title: Military field deployable antenna using origami

abstract: In this paper, origami antennas are proposed for military field deployment. A high gain tetrahedron origami antenna (antenna #1) is introduced and then extended to circularly polarized antenna (antenna #2) for military satellite applications. Both the antennas are realized on paper substrate by using origami tetrahedron structure. The radiating aperture of antenna #1 comprises a triangular shaped monopole and two strip directors. The fabricated antenna #1 demonstrates a peak gain of 9.6dBi at 2.6 GHz with impedance bandwidth of 66% (2–4 GHz). The circular polarization characteristics of antenna #2 is achieved by exciting two identical triangular shaped monopole elements. Both the elements are fed by a T-junction divider with a phase difference of 90°. The 3-dB axial-ratio bandwidth and impedance bandwidth of the proposed antenna #2 are found to be 8% (3.415 to 3.7 GHz), and 70.2% (2.4 to 5 GHz), respectively.


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title: Evaluating and Informing the Design of Chatbots

abstract: Text messaging-based conversational agents (CAs), popularly called chatbots, received significant attention in the last two years. However, chatbots are still in their nascent stage: They have a low penetration rate as 84% of the Internet users have not used a chatbot yet. Hence, understanding the usage patterns of first-time users can potentially inform and guide the design of future chatbots. In this paper, we report the findings of a study with 16 first-time chatbot users interacting with eight chatbots over multiple sessions on the Facebook Messenger platform. Analysis of chat logs and user interviews revealed that users preferred chatbots that provided either a 'human-like' natural language conversation ability, or an engaging experience that exploited the benefits of the familiar turn-based messaging interface. We conclude with implications to evolve the design of chatbots, such as: clarify chatbot capabilities, sustain conversation context, handle dialog failures, and end conversations gracefully.


title: Subgoal-labeled instructional material improves performance and transfer in learning to develop mobile applications

abstract: Mental models are mental representations of how an action changes a problem state. Creating a mental model early in the learning process is a strong predictor of success in computer science classes. One major problem in computer science education, however, is that novices have difficulty creating mental models perhaps because of the cognitive overload caused by traditional teaching methods. The present study employed subgoal-labeled instructional materials to promote the creation of mental models when teaching novices to program in Android App Inventor. Utilizing this and other well-established educational tools, such as scaffolding, to reduce cognitive load in computer science education improved the performance of participants on novel tasks when learning to develop mobile applications.


title: Weakly supervised localization of novel objects using appearance transfer

abstract: We consider the problem of localizing unseen objects in weakly labeled image collections. Given a set of images annotated at the image level, our goal is to localize the object in each image. The novelty of our proposed work is that, in addition to building object appearance model from the weakly labeled data, we also make use of existing detectors of some other object classes (which we call “familiar objects”). We propose a method for transferring the appearance models of the familiar objects to the unseen object. Our experimental results on both image and video datasets demonstrate the effectiveness of our approach.


title: Reliable State Monitoring in Cloud Datacenters

abstract: State monitoring is widely used for detecting critical events and abnormalities of distributed systems. As the scale of such systems grows and the degree of workload consolidation increases in Cloud data centers, node failures and performance interferences, especially transient ones, become the norm rather than the exception. Hence, distributed state monitoring tasks are often exposed to impaired communication caused by such dynamics on different nodes. Unfortunately, existing distributed state monitoring approaches are often designed under the assumption of always-online distributed monitoring nodes and reliable inter-node communication. As a result, these approaches often produce misleading results which in turn introduce various problems to Cloud users who rely on state monitoring results to perform automatic management tasks such as auto-scaling. This paper introduces a new state monitoring approach that tackles this challenge by exposing and handling communication dynamics such as message delay and loss in Cloud monitoring environments. Our approach delivers two distinct features. First, it quantitatively estimates the accuracy of monitoring results to capture uncertainties introduced by messaging dynamics. This feature helps users to distinguish trustworthy monitoring results from ones heavily deviated from the truth, yet significantly improves monitoring utility compared with simple techniques that invalidate all monitoring results generated with the presence of messaging dynamics. Second, our approach also adapts to non-transient messaging issues by reconfiguring distributed monitoring algorithms to minimize monitoring errors. Our experimental results show that, even under severe message loss and delay, our approach consistently improves monitoring accuracy, and when applied to Cloud application auto-scaling, outperforms existing state monitoring techniques in terms of the ability to correctly trigger dynamic provisioning.


title: Understanding the Adoption of Electronic Banking in China

abstract: Electronic banking (e-banking), facilitated by various Electronic Commerce (EC) technologies, has helped commercial banks to stay competitive through productivity gains, transaction cost reduction and customer service improvement. Despite its benefits, however, developing countries still lag behind developed countries in the adoption of e-banking. To address the lack of studies on e-banking adoption in developing countries, this paper explores factors impacting e-banking adoption in China, as an example of a developing country. Facing an intense competition from foreign-owned banks, Chinese domestic banks have recently been actively engaged in e-banking initiatives. A research framework developed based on technology-organizational-environmental framework is used to guide the study. The findings from this study involving one of the four state-owned banks complement the existing knowledge in this area to better understand the adoption and development of e-banking in China.


title: 20 years of the SMART protein domain annotation resource

abstract: SMART (Simple Modular Architecture Research Tool) is a web resource (http://smart.embl.de) for the identification and annotation of protein domains and the analysis of protein domain architectures. SMART version 8 contains manually curated models for more than 1300 protein domains, with approximately 100 new models added since our last update article (1). The underlying protein databases were synchronized with UniProt (2), Ensembl (3) and STRING (4), doubling the total number of annotated domains and other protein features to more than 200 million. In its 20th year, the SMART analysis results pages have been streamlined again and its information sources have been updated. SMART's vector based display engine has been extended to all protein schematics in SMART and rewritten to use the latest web technologies. The internal full text search engine has been redesigned and updated, resulting in greatly increased search speed.


title: Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription

abstract: We investigate the potential of Context-Dependent Deep-Neural-Network HMMs, or CD-DNN-HMMs, from a feature-engineering perspective. Recently, we had shown that for speaker-independent transcription of phone calls (NIST RT03S Fisher data), CD-DNN-HMMs reduced the word error rate by as much as one third—from 27.4%, obtained by discriminatively trained Gaussian-mixture HMMs with HLDA features, to 18.5%—using 300+ hours of training data (Switchboard), 9000+ tied triphone states, and up to 9 hidden network layers.


title: Evaluation of latex adhesives containing hydrophobic cores and poly(vinyl acetate) shells: potential to improve poly(vinyl acetate) performance

abstract: Abstract Various experimental poly(vinyl acetate) (PVAc) latices containing relatively rigid polystyrene (PS) domains were investigated in order to evaluate whether the presence of PS domains improves the performance, especially the creep resistance, of PVAc adhesives. The effects of a range of PS content, PS/PVAc morphologies and core-shell polymerisation approaches were examined by evaluating thin-film mechanical properties and adhesive performance. The results have shown that up to 50% PS (by mass) can be incorporated into a PVAc latex without compromising adhesive strength. Mechanical testing of films indicated the presence of PS maintains film performance at elevated temperature (60°C). Polymers formed using a chain transfer agent during the second stage styrene polymerisation had a concentric core-shell morphology and these performed better than either copolymers having an acorn morphology or a simple blend of PS and PVAc. Incorporation of PS domains into a commercial PVAc adhesive by second-stage polymerisation of styrene did not greatly diminish the performance of the conventional adhesive. While performance advantages acquired by adding PS into PVAc particles were demonstrated during creep testing at high humidity, the presence of PS did not impart any improved resistance to water or to heat, as determined by accelerated exposure water soak testing and shear block testing above 70°C.


title: Data Users versus Data Subjects: Are Consumers Willing to Pay for Property Rights to Personal Information?

abstract: Privacy advocates claim privacy violation is a social cost of the widespread trade in personal information driven by advances in information management technologies and the Internet. Data users claim that significant benefits from free information exchange are passed on to data subjects and that these benefits outweigh any potential privacy violations. A central issue in this ongoing debate is answering the question, "who owns and who controls personal information". Using the theory of property rights, we argue that personal information is a public good. We analyze responses from 459 New Zealanders to a contingent valuation survey to estimate the economic value data subject's place on a hypothetical change to data protection laws that gives data subject's an enforceable, property right in their personal information. The results are compared with other recent property rights studies to provide guidance for the use of opt-in versus opt-out legislative exclusion to protect information privacy.


title: Enhancement of adaptive FEC mechanism for video transmission over 802.11 WLANS

abstract: Forward Error Correction (FEC) techniques have been adopted with Automatic Repeat reQuest (ARQ) to overcome packet losses and avoid network congestion in various wireless network conditions. The number of FEC packets need to be generated adaptively because usually wireless network has varying network condition. In the current Adaptive FEC mechanism, the FEC packets are determined based on average queue length and average packet retransmission time. However, in order to determine average queue length, estimating its weight value (i.e. smoothing factor) is a challenging task. Smoothing factor is an important parameter as it affects the generation of FEC packets. Thus, this work conducted the estimation of suitable smoothing factor value to determine the average queue length according to packet loss rate over the wireless network. The simulation results show that the enhanced FEC mechanism outperformed other Adaptive FEC mechanisms in terms of recovery efficiency.


title: Confirmatory Factor Analysis of the WAIS-IV/WMS-IV

abstract: The factor structure of the Wechsler intelligence scales, adult, child, and international versions, has been the subject of numerous research studies. For the editions of these tests published in the past two decades, there is strong evidence that a four-factor model—Verbal Comprehension, Perceptual Reasoning, Working Memory, and Processing Speed— best describes the latent structure of the intelligence scales (Bowden, Lissner, McCarthy, Weiss, & Holdnack, 2007; Bowden, Weiss, Holdnack, Bardenhagen, & Cook, 2008; Dickinson, Iannone, & Gold, 2002; Donders, & Warschausky, 1996; Taub, McGrew, & Witta, 2004; Wechsler, 2003, 2008); although some studies disagree with such conclusion (Kaufmann, Lichtenberger, & McLean, 2001; Keith, Fine, Taub, Reynolds, & Kranzler, 2006). Although there is a substantial body of research evaluating the factor structure of the Wechsler intelligence scales, the study on the Wechsler memory scales has not been as extensive, and obtaining consistent factor structure across studies and test editions has been elusive (Wechsler, 2009). ::: ::: Factor analytic studies of previous versions of the Wechsler memory scale have yielded inconsistent results. Initial factor analytic results published for the Wechsler Memory Scale–third edition (WMS-III) indicated that a five-factor model (i.e., Auditory Immediate, Auditory Delayed, Visual Immediate, Visual Delayed, and Working Memory) best fit the data from the normative sample (Wechsler, 1997). Subsequent reanalysis of the factor structure of WMS-III found that a five-factor model yielded inadmissible parameter estimates (i.e., correlations >1.0) because of the high correlation between immediate and delayed memory measures (Millis, Malina, Bowers, & Ricker, 1999). Additionally, low communality estimates for Faces contributed to model specification errors for the visual memory factor (Millis et al., 1999). This finding was confirmed by Price, Tulsky, Millis, & Weiss (2002). For special groups, a two-factor model consisting of working memory and general memory was found in a large left and right temporal lobe epilepsy sample (Wilde et al., 2003); whereas four factors—Auditory, Visual, Working Memory, and Learning—were reported for a mixed clinical and control sample (Burton, Ryan, Axelrod, Schellenberger, & Richards, 2003). Inconsistent findings suggest that the relationship among memory scales may be moderated by methodological and cognitive factors beyond memory itself. ::: ::: Memory functioning cannot be directly assessed; rather, memory is measured in the context of other cognitive skills such as visual spatial or verbal abilities (e.g., story recall requires intact language functioning). Heilbronner (1992) details the nature of additional cognitive functions that affect performance on visual memory tests and concludes that the concept of pure visual memory measures is untenable. The methodology used to assess memory (e.g., free recall, learning, and recognition) could also affect the psychometric properties of the test and its relationship to other memory measures (Compton, Sherer, & Adams, 1992). Therefore, performance on the memory tests could vary by the nature of additional cognitive skills measured and the methodology employed, which may facilitate or inhibit efficient memory storage and retrieval and, in turn, attenuate or facilitate the correlation between two memory measures. These variations among memory measures result in memory tests being only moderately correlated with each other (Compton et al., 1992; Golden, White, Combs, Morgan, & McLane, 1999). ::: ::: The first combined factor analysis of the Wechsler Adult Intelligence Scale (WAIS) and the original WMS clarified the construct validity of the tests comprising the WMS by analyzing the factor structure in the context of other measures of verbal and visuospatial skills, attention, and processing speed (Larrabee, Kane, & Schuck, 1983). This exploratory factor analysis yielded a memory factor defined by Logical Memory and Paired Associate Learning, an information/orientation factor, and an attention/concentration factor defined by Digit Span, Arithmetic, and Mental Control. Of particular interest, WMS Visual Reproduction loaded more strongly with a WAIS visuospatial ability factor (.70) than with the memory factor (.09). Subsequently, Larrabee, Kane, Schuck, and Francis (1985) factored immediate recall and delayed recall Logical Memory and Visual Reproduction subtests in separate factor analyses, thereby controlling for method variance (Larrabee, 2003), and found that Immediate Visual Reproduction loaded more strongly with a visuospatial ability factor defined by Block Design and Object Assembly than with a memory factor defined by Logical Memory and Paired Associate Learning. When delayed recall scores (e.g., Delayed Logical Memory and Visual Reproduction) were factored, the loading pattern of Delayed Visual Reproduction shifted so that the primary loading was with the memory factor, with a secondary loading on the visuospatial ability factor. This basic finding (stronger association with visuospatial ability for Immediate Visual Reproduction, with stronger association with memory for Delayed Visual Reproduction) was replicated by Leonberger, Nicks, Larrabee, and Goldfader (1992) in a factor analysis of Wechsler Adult Intelligence Scale–Revised (WAIS-R), Wechsler Memory Scale–Revised (WMS-R), and Halstead– Reitan Neuropsychological Battery subtests. ::: ::: In a series of articles employing confirmatory factor analysis, Bowden and colleagues initially found that a five-factor model: Verbal Comprehension, Perceptual Organization, Attention–Concentration/Working Memory, Verbal Memory, and Visual Memory, best accounted for the joint factor structure of the WAIS-R and WMS-R in a non-clinical sample (Bowden, Carstairs, & Shores, 1999) and a substance abuse sample (Bowden et al., 2001). In a later study, however, a six-factor model that also included Processing Speed was supported in clinical and community samples (Bowden, Cook, Bardenhagen, Shores, & Carstairs, 2004). The WAIS-R and WMS-R studies indicate that five or six factors are supported with auditory and visual memory factors identified from the memory scale. ::: ::: The Wechsler intelligence and memory scales were co-normed for the first time for the third edition of the tests (Tulsky & Ledbetter, 2000). Tulsky and Price (2003) completed an extensive evaluation of the joint factor structure of the WAIS-III and WMS-III. They reported that a six-factor model, Verbal Comprehension, Perceptual Organization, Working Memory, Processing Speed, Visual Memory, and Auditory Memory, was best supported by the data. Model specification errors were observed when immediate and delayed memory factors were evaluated in the same factor analysis; additionally, Faces was observed to have poor fit on any factor. The model fit was best when subtests were allowed to load on multiple factors specifically for Arithmetic, Spatial Span, Picture Arrangement, and Visual Reproduction (Tulsky, Ivnik, Price, & Wilkins, 2003). ::: ::: The WAIS-IV has a very similar subtest and index structure as its predecessor, the WAIS-III (Wechsler, 2008). The primary differences are the inclusion of a new subtest, Visual Puzzles, on the Perceptual Reasoning index and the addition of a sequencing procedure to the Digit Span sub-test. The four-factor model of Verbal Comprehension, Perceptual Reasoning, Working Memory, and Processing speed was confirmed by factor analysis (Wechsler, 2008). The WMS-IV, however, was substantially revised from the previous edition (Wechsler, 2009). At the subtest level, there is a new visual memory subtest, Designs, and two new visual working memory subtests: Spatial Addition and Symbol Span. The index structure includes Verbal Memory, Visual Memory, Visual Working Memory, Immediate Memory, and Delayed Memory. The Visual Memory, Auditory Memory, and Visual Working Memory indexes were confirmed by factor analysis (Wechsler, 2009). ::: ::: The purpose of the current study is to evaluate the joint factor structure of the most recent editions of the Wechsler intelligence and memory scales. Although the six-factor model has received support in previous editions, the addition of new visual working memory subtests may result in a split of the working memory factor into two components: auditory and visual. Therefore, the current analysis will evaluate multiple models that include up to seven factors. It is also anticipated that allowing subtests to load on multiple factors will improve model fit. It is not expected that confirmatory factor analysis can differentiate immediate from delayed memory functioning as this often leads to model specification errors consequent to method variance. Therefore, only the delayed memory scores are used in this analysis, which should provide better evaluation of the construct validity of WMS-IV visual memory tasks in particular (Larrabee et al., 1985).


title: New Residents and the Demand for Public Services in Growing Communities: Local Officials' Perceptions and Responses

abstract: The common wisdom in many growing communities is that newcomers demand more services than do long-term residents. Dissimilar attitudes toward local services can have important effects on local government activities and services. This study investigates whether municipal officials in growing areas perceive such a difference in the demand for services; it then examines the relationship between these perceptions and actual service delivery changes in the officials' communities to see whether such perceptions really matter. The study confirms not only that many officials believe this stereotype but also that such officials were much more likely to have increased services in the past 5 years. This suggests that the stereotype, far from being innocuous, may have implications for local taxes and service provision.


title: Dual-Output Boost Resonant Full-Bridge Topology and its Modulation Strategies for High-Performance Induction Heating Applications

abstract: Induction heating (IH) is a key technology in modern household appliances and industries. Modern design trends require the design of high efficiency and multiple output power converters. This paper proposes a dual-output boost resonant full-bridge topology to provide a multiple-output converter with improved performance while keeping a cost-effective performance. The proposed converter is detailed and analyzed, and several modulation strategies are proposed to obtain proper output power control. Finally, an experimental prototype is built, verifying the proper operation and performance of the proposed topology.


title: Ambient Interference Effects in Wi-Fi Networks

abstract: This paper presents a measurement study of interference from six common devices that use the same 2.4 GHz ISM band as the IEEE 802.11 protocol. Using both controlled experiments and production environment measurements, we quantify the impact of these devices on the performance of 802.11 Wi-Fi networks. In our controlled experiments, we characterize the interference properties of these devices, as well as measure and discuss implications of interference on data, video, and voice traffic. Finally, we use measurements from a campus network to understand the impact of interference on the operational performance of the network. Overall, we find that the campus network is exposed to a large variety of non-Wi-Fi devices, and that these devices can have a significant impact on the interference level in the network.


title: Accessing Peer Social Interaction: Using Authorable Virtual Peer Technology as a Component of a Group Social Skills Intervention Program

abstract: Autism spectrum and related communication and social disorders can severely affect some children's ability to engage in peer social interaction. In this article, we describe and evaluate an Authorable Virtual Peer (AVP), technology designed to help children access peer interactions by supporting them in developing critical social skills. Children interact with the AVP in three ways: (1) engaging in face-to-face interaction with a life-sized, computer-animated child; (2) creating new social behaviors for the AVP; and (3) controlling the AVP using a graphical user interface to select appropriate responses while the AVP interacts with another person. Our evaluation suggests that when an AVP is used as an activity during a social group intervention, a common intervention approach used with children with social and communication difficulties, that children's use of specific social behaviors critical to successful social interaction increases during role-play of common social situations with another child.


title: Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network

abstract: Resting-state functional connectivity magnetic resonance imaging (fcMRI) studies constitute a growing proportion of functional brain imaging publications. This approach detects temporal correlations in spontaneous blood oxygen level–dependent (BOLD) signal oscillations while subjects rest quietly in the scanner. Although distinct resting-state networks related to vision, language, executive processing, and other sensory and cognitive domains have been identified, considerable skepticism remains as to whether resting-state functional connectivity maps reflect neural connectivity or simply track BOLD signal correlations driven by nonneural artifact. Here we combine diffusion tensor imaging (DTI) tractography with resting-state fcMRI to test the hypothesis that resting-state functional connectivity reflects structural connectivity. These 2 modalities were used to investigate connectivity within the default mode network, a set of brain regions—including medial prefrontal cortex (MPFC), medial temporal lobes (MTLs), and posterior cingulate cortex (PCC)/retropslenial cortex (RSC)—implicated in episodic memory processing. Using seed regions from the functional connectivity maps, the DTI analysis revealed robust structural connections between the MTLs and the retrosplenial cortex whereas tracts from the MPFC contacted the PCC (just rostral to the RSC). The results demonstrate that resting-state functional connectivity reflects structural connectivity and that combining modalities can enrich our understanding of these canonical brain networks.


title: Effective Inhibition of Mannitol Crystallization in Frozen Solutions by Sodium Chloride

abstract: Purpose. The purpose of this work was to study the possibility of preventing mannitol crystallization in frozen solutions by using pharmaceutically acceptable additives.


title: Neuroaesthetics: a review

abstract: Neuroaesthetics is a relatively young field within cognitive neuroscience, concerned with the neural underpinnings of aesthetic experience of beauty, particularly in visual art. Neuroscientific investigations have approached this area using imaging and neurophysiological techniques, such as functional magnetic resonance (fMRI), magnetoencephalography (MEG) and electroencephalography (EEG). The results produced so far are very heterogeneous. Nonetheless, an overall view of the findings suggests that the aesthetic experience of visual artworks is characterized by the activation of: sensorimotor areas; core emotional centres; and reward-related centres. In the present review, we discuss the functional relevance of these activations and propose that aesthetic experience is a multilevel process exceeding a purely visual analysis of artworks and relying upon visceromotor and somatomotor resonance in the beholder.


title: Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron

abstract: The authors investigate the use of two types of features extracted from face images for recognizing facial expressions. The first type is the geometric positions of a set of fiducial points on a face. The second type is a set of multi-scale and multi-orientation Gabor wavelet coefficients extracted from the face image at the fiducial points. They can be used either independently or jointly. The architecture developed is based on a two-layer perceptron. The recognition performance with different types of features has been compared, which shows that Gabor wavelet coefficients are much more powerful than geometric positions. Furthermore, since the first layer of the perceptron actually performs a nonlinear reduction of the dimensionality of the feature space, they have also studied the desired number of hidden units, i.e., the appropriate dimension to represent a facial expression in order to achieve a good recognition rate. It turns out that five to seven hidden units are probably enough to represent the space of feature expressions.


title: Syntactic N-grams as machine learning features for natural language processing

abstract: In this paper we introduce and discuss a concept of syntactic n-grams (sn-grams). Sn-grams differ from traditional n-grams in the manner how we construct them, i.e., what elements are considered neighbors. In case of sn-grams, the neighbors are taken by following syntactic relations in syntactic trees, and not by taking words as they appear in a text, i.e., sn-grams are constructed by following paths in syntactic trees. In this manner, sn-grams allow bringing syntactic knowledge into machine learning methods; still, previous parsing is necessary for their construction. Sn-grams can be applied in any natural language processing (NLP) task where traditional n-grams are used. We describe how sn-grams were applied to authorship attribution. We used as baseline traditional n-grams of words, part of speech (POS) tags and characters; three classifiers were applied: support vector machines (SVM), naive Bayes (NB), and tree classifier J48. Sn-grams give better results with SVM classifier.


title: Approximate Knowledge Compilation by Online Collapsed Importance Sampling

abstract: We introduce collapsed compilation, a novel approximate inference algorithm for discrete probabilistic graphical models. It is a collapsed sampling algorithm that incrementally selects which variable to sample next based on the partial sample obtained so far. This online collapsing, together with knowledge compilation inference on the remaining variables, naturally exploits local structure and context-specific independence in the distribution. These properties are naturally exploited in exact inference, but are difficult to harness for approximate inference. Moreover, by having a partially compiled circuit available during sampling, collapsed compilation has access to a highly-effective proposal distribution for importance sampling. Our experimental evaluation shows that collapsed compilation performs well on standard benchmarks. In particular, when the amount of exact inference is equally limited, collapsed compilation is competitive with the state of the art.


title: A Study on Document Classification using Machine Learning Techniques

abstract: With the explosion of information fuelled by the growth of the World Wide Web it is no longer feasible for a human observer to understand all the data coming in or even classify it into categories. With this growth of information and simultaneous growth of available computing power automatic classification of data, particularly textual data, gains increasingly high importance. Text classification is a task of automatically sorting a set of documents into categories from a predefined set and is one of the important research issues in the field of text mining. This paper provides a review of generic text classification process, phases of that process and methods being


title: (How) will the revolution be retweeted?: information diffusion and the 2011 Egyptian uprising

abstract: This paper examines microblogging information diffusion activity during the 2011 Egyptian political uprisings. Specifically, we examine the use of the retweet mechanism on Twitter, using empirical evidence of information propagation to reveal aspects of work that the crowd conducts. Analysis of the widespread contagion of a popular meme reveals interaction between those who were "on the ground" in Cairo and those who were not. However, differences between information that appeals to the larger crowd and those who were doing on-the-ground work reveal important interplay between the two realms. Through both qualitative and statistical description, we show how the crowd expresses solidarity and does the work of information processing through recommendation and filtering. We discuss how these aspects of work mutually sustain crowd interaction in a politically sensitive context. In addition, we show how features of this retweet-recommendation behavior could be used in combination with other indicators to identify information that is new and likely coming from the ground.


title: A Generative Constituent-Context Model For Improved Grammar Induction

abstract: We present a generative distributional model for the unsupervised induction of natural language syntax which explicitly models constituent yields and contexts. Parameter search with EM produces higher quality analyses than previously exhibited by unsupervised systems, giving the best published un-supervised parsing results on the ATIS corpus. Experiments on Penn treebank sentences of comparable length show an even higher F1 of 71% on non-trivial brackets. We compare distributionally induced and actual part-of-speech tags as input data, and examine extensions to the basic model. We discuss errors made by the system, compare the system to previous models, and discuss upper bounds, lower bounds, and stability for this task.


title: Preliminary and incomplete version Do not quote without the permission of the authors Business Cycles and FDI: Evidence from German Sectoral Data

abstract: Globalization has effected business cycle developments in OECD countries and has increased activities of firms across national borders. This paper analyzes whether these two developments are linked. We use a new firm-level dataset on the foreign activities of German firms to test whether foreign activities are affected by business cycle developments. We aggregate the data by the sector of the reporting firm, the sector of the foreign affiliate, and the host country. Data are annual and cover the period 1989-2002. We find that German outward FDI increases in response to positive cyclical developments abroad and in response to a real depreciation of the domestic currency.


title: Sparse Representation With Kernels

abstract: Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks.


title: Package Design: Colour Harmony and Consumer Expectations

abstract: IntroductionBackgroundColour plays an important role in design. It is a trigger to arouse viewers' emotions and a carrier that designers use to deliver information. Colours that are viewed together and produce pleasing affective responses are considered to be in harmony (Burchett, 2002). In product design, it has been suggested that positive emotion would not only add extra value to a product but can further increase the possibility of a product being purchased (Cho & Lee, 2005; Desmet, Overbeeke, & Tax, 2001; Jordan, 1998). Therefore, when designing products, designers endeavour to establish emotional connections among ideas, products, services and brands (Crossley, 2003). Using harmonic colours is one way to create positive emotional connections. Most of the extant colour harmony principles found in art or design textbooks are based on well-controlled, simple, uniform colours. How well these principles are applicable to contexts that are more representative of real design situations is largely unknown.The context within which colours are used also affects how colours are perceived. For instance, the colour brown may associate with natural and warm feelings when involving wooden materials in interior design. However, the same colour may associate with different feelings such as elegant and dignified when used for a coat. More examples can be seen in a recent study examining the affective responses for 2-dimensional coloured shapes and 3-dimensional objects (Lee, Luo, & Ou, 2009). Therefore, if we are to examine how the use of colours might associate with different psychological responses or encourage positive emotions with the goal of obtaining information useful for future applications in product design, a pre-selected context (e.g., wooden products or clothes) should be used consistently for all the colours examined.Research AimIn this study, fruit juice containers are used as the context to establish a practical method for studying the relationship between colour and psychological responses. Fruit juice is one of the most popular food products and its market grows almost every year. Despite the global recession in 2009, the juice market still increased by 2% globally. In 2010 the market growth went back to pre-recession rate at 5%. Euromonitor International, a leading company in strategy research for consumer market, projects that fruit/vegetable juices will maintain the strong growth (at least) until the year 2015. (http://www.just-drinks.com/analysis/focus-juices-and-nectar-outlook-to-2015_ id103542.aspx)With the context being set to fruit juice, some important issues often raised in practice are "Do the colours of the package design look harmonious?" and "How does the colour scheme of packages influence consumer expectations of the product?" The first issue is of concern because colour harmony represents opinions of aesthetics among colours. If positive aesthetic feelings of a product are perceived, the product is more likely to be further examined and potentially be purchased (Eckman, Damhorst, & Kadolph, 1990; Morganosky, 1984). The second issue is of concern because colours affect consumer expectations, which would influence initial desire to purchase and subsequent action to purchase (Hutchings, 2003). Studying colour harmony for package design and consumer expectations of the product will lead to a better understanding of these two issues.Colour HarmonyColour harmony, as Judd and Wyszecki (1975) suggested, can be defined as "two or more colours seen in neighbouring areas which produce a pleasing effect". Granville (1987) also defined colour harmony as "colour usage that pleases people". For many years, specialists have sought to understand which colour combinations produce pleasant feelings. Their studies can generally be categorised into two groups, including one group of studies based on the orderly arrangement of colours and the other group based on the interrelationship between colours (Ou & Luo, 2006). …


title: Modeling Location-Based User Rating Profiles for Personalized Recommendation

abstract: This article proposes LA-LDA, a location-aware probabilistic generative model that exploits location-based ratings to model user profiles and produce recommendations. Most of the existing recommendation models do not consider the spatial information of users or items; however, LA-LDA supports three classes of location-based ratings, namely spatial user ratings for nonspatial items, nonspatial user ratings for spatial items, and spatial user ratings for spatial items. LA-LDA consists of two components, ULA-LDA and ILA-LDA, which are designed to take into account user and item location information, respectively. The component ULA-LDA explicitly incorporates and quantifies the influence from local public preferences to produce recommendations by considering user home locations, whereas the component ILA-LDA recommends items that are closer in both taste and travel distance to the querying users by capturing item co-occurrence patterns, as well as item location co-occurrence patterns. The two components of LA-LDA can be applied either separately or collectively, depending on the available types of location-based ratings. To demonstrate the applicability and flexibility of the LA-LDA model, we deploy it to both top-k recommendation and cold start recommendation scenarios. Experimental evidence on large-scale real-world data, including the data from Gowalla (a location-based social network), DoubanEvent (an event-based social network), and MovieLens (a movie recommendation system), reveal that LA-LDA models user profiles more accurately by outperforming existing recommendation models for top-k recommendation and the cold start problem.


title: Faceted Search

abstract: We live in an information age that requires us, more than ever, to represent, access, and use information. Over the last several decades, we have developed a modern science and technology for information retrieval, relentlessly pursuing the vision of a "memex" that Vannevar Bush proposed in his seminal article, "As We May Think." Faceted search plays a key role in this program. Faceted search addresses weaknesses of conventional search approaches and has emerged as a foundation for interactive information retrieval. User studies demonstrate that faceted search provides more effective information-seeking support to users than best-first search. Indeed, faceted search has become increasingly prevalent in online information access systems, particularly for e-commerce and site search. In this lecture, we explore the history, theory, and practice of faceted search. Although we cannot hope to be exhaustive, our aim is to provide sufficient depth and breadth to offer a useful resource to both researchers and practitioners. Because faceted search is an area of interest to computer scientists, information scientists, interface designers, and usability researchers, we do not assume that the reader is a specialist in any of these fields. Rather, we offer a self-contained treatment of the topic, with an extensive bibliography for those who would like to pursue particular aspects in more depth. Table of Contents: I. Key Concepts / Introduction: What Are Facets? / Information Retrieval / Faceted Information Retrieval / II. Research and Practice / Academic Research / Commercial Applications / III. Practical Concerns / Back-End Concerns / Front-End Concerns / Conclusion / Glossary


title: "What Parts of Your Apps are Loved by Users?" (T)

abstract: Recently, Begel et al. found that one of the most important questions software developers ask is "what parts of software are used/loved by users." User reviews provide an effective channel to address this question. However, most existing review summarization tools treat reviews as bags-of-words (i.e., mixed review categories) and are limited to extract software aspects and user preferences. We present a novel review summarization framework, SUR-Miner. Instead of a bags-of-words assumption, it classifies reviews into five categories and extracts aspects in sentences which include evaluation of aspect using a pattern-based parser. Then, SUR-Miner visualizes the summaries using two interactive diagrams. Our evaluation on 17 popular apps shows that SUR-Miner summarizes more accurate and clearer aspects than state-of-the-art techniques, with an average F1-score of 0.81, significantly greater than that of ReviewSpotlight (0.56) and Guzmans' method (0.55). Feedback from developers shows that 88% developers agreed with the usefulness of the summaries from SUR-Miner.


title: Pedestrian-movement prediction based on mixed Markov-chain model

abstract: A method for predicting pedestrian movement on the basis of a mixed Markov-chain model (MMM) is proposed. MMM takes into account a pedestrian's personality as an unobservable parameter. It also takes into account the effects of the pedestrian's previous status. A promotional experiment in a major shopping mall demonstrated that the highest prediction accuracy of the MMM method is 74.4%. In comparison with methods based on a Markov-chain model (MM) and a hidden-Markov model (HMM) (i.e., prediction rates of about 45% and 2%, respectively), the proposed MMM-based prediction method is substantially more accurate. This pedestrian-movement prediction based on MMM using tracking data will make it possible to provide so-called "adaptive mobile services" with proactive functions.


title: A clock gated flip-flop for low power applications in 90 nm CMOS

abstract: A new clock gated flip-flop is presented. The circuit is based on a new clock gating approach to reduce the consumption of clock signal's switching power. It operates with no redundant clock cycles and has reduced number of transistors to minimize the overhead and to make it suitable for data signals with higher switching activity. The proposed flip-flop is used to design 10 bits binary counter and 14 bits successive approximation register. These applications have been designed up to the layout level with 1 V power supply in 90 nm CMOS technology and have been simulated using Spectre. Simulations with the inclusion of parasitics have shown the effectiveness of the new approach on power consumption and transistor count.


title: Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

abstract: The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN) can be applied to multispectral orthoimagery and a digital surface model (DSM) of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water), and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.


title: Connecting people and place : a new framework for reducing urban vulnerability to extreme heat

abstract: Climate change is predicted to increase the intensity and negative impacts of urban heat events, prompting the need to develop preparedness and adaptation strategies that reduce societal vulnerability to extreme heat. Analysis of societal vulnerability to extreme heat events requires an interdisciplinary approach that includes information about weather and climate, the natural and built environment, social processes and characteristics, interactions with stakeholders, and an assessment of community vulnerability at a local level. In this letter, we explore the relationships between people and places, in the context of urban heat stress, and present a new research framework for a multi-faceted, top-down and bottom-up analysis of local-level vulnerability to extreme heat. This framework aims to better represent societal vulnerability through the integration of quantitative and qualitative data that go beyond aggregate demographic information. We discuss how different elements of the framework help to focus attention and resources on more targeted health interventions, heat hazard mitigation and climate adaptation strategies.


HyperFlow: A Heterogeneous Dataflow Architecture


title: Traumatic neuroma of the tympanic (Jacobson's) nerve as a possible cause of otalgia

abstract: OBJECTIVE ::: To show that traumatic neuroma of the tympanic (Jacobson's) nerve may be a cause of recurrent intractable otalgia in patients following radical mastoidectomies. ::: ::: ::: STUDY DESIGN ::: Histologic evaluation of four temporal bones from three patients with a history of recurrent otalgia following radical mastoidectomy. ::: ::: ::: SUBJECTS AND METHODS ::: The medical records of three patients with multiple middle ear surgeries in four ears because of recurrent otalgia were reviewed. Histopathologic studies of the four temporal bones were performed. ::: ::: ::: RESULTS ::: All four of the temporal bones that underwent multiple surgeries were found to have traumatic neuromas of the tympanic (Jacobson's) nerve. ::: ::: ::: CONCLUSION ::: Recurrent otalgia in patients after radical middle ear surgery may be caused by a traumatic neuroma of the tympanic (Jacobson's) nerve.


title: Combating the Small Sample Class Imbalance Problem Using Feature Selection

abstract: The class imbalance problem is encountered in real-world applications of machine learning and results in a classifier's suboptimal performance. Researchers have rigorously studied the resampling, algorithms, and feature selection approaches to this problem. No systematic studies have been conducted to understand how well these methods combat the class imbalance problem and which of these methods best manage the different challenges posed by imbalanced data sets. In particular, feature selection has rarely been studied outside of text classification problems. Additionally, no studies have looked at the additional problem of learning from small samples. This paper presents a first systematic comparison of the three types of methods developed for imbalanced data classification problems and of seven feature selection metrics evaluated on small sample data sets from different applications. We evaluated the performance of these metrics using area under the receiver operating characteristic (AUC) and area under the precision-recall curve (PRC). We compared each metric on the average performance across all problems and on the likelihood of a metric yielding the best performance on a specific problem. We examined the performance of these metrics inside each problem domain. Finally, we evaluated the efficacy of these metrics to see which perform best across algorithms. Our results showed that signal-to-noise correlation coefficient (S2N) and Feature Assessment by Sliding Thresholds (FAST) are great candidates for feature selection in most applications, especially when selecting very small numbers of features.


title: An implementation of the FP-growth algorithm

abstract: The FP-growth algorithm is currently one of the fastest approaches to frequent item set mining. In this paper I describe a C implementation of this algorithm, which contains two variants of the core operation of computing a projection of an FP-tree (the fundamental data structure of the FP-growth algorithm). In addition, projected FP-trees are (optionally) pruned by removing items that have become infrequent due to the projection (an approach that has been called FP-Bonsai). I report experimental results comparing this implementation of the FP-growth algorithm with three other frequent item set mining algorithms I implemented (Apriori, Eclat, and Relim).


title: Rigi-An environment for software reverse engineering, exploration, visualization, and redocumentation

abstract: The Rigi environment is a mature research tool that provides functionality to reverse engineer software systems. With Rigi large systems can be analyzed, interactively explored, summarized, and documented. This is supported with parsers to extract information from source code, an exchange format to store extracted information, analyses to transform and abstract information, a scripting language and library to automate the process, and a visualization engine to interactively explore and manipulate information in the form of typed, directed, hierarchical graphs. In this paper we describe Rigi's main components and functionalities, and assess its impact on reverse engineering research. Furthermore, we discuss Rigi's architecture and design decisions that led to a decoupling of major functionalities, and enable tool extensibility, interoperability and end-user programmability.


title: Difficulty control for blockchain-based consensus systems

abstract: Crypto-currencies like Bitcoin have recently attracted a lot of interest. A crucial ingredient into such systems is the “mining” of a Nakamoto blockchain. We model mining as a Poisson process with time-dependent intensity and use this model to derive predictions about block times for various hash-rate scenarios (exponentially rising hash rate being the most important). We also analyse Bitcoin’s method to update the “network difficulty” as a mechanism to keep block times stable. Since it yields systematically too fast blocks for exponential hash-rate growth, we propose a new method to update difficulty. Our proposed method performs much better at ensuring stable average block times over longer periods of time, which we verify both in simulations of artificial growth scenarios and with real-world data. Besides Bitcoin itself, this has practical benefits particularly for systems like Namecoin. It can be used to make name expiration times more predictable, preventing accidental loss of names.


title: An engineering applicable dynamic equivalent method for large-scale AC system connected to VSC-HVDC

abstract: To test the software and hardware functions of VSC-HVDC control and protection device, an efficient way is to carry out the hardware-in-loop (HIL) tests and setup a detailed AC/DC transient model in real-time digital simulator. However, taking into account capability of the simulator and details of the simulation, the large scale AC network outside the converters should be simplified and equivalent. In this paper, based on a principle that the short circuit levels should be consistent before and after equivalent, a new equivalent method to calculate the equivalent generator parameters for large scale AC power system is proposed. Firstly, rated capacity and access reactance of equivalent generators are calculated according to equivalent bus's short-current. Secondly, a new criterion is provided based on short-current contribution ratio, according to short-current and short-reactance of the equivalent bus. Finally, this criterion is used to choose dominate controller of the equivalent generators, and calculate controller parameters. At the same time, the Ward method is improved. The negative line resistance and generator active output are corrected. Finally, the proposed method is used for Xiamen equivalent grid which derived from a large scale of “Sanhua” China power grids and validated by comparison of the bus voltages, power flows, short-circuit levels and the dynamic response characteristics of the equivalent network, the validity of the proposed method is proved.


title: Electromagnetic wireless nanosensor networks

abstract: Abstract This paper provides an in-depth view on nanosensor technology and electromagnetic communication among nanosensors. First, the state of the art in nanosensor technology is surveyed from the device perspective, by explaining the details of the architecture and components of individual nanosensors, as well as the existing manufacturing and integration techniques for nanosensor devices. Some interesting applications of wireless nanosensor networks are highlighted to emphasize the need for communication among nanosensor devices. A new network architecture for the interconnection of nanosensor devices with existing communication networks is provided. The communication challenges in terms of terahertz channel modeling, information encoding and protocols for nanosensor networks are highlighted, defining a roadmap for the development of this new networking paradigm.


title: Mining semantic representation from medical text: A Bayesian approach

abstract: Machine learning is a subfield of artificial intelligence that deals with the exploration and construction of systems that can learn from data. Machine learning trains the computers to manage the critical situations via examining, self- training, inference by observation and previous experience. This paper provides an overview of the development of an efficient classifier that represents the semantics in medical data (Medline) using a Machine Learning (ML) perspective. In recent days people are more concerned about their health and explore ways to identify health related information. But the process of identifying the semantic representation for the medical terms is a difficult task. The main goal of our work was to identify the semantic representation for the medical abstracts in the Medline repository using Machine Learning and Natural Language Processing (NLP). Keywords—Machine Learning (ML), Natural Language Processing (NLP), Classification, Medline and Healthcare.


title: A CAPTCHA Implementation Based on 3D Animation

abstract: In order to distinguish between human users and computer programs, CAPTCHA (Completely Automated Public Turing test to tell Computers and Human Apart) mechanism is widely applied in websites such as accounts application website. While the major implementation of CAPTCHA method—2D still image verification code based on OCR technology is threatened by developing artificial intelligence and image recognition technologies. In this paper, we propose a new approach to implement CAPTCHA mechanism based on 3D Animation, utilizing the weakness of computer vision, which make it robust to computer attacks and convenient for users to recognize, and implemented this method to generate a 3D animation verification code.


title: Credibility of Electronic Word-of-Mouth: Informational and Normative Determinants of On-line Consumer Recommendations

abstract: Word-of-mouth (WOM) study is extended to the on-line context (eWOM) by examining the informational and normative determinants of the perceived credibility of on-line consumer recommendations. A survey of users of an on-line consumer discussion forum in China substantiated the effects of the determinants, although post-hoc analyses revealed that prior knowledge and involvement level moderate some of them. Implications for research and practice are discussed.


title: Microscale Glass-Blown Three-Dimensional Spherical Shell Resonators

abstract: This paper introduces a new paradigm for design and batch fabrication of isotropic 3-D spherical shell resonators. The approach uses pressure and surface tension driven plastic deformation (glassblowing) on a wafer scale as a mechanism for creating inherently smooth and symmetric 3-D resonant structures. The feasibility of the new approach was demonstrated by fabrication and characterization of Pyrex glass spherical shell resonators with millimeter-scale diameter and average thickness of 10 μm . Metal electrodes cofabricated along with the shell were used to actuate the two dynamically balanced four- and six-node vibratory modes. For 1-MHz glass-blown resonators, the relative frequency mismatch Δf/f between the two degenerate four-node wineglass modes was measured as 0.63% without any trimming or tuning. For the higher order six-node wineglass modes, the relative frequency mismatch was only 0.2%, demonstrating the potential for precision manufacturing. The intrinsic manufacturing symmetry enabled by the technology may inspire new classes of high-performance 3-D MEMS for communication and inertial navigation.


title: An application of the supervoxel-based Fuzzy C-Means with a GPU support to segmentation of volumetric brain images

abstract: In this paper the problem of segmentation of vol- umetric medical images is considered. The fast and effective segmentation is obtained by applying the proposed approach which combines the idea of supervoxels and the Fuzzy C-Means algorithm. In particular, Fuzzy C-Means is used to cluster supervoxels produced by the fast 3D region growing. Additional acceleration of the method is achieved with the support of graphical processor (GPU). The detailed description of the proposed approach is given. The results of applying the method to volumetric CT and MRI brain images and CT images of various phantoms are presented, analysed and discussed. The issues related to accuracy of the method, memory workload and the running time are also considered.


title: Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty

abstract: Abstract Optimization under uncertainty has been an active area of research for many years. However, its application in Process Systems Engineering has faced a number of important barriers that have prevented its effective application. Barriers include availability of information on the uncertainty of the data (ad-hoc or historical), determination of the nature of the uncertainties (exogenous vs. endogenous), selection of an appropriate strategy for hedging against uncertainty (robust/chance constrained optimization vs. stochastic programming), large computational expense (often orders of magnitude larger than deterministic models), and difficulty of interpretation of the results by non-expert users. In this paper, we describe recent advances that have addressed some of these barriers for mostly linear models.


title: A Generic Import Framework for Process Event Logs

abstract: The application of process mining techniques to real-life corporate environments has been of an ad-hoc nature so far, focused on proving the concept. One major reason for this rather slow adoption has been the complicated task of transforming real-life event log data to the MXML format used by advanced process mining tools, such as ProM. In this paper, the ProM Import Framework is presented, which has been designed to bridge this gap and to build a stable foundation for the extraction of event log data from any given PAIS implementation. Its flexible and extensible architecture, adherence to open standards, and open source availability make it a versatile contribution to the general BPI community.


title: Using Deep Convolutional Neural Networks to Predict Goal-Scoring Opportunities in Soccer

abstract: Deep learning approaches have successfully been applied to several image recognition tasks, such as face, object, animal and plant classification. However, almost no research has examined on how to use the field of machine learning to predict goal-scoring opportunities in soccer from position data. In this paper, we propose the use of deep convolutional neural networks (DCNNs) for the above stated problem. This aim is actualized using the following steps: 1) development of novel algorithms for finding goal-scoring opportunities and ball possession which are used to obtain positive and negative examples. The dataset consists of position data from 29 matches played by a German Bundlesliga team. 2) These examples are used to create original and enhanced images (which contain object trails of soccer positions) with a resolution size of $256 \times 256$ pixels. 3) Both the original and enhanced images are fed independently as input to two DCNN methods: instances of both GoogLeNet and a 3-layered CNN architecture. A K-nearest neighbor classifier was trained and evaluated on ball positions as a baseline experiment. The results show that the GoogLeNet architecture outperforms all other methods with an accuracy of 67.1%.


title: Mathematical modeling and simulated annealing algorithm for spatial layout problem

abstract: The spatial layout of the port industrial zone problem is a core issue in port industrial zone planning, and it directly affects the actual effects of the port industrial zone. Firstly, considering that existing port industrial zone planning lacks in methods of quantitative analysis, this paper constructs a Mathematical model based on multi-objective programming, and the optimal scale of various industries of port industrial zone is obtained. Secondly, the paper takes the maximum dependence degree of port as objective function by using systematic layout planning tools, and solves the spatial layout of the port industrial zone. Finally, by taking Binhai Port industrial zone of China as an example, a port industrial zone spatial layout model is constructed and solved through simulated annealing algorithm. The optimal spatial layout program for Binhai Port industrial zone of China was obtained, which verifies the feasibility and accuracy of the model.


title: KDEEP: Protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks.

abstract: Accurately predicting protein–ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-learning approach for predicting binding affinities using state-of-the-art 3D-convolutional neural networks and compare this approach to other machine-learning and scoring methods using several diverse data sets. The results for the standard PDBbind (v.2016) core test-set are state-of-the-art with a Pearson’s correlation coefficient of 0.82 and a RMSE of 1.27 in pK units between experimental and predicted affinity, but accuracy is still very sensitive to the specific protein used. KDEEP is made available via PlayMolecule.org for users to test easily their own protein–ligand complexes, with each prediction taking a fraction of a second. We believe that the speed, performance, and ease of use of KDEEP makes it already an attractive scoring function for modern computational ch...


title: Reinforcement learning to train Ms. Pac-Man using higher-order action-relative inputs

abstract: Reinforcement learning algorithms enable an agent to optimize its behavior from interacting with a specific environment. Although some very successful applications of reinforcement learning algorithms have been developed, it is still an open research question how to scale up to large dynamic environments. In this paper we will study the use of reinforcement learning on the popular arcade video game Ms. Pac-Man. In order to let Ms. Pac-Man quickly learn, we designed particular smart feature extraction algorithms that produce higher-order inputs from the game-state. These inputs are then given to a neural network that is trained using Q-learning. We constructed higher-order features which are relative to the action of Ms. Pac-Man. These relative inputs are then given to a single neural network which sequentially propagates the action-relative inputs to obtain the different Q-values of different actions. The experimental results show that this approach allows the use of only 7 input units in the neural network, while still quickly obtaining very good playing behavior. Furthermore, the experiments show that our approach enables Ms. Pac-Man to successfully transfer its learned policy to a different maze on which it was not trained before.


Cretaceous fossils from the Orapa Diamond Mine


title: A Highly-Digital VCO-Based Analog-to-Digital Converter Using Phase Interpolator and Digital Calibration

abstract: A first-order time-based ΔΣ modulator using voltage-controlled oscillator (VCO) is presented. The proposed modulator employs phase interpolation technique to enhance the time resolution of the VCO and digital calibration to improve the linearity of the VCO tuning curve. The proposed modulator, implemented in 0.13 μm CMOS process, achieves 55 dB peak signal-to-noise ratio and 52.5 dB peak signal-to-noise-and-distortion ratio at 600 MHz sampling frequency for 20 MHz input bandwidth and consumes 14.3 mW.


title: The Necessity of Others is The Mother of Invention: Intrinsic and Prosocial Motivations, Perspective Taking, and Creativity

abstract: Although many scholars believe that intrinsic motivation fuels creativity, research has returned equivocal results. Drawing on motivated information processing theory, we propose that the relationship between intrinsic motivation and creativity is enhanced by other-focused psychological processes. Perspective taking, as generated by prosocial motivation, encourages employees to develop ideas that are useful as well as novel. In three studies, using both field and lab data, we found that prosocial motivation strengthened the association between intrinsic motivation and independent creativity ratings. In our second and third studies, perspective taking mediated this moderating effect. We discuss theoretical implications for creativity and motivation.


title: SAFER: System-level Architecture for Failure Evasion in Real-time Applications

abstract: Recent trends towards increasing complexity in distributed embedded real-time systems pose challenges in designing and implementing a reliable system such as a self-driving car. The conventional way of improving reliability is to use redundant hardware to replicate the whole (sub)system. Although hardware replication has been widely deployed in hard real-time systems such as avionics, space shuttles and nuclear power plants, it is significantly less attractive to many applications because the amount of necessary hardware multiplies as the size of the system increases. The growing needs of flexible system design are also not consistent with hardware replication techniques. To address the needs of dependability through redundancy operating in real-time, we propose a layer called SAFER(System-level Architecture for Failure Evasion in Real-time applications) to incorporate configurable task-level fault-tolerance features to tolerate fail-stop processor and task failures for distributed embedded real-time systems. To detect such failures, SAFER monitors the health status and state information of each task and broadcasts the information. When a failure is detected using either time-based failure detection or event-based failure detection, SAFER reconfigures the system to retain the functionality of the whole system. We provide a formal analysis of the worst-case timing behaviors of SAFER features. We also describe the modeling of a system equipped with SAFER to analyze timing characteristics through a model-based design tool called SysWeaver. SAFER has been implemented on Ubuntu 10.04 LTS and deployed on Boss, an award-winning autonomous vehicle developed at Carnegie Mellon University. We show various measurements using simulation scenarios used during the 2007 DARPA Urban Challenge. Finally, we present a case study of failure recovery by SAFER when node failures are injected.


title: Multimodal emotion recognition from expressive faces, body gestures

abstract: In this paper we present a multimodal approach for the recognition of eight emotions that integrates information from facial expressions, body movement and gestures and speech. We trained and tested a model with a Bayesian classifier, using a multimodal corpus with eight emotions and ten subjects. First individual classifiers were trained for each modality. Then data were fused at the feature level and the decision level. Fusing multimodal data increased very much the recognition rates in comparison with the unimodal systems: the multimodal approach gave an improvement of more than 10% with respect to the most successful unimodal system. Further, the fusion performed at the feature level showed better results than the one performed at the decision level.


title: Significance of Softmax-based Features in Comparison to Distance Metric Learning-based Features

abstract: End-to-end distance metric learning (DML) has been applied to obtain features useful in many computer vision tasks. However, these DML studies have not provided equitable comparisons between features extracted from DML-based networks and softmax-based networks. In this paper, we present objective comparisons between these two approaches under the same network architecture.


Predicting Canopy Light-Use Efficiency from Leaf Characteristics


title: The career of metaphor

abstract: �� A central question in metaphor research is how metaphors establish mappings between concepts from different domains. The authors propose an evolutionary path based on structure-mapping theory. This hypothesis-the career of metaphor-postulates a shift in mode of mapping from comparison to categorization as metaphors are conventionalized. Moreover, as demonstrated by 3 experiments, this processing shift is reflected in the very language that people use to make figurative assertions. The career of metaphor hypothesis offers a unified theoretical framework that can resolve the debate between comparison and categorization models of metaphor. This account further suggests that whether metaphors are processed directly or indirectly, and whether they operate at the level of individual concepts or entire conceptual domains, will depend both on their degree of conventionality and on their linguistic form.


title: Gender and genre variation in weblogs

abstract: A relationship among language, gender, and discourse genre has previously been observed in informal, spoken interaction and formal, written texts. This study investigates the language/gender/genre relationship in weblogs, a popular new mode of computer-mediated communication (CMC). Taking as the dependent variables stylistic features identified in machine learning research and popularized in a Web interface called the Gender Genie, a multivariate analysis was conducted of entries from random weblogs in a sample balanced for author gender and weblog sub-genre (diary or filter). The results show that the diary entries contained more 'female' stylistic features, and the filter entries more 'male' stylistic features, independent of author gender. These findings problematize the characterization of the stylistic features as gendered, and suggest a need for more fine-grained genre analysis in CMC research. At the same time, it is observed that conventional associations of gender with certain spoken and written genres are reproduced in weblogs, along with their societal valuations.


title: Structure Comparison of High-Frequency Planar Power Integrated Magnetic Circuits

abstract: World power consumption increases from year to year; hence, it is essential to strike new paths in finding effective results for power efficient devices. DC-DC converters are applied in various equipment such as solar photovoltaic installations as well as consumer electronic devices such as LED illumination. To satisfy the need of room saving constructions, planar transformers and integrations of passive parts of the converter are adopted for these purposes. This paper compares two different types of high-frequency planar integrated systems via finite element method and investigates the strengths and weaknesses of both structures.


title: Comparative analysis of RADAR-IR sensor fusion methods for object detection

abstract: This paper presents the Radar and IR sensor fusion method for objection detection. The infrared camera parameter calibration with Levenberg-Marquardt (LM) optimization method is proposed based on the Radar ranging data represented by Cartesian coordinate compared with 6 fusion methods. The proposed method firstly performs the estimation of the intrinsic parameter matrix of infrared camera with some optical trick. Then the method searches the extrinsic parameters using the generative approach. The initial angle and translation of the extrinsic parameters are optimized by the LM method with the geometrical cost function. In the experiments, the performance of proposed method outperforms by a maximum 13 times the performance of the other baseline methods on the averaged Euclidian distance error. In future work, the angular noise of the Radar information will be improved and the proposed method will provide the effective proposals for the deep neural network.


title: Smart Helmet for Accident Detection and Notification

abstract: Aim to create a system utilizing the Internet of Things (IoT) concept to detect accidents in two wheelers using a microcontroller and accelerometer and notify the authorities as well as the emergency contact of the rider. Also, plot the accidents occurring in the city for further analysis.


title: Thermal and Electrical Characteristics of EV Traction Motor Considering AC Resistance of MSO Coil

abstract: This paper reports the variation in the parameters of PMSM(Permanent Magnet Synchronous Motor) according to the fill factor (i.e., ratio of slot area to conductor area) for a traction motor of EV(Electric Vehicle) and HEV(Hybrid Electric Vehicles). Specifically, we compared fill factors (Concuctor area / Slot area) of 54% from a typical rectangular coil and S6.7% from a MSO (maximum slot occupation, patented design) coil considering a 16-pole 24-slot surface permanent-magnet motor. In addition, we evaluated the current density distribution and thermal characteristics of the conductor (i.e., copper) by 2-D time harmonic and 2-D transient thermal analyses. Moreover, we characterized the copper loss at the d-q axis equivalent circuit of motor and determined the motor characteristics under load operation. The results show that the MSO coil performs higher effeciency of motor at low speed, also lower thermal generation under the same operational conditions.


Closed-loop Servoing using Real-time Markerless Arm Tracking


title: Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion

abstract: Action recognition is an important yet challenging task in computer vision. In this paper, we propose a novel deep-based framework for action recognition, which improves the recognition accuracy by: 1) deriving more precise features for representing actions, and 2) reducing the asynchrony between different information streams. We first introduce a coarse-to-fine network which extracts shared deep features at different action class granularities and progressively integrates them to obtain a more accurate feature representation for input actions. We further introduce an asynchronous fusion network. It fuses information from different streams by asynchronously integrating stream-wise features at different time points, hence better leveraging the complementary information in different streams. Experimental results on action recognition benchmarks demonstrate that our approach achieves the state-of-the-art performance.


title: Markov chain Monte Carlo (MCMC) sampling methods to determine optimal models, model resolution and model choice for Earth Science problems

abstract: We present an overview of Markov chain Monte Carlo, a sampling method for model inference and uncertainty quantification. We focus on the Bayesian approach to MCMC, which allows us to estimate the posterior distribution of model parameters, without needing to know the normalising constant in Bayes' theorem. Given an estimate of the posterior, we can then determine representative models (such as the expected model, and the maximum posterior probability model), the probability distributions for individual parameters, and the uncertainty about the predictions from these models. We also consider variable dimensional problems in which the number of model parameters is unknown and needs to be inferred. Such problems can be addressed with reversible jump (RJ) MCMC. This leads us to model choice, where we may want to discriminate between models or theories of differing complexity. For problems where the models are hierarchical (e.g. similar structure but with a different number of parameters), the Bayesian approach naturally selects the simpler models. More complex problems require an estimate of the normalising constant in Bayes' theorem (also known as the evidence) and this is difficult to do reliably for high dimensional problems. We illustrate the applications of RJMCMC with 3 examples from our earlier working involving modelling distributions of geochronological age data, inference of sea-level and sediment supply histories from 2D stratigraphic cross-sections, and identification of spatially discontinuous thermal histories from a suite of apatite fission track samples distributed in 3D.


title: Digital Life and Youth Well-being, Social Connectedness, Empathy, and Narcissism

abstract: Youth well-being, social connectedness, and personality traits, such as empathy and narcissism, are at the crux of concerns often raised about the impacts of digital life. Understanding known impacts, and research gaps, in these areas is an important first step toward supporting media use that contributes positively to youth's happiness, life satisfaction, and prosocial attitudes and behaviors. By examining existing work addressing these issues across domains, we found that a complex interplay of individual factors, type of digital media engagement, and experiences in media contexts informs outcomes related to well-being, social connectedness, empathy, and narcissism. We argue that further research is needed to uncover how, where, when, and for whom digital media practices support positive well-being and social connectedness outcomes. Specifically, research needs to move beyond correlational studies to uncover causal connections between traits like narcissism and media use. Longitudinal studies are also needed to explore patterns of media use over time and related impacts. Further research is needed to explore how specific technologies can be designed to support positive well-being, social outcomes, and prosocial personality traits. Finally, research is needed regarding parenting, educational practices, and policies that support positive digital media use and related outcomes. Although existing research suggests that digital life has mixed potentials and effects for well-being, social connectedness, empathy, and narcissism, we provide recommendations for clinicians, policy makers, and educators in partnering with caregivers and youth to support media use that promotes positive outcomes in these areas.


title: A fingerprint cryptosystem based on minutiae phase spectrum

abstract: Public acceptance of biometric technology is hindered by security and privacy concerns regarding leakage of biometric templates. Biometric cryptosystems alleviate this problem by storing a secure sketch that is typically derived by binding the template with a cryptographic key. However, designing fingerprint cryptosystems is difficult because fingerprint matching is usually based on unordered sets of minutia features having large intra-user variations. To overcome this challenge, we propose a novel minutiae representation known as the Binarized Phase Spectrum (BiPS), which is a fixed-length binary string obtained by quantizing the Fourier phase spectrum of a minutia set. We secure the BiPS representation using a fuzzy commitment scheme employing turbo codes. We also propose a technique for aligning fingerprints based on the focal point of high curvature regions. The proposed system achieves a FNMR of 16.2% and 12.6% on FVC2002-DB1 and DB2 databases, respectively, at zero FMR.


Impact of Utilitarian and Hedonic Shopping Values on Individual ’ s Perceived Benefits and Risks in Online Shopping


title: Interacting with Financial Data using Natural Language

abstract: Financial and economic data are typically available in the form of tables and comprise mostly of monetary amounts, numeric and other domain-specific fields. They can be very hard to search and they are often made available out of context, or in forms which cannot be integrated with systems where text is required, such as voice-enabled devices. This work presents a novel system that enables both experts in the finance domain and non-expert users to search financial data with both keyword and natural language queries. Our system answers the queries with an automatically generated textual description using Natural Language Generation (NLG). The answers are further enriched with derived information, not explicitly asked in the user query, to provide the context of the answer. The system is designed to be flexible in order to accommodate new use cases without significant development effort, thus allowing fast integration of new datasets.


title: SCENE UNDERSTANDING USING BACK PROPAGATION BY NEURAL NETWORK

abstract: In this paper, we proposed an efficient method to address the problem of scene understanding that is based on neural network (NN) and image segmentation. We utilized a multilayer perceptron (MLP) to train the network and features are extracted using pixels in the RGB color space. In this work, object samples in images with varying lighting conditions are used to obtain a wide object color distribution. The training data is generated from positive and negative training patterns in the color planes. Subsequently, training set is fed to an MLP, trained by the back propagation algorithm using these object samples. We apply the above mentioned NN-based object classifier to the test image which is applied to image segmentation and corresponding to the pixel level of the object in test image particular object is determined.


abstract: spence laschinger h.k., gilbert s., smith l.m. & leslie k. (2010) Journal of Nursing Management18, 4–13 ::: Towards a comprehensive theory of nurse/patient empowerment: applying Kanter’s empowerment theory to patient care ::: ::: Aim The purpose of this theoretical paper is to propose an integrated model of nurse/patient empowerment that could be used as a guide for creating high-quality nursing practice work environments that ensure positive outcomes for both nurses and their patients. ::: ::: Background There are few integrated theoretical approaches to nurse and patient empowerment in the literature, although nurse empowerment is assumed to positively affect patient outcomes. ::: ::: Evaluation The constructs described in Kanter’s (1993) work empowerment theory are conceptually consistent with the nursing care process and can be logically extended to nurses’ interactions with their patients and the outcomes of nursing care. ::: ::: Key issues We propose a model of nurse/patient empowerment derived from Kanter’s theory that suggests that empowering working conditions increase feelings of psychological empowerment in nurses, resulting in greater use of patient empowerment strategies by nurses, and, ultimately, greater patient empowerment and better health outcomes. ::: ::: Conclusions Empirical testing of the model is recommended prior to use of the model in clinical practice. ::: ::: Implications for Nursing Management We argue that empowered nurses are more likely to empower their patients, which results in better patient and system outcomes. Strategies for managers to empower nurses and for nurses to empower patients are suggested.


title: A Conceptual Security Approach with Awareness Strategy and Implementation Policy to Eliminate Ransomware

abstract: Undeterred by numerous efforts deployed by antivirus software that shields users from various security threats, ransomware is constantly evolving as technology advances. The impact includes hackers hindering the user's accessibility to their data, and the user will pay ransom to retrieve their data. Ransomware also targets multimillion-dollar organizations, and it can cause colossal data loss. The organizations could face catastrophic consequences, and business operations could be ceased. This research contributes by spreading awareness of ransomware to alert people to tackle ransomware. The solution of this research is the conceptual development of a browser extension that provides assistance to warn users of plausible dangers while surfing the Internet. It allows the users to surf the web safely. Since the contribution of this research is conceptual, we can assume that technology users will adopt the proposed idea to prevent ransomware attacks on their personal computers once the solution is fully implemented in future research.


title: Image compression using the 2-D wavelet transform

abstract: The 2-D orthogonal wavelet transform decomposes images into both spatial and spectrally local coefficients. The transformed coefficients were coded hierarchically and individually quantized in accordance with the local estimated noise sensitivity of the human visual system (HVS). The algorithm can be mapped easily onto VLSI. For the Miss America and Lena monochrome images, the technique gave high to acceptable quality reconstruction at compression ratios of 0.3-0.2 and 0.64-0.43 bits per pixel (bpp), respectively. >


title: A Smart City Application: A Fully Controlled Street Lighting Isle Based on Raspberry-Pi Card, a ZigBee Sensor Network and WiMAX

abstract: A smart city application has been realized and tested. It is a fully remote controlled isle of lamp posts based on new technologies. It has been designed and organized in different hierarchical layers, which perform local activities to physically control the lamp posts and transmit information with another for remote control. Locally, each lamp post uses an electronic card for management and a ZigBee tlc network transmits data to a central control unit, which manages the whole isle. The central unit is realized with a Raspberry-Pi control card due to its good computing performance at very low price. Finally, a WiMAX connection was tested and used to remotely control the smart grid, thus overcoming the distance limitations of commercial Wi-Fi networks. The isle has been realized and tested for some months in the field.


title: Modeling and Using Product Architectures in Industrial Mechatronic Product Development: Experiments and Observations

abstract: The goal of this work is to practically determine the role of product architecture models to support communication for improving development practices of complex mechatronic products. This paper contains descriptions, observations, and lessons learned from case studies in which the authors tested a language to represent product architectures during product development in a company, as well as the reasons leading to the use of the specific language/model. The tests include construction of architecture models, direct use of the architecture information, model generation from the architecture model, reuse of architecture model information, clarification of existing documentation, and transition towards model-based product development. The work points out desired characteristics of product architecture models as well as characteristics of the necessary implementation tools and framework.Copyright © 2011 by ASME


Bootstrapping for Numerical Open IE


title: Conducted noise prediction for zero-crossing issue in totem-pole bridgeless PFC converter

abstract: This paper focuses on zero-crossing spike current issue in totem-pole bridgeless PFC converter. Spike current occurs at every input voltage zero-crossing and becomes a severe conducted common mode noise source. The zero-crossing issue is discussed by theoretical analysis and mathematical calculation, the conducted noise caused by zero-crossing issue can be predicted by calculating Fourier coefficient of approximate spike current waveform. Based on the analysis, a noise reduction method is proposed to solve zero-crossing issue. Finally, the experiments are achieved on a GaN-based totem-pole bridgeless PFC converter and show that the common mode conducted noise caused by spike current can by reduced effectively.


title: The Natural Language Decathlon: Multitask Learning as Question Answering

abstract: Presented on August 28, 2018 at 12:15 p.m. in the Pettit Microelectronics Research Center, Room 102 A/B.


title: DRACO: Byzantine-resilient Distributed Training via Redundant Gradients

abstract: Distributed model training is vulnerable to byzantine system failures and adversarial compute nodes, i.e., nodes that use malicious updates to corrupt the global model stored at a parameter server (PS). To guarantee some form of robustness, recent work suggests using variants of the geometric median as an aggregation rule, in place of gradient averaging. Unfortunately, median-based rules can incur a prohibitive computational overhead in large-scale settings, and their convergence guarantees often require strong assumptions. In this work, we present DRACO, a scalable framework for robust distributed training that uses ideas from coding theory. In DRACO, each compute node evaluates redundant gradients that are used by the parameter server to eliminate the effects of adversarial updates. DRACO comes with problem-independent robustness guarantees, and the model that it trains is identical to the one trained in the adversary-free setup. We provide extensive experiments on real datasets and distributed setups across a variety of large-scale models, where we show that DRACO is several times, to orders of magnitude faster than median-based approaches.


title: Modeling, design, and demonstration of 2.5D glass interposers for 16-channel 28 Gbps signaling applications

abstract: This paper describes the modeling, design, and demonstration of high-speed differential transmission lines on a 130µm thin glass interposer with two re-distribution layers (RDL), line lengths of 1–50mm, and turn radii of 0.15–8mm for 16-channel signal transmission at 28 Gbps per channel. Next generation photonic systems such as 400 Gigabit Ethernet (400 GbE) require low power and low loss channels between photodetectors and trans-impedance amplifiers (TIA) or between laser arrays and driver ICs. Glass, with low dielectric constant and loss tangent, has higher electrical performance and channel power efficiency compared to silicon interposers. Furthermore, low surface roughness and high-dimensional stability of glass enable finer lithographic dimensions and higher interconnection density during large panel processing compared to organic interposers. Interconnection of optical and electrical ICs on 2.5D glass interposers provides the best combination of electrical and optical signal performance. For 400 GbE modules, a 16-channel bus at 28 Gbps per channel is required for communication to the backplane. Electrical modeling and simulation was performed to arrive at an appropriate design for the 16×28 Gbps I/O interface on a two-metal layer glass interposer. An ultra-thin 130µm glass interposer was fabricated using low-cost, double-side panel processing providing for a lower cost, higher performance solution compared to silicon interposers.


title: Analysis of Heart Diseases Dataset using Neural Network Approach

abstract: One of the important techniques of Data mining is Classification. Many real world problems in various fields such as business, science, industry and medicine can be solved by using classification approach. Neural Networks have emerged as an important tool for classification. The advantages of Neural Networks helps for efficient classification of given data. In this study a Heart diseases dataset is analyzed using Neural Network approach. To increase the efficiency of the classification process parallel approach is also adopted in the training phase.


title: Effects of sex steroids on proliferation in normal mammary tissue.

abstract: Numerous women are treated with a combination of oestrogen and progestogen for contraception and hormone replacement therapy worldwide. A possible increased risk of cancer in target organs has been discussed vividly for many years. While oestrogens are clearly mitogenic for breast epithelial cells, there has been considerable uncertainty about the effects of progestogens. This article reviews current knowledge on this field, including our own data. Oestrogen receptors are down-regulated during the luteal phase, while progesterone receptors remain at a high level throughout the menstrual cycle. According to most studies, in vivo proliferation of normal breast epithelial cells is higher during the luteal phase in the vast majority of women. Normal breast tissue can convert oestrone sulphate to oestradiol. A negative correlation between the levels of circulating oestradiol and the enzyme converting oestrone into oestradiol suggests a local regulatory mechanism of tissue oestradiol formation. Serum progesterone levels correlate positively with sulphatase activity while 19-norsteroid progestogens may be inhibitory. We found that long-term continuous combined hormonal treatment with conjugated equine oestrogens and medroxyprogesterone acetate induced a proliferative response in the breasts of surgically postmenopausal macaques. The effect of combined treatment was more pronounced than that of oestrogen treatment alone. Both endogenous progesterone and exogenous progestogens increase proliferation of breast epithelial cells. Exogenous progestogens down-regulate both oestrogen and progesterone receptors. Oestrogen and progestogens may have both direct and indirect stimulating effects on proliferation. The finding of a positive correlation between insulin-like growth factor I messenger RNA and proliferation found in hormonally treated women with low receptor levels suggests the possibility of nonreceptor-mediated effects of sex steroids on proliferation, which needs to be investigated further.