to grammatical role labels (7 labels), and 84.4% for the maximum set (50 labels).


title: In vivo drug discovery in the zebrafish

abstract: The zebrafish has become a widely used model organism because of its fecundity, its morphological and physiological similarity to mammals, the existence of many genomic tools and the ease with which large, phenotype-based screens can be performed. Because of these attributes, the zebrafish might also provide opportunities to accelerate the process of drug discovery. By combining the scale and throughput of in vitro screens with the physiological complexity of animal studies, the zebrafish promises to contribute to several aspects of the drug development process, including target identification, disease modelling, lead discovery and toxicology.


title: Predicting web search success with fine-grained interaction data

abstract: Detecting and predicting searcher success is essential for automatically evaluating and improving Web search engine performance. In the past, Web searcher behavior data, such as result clickthrough, dwell time, and query reformulation sequences, have been successfully used for a variety of tasks, including prediction of success in a search session. However, the effectiveness of the previous approaches has been limited, as they tend to ignore how searchers actually view and interact with the visited pages. We show that fine-grained interactions, such as mouse cursor movements and scrolling, provide additional clues for better predicting success of a search session as a whole. To this end, we identify patterns of examination and interaction behavior that correspond to search success, and design a new Fine-grained Session Behavior (FSB) model to capture these patterns. Our experimental results show that FSB is significantly more effective than the state-of-the-art approaches that do not use these additional interaction data.


title: Antipodal Vivaldi antenna array with high gain and reduced mutual coupling for UWB applications

abstract: An Antipodal Vivaldi antenna (A VA) with enhanced radiation characteristics for UWB applications is presented in this paper. By using of a triangular director with high permittivity in the end of the antenna aperture, the directivity, the beamwidth and the mutual decoupling are ameliorated over the wideband applications. In order to achieve a compact structure with higher gain and ameliorated mutual decoupling, 1×4 planar antenna array is proposed.


title: Design and Implementation of Low-Area and Low-Power AES Encryption Hardware Core

abstract: The Advanced Encryption Standard (AES) algorithm has become the default choice for various security services in numerous applications. In this paper we present an AES encryption hardware core suited for devices in which low cost and low power consumption are desired. The core constitutes of a novel 8-bit architecture and supports encryption with 128-bit keys. In a 0.13 im CMOS technology our area optimized implementation consumes 3.1 kgates. The throughput at the maximum clock frequency of 153 MHz is 121 Mbps, also in feedback encryption modes. Compared to previous 8-bit implementations, we achieve significantly higher throughput with corresponding area. The energy consumption per processed block is also lower.


title: Towards a Load Balancing in a three-level cloud computing network

abstract: Network bandwidth and hardware technology are developing rapidly, resulting in the vigorous development of the Internet. A new concept, cloud computing, uses low-power hosts to achieve high reliability. The cloud computing, an Internet-based development in which dynamically scalable and often virtualized resources are provided as a service over the Internet has become a significant issue. The cloud computing refers to a class of systems and applications that employ distributed resources to perform a function in a decentralized manner. Cloud computing is to utilize the computing resources (service nodes) on the network to facilitate the execution of complicated tasks that require large-scale computation. Thus, the selecting nodes for executing a task in the cloud computing must be considered, and to exploit the effectiveness of the resources, they have to be properly selected according to the properties of the task. However, in this study, a two-phase scheduling algorithm under a three-level cloud computing network is advanced. The proposed scheduling algorithm combines OLB (Opportunistic Load Balancing) and LBMM (Load Balance Min-Min) scheduling algorithms that can utilize more better executing efficiency and maintain the load balancing of system.


Satellite Signal Acquisition , Tracking , and Data Demodulation


title: Semi-Supervised Apprenticeship Learning

abstract: In apprenticeship learning we aim to learn a good policy by observing the behavior of an expert or a set of experts. In particular, we consider the case where the expert acts so as to maximize an unknown reward function defined as a linear combination of a set of state features. In this paper, we consider the setting where we observe many sample trajectories (i.e., sequences of states) but only one or a few of them are labeled as experts’ trajectories. We investigate the conditions under which the remaining unlabeled trajectories can help in learning a policy with a good performance. In particular, we define an extension to the max-margin inverse reinforcement learning proposed by Abbeel and Ng [2004] where, at each iteration, the max-margin optimization step is replaced by a semi-supervised optimization problem which favors classifiers separating clusters of trajectories. Finally, we report empirical results on two grid-world domains showing that the semi-supervised algorithm is able to output a better policy in fewer iterations than the related algorithm that does not take the unlabeled trajectories into account.


title: Data Mining in Telecommunications

abstract: Telecommunication companies generate a tremendous amount of data. These data include call detail data, which describes the calls that traverse the telecommunication networks, network data, which describes the state of the hardware and software components in the network, and customer data, which decsribes the telecommmunication customers. This chapter describes how Data Mining can be used to uncover useful information buried within these data sets. Several Data Mining applications are described and together they demonstrate that Data Mining can be used to identify telecommunication fraud, improve marketing effectiveness, and identify network faults.


title: Feature-based Product Review Summarization Utilizing User Score

abstract: With the steadily increasing volume of e-commerce transactions, the amount of user- provided product reviews is increasing on the Web. Because many customers feel that they can purchase product based on the experiences of others that are obtainable through product reviews, the review summarization process has become important. In particular, feature-based product review summarization is needed in order to satisfy the detailed needs of some customers. To achieve such summarization, numerous techniques using natural language processing (NLP), machine learning, and statistical approaches that can evaluate product features within a collection of review documents have been studied. Many of these techniques require sentiment analysis or feature scoring methods. How-ever, existing sentiment analysis methods are limited when determining the sentiment polarity of context-sensitive words, and existing feature scoring methods are limited when only the overall user score is used to evaluate individual product features. In our summarization approach, context-sensitive information is used to determine sentiment polarity while opinioned-feature frequency is used to determine feature scores. Based on experiments with actual review data, our method improved the accuracy of the calcu-lated feature scores and outperformed existing methods.


title: Deep Hybrid Similarity Learning for Person Re-Identification

abstract: Person re-identification (Re-ID) aims to match person images captured from two non-overlapping cameras. In this paper, a deep hybrid similarity learning (DHSL) method for person Re-ID based on a convolution neural network (CNN) is proposed. In our approach, a light CNN learning feature pair for the input image pair is simultaneously extracted. Then, both the elementwise absolute difference and multiplication of the CNN learning feature pair are calculated. Finally, a hybrid similarity function is designed to measure the similarity between the feature pair, which is realized by learning a group of weight coefficients to project the elementwise absolute difference and multiplication into a similarity score. Consequently, the proposed DHSL method is able to reasonably assign complexities of feature learning and metric learning in a CNN, so that the performance of person Re-ID is improved. Experiments on three challenging person Re-ID databases, QMUL GRID, VIPeR, and CUHK03, illustrate that the proposed DHSL method is superior to multiple state-of-the-art person Re-ID methods.


title: Selections: Internet voting with over-the-shoulder coercion-resistance

abstract: We present Selections, a new cryptographic voting protocol that is end-to-end verifiable and suitable for Internet voting. After a one-time in-person registration, voters can cast ballots in an arbitrary number of elections. We say a system provides over-the-shoulder coercion-resistance if a voter can undetectably avoid complying with an adversary that is present during the vote casting process. Our system is the first in the literature to offer this property without the voter having to anticipate coercion and precompute values. Instead, a voter can employ a panic password. We prove that Selections is coercion-resistant against a non-adaptive adversary.


title: TimeFork: Interactive Prediction of Time Series

abstract: We present TimeFork, an interactive prediction technique to support users predicting the future of time-series data, such as in financial, scientific, or medical domains. TimeFork combines visual representations of multiple time series with prediction information generated by computational models. Using this method, analysts engage in a back-and-forth dialogue with the computational model by alternating between manually predicting future changes through interaction and letting the model automatically determine the most likely outcomes, to eventually come to a common prediction using the model. This computer-supported prediction approach allows for harnessing the user's knowledge of factors influencing future behavior, as well as sophisticated computational models drawing on past performance. To validate the TimeFork technique, we conducted a user study in a stock market prediction game. We present evidence of improved performance for participants using TimeFork compared to fully manual or fully automatic predictions, and characterize qualitative usage patterns observed during the user study.


title: Segment-Based Predominant Learning Swarm Optimizer for Large-Scale Optimization

abstract: Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, this paper proposes a novel segment-based predominant learning swarm optimizer (SPLSO) swarm optimizer through letting several predominant particles guide the learning of a particle. First, a segment-based learning strategy is proposed to randomly divide the whole dimensions into segments. During update, variables in different segments are evolved by learning from different exemplars while the ones in the same segment are evolved by the same exemplar. Second, to accelerate search speed and enhance search diversity, a predominant learning strategy is also proposed, which lets several predominant particles guide the update of a particle with each predominant particle responsible for one segment of dimensions. By combining these two learning strategies together, SPLSO evolves all dimensions simultaneously and possesses competitive exploration and exploitation abilities. Extensive experiments are conducted on two large-scale benchmark function sets to investigate the influence of each algorithmic component and comparisons with several state-of-the-art meta-heuristic algorithms dealing with large-scale problems demonstrate the competitive efficiency and effectiveness of the proposed optimizer. Further the scalability of the optimizer to solve problems with dimensionality up to 2000 is also verified.


title: Simulation and Hardware Implementation of New Maximum Power Point Tracking Technique for Partially Shaded PV System Using Hybrid DEPSO Method

abstract: In photovoltaic (PV) power generation, partial shading is an unavoidable complication that significantly reduces the efficiency of the overall system. Under this condition, the PV system produces a multiple-peak function in its output power characteristic. Thus, a reliable technique is required to track the global maximum power point (GMPP) within an appropriate time. This study aims to employ a hybrid evolutionary algorithm called the DEPSO technique, a combination of the differential evolutionary (DE) algorithm and particle swarm optimization (PSO), to detect the maximum power point under partial shading conditions. The paper starts with a brief description about the behavior of PV systems under partial shading conditions. Then, the DEPSO technique along with its implementation in maximum power point tracking (MPPT) is explained in detail. Finally, Simulation and experimental results are presented to verify the performance of the proposed technique under different partial shading conditions. Results prove the advantages of the proposed method, such as its reliability, system-independence, and accuracy in tracking the GMPP under partial shading conditions.


title: The multiple sweep method of moments (MSMM) analysis of electrically large bodies

abstract: This paper presents the multiple sweep method of moments (MSMM) analysis of the radiation and scattering from electrically large, smooth, perfectly conducting bodies. The MSMM is an O(N/sup 2/) recursive method for solving the large matrix equations, which arise in the method of moments (MM) analysis of electrically large bodies. In the MSMM, the body is split into P sections and the currents on these sections are found in a recursive fashion. The first MSMM recursion or sweep includes the dominant scattering mechanisms and each subsequent recursion or sweep includes higher order mechanisms. Thus, as the electrical size of the body increases, fewer and fewer sweeps are required to accurately compute the current on the body.


title: Adaptive Statistical Optimization Techniques for Firewall Packet Filtering

abstract: Packet filtering plays a critical role in the performance of many network devices such as firewalls, IPSec gateways, DiffServ and QoS routers. A tremendous amount of research was proposed to optimize packet filters. However, most of the related works use deterministic techniques and do not exploit the traffic characteristics in their optimization schemes. In addition, most packet classifiers give no specific consideration for optimizing packet rejection, which is important for many filtering devices like firewalls. Our contribution in this paper is twofold. First, we present a novel algorithm for maximizing early rejection of unwanted flows without impacting other flows significantly. Second, we present a new packet filtering optimization technique that uses adaptive statistical search trees to utilize important traffic characteristics and minimize the average packet matching time. The proposed techniques timely adapt to changes in the traffic conditions by performing simple calculations for optimizing the search data structure. Our techniques are practically attractive because they exhibit simple-to-implement and easy-to-deploy algorithms. Our extensive evaluation study using Internet traces shows that the proposed techniques can significantly minimize the packet filtering time with reasonable memory space requirements.


title: Advances in Human Action Recognition: A Survey

abstract: Human action recognition has been an important topic in computer vision due to its many applications such as video surveillance, human machine interaction and video retrieval. One core problem behind these applications is automatically recognizing low-level actions and high-level activities of interest. The former is usually the basis for the latter. This survey gives an overview of the most recent advances in human action recognition during the past several years, following a well-formed taxonomy proposed by a previous survey [1]. From this state-of-the-art survey, researchers can view a panorama of progress in this area for future research.


title: V.: W-kmeans: Clustering News Articles Using WordNet

abstract: Document clustering is a powerful technique that has been widely used for organizing data into smaller and manageable information kernels. Several approaches have been proposed suffering however from problems like synonymy, ambiguity and lack of a descriptive content marking of the generated clusters. We are proposing the enhancement of standard kmeans algorithm using the external knowledge from WordNet hypernyms in a twofold manner: enriching the "bag of words" used prior to the clustering process and assisting the label generation procedure following it. Our experimentation revealed a significant improvement over standard kmeans for a corpus of news articles derived from major news portals. Moreover, the cluster labeling process generates useful and of high quality cluster tags.


title: Miniaturized High-Performance Filters for 5G Small-Cell Applications

abstract: This paper demonstrates the first panel-based ultra-miniaturized filters with footprint smaller than half of the free-space wavelength at the operating frequencies of 28 and 39 GHz bands for 5G and mm-wave small-cell applications. The thin-film filters can be utilized either as ultra-thin integrated passive devices (IPDs) or embedded into the module substrates. Two filter types: lowpass and bandpass, with three topologies in total, are modeled, designed and fabricated on precision thin-film build-up layers on glass and traditional laminate cores. The modeling, design and optimization phase included the considerations of fabrication tolerances and testability of the filters. Glass is an ideal core material for mm-wave 5G modules and IPDs since it combines the benefits of ceramics for high frequency electrical performance, laminates for large panel processing and low cost, silicon-like dimensional stability and precision patterning, which is essential for mm-wave circuits. Unlike printing used in ceramics, or subtractive etching used in multilayer organics (MLO), this research utilizes semi-additive patterning (SAP) process to form high precision, multilayer redistribution layers (RDL) to design ultra-compact filter topologies with low insertion loss and improved stopband rejection, due to the close-to-ideal translation of lumped-to-distributed components. The simulated results of bandwidth, in-band insertion loss and out-of-band rejection of filters show excellent correlation with the measured results.


title: Heterogeneous Knowledge-Based Attentive Neural Networks for Short-Term Music Recommendations

abstract: The current existing data in online music service platforms are heterogeneous, extensive, and disorganized. Finding an effective method to use these data in recommending appropriate music to users during a short-term session is a significant challenge. Another serious problem is that most of the data, in reality, obey the long-tailed distribution, which consequently leads to traditional music recommendation systems recommending a lot of popular music that users do not like on a specific occasion. To solve these problems, we propose a heterogeneous knowledge-based attentive neural network model for short-term music recommendations. First, we collect three types of data for modeling entities in user–music interaction network, i.e., graphic, textual, and visual data, and then embed them into high-dimensional spaces using the TransR, distributed memory version of paragraph vector, and variational autoencoder methods, respectively. The concatenation of these embedding results is an abstract representation of the entity. Based on this, a recurrent neural network with an attention mechanism is built, which is capable of obtaining users’ preferences in the current session and consequently making recommendations. The experimental results show that our proposed approach outperforms the current state-of-the-art short-term music recommendation systems on one real-world dataset. In addition, it can also recommend more relatively unpopular songs compared with classic models.


title: Joint Discriminative Learning of Deep Dynamic Textures for 3D Mask Face Anti-Spoofing

abstract: Three-dimensional mask spoofing attacks have been one of the main challenges in face recognition. Compared with a 3D mask, a real face displays different facial motion patterns that are reflected by different facial dynamic textures. However, a large portion of these facial motion differences is subtle. We find that the subtle facial motion can be fully captured by multiple deep dynamic textures from a convolutional layer of a convolutional neural network, but not all deep dynamic textures from different spatial regions and different channels of a convolutional layer are useful for differentiation of subtle motions between real faces and 3D masks. In this paper, we propose a novel feature learning model to learn discriminative deep dynamic textures for 3D mask face anti-spoofing. A novel joint discriminative learning strategy is further incorporated in the learning model to jointly learn the spatial- and channel-discriminability of the deep dynamic textures. The proposed joint discriminative learning strategy can be used to adaptively weight the discriminability of the learned feature from different spatial regions or channels, which ensures that more discriminative deep dynamic textures play more important roles in face/mask classification. Experiments on several publicly available data sets validate that the proposed method achieves promising results in intra- and cross-data set scenarios.


title: Decoding Superposed LoRa Signals

abstract: Long-range low-power wireless communications, such as LoRa, are used in many IoT and environmental monitoring applications. They typically increase the communication range to several kilometers, at the cost of reducing the bitrate to a few bits per seconds. Collisions further reduce the performance of these communications. In this paper, we propose two algorithms to decode colliding signals: one algorithm requires the transmitters to be slightly desynchronized, and the other requires the transmitters to be synchronized. To do so, we use the timing information to match the correct symbols to the correct transmitters. We show that our algorithms are able to significantly improve the overall throughput of LoRa.


title: Anonymizing moving objects: how to hide a MOB in a crowd?

abstract: Moving object databases (MOD) have gained much interest in recent years due to the advances in mobile communications and positioning technologies. Study of MOD can reveal useful information (e.g., traffic patterns and congestion trends) that can be used in applications for the common benefit. In order to mine and/or analyze the data, MOD must be published, which can pose a threat to the location privacy of a user. Indeed, based on prior knowledge of a user's location at several time points, an attacker can potentially associate that user to a specific moving object (MOB) in the published database and learn her position information at other time points. In this paper, we study the problem of privacy-preserving publishing of moving object database. Unlike in microdata, we argue that in MOD, there does not exist a fixed set of quasi-identifier (QID) attributes for all the MOBs. Consequently the anonymization groups of MOBs (i.e., the sets of other MOBs within which to hide) may not be disjoint. Thus, there may exist MOBs that can be identified explicitly by combining different anonymization groups. We illustrate the pitfalls of simple adaptations of classical k-anonymity and develop a notion which we prove is robust against privacy attacks. We propose two approaches, namely extreme-union and symmetric anonymization, to build anonymization groups that provably satisfy our proposed k-anonymity requirement, as well as yield low information loss. We ran an extensive set of experiments on large real-world and synthetic datasets of vehicular traffic. Our results demonstrate the effectiveness of our approach.


title: Oral misoprostol vs. intravaginal prostaglandin E2 for preinduction cervical ripening. A randomized trial.

abstract: OBJECTIVE ::: To compare orally administered misoprostol with intravaginal prostaglandin E2 for cervical ripening and labor induction. ::: ::: ::: STUDY DESIGN ::: Patients presenting with medical or obstetric indications for labor induction whose Bishop's score was < or = 6 were randomly allocated to receive either 50 micrograms of oral misoprostol or 4 mg of intravaginal prostaglandin E2. If adequate cervical ripening (Bishop score of 9 or cervical dilatation of 3) or active labor did not ensue, repeat doses of each medication were administered every four hours. A maximum of six doses of either oral misoprostol or intravaginal prostaglandin E2 was permitted. Intravenous oxytocin was subsequently administered according to a standardized infusion protocol. ::: ::: ::: RESULTS ::: Sixty patients were enrolled, with 29 randomized to the oral misoprostol arm and 31 to the prostaglandin E2 group. The data on 58 patients were eligible for analysis. Delivery occurred within 48 hours in 96.4% (27/28) of those administered oral misoprostol as compared to 76.7% (23/30) of those who received intravaginal prostaglandin E2 (P = .03). The mean time intervals from the start of induction to delivery were similar between the two groups (1,496 +/- 120 vs. 1,723 +/- 230 minutes, P = .40). No statistically significant differences existed between the two groups with respect to intrapartum complications, tachysystole, uterine hyperstimulation or adverse neonatal outcomes. ::: ::: ::: CONCLUSION ::: Oral administration of misoprostol is an effective alternative to intravaginal prostaglandin E2 for preinduction cervical ripening.


Early writings on visual language


title: Spatial domain quantization noise based image filtering detection

abstract: Smart image editing and processing techniques make it easier to manipulate an image convincingly and also hide any artifacts of tampering. Common real world forgeries can be accompanied by enhancement operations like filtering, compression and/or format conversion to suppress forgery artifacts. Out of these enhancement operations, filtering is very common and has received a lot of attention in forensics research lately. However, different filtering operations and image formats are not investigated deeply and simultaneously. We propose an algorithm to detect if a given image has undergone filtering based enhancement irrespective of the format of image or the type of filter applied. In the proposed algorithm, we exploit the correlation of spatial domain quantization noise of an image by extracting transition probability features and classify the image as filtered or unfiltered. Experiments are performed to evaluate the robustness and compare the performance of the proposed technique with popular forensic filtering detection algorithms and is found to be superior in most of the cases.


title: Integrating Kano and EMF model to measure e-book user experience service quality

abstract: In order to determine the innovative service attributes of the e-book service model, the study proposed the Kano model with the strategic experiential module (SEM). First, the study identified sixteen attributes that affect the innovative services of e-book through expert panel discussion and key customer interviews. These attributes were then grouped into the five elements of the experience marketing framework (EMF). The Kano model is used to categorize these attributes, interview 292 customers and analyze their satisfaction. All these findings of this research then could be used as a reference for e-book operators. In the future, collaboration learning, cloud-based and social network service may into consideration in e-book designing phase to improve and expand the service satisfaction and scope.


title: M.: Outsourcing multi-party computation

abstract: We initiate the study of secure multi-party computation (MPC) in a server-aided setting, where the parties have access to a single server that (1) does not have any input to the computation; (2) does not receive any output from the computation; but (3) has a vast (but bounded) amount of computational resources. In this setting, we are concerned with designing protocols that minimize the computation of the parties at the expense of the server. We develop new definitions of security for this server-aided setting that generalize the standard simulation-based definitions for MPC and allow us to formally capture the existence of dishonest but non-colluding participants. This requires us to introduce a formal characterization of non-colluding adversaries that may be of independent interest. We then design general and special-purpose server-aided MPC protocols that are more efficient (in terms of computation and communication) for the parties than the alternative of running a standard MPC protocol (i.e., without the server). Our main general-purpose protocol provides security when there is at least one honest party with input. We also construct a new and efficient server-aided protocol for private set intersection and give a general transformation from any secure delegated computation scheme to a server-aided two-party protocol. ∗Microsoft Research. senyk@microsoft.com. †University of Calgary. pmohassel@cspc.ucalgary.ca. Work done while visiting Microsoft Research. ‡Columbia University. mariana@cs.columbia.edu. Work done as an intern at Microsoft Research.


title: Treatment of Unstable Thoracolumbar Burst Fractures by Indirect Reduction and Posterior Stabilization: Short-Segment Versus Long-Segment Stabilization

abstract: In order to compare short-segment stabilization with long-segment stabilization for treating unstable thoracolumbar fractures, we studied fifty patients suffered from unstable thoracolumbar burst fractures. Thirty of them were managed with long-segment posterior transpedicular instrumentation and twenty patients with short-segment stabilization. The mean follow up period was 5.2 years. Pre-operative and post-operative radiological parameters, like the Cobb angle, the kyphotic deformation and the Beck index were evaluated. A statistically significant difference between the two under study groups was noted for the Cobb angle and the kyphotic deformation, while, as far as the Beck index is concerned, no significant difference was noted. In conclusion, either the long-segment or the short-segment stabilization is able for reducing the segmental kyphosis and the vertebral body deformation postoperatively. However, as time goes by, the long-segment stabilization is associated with better results as far as the radiological parameters, the indexes and the patient’s satisfaction are concerned.


title: Tweeting celebrity suicides: Users' reaction to prominent suicide deaths on Twitter and subsequent increases in actual suicides.

abstract: A substantial amount of evidence indicates that news coverage of suicide deaths by celebrities is followed by an increase in suicide rates, suggesting a copycat behavior. However, the underlying process by which celebrity status and media coverage leads to increases in subsequent suicides is still unclear. This study collected over 1 million individual messages ("tweets") posted on Twitter that were related to 26 prominent figures in Japan who died by suicide between 2010 and 2014 and investigated whether media reports on suicide deaths that generated a greater level of reactions by the public are likely to be followed by a larger increase in actual suicides. We also compared the number of Twitter posts and the number of media reports in newspaper and on television to understand whether the number of messages on Twitter in response to the deaths corresponds to the amount of coverage in the traditional media. Using daily data from Japan's national death registry between 2010 and 2014, our analysis found an increase in actual suicides only when suicide deaths generated a large reaction from Twitter users. In contrast, no discernible increase in suicide counts was observed when the analysis included suicide deaths to which Twitter users did not show much interest, even when these deaths were covered considerably by the traditional media. This study also found suicides by relatively young entertainers generated a large number of posts on Twitter. This sharply contrasts with the relatively smaller volume of reaction to them generated by traditional forms of media, which focuses more on the deaths of non-entertainers. The results of this study strongly suggest that it is not sufficient to examine only traditional news media when investigating the impact of media reports on actual suicides.


title: Efficient randomized pattern-matching algorithms

abstract: We present randomized algorithms to solve the following string-matching problem and some of its generalizations: Given a string X of length n (the pattern) and a string Y (the text), find the first occurrence of X as a consecutive block within Y. The algorithms represent strings of length n by much shorter strings called fingerprints, and achieve their efficiency by manipulating fingerprints instead of longer strings. The algorithms require a constant number of storage locations, and essentially run in real time. They are conceptually simple and easy to implement. The method readily generalizes to higher-dimensional patternmatching problems.


title: What Differences Make a Difference? The Promise and Reality of Diverse Teams in Organizations

abstract: SUMMARY—As the workplace has become increasingly diverse, there has been a tension between the promise and the reality of diversity in team process and performance. The optimistic view holds that diversity will lead to an increase in the variety of perspectives and approaches brought to a problem and to opportunities for knowledge sharing, and hence lead to greater creativity and quality of team performance. However, the preponderance of the evidence favors a more pessimistic view: that diversity creates social divisions, which in turn create negative performance outcomes for the group.Why is the reality of diversity less than the promise? Answering this requires understanding a variety of factors, including how diversity is defined and categorized, and the moderating as well as mediating processes that affect the diversity–process–performance linkage.We start with a definition. The word diversity has been used to refer to so many types of differences among people that the most commonly used definition—“a...


title: Network Community Detection: A Review and Visual Survey

abstract: Community structure is an important area of research. It has received a considerable attention from the scientific community. Despite its importance, one of the key problems in locating information about community detection is the diverse spread of related articles across various disciplines. To the best of our knowledge, there is no current comprehensive review of recent literature which uses a scientometric analysis using complex networks analysis covering all relevant articles from the Web of Science (WoS). Here we present a visual survey of key literature using CiteSpace. The idea is to identify emerging trends besides using network techniques to examine the evolution of the domain. Towards that end, we identify the most influential, central, as well as active nodes using scientometric analyses. We examine authors, key articles, cited references, core subject categories, key journals, institutions, as well as countries. The exploration of the scientometric literature of the domain reveals that Yong Wang is a pivot node with the highest centrality. Additionally, we have observed that Mark Newman is the most highly cited author in the network. We have also identified that the journal, "Reviews of Modern Physics" has the strongest citation burst. In terms of cited documents, an article by Andrea Lancichinetti has the highest centrality score. We have also discovered that the origin of the key publications in this domain is from the United States. Whereas Scotland has the strongest and longest citation burst. Additionally, we have found that the categories of "Computer Science" and "Engineering" lead other categories based on frequency and centrality respectively.


Modeling Interestingness with Deep Neural Networks


title: Spatially Transformed Adversarial Examples

abstract: Recent studies show that widely used deep neural networks (DNNs) are vulnerable to carefully crafted adversarial examples. Many advanced algorithms have been proposed to generate adversarial examples by leveraging the $\mathcal{L}_p$ distance for penalizing perturbations. Researchers have explored different defense methods to defend against such adversarial attacks. While the effectiveness of $\mathcal{L}_p$ distance as a metric of perceptual quality remains an active research area, in this paper we will instead focus on a different type of perturbation, namely spatial transformation, as opposed to manipulating the pixel values directly as in prior works. Perturbations generated through spatial transformation could result in large $\mathcal{L}_p$ distance measures, but our extensive experiments show that such spatially transformed adversarial examples are perceptually realistic and more difficult to defend against with existing defense systems. This potentially provides a new direction in adversarial example generation and the design of corresponding defenses. We visualize the spatial transformation based perturbation for different examples and show that our technique can produce realistic adversarial examples with smooth image deformation. Finally, we visualize the attention of deep networks with different types of adversarial examples to better understand how these examples are interpreted.


title: Automatic White-Box Testing of First-Order Logic Ontologies

abstract: Formal ontologies are axiomatizations in a logic-based formalism. The development of formal ontologies, and their important role in the Semantic Web area, is generating considerable research on the use of automated reasoning techniques and tools that help in ontology engineering. One of the main aims is to refine and to improve axiomatizations for enabling automated reasoning tools to efficiently infer reliable information. Defects in the axiomatization can not only cause wrong inferences, but can also hinder the inference of expected information, either by increasing the computational cost of, or even preventing, the inference. In this paper, we introduce a novel, fully automatic white-box testing framework for first-order logic ontologies. Our methodology is based on the detection of inference-based redundancies in the given axiomatization. The application of the proposed testing method is fully automatic since a) the automated generation of tests is guided only by the syntax of axioms and b) the evaluation of tests is performed by automated theorem provers. Our proposal enables the detection of defects and serves to certify the grade of suitability --for reasoning purposes-- of every axiom. We formally define the set of tests that are generated from any axiom and prove that every test is logically related to redundancies in the axiom from which the test has been generated. We have implemented our method and used this implementation to automatically detect several non-trivial defects that were hidden in various first-order logic ontologies. Throughout the paper we provide illustrative examples of these defects, explain how they were found, and how each proof --given by an automated theorem-prover-- provides useful hints on the nature of each defect. Additionally, by correcting all the detected defects, we have obtained an improved version of one of the tested ontologies: Adimen-SUMO.


title: QSGD : Communication-Optimal Stochastic Gradient Descent , with Applications to Training Neural Networks

abstract: Parallel implementations of stochastic gradient descent (SGD) have received significant research attention, thanks to excellent scalability properties of this algorithm, and to its efficiency in the context of training deep neural networks. A fundamental barrier for parallelizing large-scale SGD is the fact that the cost of communicating the gradient updates between nodes can be very large. Consequently, lossy compression heuristics have been proposed, by which nodes only communicate quantized gradients. Although effective in practice, these heuristics do not always provably converge, and it is not clear whether they are optimal. ::: In this paper, we propose Quantized SGD (QSGD), a family of compression schemes which allow the compression of gradient updates at each node, while guaranteeing convergence under standard assumptions. QSGD allows the user to trade off compression and convergence time: it can communicate a sublinear number of bits per iteration in the model dimension, and can achieve asymptotically optimal communication cost. We complement our theoretical results with empirical data, showing that QSGD can significantly reduce communication cost, while being competitive with standard uncompressed techniques on a variety of real tasks. ::: In particular, experiments show that gradient quantization applied to training of deep neural networks for image classification and automated speech recognition can lead to significant reductions in communication cost, and end-to-end training time. For instance, on 16 GPUs, we are able to train a ResNet-152 network on ImageNet 1.8x faster to full accuracy. Of note, we show that there exist generic parameter settings under which all known network architectures preserve or slightly improve their full accuracy when using quantization.


title: Embedded Design of an Emotion-Aware Music Player

abstract: In this paper, a novel human-robot interaction(HRI) design is proposed where emotional recognition from the speech signal is used to create an emotion-aware music player that can be implemented in an embedded platform. The proposed system maps an inputted short-speech utterance to a two dimensional emotional plane of valence and arousal. This strategy allows the system to automatically select a piece of music from a database of songs, of which emotions are also expressed using arousal and valence values. Furthermore, a cheer-up strategy is proposed such that music songs with varying emotional content are played in order to cheer up the user to a more neutral/happy state. The proposed system has been implemented in a Beagle board. The online test verified the feasibility of the system. A questionnaire survey shows that 80% of subjects agree with the songs selected by the proposed cheer-up strategy based on the emotional model.


title: A dive into Web Scraper world

abstract: This paper talks about the World of Web Scraper, Web scraping is related to web indexing, whose task is to index information on the web with the help of a bot or web crawler. Here the legal aspect, both positive and negative sides are taken into view. Some cases regarding the legal issues are also taken into account. The Web Scraper's designing principles and methods are contrasted, it tells how a working Scraper is designed. The implementation is divided into three parts: the Web Crawler to fetch the desired links, the data extractor to fetch the data from the links and storing that data into a csv file. The Python language is used for the implementation. On combining all these with the good knowledge of libraries and working experience, we can have a fully-fledged Scraper. Due to a vast community and library support for Python and the beauty of coding style of python language, it is most suitable for Scraping data from Websites.


title: A dynamically configurable coprocessor for convolutional neural networks

abstract: Convolutional neural networks (CNN) applications range from recognition and reasoning (such as handwriting recognition, facial expression recognition and video surveillance) to intelligent text applications such as semantic text analysis and natural language processing applications. Two key observations drive the design of a new architecture for CNN. First, CNN workloads exhibit a widely varying mix of three types of parallelism: parallelism within a convolution operation, intra-output parallelism where multiple input sources (features) are combined to create a single output, and inter-output parallelism where multiple, independent outputs (features) are computed simultaneously. Workloads differ significantly across different CNN applications, and across different layers of a CNN. Second, the number of processing elements in an architecture continues to scale (as per Moore's law) much faster than off-chip memory bandwidth (or pin-count) of chips. Based on these two observations, we show that for a given number of processing elements and off-chip memory bandwidth, a new CNN hardware architecture that dynamically configures the hardware on-the-fly to match the specific mix of parallelism in a given workload gives the best throughput performance. Our CNN compiler automatically translates high abstraction network specification into a parallel microprogram (a sequence of low-level VLIW instructions) that is mapped, scheduled and executed by the coprocessor. Compared to a 2.3 GHz quad-core, dual socket Intel Xeon, 1.35 GHz C870 GPU, and a 200 MHz FPGA implementation, our 120 MHz dynamically configurable architecture is 4x to 8x faster. This is the first CNN architecture to achieve real-time video stream processing (25 to 30 frames per second) on a wide range of object detection and recognition tasks.


title: Adversarial Examples for Semantic Segmentation and Object Detection

abstract: It has been well demonstrated that adversarial examples, i.e., natural images with visually imperceptible perturbations added, cause deep networks to fail on image classification. In this paper, we extend adversarial examples to semantic segmentation and object detection which are much more difficult. Our observation is that both segmentation and detection are based on classifying multiple targets on an image (e.g., the target is a pixel or a receptive field in segmentation, and an object proposal in detection). This inspires us to optimize a loss function over a set of targets for generating adversarial perturbations. Based on this, we propose a novel algorithm named Dense Adversary Generation (DAG), which applies to the state-of-the-art networks for segmentation and detection. We find that the adversarial perturbations can be transferred across networks with different training data, based on different architectures, and even for different recognition tasks. In particular, the transfer ability across networks with the same architecture is more significant than in other cases. Besides, we show that summing up heterogeneous perturbations often leads to better transfer performance, which provides an effective method of black-box adversarial attack.


title: Abductive reasoning as the basis to reproduce expert criteria in ECG Atrial Fibrillation identification

abstract: Objective: This work aims at providing a new method for the automatic detection of atrial fibrillation, other arrhythmia and noise on short single lead ECG signals, emphasizing the importance of the interpretability of the classification results. ::: Approach: A morphological and rhythm description of the cardiac behavior is obtained by a knowledge-based interpretation of the signal using the \textit{Construe} abductive framework. Then, a set of meaningful features are extracted for each individual heartbeat and as a summary of the full record. The feature distributions were used to elucidate the expert criteria underlying the labeling of the 2017 Physionet/CinC Challenge dataset, enabling a manual partial relabeling to improve the consistency of the classification rules. Finally, state-of-the-art machine learning methods are combined to provide an answer on the basis of the feature values. ::: Main results: The proposal tied for the first place in the official stage of the Challenge, with a combined $F_1$ score of 0.83, and was even improved in the follow-up stage to 0.85 with a significant simplification of the model. ::: Significance: This approach demonstrates the potential of \textit{Construe} to provide robust and valuable descriptions of temporal data even with significant amounts of noise and artifacts. Also, we discuss the importance of a consistent classification criteria in manually labeled training datasets, and the fundamental advantages of knowledge-based approaches to formalize and validate that criteria.


title: Disney's Aladdin: first steps toward storytelling in virtual reality

abstract: Disney Imagineering has developed a high-fidelity virtual reality (VR) attraction where guests fly a magic carpet through a virtual world based on the animated film “Aladdin.” Unlike most existing work on VR, which has focused on hardware and systems software, we assumed high fidelity and focused on using VR as a new medium to tell stories. We fielded our system at EPCOT Center for a period of fourteen months and conducted controlled experiments, observing the reactions of over 45,000 guests. contact author: Randy Pausch, Computer Science Department, Thornton Hall, University of Virginia, Charlottesville, VA 22903. Pausch@virginia.edu, 804/982-2211 Permission to make digital or hard copies of part or all of this work or personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to st on servers, or to redistribute to lists, requires prior sion and/or a fee. -89791-746-4/96/008...$3.50 193 Riders filled out an exit survey after the experience, and with select groups we used a number of other data-gathering techniques, including interviews and mechanically logging where guests looked and flew. Our major finding is that in a high fidelity VR experience, men and women of all ages suspend disbelief and accept the illusion that they are in a different place. We have found that in VR, as in all media, content matters. Novices are unimpressed with the technology for its own sake; they care about what there is to do in the virtual world. We can improve the experience by telling a pre-immersion “background story” and by giving the guest a concrete goal to perform in the virtual environment. Our eventual goal is to develop the lexicon for this new storytelling medium: the set of communication techniques shared between directors and the audience. We conclude with a discussion of our second version of the Aladdin project, which contains a large number of synthetic characters and a narrative story line. Figure 1: A Guest’s View of the Virtual Environment


Patent Citations and Empirical Analysis


title: Night-to-Day Image Translation for Retrieval-based Localization

abstract: Visual localization is a key step in many robotics pipelines, allowing the robot to (approximately) determine its position and orientation in the world. An efficient and scalable approach to visual localization is to use image retrieval techniques. These approaches identify the image most similar to a query photo in a database of geo-tagged images and approximate the query’s pose via the pose of the retrieved database image. However, image retrieval across drastically different illumination conditions, e.g. day and night, is still a problem with unsatisfactory results, even in this age of powerful neural models. This is due to a lack of a suitably diverse dataset with true correspondences to perform end-to-end learning. A recent class of neural models allows for realistic translation of images among visual domains with relatively little training data and, most importantly, without ground-truth pairings.In this paper, we explore the task of accurately localizing images captured from two traversals of the same area in both day and night. We propose ToDayGAN – a modified image-translation model to alter nighttime driving images to a more useful daytime representation. We then compare the daytime and translated night images to obtain a pose estimate for the night image using the known 6-DOF position of the closest day image. Our approach improves localization performance by over 250% compared the current state-of-the-art, in the context of standard metrics in multiple categories.


title: “Switching On” Creativity: Task Switching Increases Creativity by Reducing Cognitive Fixation

abstract: The contemporary workplace increasingly demands individuals to switch among cognitive tasks. Whereas past research has focused on the downsides of task switching, the present research is among the first to uncover a potential upside-increased creativity. In two experiments using three distinct measures of creativity, we show that task switching enhances two principal forms of creativity: divergent thinking (Study 1) and convergent thinking (Study 2). Participants who continually alternated back and forth between two creativity tasks (i.e., continual-switch condition) outperformed both those who switched between the tasks at their discretion (i.e., discretionary-switch condition) and those who focused on one task for the first half of the allotted time before switching to the other task for the second half (i.e., midpoint- switch condition). We present evidence that task switching, which involves temporarily setting a task aside, fosters creativity by reducing cognitive fixation. This finding is all the mo...


title: Attribute based object identification

abstract: Over the last years, the robotics community has made substantial progress in detection and 3D pose estimation of known and unknown objects. However, the question of how to identify objects based on language descriptions has not been investigated in detail. While the computer vision community recently started to investigate the use of attributes for object recognition, these approaches do not consider the task settings typically observed in robotics, where a combination of appearance attributes and object names might be used in referral language to identify specific objects in a scene. In this paper, we introduce an approach for identifying objects based on natural language containing appearance and name attributes. To learn rich RGB-D features needed for attribute classification, we extend recently introduced sparse coding techniques so as to automatically learn attribute-dependent features. We introduce a large data set of attribute descriptions of objects in the RGB-D object dataset. Experiments on this data set demonstrate the strong performance of our approach to language based object identification. We also show that our attribute-dependent features provide significantly better generalization to previously unseen attribute values, thereby enabling more rapid learning of new attribute values.


title: Opposing effects of viral mediated brain expression of apolipoprotein E2 (apoE2) and apoE4 on apoE lipidation and Aβ metabolism in apoE4-targeted replacement mice

abstract: BackgroundHuman apolipoprotein E (apoE) exists in three major isoforms: apoE2, apoE3 and apoE4. In the brain, apoE is produced mostly by astrocytes and transports cholesterol to neurons via apoE receptors. Among the gene alleles encoding the three isoforms, the APOE4 allele is the strongest genetic risk factor for late-onset Alzheimer’s disease (AD), whereas APOE2 is protective. ApoE4 confers a gain of toxic function, a loss of neuroprotective function or a combination of both in AD pathogenesis. Given that therapeutic impacts of modulating apoE expression may be isoform-dependent, we sought to investigate the relationship between overexpressing apoE isoform and apoE-related functions in apoE-targeted replacement (TR) mice. Specifically, apoE isoform expression driven by the astrocyte-specific glial fibrillary acidic protein (GFAP) promoter was built into an adeno-associated virus serotype 8 (AAV8) vector and injected into the ventricles of postnatal day 2 (P2) apoE3-TR or apoE4-TR mice. Upon confirmation of apoE isoform expression, effects on apoE lipidation and the levels of amyloid-β (Aβ) in the brain were assessed.ResultsAAV8-GFAP-apoE isoforms were specifically expressed in astrocytes throughout all brain regions, which led to overall increased apoE levels in the brain. Viral mediated overexpression of apoE4 in the apoE4-TR background increased poorly-lipidated apoE lipoprotein particles and decreased apoE-associated cholesterol in apoE4-TR mice. Conversely, apoE2 overexpression in apoE4-TR mice enhanced apoE lipidation and associated cholesterol. Furthermore, overexpression of apoE4 elevated the levels of endogenous Aβ, whereas apoE2 overexpression trended to lower endogenous Aβ.ConclusionsOverexpression of apoE isoforms induces differential effects in the apoE4-TR background: apoE4 decreases apoE lipidation and enhances Aβ accumulation, whereas apoE2 has the opposite effects. Our findings suggest that increasing apoE2 in APOE4 carriers is a beneficial strategy to treat AD, whereas increasing apoE4 in APOE4 carriers is likely harmful. We have also established novel methods to express apoE isoforms in mouse brain to study apoE-related pathways in AD and related dementia.


title: Young women's genital self-image and effects of exposure to pictures of natural vulvas.

abstract: INTRODUCTION ::: Many women have doubts about the normality of the physical appearance of their vulvas. This study measured genital self-image in a convenience sample of college-educated women, and assessed whether exposure to pictures of natural vulvas influenced their genital self-image. ::: ::: ::: METHOD ::: Forty-three women were either shown pictures of natural vulvas (N = 29) or pictures of neutral objects (N = 14). Genital self-image was measured before and after exposure to the pictures and two weeks later. Sexual function, sexual distress, self-esteem and trait anxiety were measured to investigate whether these factors influenced genital self-image scores after vulva picture exposure. ::: ::: ::: RESULTS ::: A majority of the participants felt generally positively about their genitals. Having been exposed to pictures of natural vulvas resulted in an even more positive genital self-image, irrespective of levels of sexual function, sexual distress, self-esteem and trait anxiety. In the women who had seen the vulva pictures, the positive effect on genital self-image was still present after two weeks. ::: ::: ::: DISCUSSION ::: The results of this study seem to indicate that even in young women with a relatively positive genital self-image, exposure to pictures of a large variety of natural vulvas positively affects genital self-image. This finding may suggest that exposure to pictures of natural vulvas may also lead to a more positive genital self-image in women who consider labiaplasty.


title: Social Learning Theory and the Health Belief Model

abstract: The Health Belief Model, social learning theory (recently relabelled social cognitive theory), self-efficacy, and locus of control have all been applied with varying success to problems of explaining, predicting, and influencing behavior. Yet, there is con ceptual confusion among researchers and practitioners about the interrelationships of these theories and variables. This article attempts to show how these explanatory fac tors may be related, and in so doing, posits a revised explanatory model which incor porates self-efficacy into the Health Belief Model. Specifically, self-efficacy is pro posed as a separate independent variable along with the traditional health belief var iables of perceived susceptibility, severity, benefits, and barriers. Incentive to behave (health motivation) is also a component of the model. Locus of control is not included explicitly because it is believed to be incorporated within other elements of the model. It is predicted that the new formulation will more fully account fo...


title: A ZVS Grid-Connected Three-Phase Inverter

abstract: A six-switch three-phase inverter is widely used in a high-power grid-connected system. However, the antiparallel diodes in the topology operate in the hard-switching state under the traditional control method causing severe switch loss and high electromagnetic interference problems. In order to solve the problem, this paper proposes a topology of the traditional six-switch three-phase inverter but with an additional switch and gave a new space vector modulation (SVM) scheme. In this way, the inverter can realize zero-voltage switching (ZVS) operation in all switching devices and suppress the reverse recovery current in all antiparallel diodes very well. And all the switches can operate at a fixed frequency with the new SVM scheme and have the same voltage stress as the dc-link voltage. In grid-connected application, the inverter can achieve ZVS in all the switches under the load with unity power factor or less. The aforementioned theory is verified in a 30-kW inverter prototype.


title: PrefMiner: mining user's preferences for intelligent mobile notification management

abstract: Mobile notifications are increasingly used by a variety of applications to inform users about events, news or just to send alerts and reminders to them. However, many notifications are neither useful nor relevant to users' interests and, also for this reason, they are considered disruptive and potentially annoying. In this paper we present the design, implementation and evaluation of PrefMiner, a novel interruptibility management solution that learns users' preferences for receiving notifications based on automatic extraction of rules by mining their interaction with mobile phones. The goal is to build a system that is intelligible for users, i.e., not just a "black-box" solution. Rules are shown to users who might decide to accept or discard them at run-time. The design of PrefMiner is based on a large scale mobile notification dataset and its effectiveness is evaluated by means of an in-the-wild deployment.


title: A Narrative Review of Dignity Therapy Research

abstract: Introduction ::: Dignity therapy is an end-of-life psychological intervention that focuses on the creation of a legacy document to alleviate end-of-life distress. Dignity therapy is based on an empirical model of dignity at the end of life. Research on dignity therapy has been ongoing for over 12 years in 11 countries, which has created a solid empirical base. ::: ::: Objective ::: This article presents a narrative overview of the literature on dignity therapy to provide a comprehensive narrative review and critical synthesis of published research. ::: ::: Method ::: Electronic databases (PubMed, CINAHL, PsycINFO, and Scopus) were searched using the key terms “dignity therapy,” “dignity psychotherapy,” and “Chochinov” from 2000 to March 2016. ::: ::: Results ::: Thirty-nine publications were included and findings were grouped into the following areas: Efficacy; Feasibility with different study populations; Web-assisted delivery; Impact on families; Cultural studies; Case studies; Themes found in documents; Clinical perspectives; and Implementation studies. ::: ::: Conclusions ::: While dignity therapy is well accepted in most cases, it may not always be effective, therapeutically valid, or practical, and may cause family or cultural frictions. It is recommended that clinicians take into consideration each person's unique circumstances in relation to the current literature before undertaking dignity therapy. Future research is indicated to evaluate dignity therapy with different cultural groups, to investigate the views of recipients of dignity therapy documents, to discover the time and resource commitments required to deliver dignity therapy, to identify who should provide dignity therapy, and to examine the experiences of clinicians who deliver dignity therapy. More research is also needed comparing dignity therapy to other end-of-life interventions.


title: The Influence of Social Curiosity on the Observing Behavior of Users on Social Network Sites

abstract: Social Network Site (SNS) service providers have a strong interest in knowing which factors drive different kinds of behavior in SNSs, in order to be able to foster behavior that is beneficial to their business models. Current studies neglect the factors driving Observing behavior: behavior that provides members with information within SNSs. Drawing from the Uses and Gratifications Theory, we postulate that Curiosity as well as its people-oriented facet, Social Curiosity, positively influence SNS members’ Observing behavior. After surveying Facebook users and applying a structural equation modeling approach, we confirmed that two facets of Social Curiosity, General Social Curiosity and Covert Social Curiosity, positively influence Observing behavior. However, we were not able to confirm a corresponding influence of general Curiosity. Overall, our findings suggest that just being curious is not enough in order to be attracted to SNSs’ Observing functions. Rather, people need to have a curiosity for people.


title: Clinical investigation of asmofilcon A silicone hydrogel lenses

abstract: Purpose. To investigate the clinical and subjective performance of asmofilcon A, a new third generation silicone hydrogel contact lens during 6-night extended wear (EW) over 6 months. ::: ::: Methods. A prospective, randomized, single-masked study was conducted. Sixty experienced daily wear soft contact lens wearers were randomly assigned to wear either asmofilcon A or senofilcon A contact lenses bilaterally for 6 months on an EW basis. Evaluations were conducted at contact lens delivery and after 1 week, 4 weeks, 3 and 6 months of EW. ::: ::: Results. Fifty subjects (83%) successfully completed the study. Two subjects experienced adverse events; one unilateral red eye with asmofilcon A and one asymptomatic infiltrate with senofilcon A. There were no significant differences in high or low contrast distance visual acuity between asmofilcon A and senofilcon A; however, low contrast distance visual acuity decreased significantly over time with both contact lens types (p 0.05). Superior palpebral conjunctival injection showed a statistically significant increase over time with both lens types (p 0.05). Overall satisfaction ratings were also very high at all visits, with median scores of 95 (86 to 99) for asmofilcon A and 90 (85 to 96) for senofilcon A at 6 months (p > 0.05). ::: ::: Conclusions. Over 6 months of EW, the asmofilcon A contact lens performed in a similar manner to senofilcon A with respect to visual acuity, ocular health, and contact lens performance measures. Longer-term EW studies are required to investigate the changes over time observed with both lens types.


title: Contactless DC Connector Based on GaN LLC Converter for Next-Generation Data Centers

abstract: An inductively coupled contactless dc connector has been proposed for the next-generation 380-V dc distribution system in data centers. A LLC resonant dc–dc converter topology with gallium nitride (GaN) power transistors has been applied to realize the short-distance highly efficient contactless power transfer. A prototype of a 1.2-kW 384- to 192-V connector has been fabricated and the conversion efficiency of over 95% with the power density of $8.1\ \hbox{W}/\hbox{cm}^{3} $ has been confirmed experimentally under 1000-kHz operation. The design consideration has been carried out and the potential to achieve $10.0\ \hbox{W}/\hbox{cm}^{3} $ has been also shown taking the feature of the GaN power device and the characteristics of the magnetic core material for the transformer into account. The contactless dc connector integrates the functioning of an isolated dc–dc converter into a connector for space saving, and the dc current can be cut off without arc because of the inductive coupling. The proposed connector contributes to realizing a highly efficient, space saving, and reliable future 380-V dc distribution system.


title: ARMageddon: Last-Level Cache Attacks on Mobile Devices

abstract: In the last 10 years cache attacks on Intel CPUs have gained increasing attention among the scientific community. More specifically, powerful techniques to exploit the cache side channel have been developed. However, so far only a few investigations have been performed on modern smartphones and mobile devices in general. In this work, we describe Evict+Reload, the first access-based cross-core cache attack on modern ARM Cortex-A architectures as used in most of today’s mobile devices. Our attack approach overcomes several limitations of existing cache attacks on ARM-based devices, for instance, the requirement of a rooted device or specific permissions. Thereby, we broaden the scope of cache attacks in two dimensions. First, we show that all existing attacks on the x86 architecture can also be applied to mobile devices. Second, despite the general belief these attacks can also be launched on non-rooted devices and, thus, on millions of off-the-shelf devices. Similarly to the well-known Flush+Reload attack for the x86 architecture, Evict+Reload allows to launch generic cache attacks on mobile devices. Based on cache template attacks we identify information leaking through the last-level cache that can be exploited, for instance, to infer tap and swipe events, inter-keystroke timings as well as the length of words entered on the touchscreen, and even cryptographic primitives implemented in Java. Furthermore, we demonstrate the applicability of Prime+Probe attacks on ARM Cortex-A CPUs. The performed example attacks demonstrate the immense potential of our proposed attack techniques.


title: Roles of DNA polymerase I in leading and lagging-strand replication defined by a high-resolution mutation footprint of ColE1 plasmid replication

abstract: DNA polymerase I (pol I) processes RNA primers during lagging-strand synthesis and fills small gaps during DNA repair reactions. However, it is unclear how pol I and pol III work together during replication and repair or how extensive pol I processing of Okazaki fragments is in vivo. Here, we address these questions by analyzing pol I mutations generated through error-prone replication of ColE1 plasmids. The data were obtained by direct sequencing, allowing an accurate determination of the mutation spectrum and distribution. Pol I's mutational footprint suggests: (i) during leading-strand replication pol I is gradually replaced by pol III over at least 1.3 kb; (ii) pol I processing of Okazaki fragments is limited to ∼20 nt and (iii) the size of Okazaki fragments is short (∼250 nt). While based on ColE1 plasmid replication, our findings are likely relevant to other pol I replicative processes such as chromosomal replication and DNA repair, which differ from ColE1 replication mostly at the recruitment steps. This mutation footprinting approach should help establish the role of other prokaryotic or eukaryotic polymerases in vivo, and provides a tool to investigate how sequence topology, DNA damage, or interactions with protein partners may affect the function of individual DNA polymerases.


title: Action Recognition using Visual Attention

abstract: We propose a soft attention based model for the task of action recognition in videos. We use multi-layered Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units which are deep both spatially and temporally. Our model learns to focus selectively on parts of the video frames and classifies videos after taking a few glimpses. The model essentially learns which parts in the frames are relevant for the task at hand and attaches higher importance to them. We evaluate the model on UCF-11 (YouTube Action), HMDB-51 and Hollywood2 datasets and analyze how the model focuses its attention depending on the scene and the action being performed.


title: Sentiment analysis on tweets for social events

abstract: Sentiment analysis or opinion mining is an important type of text analysis that aims to support decision making by extracting and analyzing opinion oriented text, identifying positive and negative opinions, and measuring how positively or negatively an entity (i.e., people, organization, event, location, product, topic, etc.) is regarded. As more and more users express their political and religious views on Twitter, tweets become valuable sources of people's opinions. Tweets data can be efficiently used to infer people's opinions for marketing or social studies. This paper proposes a Tweets Sentiment Analysis Model (TSAM) that can spot the societal interest and general people's opinions in regard to a social event. In this paper, Australian federal election 2010 event was taken as an example for sentiment analysis experiments. We are primarily interested in the sentiment of the specific political candidates, i.e., two primary minister candidates - Julia Gillard and Tony Abbot. Our experimental results demonstrate the effectiveness of the system.


title: The Reduceron reconfigured and re-evaluated

abstract: A new version of a special-purpose processor for running lazy functional programs is presented. This processor-the Reduceron-exploits parallel memories and dynamic analyses to increase evaluation speed, and is implemented using reconfigurable hardware. Compared to a more conventional functional language implementation targeting a standard RISC processor running on the same reconfigurable hardware, the Reduceron offers a significant improvement in run-time performance.


title: Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension

abstract: We introduce the task of Multi-Modal Machine Comprehension (M3C), which aims at answering multimodal questions given a context of text, diagrams and images. We present the Textbook Question Answering (TQA) dataset that includes 1,076 lessons and 26,260 multi-modal questions, taken from middle school science curricula. Our analysis shows that a significant portion of questions require complex parsing of the text and the diagrams and reasoning, indicating that our dataset is more complex compared to previous machine comprehension and visual question answering datasets. We extend state-of-the-art methods for textual machine comprehension and visual question answering to the TQA dataset. Our experiments show that these models do not perform well on TQA. The presented dataset opens new challenges for research in question answering and reasoning across multiple modalities.


Bayesian Nonstationary Gaussian Process Models for Large Datasets via Treed Process Convolutions


title: Customer satisfaction evaluation method for customized product development using Entropy weight and Analytic Hierarchy Process

abstract: Customer requirements are identified and classified into four categories.A customer satisfaction evaluation model is developed.It gives the quantitative methods for each category of the evaluation criteria.Criteria weights are obtained by integrating Entropy weight and AHP.A case study of applying to customized portrait-based product industry is given. Providing customized products with high customer satisfaction (CS) has become an inevitable trend for modern customized companies to remain competitive. This study proposes a CS evaluation method for customized product development using Entropy weight and Analytic Hierarchy Process (AHP). Firstly, customer requirements are identified based on Voice-of-Customer (VoC), and classified into four categories (positive, negative, must-be and fuzzy attributes) according to the characteristics of CS criteria. Then, CS evaluation model and its solution method are introduced in detail. This study especially focuses on the quantitative methods for each category of the evaluation criteria through formulating several mathematical models, and criteria weights are obtained through integrating Entropy weight and AHP method. Finally, a case study of applying the proposed methodology to customized portrait-based product industry demonstrates the functionality of the proposed methodology.


title: Effects of current and voltage harmonics on distribution transformer losses

abstract: The drive to improve energy efficiency and reduce electrical loading has resulted in energy efficient lighting such as compact fluorescent lamps (CFLs) replacing conventional incandescent lamps. However, the presence of such non-linear loads has brought about the injection of voltage and current harmonics into electrical networks. As transformers are the interface between the supply and the non-linear loads, the investigation of their effects on transformer losses is of great importance. These harmonics can cause excessive loss and abnormal temperature rise in the transformers, thus reducing their operational life span. This paper investigates the impact of current and voltage harmonics of the loads on a single-phase 25kVA distribution transformer. Harmonic spectra of a range of non-linear loads including CFL, LED tube, PC and fluorescent lamp are obtained. A single-phase inverter is used for harmonic generation to simulate power supply harmonics injected into the transformer. Open circuit and short circuit tests are conducted on the transformer under the effect of harmonics, and the impacts on core loss are analyzed.


title: Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN

abstract: Discriminative localization is essential for fine-grained image classification task, which devotes to recognizing hundreds of subcategories in the same basic-level category. Reflecting on discriminative regions of objects, key differences among different subcategories are subtle and local. Existing methods generally adopt a two-stage learning framework: The first stage is to localize the discriminative regions of objects, and the second is to encode the discriminative features for training classifiers. However, these methods generally have two limitations: (1) Separation of the two-stage learning is time-consuming. (2) Dependence on object and parts annotations for discriminative localization learning leads to heavily labor-consuming labeling. It is highly challenging to address these two important limitations simultaneously. Existing methods only focus on one of them. Therefore, this paper proposes the discriminative localization approach via saliency-guided Faster R-CNN to address the above two limitations at the same time, and our main novelties and advantages are: (1) End-to-end network based on Faster R-CNN is designed to simultaneously localize discriminative regions and encode discriminative features, which accelerates classification speed. (2) Saliency-guided localization learning is proposed to localize the discriminative region automatically, avoiding labor-consuming labeling. Both are jointly employed to simultaneously accelerate classification speed and eliminate dependence on object and parts annotations. Comparing with the state-of-the-art methods on the widely-used CUB-200-2011 dataset, our approach achieves both the best classification accuracy and efficiency.


title: High-density MOM capacitor array with novel mortise-tenon structure for low-power SAR ADC

abstract: The design of capacitor structures have great impact on capacitance density, parasitic capacitance, routability, and matching quality of capacitor network in a SAR ADC, which may affect power, performance, and area of the whole data converter. Most of the recent studies focused on common-centroid placement and routing optimization of the capacitor network. Only few of them investigated the structures of highly integrated capacitors. In this paper, a novel mortise-tenon metal-oxide-metal capacitor structure is proposed, which has the advantages of high capacitance density and small parasitic capacitance. Based on the proposed structure, an integer-linear-programming based capacitor sizing and routing parasitic matching method is further introduced. Experimental results show that the proposed structure and method can achieve the best capacitance density and matching quality of the capacitor network in a SAR ADC.


title: Structural Correlation Filter for Robust Visual Tracking

abstract: In this paper, we propose a novel structural correlation filter (SCF) model for robust visual tracking. The proposed SCF model takes part-based tracking strategies into account in a correlation filter tracker, and exploits circular shifts of all parts for their motion modeling to preserve target object structure. Compared with existing correlation filter trackers, our proposed tracker has several advantages: (1) Due to the part strategy, the learned structural correlation filters are less sensitive to partial occlusion, and have computational efficiency and robustness. (2) The learned filters are able to not only distinguish the parts from the background as the traditional correlation filters, but also exploit the intrinsic relationship among local parts via spatial constraints to preserve object structure. (3) The learned correlation filters not only make most parts share similar motion, but also tolerate outlier parts that have different motion. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SCF tracking algorithm performs favorably against several state-of-the-art methods.


title: Ontology based combined approach for sentiment classification

abstract: Text documents contain opinions or sentiments about the objects, such as movie reviews, book reviews, and product reviews etc. Sentiment analysis is the mining the sentiment or opinion words and identification and analysis of the opinion and arguments in the text. In this paper, we proposed an ontology based combination approach to enhance the exits approaches of sentiment classifications and use supervised learning techniques for classifications.


title: M4: A Visualization-Oriented Time Series Data Aggregation

abstract: Visual analysis of high-volume time series data is ubiquitous in many industries, including finance, banking, and discrete manufacturing. Contemporary, RDBMS-based systems for visualization of high-volume time series data have difficulty to cope with the hard latency requirements and high ingestion rates of interactive visualizations. Existing solutions for lowering the volume of time series data disregard the semantics of visualizations and result in visualization errors. ::: ::: In this work, we introduce M4, an aggregation-based time series dimensionality reduction technique that provides error-free visualizations at high data reduction rates. Focusing on line charts, as the predominant form of time series visualization, we explain in detail the drawbacks of existing data reduction techniques and how our approach outperforms state of the art, by respecting the process of line rasterization. ::: ::: We describe how to incorporate aggregation-based dimensionality reduction at the query level in a visualization-driven query rewriting system. Our approach is generic and applicable to any visualization system that uses an RDBMS as data source. Using real world data sets from high tech manufacturing, stock markets, and sports analytics domains we demonstrate that our visualization-oriented data aggregation can reduce data volumes by up to two orders of magnitude, while preserving perfect visualizations.


title: Theories and computational models of affordance and mirror systems: An integrative review

abstract: Neuroscientific and psychological data suggest a close link between affordance and mirror systems in the brain. However, we still lack a full understanding of both the individual systems and their interactions. Here, we propose that the architecture and functioning of the two systems is best understood in terms of two challenges faced by complex organisms, namely: (a) the need to select among multiple affordances and possible actions dependent on context and high-level goals and (b) the exploitation of the advantages deriving from a hierarchical organisation of behaviour based on actions and action-goals. We first review and analyse the psychological and neuroscientific literature on the mechanisms and processes organisms use to deal with these challenges. We then analyse existing computational models thereof. Finally we present the design of a computational framework that integrates the reviewed knowledge. The framework can be used both as a theoretical guidance to interpret empirical data and design new experiments, and to design computational models addressing specific problems debated in the literature.


title: Collaborative Authoring on the Web: A Genre Analysis of Online Encyclopedias

abstract: This paper presents the results of a genre analysis of two web-based collaborative authoring environments, Wikipedia and Everything2, both of which are intended as repositories of encyclopedic knowledge and are open to contributions from the public. Using corpus linguistic methods and factor analysis of word counts for features of formality and informality, we show that the greater the degree of post-production editorial control afforded by the system, the more formal and standardized the language of the collaboratively-authored documents becomes, analogous to that found in traditional print encyclopedias. Paradoxically, users who faithfully appropriate such systems create homogeneous entries, at odds with the goal of open-access authoring environments to create diverse content. The findings shed light on how users, acting through mechanisms provided by the system, can shape (or not) features of content in particular ways. We conclude by identifying sub-genres of web-based collaborative authoring environments based on their technical affordances.


title: A multidimensional approach to measuring well-being in students: Application of the PERMA framework

abstract: Seligman recently introduced the PERMA model with five core elements of psychological well-being: positive emotions, engagement, relationships, meaning, and accomplishment. We empirically tested this multidimensional theory with 516 Australian male students (age 13–18). From an extensive well-being assessment, we selected a subset of items theoretically relevant to PERMA. Factor analyses recovered four of the five PERMA elements, and two ill-being factors (depression and anxiety). We then explored the nomological net surrounding each factor by examining cross-sectional associations with life satisfaction, hope, gratitude, school engagement, growth mindset, spirituality, physical vitality, physical activity, somatic symptoms, and stressful life events. Factors differentially related to these correlates, offering support for the multidimensional approach to measuring well-being. Directly assessing subjective well-being across multiple domains offers the potential for schools to more systematically understand and promote well-being.


title: Cloud computing infrastructure prototype for university education and research

abstract: In this paper we provide an overview of the cloud computing paradigm and discuss its application in the form of a prototype for collaboration between academic institutions. "Cloud computing" is increasing in popularity nowadays, but many organizations understand cloud computing in different ways. Although almost everyone acknowledges the importance of more efficient resource utilization and a shift from desktop and mainframe applications to the client-server, distributed, multi-processing, n-tier, grid and, finally, to the cloud computing paradigm. We briefly analyze the cloud computing approach of computation and describe our current and accomplished educational and research projects, related to the discussed topic and our experience with educational infrastructure design. Then we evaluate the successful implementations of cloud computing models at educational institutions and the different ways to implement cloud computing. As the result, we present our research and education prototype for education infrastructures which is ready for experiment. In the conclusion, we summarize the results and provide a description for taking cloud computing infrastructure implementation and development to the next step.


title: Behave or be watched: debugging with behavioral watchpoints

abstract: Finding, understanding, and fixing bugs in an operating system is challenging. Dynamic binary translation (DBT) systems provide a powerful facility for building program analysis and debugging tools. However, DBT abstractions are too low-level and provide limited contextual information for instrumentation tools. We introduce behavioral watchpoints, a new software-based watchpoint framework that simplifies the implementation of DBT-based program analysis and debugging tools. Behavioral watchpoints extend the traditional approach of using a DBT system by providing context-specific information at the instruction level and specializing instrumentation to individual data structures. We describe four applications developed using our watchpoint framework: detecting buffer overflows, detecting read-before-write and memory freeing bugs, detecting memory leaks and enforcing fine-grained memory access policies. We implemented behavioral watchpoints using Granary, a new DBT framework. We show that the overheads are reasonable for their intended use in analyzing and debugging kernel modules.


title: An ultra wide band power divider/combiner based on Y-structure waveguide

abstract: In this paper, an improved power divider/combiner based on Y-structure waveguide is proposed at ka-band (26–40 GHz). To widen the bandwidth of the divider/combiner, a stepped-impedance waveguide transformer is used, and a vertical resistive card is inserted into the divider/combiner to realize high isolation. According to EM simulations, the power divider/combiner illustrates a 42.4% effective bandwidth (ranging from 26 to 40 GHz), the return loss is better than −24 dB, and the isolation is better than −21 dB. This structure has properties of not only a wide band, low insertion loss and high output power, but also a small size, a high power combining efficiency and efficient heat sinking, and has potential for millimeter-wave broad-band high power combination.


title: Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos

abstract: Invasive Weed Optimization (IWO) algorithm is a simple but powerful algorithm which is capable of solving general multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. Recently IWO algorithm is being used in several engineering design owing to its superior performance in comparison with many other existing algorithms. This paper presents a Chaotic IWO (CIWO) algorithms based on chaos, and investigates its performance for optimal settings of Optimal Power Flow (OPF) control variables of OPF problem with non-smooth and non-convex generator fuel cost curves (non-smooth and non-convex OPF). The performance of CIWO algorithms are studied and evaluated on the standard IEEE 30-bus test system with different objective functions. The experimental results suggest that IWO algorithm holds immense promise to appear as an efficient and powerful algorithm for optimization in the power system.


title: The cognitive-energetic model: an empirical approach to Attention-Deficit Hyperactivity Disorder

abstract: Abstract Attention Deficit/Hyperactivity Disorder (ADHD) is a childhood psychiatric disorder which when carefully defined, affects around 1% of the childhood population [Swanson JM, Sergeant JA, Taylor E, Sonuga-Barke EJS, Jensen PS, Canwell DP. Attention-deficit hyperactivity disorder and hyperkinetic disorder. Lancet 1998;351:429–433]. The primary symptoms: distractibility, impulsivity and overactivity vary in degree and association in such children, which led DSM IV to propose three subgroups. Only one of these subgroups, the combined subtype: deficits in all three areas, meets the ICD-10 criteria. Since the other two subtypes are used extensively in North America (but not in Europe), widely different results between centres are to be expected and have been reported. Central to the ADHD syndrome is the idea of an attention deficit. In order to investigate attention, it is necessary to define what one means by this term and to operationalize it in such a manner that others can test and replicate findings. We have advocated the use of a cognitive-energetic model [Sanders, AF. Towards a model of stress and performance. Acta Psychologica 1983;53: 61–97]. The cognitive-energetic model of ADHD approaches the ADHD deficiency at three distinct levels. First, a lower set of cognitive processes: encoding, central processing and response organisation is postulated. Study of these processes has indicated that there are no deficits of processing at encoding or central processing but are present in motor organisation [Sergeant JA, van der Meere JJ. Convergence of approaches in localizing the hyperactivity deficit. In Lahey BB, Kazdin AE, editors. Advancements in clinical child psychology, vol. 13. New York: Plenum press, 1990. p. 207–45; Sergeant, JA, van der Meere JJ. Additive factor methodology applied to psychopathology with special reference to hyperactivity. Acta Psychologica 1990;74:277–295]. A second level of the cognitive-energetic model consists of the energetic pools: arousal, activation and effort. At this level, the primary deficits of ADHD are associated with the activation pool and (to some extent) effort. The third level of the model contains a management or executive function system. Barkley [Barkley RA, Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychological Bulletin 1997;121:65–94] reviewed the literature and concluded that executive function deficiencies were primarily due to a failure of inhibition. Oosterlaan, Logan and Sergeant [Oosterlaan J, Logan GD, Sergeant JA. Response inhibition in ADHD, CD, comorbid ADHD+CD, anxious and normal children: a meta-analysis of studies with the stop task. Journal of Child Psychology and Psychiatry 1998;39:411–426] demonstrated that this explanation was not specific to ADHD but also applied to children with the associated disorders of oppositional defiant and conduct disorder. Other executive functions seem to be intact, while others, are deficient. It is argued here that the cognitive-energetic model is a useful guide for determining not only ADHD deficiencies and associated disorders but also linking human cognitive neuroscience studies with neurobiological models of ADHD using animals [Sadile AG. Multiple evidence of a segmental defect in the anterior forebrain of an animal model of hyperactivity and attention deficits. Neuroscience and Biobehavioral Reviews, in press; Sagvolden T, Sergeant JA. Attention-deficit hyperactivity disorder: from brain dysyfunctions to behaviour. Behavioural Brain Research 1998;94:1–10]. A plea for an integrated attack on this research problem is made and the suggestion that conceptual refinement between levels of analysis is essential for further fundamental work to succeed is offered here.