title: Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation

abstract: Segmentation of 3D images is a fundamental problem in biomedical image analysis. Deep learning (DL) approaches have achieved state-of-the-art segmentation perfor- mance. To exploit the 3D contexts using neural networks, known DL segmentation methods, including 3D convolution, 2D convolution on planes orthogonal to 2D image slices, and LSTM in multiple directions, all suffer incompatibility with the highly anisotropic dimensions in common 3D biomedical images. In this paper, we propose a new DL framework for 3D image segmentation, based on a com- bination of a fully convolutional network (FCN) and a recurrent neural network (RNN), which are responsible for exploiting the intra-slice and inter-slice contexts, respectively. To our best knowledge, this is the first DL framework for 3D image segmentation that explicitly leverages 3D image anisotropism. Evaluating using a dataset from the ISBI Neuronal Structure Segmentation Challenge and in-house image stacks for 3D fungus segmentation, our approach achieves promising results comparing to the known DL-based 3D segmentation approaches.


title: Breast Reinnervation: DIEP Neurotization Using the Third Anterior Intercostal Nerve

abstract: Background:The purpose of this article is to evaluate a new method of DIEP flap neurotization using a reliably located recipient nerve. We hypothesize that neurotization by this method (with either nerve conduit or direct nerve coaptation) will have a positive effect on sensory recovery.Methods:Fift


Identifying Causal Relations Using Parallel Wikipedia Articles


title: Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping

abstract: Most time series data mining algorithms use similarity search as a core subroutine, and thus the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms, including classification, clustering, motif discovery, anomaly detection, and so on. The difficulty of scaling a search to large datasets explains to a great extent why most academic work on time series data mining has plateaued at considering a few millions of time series objects, while much of industry and science sits on billions of time series objects waiting to be explored. In this work we show that by using a combination of four novel ideas we can search and mine massive time series for the first time. We demonstrate the following unintuitive fact: in large datasets we can exactly search under Dynamic Time Warping (DTW) much more quickly than the current state-of-the-art Euclidean distance search algorithms. We demonstrate our work on the largest set of time series experiments ever attempted. In particular, the largest dataset we consider is larger than the combined size of all of the time series datasets considered in all data mining papers ever published. We explain how our ideas allow us to solve higher-level time series data mining problems such as motif discovery and clustering at scales that would otherwise be untenable. Moreover, we show how our ideas allow us to efficiently support the uniform scaling distance measure, a measure whose utility seems to be underappreciated, but which we demonstrate here. In addition to mining massive datasets with up to one trillion datapoints, we will show that our ideas also have implications for real-time monitoring of data streams, allowing us to handle much faster arrival rates and/or use cheaper and lower powered devices than are currently possible.


title: Data Recovery Function Testing for Digital Forensic Tools

abstract: Many digital forensic tools used by investigators were not originally designed for forensic applications. Even in the case of tools created with the forensic process in mind, there is the issue of assuring their reliability and dependability. Given the nature of investigations and the fact that the data collected and analyzed by the tools must be presented as evidence, it is important that digital forensic tools be validated and verified before they are deployed. This paper engages a systematic description of the digital forensic discipline that is obtained by mapping its fundamental functions. The function mapping is used to construct a detailed function-oriented validation and verification framework for digital forensic tools. This paper focuses on the data recovery function. The data recovery requirements are specified and a reference set is presented to test forensic tools that implement the data recovery function.


title: A Single-Stage Soft-Switching Flyback Converter for Power-Factor-Correction Applications

abstract: This letter presents a single-stage soft-switching Flyback converter for power-factor-correction (PFC) applications. High power factor and high conversion efficiency can be achieved by a simple single-stage circuit with soft-switching features. The operation principles and design criteria for the studied PFC converter are analyzed and discussed. A laboratory prototype is also built and tested. Finally, the experimental waveforms for this prototype circuit are shown to verify the feasibility of the proposed scheme.


A BUILDING‐BLOCK MODEL FOR THE CHARACTERIZATION OF ODORANT MOLECULES AND THEIR ODORS


A novel laser ranging system for measurement of ground-to-satellite distances


title: Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

abstract: Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives. In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between each task's loss. Tuning these weights by hand is a difficult and expensive process, making multi-task learning prohibitive in practice. We propose a principled approach to multi-task deep learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. This allows us to simultaneously learn various quantities with different units or scales in both classification and regression settings. We demonstrate our model learning per-pixel depth regression, semantic and instance segmentation from a monocular input image. Perhaps surprisingly, we show our model can learn multi-task weightings and outperform separate models trained individually on each task.


title: Forensic hash for multimedia information

abstract: Digital multimedia such as images and videos are prevalent on today's internet and cause significant ::: social impact, which can be evidenced by the proliferation of social networking sites with user generated ::: contents. Due to the ease of generating and modifying images and videos, it is critical to establish ::: trustworthiness for online multimedia information. In this paper, we propose novel approaches to ::: perform multimedia forensics using compact side information to reconstruct the processing history of ::: a document. We refer to this as FASHION, standing for Forensic hASH for informatION assurance. ::: Based on the Radon transform and scale space theory, the proposed forensic hash is compact and ::: can effectively estimate the parameters of geometric transforms and detect local tampering that an ::: image may have undergone. Forensic hash is designed to answer a broader range of questions regarding ::: the processing history of multimedia data than the simple binary decision from traditional robust ::: image hashing, and also offers more efficient and accurate forensic analysis than multimedia forensic ::: techniques that do not use any side information.


title: An Effective News Recommendation Method for Microblog User

abstract: Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. Traditional systems strive to satisfy their user by tracing users' reading history and choosing the proper candidate news articles to recommend. However, most of news websites hardly require any user to register before reading news. Besides, the latent relations between news and microblog, the popularity of particular news, and the news organization are not addressed or solved efficiently in previous approaches. In order to solve these issues, we propose an effective personalized news recommendation method based on microblog user profile building and sub class popularity prediction, in which we propose a news organization method using hybrid classification and clustering, implement a sub class popularity prediction method, and construct user profile according to our actual situation. We had designed several experiments compared to the state-of-the-art approaches on a real world dataset, and the experimental results demonstrate that our system significantly improves the accuracy and diversity in mass text data.


title: An Automatic 3D Ear Recognition System

abstract: Previous works have shown that the ear is a good candidate for a human biometric. However, in prior work, the pre-processing of ear images has been a manual process. Also, prior algorithms were sensitive to noise in the data, especially that caused by hair and earrings. We present a novel solution to the automated cropping of the ear and implement it in an end-to-end solution for biometric recognition. We demonstrate our automatic recognition process with the largest study to date in ear biometrics, 415 subjects, achieving a rank one recognition rate of 97.6%. This work represents a breakthrough in ear biometrics and paves the way for commercial quality, fully automatic systems.


title: For personal use only--not for distribution. Positive Illusions and Well-Being Revisited Separating Fact From Fiction

abstract: In 1988, we published an article that challenged the notion that accurate perceptions of self and the world are essential for mental health (Taylor & Brown, 1988). We argued instead that people's perceptions in these domains are positively biased and that these positive illusions promote psychological well-being. In the current article, we review our theoretical model, correct certain misconceptions in its empirical application, and address the criticisms made by Colvin and Block. Taylor and Brown's (1988) model of mental health maintains that certain positive illusions are highly prevalent in normal thought and predictive of criteria traditionally associated with mental health. The work initially derived from research with cancer patients (Taylor, 1983) but was integrated in the 1988 essay with literature on social cognition, suggesting that the formulation could also make sense of previously anomalous and somewhat unrelated errors and biases in human thought. As is the case with any theoretical model, the goal of the article was to generate research. On this ground, the model appears to have been quite successful. According to Colvin and Block's estimation, approximately 250 studies have made use of the formulation. Given its popularity and influence, a critical examination of the Taylor and Brown argument is appropriate. In this article, we (a) review some of the central points of our theoretical model; (b) examine Colvin and Block's article in view of these issues; (c) present research germane to our thesis but not discussed by Colvin and Block; and (d) raise some important issues that have yet to be resolved. As before, our aim is to contribute to an informed dialogue regarding the nature of psychological well-being.


title: Image Patch Matching Using Convolutional Descriptors with Euclidean Distance

abstract: In this work we propose a neural network based image descriptor suitable for image patch matching, which is an important task in many computer vision applications. Our approach is influenced by recent success of deep convolutional neural networks (CNNs) in object detection and classification tasks. We develop a model which maps the raw input patch to a low dimensional feature vector so that the distance between representations is small for similar patches and large otherwise. As a distance metric we utilize \(L_2\) norm, i.e. Euclidean distance, which is fast to evaluate and used in most popular hand-crafted descriptors, such as SIFT. According to the results, our approach outperforms state-of-the-art \(L_2\)-based descriptors and can be considered as a direct replacement of SIFT. In addition, we conducted experiments with batch normalization and histogram equalization as a preprocessing method of the input data. The results confirm that these techniques further improve the performance of the proposed descriptor. Finally, we show promising preliminary results by appending our CNNs with recently proposed spatial transformer networks and provide a visualisation and interpretation of their impact.


title: Situated Engineering Learning: Bridging Engineering Education Research and the Learning Sciences

abstract: Contributors ::: ::: Indigo Esmonde, University of Toronto; Krishna Madhavan, Purdue University; Wolff-Michael Roth, University of Victoria; Dan L. Schwartz and Jessica Tsang, Stanford University; Estrid Sorensen, Humboldt University and Aarhus University; Iris Tabak, Ben Gurion University of the Negev ::: ::: ::: ::: Background ::: ::: The field of engineering education research has seen substantial growth in the last five years but it often lacks theoretical and empirical work on engineering learning that could be supplied by the learning sciences. In addition, the learning sciences have focused very little on engineering learning to date. ::: ::: ::: ::: Purpose ::: ::: This article summarizes prior work in the learning sciences and discusses one perspective—situative learning— in depth. Situativity refers to the central role of context, including the physical and social aspects of the environment, on learning. Furthermore, it emphasizes the socially and culturally negotiated nature of thought and action of persons in interaction. The aim of the article is to provide a foundation for future work on engineering learning and to suggest ways in which the learning sciences and engineering education research communities might work to their mutual benefit. ::: ::: ::: ::: Scope/Method ::: ::: The article begins with a brief discussion of recent developments in engineering education research. After an initial overview of the field of learning sciences, situative learning is discussed and three analytical aspects of the perspective are outlined: social and material context, activities and interactions, and participation and identity. Relevant expert commentaries are interspersed throughout the article. The article concludes with an exploration of the potential for contributions from the learning sciences to understanding engineering learning. ::: ::: ::: ::: Conclusion ::: ::: There are many areas of mutual benefit for engineering education and the learning sciences and many potential areas of collaborative research that can contribute not only to engineering learning but to the learning sciences.


title: First-Principles Study of Electron Linewidths in Graphene

abstract: We present first-principles calculations of the linewidths of low-energy quasiparticles in n-doped graphene arising from both the electron-electron and the electron-phonon interactions. The contribution to the electron linewidth arising from the electron-electron interactions varies significantly with wave vector at fixed energy; in contrast, the electron-phonon contribution is virtually wave vector independent. These two contributions are comparable in magnitude at a binding energy of approximately 0.2 eV, corresponding to the optical phonon energy. The calculated linewidths, with both electron-electron and electron-phonon interactions included, explain to a large extent the linewidths seen in recent photoemission experiments.


title: Fog Orchestration for Internet of Things Services

abstract: Large-scale Internet of Things (IoT) services such as healthcare, smart cities, and marine monitoring are pervasive in cyber-physical environments strongly supported by Internet technologies and fog computing. Complex IoT services are increasingly composed of sensors, devices, and compute resources within fog computing infrastructures. The orchestration of such applications can be leveraged to alleviate the difficulties of maintenance and enhance data security and system reliability. However, efficiently dealing with dynamic variations and transient operational behavior is a crucial challenge within the context of choreographing complex services. Furthermore, with the rapid increase of the scale of IoT deployments, the heterogeneity, dynamicity, and uncertainty within fog environments and increased computational complexity further aggravate this challenge. This article gives an overview of the core issues, challenges, and future research directions in fog-enabled orchestration for IoT services. Additionally, it presents early experiences of an orchestration scenario, demonstrating the feasibility and initial results of using a distributed genetic algorithm in this context.


title: Design and Experimental Evaluation of Miniaturized Monopole UHF RFID Tag Antennas

abstract: This letter reports the design and experimental evaluation of three compact printed monopole antennas for UHF RFID applications. The antennas that include folded, 2-D meander, and 3-D meander monopole designs are inductively coupled to RFID chips and are fabricated using a 1.524-mm-thick Rogers RB4350B substrate with the relative permittivity of 3.66. Experimental results reveal that these tags cover the worldwide UHF RFID operation band (860-960 MHz) with a maximum readable range of 6.3 m for the folded monopole, 5.39 m for the 2-D meander, and 5.24 m for the 3-D meander monopole designs with an effective isotropic radiated power of 3.28 W. A method for measuring the radiation pattern of an assembled UHF RFID tag without the use of any additional matching circuits or feeding wires is presented. This method is used to measure the radiation pattern of the fabricated 3-D meander monopole tag antenna.


title: Defining transparency movements

abstract: Purpose ::: ::: ::: ::: ::: A multitude of transparency movements have been developed and grown strong in recent decades. Despite their growing influence, scholarly studies have focused on individual movements. The purpose of this paper is to make a pioneering contribution in defining transparency movements. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: An exploratory approach has been used utilizing movement-specific professional and scholarly documents concerning 18 transparency movements. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: Different traditions, ideologies of openness and aspects involving connections between movements have been identified as well as forms of organization. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This is the first attempt at identifying and defining transparency movements as a contemporary phenomenon.


title: 200-Gb/s compact card-edge optical transceiver utilizing cost-effective FPC-based module for optical interconnect

abstract: A compact card-edge optical transceiver with a cost-effective flexible printed circuit (FPC) based module structure has been developed. Clear eye-openings operating at 200 Gb/s (8-channel × 25 Gb/s) were successfully demonstrated. The optical power budget more than 4.5 dB was obtained even when the channel-to-channel cross-talk was included.


title: The Constant Comparative Analysis Method Outside of Grounded Theory

abstract: This commentary addresses the gap in the literature regarding discussion of the legitimate use of Constant Comparative Analysis Method (CCA) outside of Grounded Theory. The purpose is to show the strength of using CCA to maintain the emic perspective and how theoretical frameworks can maintain the etic perspective throughout the analysis. My naturalistic inquiry model shows how conceptual frameworks and theoretical frameworks can be integrated when using the CCA method. Keywords: Constant Comparison, Constant Comparative Analysis, Theoretical Framework, Conceptual Framework, Critical Discourse Analysis, Naturalistic Inquiry Grounded Theory (GT) use has spread to various fields of study since Glaser and Strauss’ (1967) introduction of the methodology (Tan, 2010, p. 94). A review of the literature on the Constant Comparative Method shows the same movement. CCA, as a technique or method, appears to be considered as synonymous with GT. O’Connor, Netting, and Thomas’ (2008) review of the GT literature shows a steady growth over the last several decades in the use of CCA within GT methodology. A review of dissertation abstracts from 2004 shows that GT is used in various ways. The authors highlight that 35% of the dissertations that state grounded theory in their dissertation abstracts, keywords, and/or titles focus on the use of the CCA method, but lack a definitive approach towards the development of a substantive theory. The problem appears to be a gap in the literature regarding discussion of the legitimate use of the CCA method outside of GT. The purpose of this commentary is to show the strength in using CCA to maintain the emic perspective (participant’s view as insider) and how theoretical frameworks can maintain the etic perspective (outsider/ distant concepts) throughout the analysis. The commentary answers the question: What is the benefit of using CCA method outside of GT? Tan’s (2010) review of the literature presents the question about the use of the terms


title: Sentiment analysis in a cross-media analysis framework

abstract: This paper introduces the implementation and integration of a sentiment analysis pipeline into the ongoing open source cross-media analysis framework. The pipeline includes the following components; chat room cleaner, NLP and sentiment analyzer. Before the integration, we also compare two broad categories of sentiment analysis methods, namely lexicon-based and machine learning approaches. We mainly focus on finding out which method is appropriate to detect sentiments from forum discussion posts. In order to conduct our experiments, we use the apache-hadoop framework with its lexicon-based sentiment prediction algorithm and Stanford coreNLP library with the Recursive Neural Tensor Network (RNTN) model. The lexicon-based uses sentiment dictionary containing words annotated with sentiment labels and other basic lexical features, and the later one is trained on Sentiment Treebank with 215,154 phrases, labeled using Amazon Turk. Our overall performance evaluation shows that RNTN outperforms the lexicon-based by 9.88% accuracy on variable length positive, negative, and neutral comments. However, the lexicon-based shows better performance on classifying positive comments. We also found out that the F1-score values of the Lexicon-based is greater by 0.16 from the RNTN.


title: Multimodal Machine Learning: A Survey and Taxonomy

abstract: Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities. In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals together. Multimodal machine learning aims to build models that can process and relate information from multiple modalities. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy. We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning. This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research.


title: A non-pharmacologic approach to decrease restraint use.

abstract: AIMS ::: To evaluate an education intervention to decrease restraint use in patients in a Trauma Intensive Care Unit (TICU) and to evaluate nurses' perceptions regarding restraints. ::: ::: ::: OBJECTIVES ::: To measure restraint use pre/post-intervention and to measure nurses' perceptions of restraint use. ::: ::: ::: METHODS ::: Pre/post-intervention design to collate incidences of delirium and restraints pre/post-intervention. Data reporting nurses' views and preferences were collected pre-intervention. ::: ::: ::: MEASURES ::: Patients were assessed by nursing on admission and every shift with the Confusion Assessment Method for TICU. Restraint use was measured in a 24-hour period. Nurses' perception of restraints was measured using Perceptions of Restraint Use Questionnaire (PRUQ). ::: ::: ::: RESULTS ::: A statistically significant difference was demonstrated in restraint use before and after the educational intervention. Mean and standard deviation for restraints per 1000 patient days pre-intervention was 314.1 (35.4), post-intervention 237.8 (56.4) (p=0.008). Mean PRUQ overall, 3.57 (range 1-5) indicated that nurses had positive attitudes towards restraints in certain circumstances. The primary reasons for using restraints were: "protecting patients from falling out of bed", 37 (72.5%), and "protecting patients from falling out of chair", 34 (66.7%). ::: ::: ::: CONCLUSION ::: This study demonstrates that a low risk educational intervention aimed at use of an alternative device use can reduce restraint use.


title: New designs of ventricular catheters for hydrocephalus by 3-D computational fluid dynamics

abstract: Introduction ::: Based on a landmark study by Lin et al. of the two-dimensional flow in ventricular catheters (VCs) via computational fluid dynamics (CFD), we studied in a previous paper the three-dimensional flow patterns of five commercially available VC. We found that the drainage of the cerebrospinal fluid (CSF) mostly occurs through the catheter’s most proximal holes. In this paper, we design five VC prototypes with equalized flow characteristics.


title: MATBN: A Mandarin Chinese Broadcast News Corpus

abstract: The MATBN Mandarin Chinese broadcast news corpus contains a total of 198 hours of broadcast news from the Public Television Service Foundation (Taiwan) with corresponding transcripts. The primary purpose of this collection is to provide training and testing data for continuous speech recognition evaluation in the broadcast news domain. In this paper, we briefly introduce. the speech corpus and report on some preliminary statistical analysis and speech recognition evaluation results.


title: The impact of e-WOM on destination image, attitude toward destination and travel intention

abstract: Electronic word of mouth (e-WOM) is a new development of word of mouth by using social media platform to interact in the internet. Current studies only examine the model on its own basis by using theory planned behavior (TPB). Based on this idea, this research is trying to examine the model of e-WOM, Destination Image, Attitude Toward Destination together to reveal which factor that give the greater effects against travel intention both direct effect, indirect effects and total effects. The study was using the Generalized Structured Component Analysis (GSCA) methodology to test the model. The research object was the @explore_batu in Instagram as many as 167 online respondents against tourist destination in Batu City. Results show that: (1) e-WOM positively influences Destination Image; (2) e-WOM positively influences Attitude Toward Destination; (3) e-WOM positively influences Travel Intention; (4) Destination Image influences Travel Intention and (5) Attitude Towards Destination influences Travel Intention. The e-WOM is a crucial part of this research because has a greater effect on Destination Image and Travel Intention. Implication, limitation, discussion and future research direction are discussed at the end of the paper.


title: Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning

abstract: Recently there has been a lot of interest in learning common representations for multiple views of data. Typically, such common representations are learned using a parallel corpus between the two views (say, 1M images and their English captions). In this work, we address a real-world scenario where no direct parallel data is available between two views of interest (say, $V_1$ and $V_2$) but parallel data is available between each of these views and a pivot view ($V_3$). We propose a model for learning a common representation for $V_1$, $V_2$ and $V_3$ using only the parallel data available between $V_1V_3$ and $V_2V_3$. The proposed model is generic and even works when there are $n$ views of interest and only one pivot view which acts as a bridge between them. There are two specific downstream applications that we focus on (i) transfer learning between languages $L_1$,$L_2$,...,$L_n$ using a pivot language $L$ and (ii) cross modal access between images and a language $L_1$ using a pivot language $L_2$. Our model achieves state-of-the-art performance in multilingual document classification on the publicly available multilingual TED corpus and promising results in multilingual multimodal retrieval on a new dataset created and released as a part of this work.


title: A New Multi-objective Evolutionary Optimisation Algorithm: The Two-Archive Algorithm

abstract: Many multi-objective evolutionary algorithms (MOEAs) have been proposed in recent years. However, almost all MOEAs have been evaluated on problems with two to four objectives only. It is unclear how well these MOEAs will perform on problems with a large number of objectives. Our preliminary study (V. Khare et al., 2003) showed that performance of some MOEAs deteriorates significantly as the number of objectives increases. This paper proposes a new MOEA that performs well on problems with a large number of objectives. The new algorithm separates non-dominated solutions into two archives, and is thus called the two-archive algorithm. The two archives focused on convergence and diversity, respectively, in optimisation. Computational studies have been carried out to evaluate and compare our new algorithm against the best MOEA for problems with a large number of objectives. Our experimental results have shown that the two-archive algorithm outperforms existing MOEAs on problems with a large number of objectives


title: Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction

abstract: Recognizing objects in fine-grained domains can be extremely challenging due to the subtle differences between subcategories. Discriminative markings are often highly localized, leading traditional object recognition approaches to struggle with the large pose variation often present in these domains. Pose-normalization seeks to align training exemplars, either piecewise by part or globally for the whole object, effectively factoring out differences in pose and in viewing angle. Prior approaches relied on computationally-expensive filter ensembles for part localization and required extensive supervision. This paper proposes two pose-normalized descriptors based on computationally-efficient deformable part models. The first leverages the semantics inherent in strongly-supervised DPM parts. The second exploits weak semantic annotations to learn cross-component correspondences, computing pose-normalized descriptors from the latent parts of a weakly-supervised DPM. These representations enable pooling across pose and viewpoint, in turn facilitating tasks such as fine-grained recognition and attribute prediction. Experiments conducted on the Caltech-UCSD Birds 200 dataset and Berkeley Human Attribute dataset demonstrate significant improvements of our approach over state-of-art algorithms.


title: Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone

abstract: We present the design of a competitive artificial intelligence for Scopone , a popular Italian card game. We compare rule-based players using the most established strategies (one for beginners and two for advanced players) against players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo Tree Search (ISMCTS) with different reward functions and simulation strategies. MCTS requires complete information about the game state and thus implements a cheating player, whereas ISMCTS can deal with incomplete information and thus implements a fair player. Our results show that, as expected, the cheating MCTS outperforms all the other strategies; ISMCTS is stronger than all the rule-based players implementing well-known and most advanced strategies and it also turns out to be a challenging opponent for human players.


title: Testosterone and dominance in men

abstract: In men, high levels of endogenous testosterone (T) seem to encourage behavior intended to dominate - to enhance one's status over - other people. Sometimes dominant behavior is aggressive, its apparent intent being to inflict harm on another person, but often dominance is expressed nonaggressively. Sometimes dominant behavior takes the form of antisocial behavior, including rebellion against authority and law breaking. Measurement of T at a single point in time, presumably indicative of a man's basal T level, predicts many of these dominant or antisocial behaviors. T not only affects behavior but also responds to it. The act of competing for dominant status affects male T levels in two ways. First, T rises in the face of a challenge, as if it were an anticipatory response to impending competition. Second, after the competition, T rises in winners and declines in losers. Thus, there is a reciprocity between T and dominance behavior, each affecting the other. We contrast a reciprocal model, in which T level is variable, acting as both a cause and effect of behavior, with a basal model, in which T level is assumed to be a persistent trait that influences behavior. An unusual data set on Air Force veterans, in which data were collected four times over a decade, enables us to compare the basal and reciprocal models as explanations for the relationship between T and divorce. We discuss sociological implications of these models.


title: Onset detection: A new approach to QBH system

abstract: Query by Humming (QBH) is an system to provide a user with the song(s) which the user hums to the system. Current QBH method requires the extraction of onset and pitch information in order to track similarity with various versions of different songs. However, we here focus on detecting precise onsets only and use them to build a QBH system which is better than existing methods in terms of speed and memory and empirically in terms of accuracy. We also provide statistical analogy for onset detection functions and provide a measure of error in our algorithm.


Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines


title: Clunealgia: CT-guided therapeutic posterior femoral cutaneous nerve block

abstract: article i nfo Clunealgia is caused by neuropathy of inferior cluneal branches of the posterior femoral cutaneous nerve resulting in pain in the inferior gluteal region. Image-guided anesthetic nerve injections are a viable and safe therapeutic option in sensory peripheral neuropathies that provides significant pain relief when conservative therapy fails and surgery is not desired or contemplated. The authors describe two cases of clunealgia, where computed-tomography-guided technique for nerve blocks of the posterior femoral cutaneous nerve and its branches was used as a cheaper, more convenient, and faster alternative with similar face validity as the previously described magnetic-resonance-guided injection.


title: Compact UWB Antenna Design for MIMO Applications

abstract: In this study, a compact printed Multiple-Input-Multiple-Output (MIMO) antenna with a dimension of 32×70 mm 2 has been proposed for Ultra-Wideband (UWB) systems applications. The design constitutes of two identical UWB antenna elements, which is etched onto a Taconic TLC-30 printed circuit board. The proposed antenna has been designed and simulated using computer simulation software. For validation purposes, antenna prototype is fabricated and tested. The UWB-MIMO antenna yields an impedance bandwidth of 2.9 to 12 GHz with a return loss of less than-10 dB. Furthermore, the isolation characteristic between the two antenna elements is more than 15 dB within the operating frequency range. The designed structure is found to provide good MIMO/diversity characteristic across the UWB band.


title: Guidelines for transepidermal water loss (TEWL) measurement. A report from the Standardization Group of the European Society of Contact Dermatitis.

abstract: This report reviews individual-related variables, environment-related variables and instrument-related variables, with a focus on the Evaporimeter EP1 (ServoMed). Start-up and use is described, and guidelines for good laboratory practice given.


title: A review on the cosine modulated filter bank studies using meta-heuristic optimization algorithms

abstract: There has been a significant increase in the number of designs based on optimization techniques as the usage areas of computers have increased day by day. In this paper, Cosine Modulated Filter Banks (CMFBs) using meta-heuristic optimization techniques are reviewed. The basic features of the meta-heuristic optimization algorithms which used in related studies and the purpose of these algorithms in CMFB design are explored. The paper begins with a definition of CMFBs and continues with meta-heuristic algorithms used in CMFB studies. Later, the meta-heuristic algorithms used to design the CMFBs are briefly described. Finally, it is reviewed where algorithms are used in the CMFB designs. This study aims to clarify the role of meta-heuristic algorithms in CMFB design. The main contribution of the study to the literature is not just describing meta-heuristic algorithms, the proposed methods in the literature to design the CMFB are fully described and compared.


title: Generalizing Sudoku to three dimensions

abstract: The well-known logic puzzle Sudoku can be generalized from two to three dimensions by designing a puzzle that is played on the faces of a cube. One variation, already introduced as a puzzle by Dion Church, uses three adjacent faces. Another variation uses all six faces. We have developed a set of rules and constraints for both three-dimensional Sudoku variations and have studied the properties using the method of simulated annealing.


An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset


title: Tuning out of phase: Resonance attacks

abstract: This work presents a new class of cyber-physical switching attacks of power transmission systems. The proposed resonance attack relies on enhancing inter-area oscillation modes to drive groups of system generators out of step. This switching attack targets inter-area oscillations by switching a relatively small part of the system load at a (low) frequency that resonates with one of the inter-area oscillation modes observed in the power system. The targeted frequency of the attack is chosen through measurement-based analysis of the frequency deviation at a targeted bus that is observable by the adversary. Numerical results show the effectiveness of the proposed switching attack when applied to a two-area system.


title: Texture enhancement for improving single-image super-resolution performance

abstract: Although various single-image super-resolution algorithms have been developed to increase image resolution, they still do not provide adequate performance in the texture region due to the lack of fine textures in the processed image. In this paper, we present a novel texture enhancement strategy in order to improve the super-resolution performance in the texture region. For texture enhancement, we extract a low-resolution texture layer from an input image and generate a high-resolution texture layer by applying the proposed texture synthesis algorithm. A texture enhanced high-resolution image is then obtained by properly combining the generated high-resolution texture layer with an image obtained by using an existing single-image super-resolution algorithm. Experimental results show that the proposed texture enhancement strategy provides sharper and more natural looking textures compared with the existing super-resolution algorithms. This paper addresses a key remaining issue, texture SR, which is yet unresolved.Patch-based texture synthesis is performed by using a LR texture layer.A HR texture patch is synthesized based on both AR and reconstruction models.The proposed algorithm provides realistic SR images with sharp and fine textures.


title: SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes

abstract: The objective of this paper is 3D shape understanding from single and multiple images. To this end, we introduce a new deep-learning architecture and loss function, SilNet, that can handle multiple views in an order-agnostic manner. The architecture is fully convolutional, and for training we use a proxy task of silhouette prediction, rather than directly learning a mapping from 2D images to 3D shape as has been the target in most recent work. ::: We demonstrate that with the SilNet architecture there is generalisation over the number of views -- for example, SilNet trained on 2 views can be used with 3 or 4 views at test-time; and performance improves with more views. ::: We introduce two new synthetics datasets: a blobby object dataset useful for pre-training, and a challenging and realistic sculpture dataset; and demonstrate on these datasets that SilNet has indeed learnt 3D shape. Finally, we show that SilNet exceeds the state of the art on the ShapeNet benchmark dataset, and use SilNet to generate novel views of the sculpture dataset.


title: A revolutionary crowd model: Implemented to contrast oscillating to consistent media influence on crowd behavior

abstract: Since Le Bon introduced his profound theory about the crowd in 1895, the phenomenon has been investigated across a range of scientific disciplines. Nevertheless, the mystery of the popular mind seems so far unrevealed, especially with the emergence of unfamiliar crowd movements, such as Twitter revolutions, which are triggered by novel types of media and interpersonal communication. Such original collective behavior, along with the current turbulent sociopolitical global environment, has necessitated the development of explanatory contemporary models. This research introduces a new revolutionary crowd model with a unique set of internal and external factors that can fit the modern uprisings in order to enable the understanding of the conditions that could lead to or prohibit the formation of revolutionary crowds. The model is implemented to examine the effectiveness of oscillating intensified mass media on the crowd pattern and dynamics. Among several emergent behaviors, the model shows an insignificant impact of disrupted intensified media on the crowd, in contrast with consistent low-intensity media, the failure of contagion theory in sustaining a revolution without a persistent stimulus, and the refutation of current claims of the insignificancy of leaders’ roles in igniting and maintaining modern crowds.


title: Hallucinogenic plants of the Shuar and related indigenous groups in Amazonian Ecuador and Peru

abstract: Banisteriopsis caapi, Brugmansia suaveolens, andNicotiana tabacum are the principal hallucinogens used by the Shuar and related ethnic groups in Amazonian Ecuador and Peru. These three species are common hallucinogens throughout northwestern Amazonia.Banisteriopsis caapi (natem) is the hallucinogen most frequently employed by the Shuar. The Shuar drink the juice ofN. tabacum duringnatem healing ceremonies. They also believe that smoke fromN. tabacum cigarettes repel evil spirits.Brugmansia suaveolens is the strongest Shuar hallucinogen. Considered very dangerous, it sometimes is added tonatem mixtures or it may be taken alone. Other plants used in hallucinogens or in narcotic beverages includeBrunfelsia grandiflora, Cyperus spp.,Diplopterys cabrerana, Heliconia stricta, Herrania spp., andIlex guayusa.


title: Very Fast Rise-Time Short-Pulse High-Voltage Generator

abstract: This paper relates to the development of an ultrafast compact system readily applicable to the field of ultrawideband microwave applications such as transient radar or laser drivers. The design, production, and experimental results of a short-pulse generation system are presented in this paper. The coaxial generator technology based on the principle of a line discharge by means of a high-pressure gas switch in a simple transmission line arrangement is described. In the first stage of the process, the coaxial generator was charged by a high-voltage direct current source. Interesting results were obtained, but due to the jitter of the discharge formative time, the reproducibility of the output pulses was not sufficient. To improve the performance, a pulsed source was developed in order to apply an overvoltage to the gap switch and thus reduce the jitter of the discharge formative time. The characteristics of the pulsed source are defined. A thyristor Marx generator and a pulse transformer using magnetic properties of ferrite cores were combined in order to produce this pulsed source. The main performances of this source are a pulse amplitude of 60 kV, a rise time of 250 ns, and a maximum frequency of 900 Hz from a 1-kV dc supply. Finally, the complete generation system is able to generate pulses with 25-kV amplitude and 70-ps rise time through a 50-Omega impedance from the single shot mode up to 900 Hz. The output pulses reproducibility obtained is better than plusmn5%


title: Sparse Reconstruction by Separable Approximation

abstract: Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing (CS) are a few well-known areas in which problems of this type appear. One standard approach is to minimize an objective function that includes a quadratic (pound 2) error term added to a sparsity-inducing (usually pound 1) regularizer. We present an algorithmic framework for the more general problem of minimizing the sum of a smooth convex function and a nonsmooth, possibly nonconvex, sparsity-inducing function. We propose iterative methods in which each step is an optimization subproblem involving a separable quadratic term (diagonal Hessian) plus the original sparsity-inducing term. Our approach is suitable for cases in which this subproblem can be solved much more rapidly than the original problem. In addition to solving the standard pound 2 - pound 1 case, our approach handles other problems, e.g., pound p regularizers with p ne 1, or group-separable (GS) regularizers. Experiments with CS problems show that our approach provides state-of-the-art speed for the standard pound 2 - pound 1 problem, and is also efficient on problems with GS regularizers.


title: Discovering emerging entities with ambiguous names

abstract: Knowledge bases (KB's) contain data about a large number of people, organizations, and other entities. However, this knowledge can never be complete due to the dynamics of the ever-changing world: new companies are formed every day, new songs are composed every minute and become of interest for addition to a KB. To keep up with the real world's entities, the KB maintenance process needs to continuously discover newly emerging entities in news and other Web streams. In this paper we focus on the most difficult case where the names of new entities are ambiguous. This raises the technical problem to decide whether an observed name refers to a known entity or represents a new entity. This paper presents a method to solve this problem with high accuracy. It is based on a new model of measuring the confidence of mapping an ambiguous mention to an existing entity, and a new model of representing a new entity with the same ambiguous name as a set of weighted keyphrases. The method can handle both Wikipedia-derived entities that typically constitute the bulk of large KB's as well as entities that exist only in other Web sources such as online communities about music or movies. Experiments show that our entity discovery method outperforms previous methods for coping with out-of-KB entities (called unlinkable in entity linking).


title: Schedulability analysis for tasks with static and dynamic offsets

abstract: In this paper we present an extension to current schedulability analysis techniques for periodic task with offsets, scheduled under a preemptive fixed priority scheduler. Previous techniques allowed only static offsets restricted to being smaller than the task periods. With the extension presented in this paper, we eliminate this restriction and we allow both static and dynamic offsets. The most significant application of this extension is in the analysis of multiprocessor and distributed systems. We show that we can achieve a significant increase of the maximum schedulable utilization by using the new technique, as opposed to using previously known worst-case analysis techniques for distributed systems.


title: Circularly Polarized Traveling-Wave Array Antenna With Novel Microstrip Patch Element

abstract: A traveling-wave microstrip array antenna with circular polarization in presented. It uses a novel circularly polarized dual-feed radiating element. The element is a square microstrip patch with a chamfered corner. It is fed by microstrip lines, so radiating element and feed lines can be realized in a single layer. A five-element array is designed and built. Measured performance is presented.


title: EFFECT OF CSR SHIELDING IN THE COMPACT LINEAR COLLIDER

abstract: The Drive Beam complex of the Compact Linear Collider must use short bunches with a large charge making beam transport susceptible to unwanted effects of Coherent Synchrotron Radiation emitted in the dipole magnets. We present the effects of transporting the beam within a limited aperture which decreases the magnitude of the CSR wake. The effect, known as CSR shielding, eases the design of key components of the facility.


title: Emotional intelligence and academic success: examining the transition from high school to university

abstract: The transition from high school to university was used as the context for examining the relationship between emotional intelligence and academic achievement. During the first month of classes 372 first-year full-time students at a small Ontario university completed the short form of the Emotional Quotient Inventory (EQ-i:Short). At the end of the academic year the EQ-i:Short data was matched with the student's academic record. Predicting academic success from emotional intelligence variables produced divergent results depending on how the former variable was operationalized. When EQ-i:Short variables were compared in groups who had achieved very different levels of academic success (highly successful students who achieved a first-year university GPA of 80% or better versus relatively unsuccessful students who received a first-year GPA of 59% or less) academic success was strongly associated with several dimensions of emotional intelligence. Results are discussed in the context of the importance of emotional and social competency during the transition from high school to university.


title: A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments

abstract: Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the ‘execution time’. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing ‘Best Resource Selection’ (BRS) algorithm. Our results show that PSO can achieve: a) as much as 3 times cost savings as compared to BRS, and b) good distribution of workload onto resources.


title: Risk of Terrorism, Trust in Government, and e-Government Services: An Exploratory Study of Citizens’ Intention to use e Government Services in a Turbulent Environment

abstract: For the last several years, US citizens have been experiencing severe threats of terrorism. While such threats can significantly impact important factors for effective government operations such as citizens’ information demand and attitude toward the government, little is known about their potential effect on citizens’ acceptance of e-Government services. In order to fill this gap, this study examines the effects of perceived risks of terrorism and trustworthiness of the government, on citizens’ e-Government usage intentions. The data was collected through two surveys with a one-year interval: during and after the war in Iraq. The analysis revealed that citizens’ e-Government usage intentions can be significantly influenced by perceived risks of terrorism and trustworthiness of the supreme government, as well as other factors suggested by previous research. These relationships appear to vary depending on the domain and the type of the particular e-Government service. Other findings and future research needs are also discussed. Lee and Rao ♦ Risk of Terrorism, Trust in Government, and e-Government Services /


title: BAYESIAN LEARNING FOR NEURAL NETWORKS

abstract: From the Publisher: ::: Artificial "neural networks" are now widely used as flexible models for regression classification applications, but questions remain regarding what these models mean, and how they can safely be used when training data is limited. Bayesian Learning for Neural Networks shows that Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional neural network learning methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. Use of these models in practice is made possible using Markov chain Monte Carlo techniques. Both the theoretical and computational aspects of this work are of wider statistical interest, as they contribute to a better understanding of how Bayesian methods can be applied to complex problems. Presupposing only the basic knowledge of probability and statistics, this book should be of interest to many researchers in statistics, engineering, and artificial intelligence. Software for Unix systems that implements the methods described is freely available over the Internet.


A simplified photometric analysis for serine and formaldehyde.


title: Diagnostic Evaluation of Low Back Pain with Emphasis on Imaging

abstract: PURPOSE ::: To review evidence on the diagnostic accuracy of clinical information and imaging for patients with low back pain in primary care settings. ::: ::: ::: DATA SOURCE ::: MEDLINE search (January 1966 to September 2001) for articles and reviews relevant to the accuracy of the clinical and radiographic examination of patients with low back pain. ::: ::: ::: STUDY SELECTION ::: The authors reviewed abstracts and selected articles for review on the basis of a combined judgment. Data on the clinical examination were based primarily on recent systematic reviews; data on imaging tests were based primarily on original articles. ::: ::: ::: DATA EXTRACTION ::: Diagnostic results were extracted by one or the other author. Quality of methods was evaluated informally. Major potential biases were identified, but neither quantitative data extraction nor scoring was done. ::: ::: ::: DATA SYNTHESIS ::: Formal meta-analysis was not used because the diagnostic hardware and software, gold standards, and patient selection methods were heterogeneous and the number of studies was small. Sensitivity for cancer was highest for magnetic resonance imaging (0.83 to 0.93) and radionuclide scanning (0.74 to 0.98); specificity was highest for magnetic resonance imaging (0.9 to 0.97) and radiography (0.95 to 0.99). Magnetic resonance imaging was the most sensitive (0.96) and specific (0.92) test for infection. The sensitivity and specificity of magnetic resonance imaging for herniated discs were slightly higher than those for computed tomography but very similar for the diagnosis of spinal stenosis. ::: ::: ::: CONCLUSIONS ::: The data suggest a diagnostic strategy similar to the 1994 Agency for Health Care Policy and Research guidelines. For adults younger than 50 years of age with no signs or symptoms of systemic disease, symptomatic therapy without imaging is appropriate. For patients 50 years of age and older or those whose findings suggest systemic disease, plain radiography and simple laboratory tests can almost completely rule out underlying systemic diseases. Advanced imaging should be reserved for patients who are considering surgery or those in whom systemic disease is strongly suspected.


title: Spatio-Temporal Topic Modeling in Mobile Social Media for Location Recommendation

abstract: Mobile networks enable users to post on social media services (e.g., Twitter) from anywhere and anytime. This new phenomenon led to the emergence of a new line of work of mining the behavior of mobile users taking into account the spatio-temporal aspects of their engagement with online social media. In this paper, we address the problem of recommending the right locations to users at the right time. We claim to propose the first comprehensive model, called STT (Spatio-Temporal Topic), to capture the spatio-temporal aspects of user check-ins in a single probabilistic model for location recommendation. Our proposed generative model does not only captures spatio-temporal aspects of check-ins, but also profiles users. We conduct experiments on real life data sets from Twitter, Go Walla, and Bright kite. We evaluate the effectiveness of STT by evaluating the accuracy of location recommendation. The experimental results show that STT achieves better performance than the state-of-the-art models in the areas of recommender systems as well as topic modeling.


title: CathNets: Detection and Single-View Depth Prediction of Catheter Electrodes

abstract: The recent success of convolutional neural networks in many computer vision tasks implies that their application could also be beneficial for vision tasks in cardiac electrophysiology procedures which are commonly carried out under guidance of C-arm fluoroscopy. Many efforts for catheter detection and reconstruction have been made, but especially robust detection of catheters in X-ray images in realtime is still not entirely solved. We propose two novel methods for (i) fully automatic electrophysiology catheter electrode detection in interventional X-ray images and (ii) single-view depth estimation of such electrodes based on convolutional neural networks. For (i), experiments on 24 different fluoroscopy sequences (1650 X-ray images) yielded a detection rate > 99 %. Our experiments on (ii) depth prediction using 20 images with depth information available revealed that we are able to estimate the depth of catheter tips in the lateral view with a remarkable mean error of \(6.08\,\pm \,4.66\) mm.


title: Designing for Uncertainty in HCI: When Does Uncertainty Help?

abstract: End-users are often exposed to uncertain data in interactive systems such as personal health apps, intelligent navigation systems, and systems driven by machine learning. On one hand, communicating uncertainty may improve the understanding of data and predictions. On the other hand, communicating uncertainty can greatly confuse users and decrease trust. While some specialized guidelines for dealing with uncertainty exist within particular fields such as information visualization or context-aware computing, HCI lacks general design guidelines around the more basic question of "will communicating uncertainty rather help or confuse my users'" The goal of this workshop is to bring together researchers and practitioners from across HCI and related fields to establish a better understanding of when and how to design for uncertainty. The outcome of the workshop will be a set of real-world application scenarios with descriptions of the impact of presenting uncertainty in that scenario. Additionally, we will create a set of design guidelines that supports designers and researchers in this emerging space in evaluating whether and how to present uncertainty.


48. Possessives and relational nouns


title: A proposal of EMG-based wheelchair for preventing disuse of lower motor function

abstract: Nowadays Japan has faced on a super aging society, there are lots of elderly who need to be cared. Supporting their daily life and reducing loads for nursing care are crucial social issue to be solved. In particular, mobility should be a significant factor for maintaining their health, since limitation of the mobiliy due to an aging or disorder causes degradation of QOL. In the paper, we propose an EMG-based wheelchair control system for preventing a disuse of lower extremities. Experimental results of consecutive five days training with the proposed system reveals that it is effective for enhancing and maintaining motor function of lower extremity.


title: A constraint-based framework to model harmony for algorithmic composition

abstract: "Copyright: c 2017 Torsten Anders. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited"


title: College Students Who Have an Eveningness Preference Report Lower Self‐Control and Greater Procrastination

abstract: Previous research suggests a possible link between eveningness and general difficulties with self‐regulation (e.g., evening types are more likely than other chronotypes to have irregular sleep schedules and social rhythms and use substances). Our study investigated the relationship between eveningness and self‐regulation by using two standardized measures of self‐regulation: the Self‐Control Scale and the Procrastination Scale. We predicted that an eveningness preference would be associated with poorer self‐control and greater procrastination than would an intermediate or morningness preference. Participants were 308 psychology students (mean age=19.92 yrs) at a small Canadian college. Students completed the self‐regulation questionnaires and Morningness/Eveningness Questionnaire (MEQ) online. The mean MEQ score was 46.69 (SD=8.20), which is intermediate between morningness and eveningness. MEQ scores ranged from definite morningness to definite eveningness, but the dispersion of scores was skewed toward ...


Business Process Languages: An Ontology-Based Perspective


title: Robot learning and use of affordances in goal-directed tasks

abstract: An affordance is a relation between an object, an action, and the effect of that action in a given environmental context. One key benefit of the concept of affordance is that it provides information about the consequence of an action which can be stored and reused in a range of tasks that a robot needs to learn and perform. In this paper, we address the challenge of the on-line learning and use of affordances simultaneously while performing goal-directed tasks. This requires efficient online performance to ensure the robot is able to achieve its goal fast. By providing conceptual knowledge of action possibilities and desired effects, we show that a humanoid robot NAO can learn and use affordances in two different task settings. We demonstrate the effectiveness of this approach by integrating affordances into an Extended Classifier System for learning general rules in a reinforcement learning framework. Our experimental results show significant speedups in learning how a robot solves a given task.


title: Learning in-contact control strategies from demonstration

abstract: Learning to perform tasks like pulling a door handle or pushing a button, inherently easy for a human, can be surprisingly difficult for a robot. A crucial problem in these kinds of in-contact tasks is the context specificity of pose and force requirements. In this paper, a robot learns in-contact tasks from human kinesthetic demonstrations. To address the need to balance between the position and force constraints, we propose a model based on the hidden semi-Markov model (HSMM) and Cartesian impedance control. The model captures uncertainty over time and space and allows the robot to smoothly satisfy a task's position and force constraints by online modulation of impedance controller stiffness according to the HSMM state belief. In experiments, a KUKA LWR 4+ robotic arm equipped with a force/torque sensor at the wrist successfully learns from human demonstrations how to pull a door handle and push a button.


title: A new theory of the origin of cancer: quantum coherent entanglement, centrioles, mitosis, and differentiation

abstract: Malignant cells are characterized by abnormal segregation of chromosomes during mitosis ("aneuploidy"), generally considered a result of malignancy originating in genetic mutations. However, recent evidence supports a century-old concept that maldistribution of chromosomes (and resultant genomic instability) due to abnormalities in mitosis itself is the primary cause of malignancy rather than a mere byproduct. In normal mitosis chromosomes replicate into sister chromatids which are then precisely separated and transported into mirror-like sets by structural protein assemblies called mitotic spindles and centrioles, both composed of microtubules. The elegant yet poorly understood ballet-like movements and geometric organization occurring in mitosis have suggested guidance by some type of organizing field, however neither electromagnetic nor chemical gradient fields have been demonstrated or shown to be sufficient. It is proposed here that normal mirror-like mitosis is organized by quantum coherence and quantum entanglement among microtubule-based centrioles and mitotic spindles which ensure precise, complementary duplication of daughter cell genomes and recognition of daughter cell boundaries. Evidence and theory supporting organized quantum states in cytoplasm/nucleoplasm (and quantum optical properties of centrioles in particular) at physiological temperature are presented. Impairment of quantum coherence and/or entanglement among microtubule-based mitotic spindles and centrioles can result in abnormal distribution of chromosomes, abnormal differentiation and uncontrolled growth, and account for all aspects of malignancy. New approaches to cancer therapy and stem cell production are suggested via non-thermal laser-mediated effects aimed at quantum optical states of centrioles.


title: A new social communication intervention for children with autism: pilot randomised controlled treatment study suggesting effectiveness

abstract: Background: Psychosocial treatments are the mainstay of management of autism in the UK but there is a notable lack of a systematic evidence base for their effectiveness. Randomised controlled trial (RCT) studies in this area have been rare but are essential because of the developmental heterogeneity of the disorder. We aimed to test a new theoretically based social communication intervention targeting parental communication in a randomised design against routine care alone. Methods: The intervention was given in addition to existing care and involved regular monthly therapist contact for 6 months with a further 6 months of 2-monthly consolidation sessions. It aimed to educate parents and train them in adapted communication tailored to their child’s individual competencies. Twenty-eight children with autism were randomised between this treatment and routine care alone, stratified for age and baseline severity. Outcome was measured at 12 months from commencement of intervention, using standardised instruments. Results: All cases studied met full Autism Diagnostic Interview (ADI) criteria for classical autism. Treatment and controls had similar routine care during the study period and there were no study dropouts after treatment had started. The active treatment group showed significant improvement compared with controls on the primary outcome measure – Autism Diagnostic Observation Schedule (ADOS) total score, particularly in reciprocal social interaction – and on secondary measures of expressive language, communicative initiation and parent–child interaction. Suggestive but non-significant results were found in Vineland Adaptive Behaviour Scales (Communication Subdomain) and ADOS stereotyped and restricted behaviour domain. Conclusions: A Randomised Treatment Trial design of this kind in classical autism is feasible and acceptable to patients. This pilot study suggests significant additional treatment benefits following a targeted (but relatively non-intensive) dyadic social communication treatment, when compared with routine care. The study needs replication on larger and independent samples. It should encourage further RCT designs in this area. Keywords: Autism, intervention, treatment, social communication, RCT. Abbreviations: ADOS: Autism Diagnostic Observation Schedule; ADI: Autism Diagnostic Interview; RCT: randomised controlled trial.


title: A Frequency-Domain Adaptive Filter (FDAF) Prediction Error Method (PEM) Framework for Double-Talk-Robust Acoustic Echo Cancellation

abstract: In this paper, we propose a new framework to tackle the double-talk (DT) problem in acoustic echo cancellation (AEC). It is based on a frequency-domain adaptive filter (FDAF) implementation of the so-called prediction error method adaptive filtering using row operations (PEM-AFROW) leading to the FDAF-PEM-AFROW algorithm. We show that FDAF-PEM-AFROW is by construction related to the best linear unbiased estimate (BLUE) of the echo path. We depart from this framework to show an improvement in performance with respect to other adaptive filters minimizing the BLUE criterion, namely the PEM-AFROW and the FDAF-NLMS with near-end signal normalization. One of the contributions is to propose the instantaneous pseudo-correlation (IPC) measure between the near-end signal and the loudspeaker signal. The IPC measure serves as an indication of the effect of a DT situation occurring during adaptation. We motivate the choice of FDAF-PEM-AFROW over PEM-AFROW and FDAF-NLMS with near-end signal normalization, based on performance, computational complexity and related IPC measure values. Moreover, we use the FDAF-PEM-AFROW framework to improve several state-of-the-art variable step-size (VSS) and variable regularization (VR) algorithms. The FDAF-PEM-AFROW versions significantly outperform the original versions in every simulation. In terms of computational complexity, the FDAF-PEM-AFROW versions are themselves about two orders of magnitude cheaper than the original versions.


Paired associate learning predicted from indices of free association strength 1


title: Face/Off: Preventing Privacy Leakage From Photos in Social Networks

abstract: The capabilities of modern devices, coupled with the almost ubiquitous availability of Internet connectivity, have resulted in photos being shared online at an unprecedented scale. This is further amplified by the popularity of social networks and the immediacy they offer in content sharing. Existing access control mechanisms are too coarse-grained to handle cases of conflicting interests between the users associated with a photo; stories of embarrassing or inappropriate photos being widely accessible have become quite common. In this paper, we propose to rethink access control when applied to photos, in a way that allows us to effectively prevent unwanted individuals from recognizing users in a photo. The core concept behind our approach is to change the granularity of access control from the level of the photo to that of a user's personally identifiable information (PII). In this work, we consider the face as the PII. When another user attempts to access a photo, the system determines which faces the user does not have the permission to view, and presents the photo with the restricted faces blurred out. Our system takes advantage of the existing face recognition functionality of social networks, and can interoperate with the current photo-level access control mechanisms. We implement a proof-of-concept application for Facebook, and demonstrate that the performance overhead of our approach is minimal. We also conduct a user study to evaluate the privacy offered by our approach, and find that it effectively prevents users from identifying their contacts in 87.35% of the restricted photos. Finally, our study reveals the misconceptions about the privacy offered by existing mechanisms, and demonstrates that users are positive towards the adoption of an intuitive, straightforward access control mechanism that allows them to manage the visibility of their face in published photos.


title: Substring matching for clone detection and change tracking

abstract: Legacy systems pose problems to maintainers that can be solved partially with effective tools. A prototype tool for determining collections of files sharing a large amount of text has been developed and applied to a 40 megabyte source tree containing two releases of the gcc compiler. Similarities in source code and documentation corresponding to software cloning, movement and inertia between releases, as well as the effects of preprocessing easily stand out in a way that immediately conveys nonobvious structural information to a maintainer taking responsibility for such a system. >


title: Update on primary cicatricial alopecias

abstract: The cicatricial alopecias encompass a diverse group of disorders characterized by permanent destruction of the hair follicle and irreversible hair loss. Destruction of the hair follicle can result from primary, folliculocentric disease or as a secondary result. This article focuses on the former, or primary cicatricial alopecias. The cause and pathogenesis of many of these disorders are largely unknown. Although unique clinicopathologic features allow for accurate diagnosis in some cases, diagnostic certainty is often elusive and reflects the limits of present understanding. Classification of the primary cicatricial alopecias on the basis of pathology provides a diagnostic and investigational framework and, it is hoped, will facilitate future enlightenment. Details of classification, etiopathogenesis, clinicopathologic features, differential diagnosis, and practical management of the primary cicatricial alopecias will be discussed. Learning objectives Upon completion of this learning activity, participants should be familiar with the following aspects of the primary cicatricial alopecias: (1) the new, consensus-issued classification scheme, (2) current understanding about etiopathogenesis, (3) salient clinicopathologic features, (4) differential diagnosis, and (5) therapeutic management.


title: Gender differences in scholastic achievement: A meta-analysis.

abstract: A female advantage in school marks is a common finding in education research, and it extends to most course subjects (e.g., language, math, science), unlike what is found on achievement tests. However, questions remain concerning the quantification of these gender differences and the identification of relevant moderator variables. The present meta-analysis answered these questions by examining studies that included an evaluation of gender differences in teacher-assigned school marks in elementary, junior/middle, or high school or at the university level (both undergraduate and graduate). The final analysis was based on 502 effect sizes drawn from 369 samples. A multilevel approach to meta-analysis was used to handle the presence of nonindependent effect sizes in the overall sample. This method was complemented with an examination of results in separate subject matters with a mixed-effects metaanalytic model. A small but significant female advantage (mean d 0.225, 95% CI [0.201, 0.249]) was demonstrated for the overall sample of effect sizes. Noteworthy findings were that the female advantage was largest for language courses (mean d 0.374, 95% CI [0.316, 0.432]) and smallest for math courses (mean d 0.069, 95% CI [0.014, 0.124]). Source of marks, nationality, racial composition of samples, and gender composition of samples were significant moderators of effect sizes. Finally, results showed that the magnitude of the female advantage was not affected by year of publication, thereby contradicting claims of a recent “boy crisis” in school achievement. The present meta-analysis demonstrated the presence of a stable female advantage in school marks while also identifying critical moderators. Implications for future educational and psychological research are discussed.


title: Alike people, alike interests? Inferring interest similarity in online social networks

abstract: Understanding how much two individuals are alike in their interests (i.e., interest similarity) has become virtually essential for many applications and services in Online Social Networks (OSNs). Since users do not always explicitly elaborate their interests in OSNs like Facebook, how to determine users' interest similarity without fully knowing their interests is a practical problem. In this paper, we investigate how users' interest similarity relates to various social features (e.g. geographic distance); and accordingly infer whether the interests of two users are alike or unalike where one of the users' interests are unknown. Relying on a large Facebook dataset, which contains 479,048 users and 5,263,351 user-generated interests, we present comprehensive empirical studies and verify the homophily of interest similarity across three interest domains (movies, music and TV shows). The homophily reveals that people tend to exhibit more similar tastes if they have similar demographic information (e.g., age, location), or if they are friends. It also shows that the individuals with a higher interest entropy usually share more interests with others. Based on these results, we provide a practical prediction model under a real OSN environment. For a given user with no interest information, this model can select some individuals who not only exhibit many interests but also probably achieve high interest similarities with the given user. Eventually, we illustrate a use case to demonstrate that the proposed prediction model could facilitate decision-making for OSN applications and services. Pose a practical research problem: how to infer the interest similarity of two users where we do not know one of the user's interests.Reveal the homophily of interest similarity with respect to various social features, relying on a large Facebook dataset (479,048 users and 5,263,351 user-generated interests).Devise an interest similarity prediction model based on the learned social features.A recommendation system for new users is illustrated to show the practicality of the proposed interest similarity prediction model.


A Survey of Mobile Augmented Reality Applications


title: Evolution and organizational information systems: an assessment of Nolan's stage model

abstract: Richard Nolan's stage model is the best known and most widely cited model of computing evolution in organizations. The model's development over a decade demonstrates its own evolution from a simple theory, based on the factoring of change states indicated by changes in computing budgets, to an elaborate account of the characteristics of six stages of computing growth. An analysis of the model's logical and empirical structure reveals a number of problems in its formulation that help to account for the fact that its principal tenets have not been independently validated. The model is shown to be an “evolutionistic” theory within the theories of evolution in the social sciences, focusing on assumed directions of growth and an implied end state toward which growth proceeds, and suffering from problems inherent in such theories. Further research based on an “evolutionary” view of computing growth is suggested as a means of improving theories of computing in organizations.


title: Energy-Based Models for Sparse Overcomplete Representations

abstract: We present a new way of extending independent components analysis (ICA) to overcomplete representations. In contrast to the causal generative extensions of ICA which maintain marginal independence of sources, we define features as deterministic (linear) functions of the inputs. This assumption results in marginal dependencies among the features, but conditional independence of the features given the inputs. By assigning energies to the features a probability distribution over the input states is defined through the Boltzmann distribution. Free parameters of this model are trained using the contrastive divergence objective (Hinton, 2002). When the number of features is equal to the number of input dimensions this energy-based model reduces to noiseless ICA and we show experimentally that the proposed learning algorithm is able to perform blind source separation on speech data. In additional experiments we train overcomplete energy-based models to extract features from various standard data-sets containing speech, natural images, hand-written digits and faces.


title: Force Sensor Integrated Surgical Forceps for Minimally Invasive Robotic Surgery

abstract: This paper presents a novel surgical instrument integrated with a four-degree-of-freedom (DOF) force sensor. By adopting the capacitive transduction principle, the sensor enables the direct sensing of normal and shear forces at surgical instrument tips. Thus, three-DOF pulling forces and a single-DOF grasping force can be measured for haptic feedback control of robotic minimally invasive surgery systems. The sensor consists of four capacitive transducers, and all the transducers including analog signal processing units are embedded in small surgical instrument tips. The four-DOF force sensing is enabled thanks to the four capacitive transducers by using the force transformation method. In this study, the instrument is designed and manufactured to be adaptable to the open-source surgical robot platform, called Raven-II. In addition, the sensing system is experimentally validated through its application to the Raven-II by using a reference force sensor.


title: Diversity-Multiplexing Tradeoff of Ostbc Over Correlated Nakagami-M Fading CHANNELS

abstract: This paper investigates the tradeoff between diversity and multiplexing of orthogonal space-time block coding (OSTBC) for correlated Nakagami-m fading. For a given transmission rate, the outage probability as function of the multiplexing gain and signal-to-noise ratio (SNR) is derived. Following the negative slope of the outage probability versus SNR curve, we obtain the diversity gain at finite SNR and its maximum attainable value to predict the system performance at operating SNR's. In addition, the asymptotic diversity gain at high SNR is given via two approaches, which provides a theoretical limit for practical SNR's. The impact of the complex component correlation among the channel links on the outage performance is also presented.


title: Image-Text Cross-Modal Retrieval via Modality-Specific Feature Learning

abstract: Cross-modal retrieval extends the ability of search engines to deal with the massive cross-modal data. The goal of image-text cross-modal retrieval is to search images (texts) by using text (image) queries by computing the similarities of images and texts directly. Many existing methods rely on low-level visual features and textual features for cross-modal retrieval, ignoring the characteristics existing in the raw data of different modalities. In this paper, a novel model based on modality-specific feature learning is proposed. Considering the characteristics of different modalities, the model uses two types of convolutional neural networks to map the raw data to the latent space representations for images and texts, respectively. Particularly, the convolution based network used for texts involves word embedding learning, which has been proved effective to extract meaningful textual features for text classification. In the latent space, the mapped features of images and texts form relevant and irrelevant image-text pairs, which are used by the one-vs-more learning scheme. This learning scheme can achieve ranking functionality by allowing for one relevant and more irrelevant pairs. The standard back-propagation technique is employed to update the parameters of two convolutional networks. Extensive cross-modal retrieval experiments are carried out on three challenging datasets that consist of image-document pairs or image-query click-through data from a search engine, and the results firmly demonstrate that the proposed model is much more effective.


title: Computer-mediated communication, de-individuation and group decision-making

abstract: Abstract This paper discusses social psychological processes in computer-mediated communication (CMC) and group decision-making, in relation to findings that groups communicating via computer produce more polarized decisions than face-to-face groups. A wide range of possible explanations for such differences have been advanced, in which a lack of social cues, disinhibition, “de-individuation” and a consequent tendency to antinormative behaviour are central themes. In these explanations, both disinhibition and greater equality of participation are thought to facilitate the exchange of extreme persuasive arguments, resulting in polarization. These accounts are briefly reviewed and attention is drawn to various problematic issues. We provide an alternative model and explanation based on social identity (SI) theory and a re-conceptualization of de-individuation, which takes into account the social and normative factors associated with group polarization. Predictions from both sets of explanations are explored empirically by means of an experiment manipulating the salience of the discussion group, and de-individuation operationalized as the isolation and anonymity of the participants. In this experiment we were able to partial out the effects of the CMC technology which have confounded comparisons with face-to-face interaction in previous research. The results challenge the explanations based on persuasive arguments, while being consistent with our SI model. We discuss our approach in relation to other very recent research in group computer-mediated communication and offer a reinterpretation of previous findings.


Vehicle Routing with Roaming Delivery Locations Damián


title: Cryptographic Public Verification of Data Integrity for Cloud Storage Systems

abstract: Cloud storage services allow users to outsource their data to cloud servers to save on local data storage costs. However, unlike using local storage devices, users don't physically own the data stored on cloud servers and can't be certain about the integrity of the cloud-stored data. Many public verification schemes have been proposed to allow a third-party auditor to verify the integrity of outsourced data. However, most of these schemes assume that the auditors are honest and reliable, so are vulnerable to malicious auditors. Moreover, in most of these schemes, an external adversary could modify the outsourced data and tamper with the interaction messages between the cloud server and the auditor, thus invalidating the outsourced data integrity verification. This article proposes an efficient and secure public verification of data integrity scheme that protects against external adversaries and malicious auditors. The proposed scheme adopts a random masking technique to protect against external adversaries, and requires users to audit auditors' behaviors to prevent malicious auditors from fabricating verification results. It uses Bitcoin to construct unbiased challenge messages to thwart collusion between malicious auditors and cloud servers. A performance analysis demonstrates that the proposed scheme is efficient in terms of the user's auditing overhead.


title: Defining Cyberbullying: A Qualitative Research into the Perceptions of Youngsters

abstract: ABSTRACT Data from 53 focus groups, which involved students from 10 to 18 years old, show that youngsters often interpret “cyberbullying” as “Internet bullying” and associate the phenomenon with a wide range of practices. In order to be considered “true” cyberbullying, these practices must meet several criteria. They should be intended to hurt (by the perpetrator) and perceived as hurtful (by the victim); be part of a repetitive pattern of negative offline or online actions; and be performed in a relationship characterized by a power imbalance (based on “real-life” power criteria, such as physical strength or age, and/or on ICT-related criteria such as technological know-how and anonymity).


title: Antioxidant defense responses: physiological plasticity in higher plants under abiotic constraints

abstract: Environmental stresses (salinity, drought, heat/cold, light and other hostile conditions) may trigger in plants oxidative stress, generating the formation of reactive oxygen species (ROS). These species are partially reduced or activated derivatives of oxygen, comprising both free radical \( ( {\text{O}}_{2}^{\cdot - } ,{\text{OH}} \cdot , {\text{OH}}_{ 2} \cdot ) \) and non-radical (H2O2) forms, leading to cellular damage, metabolic disorders and senescence processes. In order to overcome oxidative stress, plants have developed two main antioxidants defense mechanisms that can be classified as non-enzymatic and enzymatic systems. The first class (non-enzymatic) consists of small molecules such as vitamin (A, C and E), glutathione, carotenoids and phenolics that can react directly with the ROS by scavenging them. Second class is represented by enzymes among them superoxide dismutase, peroxidase and catalase which have the capacity to eliminate superoxide and hydrogen peroxide. In this review, we have tried to explore the related works, which have revealed the changes in the basic antioxidant metabolism of plants under various abiotic constraints.


title: Sequential Learning in Neural Networks: A Review and a Discussion of Pseudorehearsal Based Methods

abstract: In this review we explore the topic of sequential learning, where information to be learned and retained arrives in separate episodes over time, in the context of artificial neural networks. Most neural networks handle this kind of task very badly, as new learning completely disrupts information previously learned by the network. This problem, known as "catastrophic forgetting", has received a lot of attention in the literature. We illustrate the catastrophic forgetting effect, and summarise possible solutions. In particular, we review the literature relating to the pseudorehearsal mechanism, which is an effective solution to the catastrophic forgetting problem in back propagation type networks. We then review similar issues of capacity, forgetting, and the use of pseudorehearsal in Hopfield type networks. Finally, we briefly discuss these issues in the context of cognition, and summarise interesting topics for further research.


title: End-to-end Learning of Multi-sensor 3D Tracking by Detection

abstract: In this paper we propose a novel approach to tracking by detection that can exploit both cameras as well as LIDAR data to produce very accurate 3D trajectories. Towards this goal, we formulate the problem as a linear program that can be solved exactly, and learn convolutional networks for detection as well as matching in an end-to-end manner. We evaluate our model in the challenging KITTI dataset and show very competitive results.


title: A 16-Element W-Band Phased-Array Transceiver Chipset With Flip-Chip PCB Integrated Antennas for Multi-Gigabit Wireless Data Links

abstract: This paper describes the design and implementation of a W-band phased-array system with printed circuit board (PCB) integrated antennas in two polarizations capable of multi-gigabit spectrally efficient wireless communication. The chipset is manufactured in a 0.18- $\mu \text{m}$ SiGe BiCMOS technology with $f_{T}/f_{\mathrm {MAX}}$ of 240-/270-GHz and is flip-chipped onto a low-cost organic PCB with integrated antenna arrays. Each chip is equipped with 16-transmit/4-receive or 16-receive/4-transmit calibrated phase shifter elements and direct up- and downconverters plus a half-rate phase-locked loop. The different system tradeoffs required to establish a multi-Gb/s wireless link at millimeter waves are carefully studied. Built-in element failure detectors, power detectors, and digital interface enable factory calibration and self-test capability. Each transceiver chip operates from 1.5- and 2.5-V supplies and consumes 5.5 and 4.5 W in transmit and receive mode, respectively. The peak transmitter effective isotropic radiation power is 34 dBm in each polarization with a measured receiver noise figure of 6.5 dB at 94 GHz. At a distance of 1 m, a maximum wireless data rate of 30 Gb/s (per polarization) using 64-QAM can be achieved and at 20 m, 8 Gb/s (dual polarization) can be established using QPSK modulation.


title: Polyphonic Music Generation with Sequence Generative Adversarial Networks

abstract: We propose an application of sequence generative adversarial networks (SeqGAN), which are generative adversarial networks for discrete sequence generation, for creating polyphonic musical sequences. Instead of a monophonic melody generation suggested in the original work, we present an efficient representation of a polyphony MIDI file that simultaneously captures chords and melodies with dynamic timings. The proposed method condenses duration, octaves, and keys of both melodies and chords into a single word vector representation, and recurrent neural networks learn to predict distributions of sequences from the embedded musical word space. We experiment with the original method and the least squares method to the discriminator, which is known to stabilize the training of GANs. The network can create sequences that are musically coherent and shows an improved quantitative and qualitative measures. We also report that careful optimization of reinforcement learning signals of the model is crucial for general application of the model.


title: Cognitive Biases in Crowdsourcing

abstract: Crowdsourcing has become a popular paradigm in data curation, annotation and evaluation for many artificial intelligence and information retrieval applications. Considerable efforts have gone into devising effective quality control mechanisms that identify or discourage cheat submissions in an attempt to improve the quality of noisy crowd judgments. Besides purposeful cheating, there is another source of noise that is often alluded to but insufficiently studied: Cognitive biases. This paper investigates the prevalence and effect size of a range of common cognitive biases on a standard relevance judgment task. Our experiments are based on three sizable publicly available document collections and note significant detrimental effects on annotation quality, system ranking and the performance of derived rankers when task design does not account for such biases.


title: Towards a psychographic user model from mobile phone usage

abstract: Knowing the users' personality can be a strategic advantage for the design of adaptive and personalized user interfaces. In this paper, we present the results of a first trial conducted with the aim of inferring people's personality traits based on their mobile phone call behavior. Initial findings corroborate the efficacy of using call detail records (CDR) and Social Network Analysis (SNA) of the call graph to infer the Big Five personality factors. On-going work includes a large-scale study that shall refine the accuracy of the models with a reduced margin of error.


title: Controlling Electromagnetic Fields

abstract: Using the freedom of design that metamaterials provide, we show how electromagnetic fields can be redirected at will and propose a design strategy. The conserved fields—electric displacement field D , magnetic induction field B , and Poynting vector B —are all displaced in a consistent manner. A simple illustration is given of the cloaking of a proscribed volume of space to exclude completely all electromagnetic fields. Our work has relevance to exotic lens design and to the cloaking of objects from electromagnetic fields.


title: Urbanization and the Wealth of Nations

abstract: The proportion of a country's population living in urban areas is highly correlated with its level of income. Urban areas offer economies of scale and richer market structures, and there is strong evidence that workers in urban areas are individually more productive, and earn more, than rural workers. However, rapid urbanization is also associated with crowding, environmental degradation, and other impediments to productivity. Overall, we find no evidence that the level of urbanization affects the rate of economic growth. Our findings weaken the rationale for either encouraging or discouraging urbanization as part of a strategy for economic growth.