title: On Best-Possible Obfuscation

abstract: An obfuscator is a compiler that transforms any program (which we will view in this work as a boolean circuit) into an obfuscated program (also a circuit) that has the same input-output functionality as the original program, but is "unintelligible". Obfuscation has applications for cryptography and for software protection. Barak et al. (CRYPTO 2001, pp. 1---18, 2001) initiated a theoretical study of obfuscation, which focused on black-box obfuscation, where the obfuscated circuit should leak no information except for its (black-box) input-output functionality. A family of functionalities that cannot be obfuscated was demonstrated. Subsequent research has showed further negative results as well as positive results for obfuscating very specific families of circuits, all with respect to black box obfuscation. This work is a study of a new notion of obfuscation, which we call best-possible obfuscation. Best possible obfuscation makes the relaxed requirement that the obfuscated program leaks as little information as any other program with the same functionality (and of similar size). In particular, this definition allows the program to leak information that cannot be obtained from a black box. Best-possible obfuscation guarantees that any information that is not hidden by the obfuscated program is also not hidden by any other similar-size program computing the same functionality, and thus the obfuscation is (literally) the best possible. In this work we study best-possible obfuscation and its relationship to previously studied definitions. Our main results are: (1) A separation between black-box and best-possible obfuscation. We show a natural obfuscation task that can be achieved under the best-possible definition, but cannot be achieved under the black-box definition. (2) A hardness result for best-possible obfuscation, showing that strong (information-theoretic) best-possible obfuscation implies a collapse in the Polynomial-Time Hierarchy. (3) An impossibility result for efficient best-possible (and black-box) obfuscation in the presence of random oracles. This impossibility result uses a random oracle to construct hard-to-obfuscate circuits, and thus it does not imply impossibility in the standard model.


title: Merging Occupancy Grid Maps From Multiple Robots

abstract: Mapping can potentially be speeded up in a significant way by using multiple robots exploring different parts of the environment. But the core question of multirobot mapping is how to integrate the data of the different robots into a single global map. A significant amount of research exists in the area of multirobot mapping that deals with techniques to estimate the relative robots poses at the start or during the mapping process. With map merging, the robots in contrast individually build local maps without any knowledge about their relative positions. The goal is then to identify regions of overlap at which the local maps can be joined together. A concrete approach to this idea is presented in form of a special similarity metric and a stochastic search algorithm. Given two maps m and m', the search algorithm transforms m' by rotations and translations to find a maximum overlap between m and m'. In doing so, the heuristic similarity metric guides the search algorithm toward optimal solutions. Results from experiments with up to six robots are presented based on simulated as well as real-world map data


title: Enterprise Microblogging: Procrastination or productive use?

abstract: There are growing fears amongst corporate managers that the introduction of Social Media and related technologies such as microblogging platforms will lead to large-scale time-wasting with negative impacts on corporate productivity. Our research analyses the communication practices of a corporate team that has embraced an Enterprise Microblogging platform and embedded its use into their day-to-day activities. We conduct a genre analysis of the team’s microblogs and compare our results with a recent study of public microblogging on the Twitter platform. By analyzing genre repertoires we have shown that enterprise microblogging can be significantly different to the public equivalent, and that in the corporate context, microblogging platforms can be used in productive ways.


title: GEFPSO: A Framework for PSO Optimization based on Grammatical Evolution

abstract: In this work, we propose a framework to automatically generate effective PSO designs by adopting Grammatical Evolution (GE). In the proposed framework, GE searches for adequate structures and parameter values (e.g., acceleration constants, velocity equations and different particles' topology) in order to evolve the PSO design. For this, a high-level Backus--Naur Form (BNF) grammar was developed, representing the search space of possible PSO designs. In order to verify the performance of the proposed method, we performed experiments using 16 diverse continuous optimization problems, with different levels of difficulty. In the performed experiments, we identified the parameters and components that most affected the PSO performance, as well as identified designs that could be reused across different problems. We also demonstrated that the proposed method generates useful designs which achieved competitive solutions when compared to well succeeded algorithms from the literature.


title: Scientific workflows for process mining: building blocks, scenarios, and implementation

abstract: Over the past decade process mining has emerged as a new analytical discipline able to answer a variety of questions based on event data. Event logs have a very particular structure; events have timestamps, refer to activities and resources, and need to be correlated to form process instances. Process mining results tend to be very different from classical data mining results, e.g., process discovery may yield end-to-end process models capturing different perspectives rather than decision trees or frequent patterns. A process-mining tool like ProM provides hundreds of different process mining techniques ranging from discovery and conformance checking to filtering and prediction. Typically, a combination of techniques is needed and, for every step, there are different techniques that may be very sensitive to parameter settings. Moreover, event logs may be huge and may need to be decomposed and distributed for analysis. These aspects make it very cumbersome to analyze event logs manually. Process mining should be repeatable and automated. Therefore, we propose a framework to support the analysis of process mining workflows. Existing scientific workflow systems and data mining tools are not tailored towards process mining and the artifacts used for analysis (process models and event logs). This paper structures the basic building blocks needed for process mining and describes various analysis scenarios. Based on these requirements we implemented RapidProM, a tool supporting scientific workflows for process mining. Examples illustrating the different scenarios are provided to show the feasibility of the approach.


The Paradoxical Brain: Paradoxical functional facilitation with noninvasive brain stimulation


title: Modulated Policy Hierarchies

abstract: Solving tasks with sparse rewards is a main challenge in reinforcement learning. While hierarchical controllers are an intuitive approach to this problem, current methods often require manual reward shaping, alternating training phases, or manually defined sub tasks. We introduce modulated policy hierarchies (MPH), that can learn end-to-end to solve tasks from sparse rewards. To achieve this, we study different modulation signals and exploration for hierarchical controllers. Specifically, we find that communicating via bit-vectors is more efficient than selecting one out of multiple skills, as it enables mixing between them. To facilitate exploration, MPH uses its different time scales for temporally extended intrinsic motivation at each level of the hierarchy. We evaluate MPH on the robotics tasks of pushing and sparse block stacking, where it outperforms recent baselines.


title: Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations

abstract: Multi-agent reinforcement learning systems aim to provide interacting agents with the ability to collaboratively learn and adapt to the behaviour of other agents. In many real-world applications, the agents can only acquire a partial view of the world. Here we consider a setting whereby most agents' observations are also extremely noisy, hence only weakly correlated to the true state of the environment. Under these circumstances, learning an optimal policy becomes particularly challenging, even in the unrealistic case that an agent's policy can be made conditional upon all other agents' observations. To overcome these difficulties, we propose a multi-agent deep deterministic policy gradient algorithm enhanced by a communication medium (MADDPG-M), which implements a two-level, concurrent learning mechanism. An agent's policy depends on its own private observations as well as those explicitly shared by others through a communication medium. At any given point in time, an agent must decide whether its private observations are sufficiently informative to be shared with others. However, our environments provide no explicit feedback informing an agent whether a communication action is beneficial, rather the communication policies must also be learned through experience concurrently to the main policies. Our experimental results demonstrate that the algorithm performs well in six highly non-stationary environments of progressively higher complexity, and offers substantial performance gains compared to the baselines.


title: Multimodal Explanations by Predicting Counterfactuality in Videos

abstract: This study addresses generating counterfactual explanations with multimodal information. Our goal is not only to classify a video into a specific category, but also to provide explanations on why it is not categorized to a specific class with combinations of visual-linguistic information. Requirements that the expected output should satisfy are referred to as counterfactuality in this paper: (1) Compatibility of visual-linguistic explanations, and (2) Positiveness/negativeness for the specific positive/negative class. Exploiting a spatio-temporal region (tube) and an attribute as visual and linguistic explanations respectively, the explanation model is trained to predict the counterfactuality for possible combinations of multimodal information in a post-hoc manner. The optimization problem, which appears during training/inference, can be efficiently solved by inserting a novel neural network layer, namely the maximum subpath layer. We demonstrated the effectiveness of this method by comparison with a baseline of the action recognition datasets extended for this task. Moreover, we provide information-theoretical insight into the proposed method.


title: BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN

abstract: Human motion prediction model has applications in various fields of computer vision. Without taking into account the inherent stochasticity in the prediction of future pose dynamics, such methods often converges to a deterministic undesired mean of multiple probable outcomes. Devoid of this, we propose a novel probabilistic generative approach called Bidirectional Human motion prediction GAN, or BiHMP-GAN. To be able to generate multiple probable human-pose sequences, conditioned on a given starting sequence, we introduce a random extrinsic factor r, drawn from a predefined prior distribution. Furthermore, to enforce a direct content loss on the predicted motion sequence and also to avoid mode-collapse, a novel bidirectional framework is incorporated by modifying the usual discriminator architecture. The discriminator is trained also to regress this extrinsic factor r, which is used alongside with the intrinsic factor (encoded starting pose sequence) to generate a particular pose sequence. To further regularize the training, we introduce a novel recursive prediction strategy. In spite of being in a probabilistic framework, the enhanced discriminator architecture allows predictions of an intermediate part of pose sequence to be used as a conditioning for prediction of the latter part of the sequence. The bidirectional setup also provides a new direction to evaluate the prediction quality against a given test sequence. For a fair assessment of BiHMP-GAN, we report performance of the generated motion sequence using (i) a critic model trained to discriminate between real and fake motion sequence, and (ii) an action classifier trained on real human motion dynamics. Outcomes of both qualitative and quantitative evaluations, on the probabilistic generations of the model, demonstrate the superiority of BiHMP-GAN over previously available methods.


title: Review of Active and Reactive Power Sharing Strategies in Hierarchical Controlled Microgrids

abstract: Microgrids consist of multiple parallel-connected distributed generation (DG) units with coordinated control strategies, which are able to operate in both grid-connected and islanded modes. Microgrids are attracting considerable attention since they can alleviate the stress of main transmission systems, reduce feeder losses, and improve system power quality. When the islanded microgrids are concerned, it is important to maintain system stability and achieve load power sharing among the multiple parallel-connected DG units. However, the poor active and reactive power sharing problems due to the influence of impedance mismatch of the DG feeders and the different ratings of the DG units are inevitable when the conventional droop control scheme is adopted. Therefore, the adaptive/improved droop control, network-based control methods, and cost-based droop schemes are compared and summarized in this paper for active power sharing. Moreover, nonlinear and unbalanced loads could further affect the reactive power sharing when regulating the active power, and it is difficult to share the reactive power accurately only by using the enhanced virtual impedance method. Therefore, the hierarchical control strategies are utilized as supplements of the conventional droop controls and virtual impedance methods. The improved hierarchical control approaches such as the algorithms based on graph theory, multi-agent system, the gain scheduling method, and predictive control have been proposed to achieve proper reactive power sharing for islanded microgrids and eliminate the effect of the communication delays on hierarchical control. Finally, the future research trends on islanded microgrids are also discussed in this paper.


title: Fox in the trap: thwarting masqueraders via automated decoy document deployment

abstract: Organizations face a persistent challenge detecting malicious insiders as well as outside attackers who compromise legitimate credentials and then masquerade as insiders. No matter how good an organization's perimeter defenses are, eventually they will be compromised or betrayed from the inside. Monitored decoy documents (honey files with enticing names and content) are a promising approach to aid in the detection of malicious masqueraders and insiders. In this paper, we present a new technique for decoy document distribution that can be used to improve the scalability of insider detection. We develop a placement application that automates the deployment of decoy documents and we report on two user studies to evaluate its effectiveness. The first study indicates that our automated decoy distribution tool is capable of strategically placing decoy files in a way that offers comparable security to optimal manual deployment. In the second user study, we measure the frequency that normal users access decoy documents on their own systems and show that decoy files do not significantly interfere with normal user tasks.


title: Mining geographic-temporal-semantic patterns in trajectories for location prediction

abstract: In recent years, research on location predictions by mining trajectories of users has attracted a lot of attention. Existing studies on this topic mostly treat such predictions as just a type of location recommendation, that is, they predict the next location of a user using location recommenders. However, an user usually visits somewhere for reasons other than interestingness. In this article, we propose a novel mining-based location prediction approach called Geographic-Temporal-Semantic-based Location Prediction (GTS-LP), which takes into account a user's geographic-triggered intentions, temporal-triggered intentions, and semantic-triggered intentions, to estimate the probability of the user in visiting a location. The core idea underlying our proposal is the discovery of trajectory patterns of users, namely GTS patterns, to capture frequent movements triggered by the three kinds of intentions. To achieve this goal, we define a new trajectory pattern to capture the key properties of the behaviors that are motivated by the three kinds of intentions from trajectories of users. In our GTS-LP approach, we propose a series of novel matching strategies to calculate the similarity between the current movement of a user and discovered GTS patterns based on various moving intentions. On the basis of similitude, we make an online prediction as to the location the user intends to visit. To the best of our knowledge, this is the first work on location prediction based on trajectory pattern mining that explores the geographic, temporal, and semantic properties simultaneously. By means of a comprehensive evaluation using various real trajectory datasets, we show that our proposed GTS-LP approach delivers excellent performance and significantly outperforms existing state-of-the-art location prediction methods.


title: On the Integration of Optical Flow and Action Recognition

abstract: Most of the top performing action recognition methods use optical flow as a “black box” input. Here we take a deeper look at the combination of flow and action recognition, and investigate why optical flow is helpful, what makes a flow method good for action recognition, and how we can make it better. In particular, we investigate the impact of different flow algorithms and input transformations to better understand how these affect a state-of-the-art action recognition method. Furthermore, we fine tune two neural-network flow methods end-to-end on the most widely used action recognition dataset (UCF101). Based on these experiments, we make the following five observations: (1) optical flow is useful for action recognition because it is invariant to appearance, (2) optical flow methods are optimized to minimize end-point-error (EPE), but the EPE of current methods is not well correlated with action recognition performance, (3) for the flow methods tested, accuracy at boundaries and at small displacements is most correlated with action recognition performance, (4) training optical flow to minimize classification error instead of minimizing EPE improves recognition performance, and (5) optical flow learned for the task of action recognition differs from traditional optical flow especially inside the human body and at the boundary of the body. These observations may encourage optical flow researchers to look beyond EPE as a goal and guide action recognition researchers to seek better motion cues, leading to a tighter integration of the optical flow and action recognition communities.


Amphibians of Argentina


title: The Placenta Harbors a Unique Microbiome

abstract: Humans and their microbiomes have coevolved as a physiologic community composed of distinct body site niches with metabolic and antigenic diversity. The placental microbiome has not been robustly interrogated, despite recent demonstrations of intracellular bacteria with diverse metabolic and immune regulatory functions. A population-based cohort of placental specimens collected under sterile conditions from 320 subjects with extensive clinical data was established for comparative 16S ribosomal DNA-based and whole-genome shotgun (WGS) metagenomic studies. Identified taxa and their gene carriage patterns were compared to other human body site niches, including the oral, skin, airway (nasal), vaginal, and gut microbiomes from nonpregnant controls. We characterized a unique placental microbiome niche, composed of nonpathogenic commensal microbiota from the Firmicutes, Tenericutes, Proteobacteria, Bacteroidetes, and Fusobacteria phyla. In aggregate, the placental microbiome profiles were most akin (Bray-Curtis dissimilarity <0.3) to the human oral microbiome. 16S-based operational taxonomic unit analyses revealed associations of the placental microbiome with a remote history of antenatal infection (permutational multivariate analysis of variance, P = 0.006), such as urinary tract infection in the first trimester, as well as with preterm birth <37 weeks (P = 0.001).


title: Hotspot Patterns: The Formal Definition and Automatic Detection of Architecture Smells

abstract: In this paper, we propose and empirically validate a suite of hotspot patterns: recurring architecture problems that occur in most complex systems and incur high maintenance costs. In particular, we introduce two novel hotspot patterns, Unstable Interface and Implicit Cross-module Dependency. These patterns are defined based on Baldwin and Clark's design rule theory, and detected by the combination of history and architecture information. Through our tool-supported evaluations, we show that these patterns not only identify the most error-prone and change-prone files, they also pinpoint specific architecture problems that may be the root causes of bug-proneness and change-proneness. Significantly, we show that 1) these structure-history integrated patterns contribute more to error- and change-proneness than other hotspot patterns, and 2) the more hotspot patterns a file is involved in, the more error- and change-prone it is. Finally, we report on an industrial case study to demonstrate the practicality of these hotspot patterns. The architect and developers confirmed that our hotspot detector discovered the majority of the architecture problems causing maintenance pain, and they have started to improve the system's maintainability by refactoring and fixing the identified architecture issues.


title: Attacks and Countermeasures in Fingerprint Based Biometric Cryptosystems

abstract: We investigate implementations of biometric cryptosystems protecting fingerprint templates (which are mostly based on the fuzzy vault scheme by Juels and Sudan in 2002) with respect to the security they provide. We show that attacks taking advantage of the system's false acceptance rate, i.e. false-accept attacks, pose a very serious risk --- even if brute-force attacks are impractical to perform. Our observations lead to the clear conclusion that currently a single fingerprint is not sufficient to provide a secure biometric cryptosystem. But there remain other problems that can not be resolved by merely switching to multi-finger: Kholmatov and Yanikoglu in 2007 demonstrated that it is possible to break two matching vault records at quite a high rate via the correlation attack. ::: We propose an implementation of a minutiae fuzzy vault that is inherently resistant against cross-matching and the correlation attack. Surprisingly, achieving cross-matching resistance is not at the cost of authentication performance. In particular, we propose to use a randomized decoding procedure and find that it is possible to achieve a GAR=91% at which no false accepts are observed on a database generally used. Our ideas can be adopted into an implementation of a multibiometric cryptosystem. All experiments described in this paper can fully be reproduced using software available for download.


title: ANN and PSO based intelligent model predictive optimal control for large-scale supercritical power unit

abstract: To improve the unit load following capacity of a large-capacity coal-fired supercritical (SC) power unit under automatic generation control (AGC), an intelligent model predictive optimal control (MPOC) scheme is proposed. It employs a nonlinear autoregressive moving average (NARMA) neural network model as the prediction model of the energy conversion unit and a more efficient simplified particle swarm optimization (PSO) algorithm as the optimal solution search approach. The effectiveness of the proposed MPOC scheme is evaluated by extensive control simulation tests on a commercial-grade simulator of a 600MW SC power unit. It is shown that the proposed approach can greatly improve the load following speed of the supercritical energy conversion unit, and at the same time keeps main steam pressure within safety limits.


title: Indexing Cognitive Load using Blood Volume Pulse Features

abstract: Physiological responses contain rich affective information even when humans are not expressing any external signs. In this paper, we investigate the use of the Blood Volume Pulse (BVP) signals for indexing cognitive load. An experiment, which introduced cognitive load as a secondary task in a decision making context was conducted in the study. BVP signals were analyzed in order to establish relationships between BVP and cognitive load levels. A set of features (e.g. peak and max features) was found to be significantly distinctive across different cognitive load levels. The identified BVP features can be used to set up machine learning models for the automatic classification of CL levels in intelligent systems.


title: Enacted Software Development Routines Based on Waterfall and Agile Software Methods: Socio-Technical Event Sequence Study

abstract: In recent decades, "agile" software development methodologies have been put forth as an alternative to traditional "waterfall" methodologies. These agile methods advance a fundamentally different approach to software development. Empirical evidence indicates differences between the two with respect to outcomes and development experience. Yet little is known to what extent the actual development practices based on either agile or traditional life cycle methodologies differ. In the current study we examine the variation in performative routines during software development by contrasting agile and traditional lifecycle process models using event sequencing method for detecting activity variations among recorded performative processes in the selected projects. Our analysis shows that performative enactment of waterfall and agile ostensive routines do differ in terms of activity types carried out in the early requirements steps. However, performative routines did show conformance to ostensive specifications in iterations, affordance types, and design objects used.


title: Harmony-based feature selection to improve the nearest neighbor classification

abstract: A new approach for feature selection is presented in this paper. The proposed approach uses the Harmony Search with a novel fitness function to eliminate noisy and irrelevant features. Harmony vectors contain real weights which refer to feature space. The best and significant features are selected according to a threshold. Fitness function of Harmony Search is based on the Area Under the receiver operating characteristics Curve (AUC). All of the selected features are employed to improve the classification of the k Nearest Neighbor (k-NN) classifier. Experimental results claim that the proposed method is able to improve the classification performance of k-NN algorithm in comparison with the other important methods in realm of feature selection such as BAHSIC, FSS, BSS and MFS.


title: Feelings of Alienation and Community among Higher Education Students in a Virtual Classroom.

abstract: Although the professional literature identifies feelings of alienation and low sense of community as factors that help explain relatively low student persistence rates in distance education programs, no studies have attempted to investigate the relationship between these two constructs. Accordingly, the present study uses canonical correlation analysis to determine if and how a set of three alienation variables is related to a set of two classroom community variables in a sample (N=117) of online graduate students. The results suggest that the two sets of variables are related along two reliable multivariate dimensions. Implications for college teaching are discussed.


title: A kernel method for multi-labelled classification

abstract: This article presents a Support Vector Machine (SVM) like learning system to handle multi-label problems. Such problems are usually decomposed into many two-class problems but the expressive power of such a system can be weak [5, 7]. We explore a new direct approach. It is based on a large margin ranking system that shares a lot of common properties with SVMs. We tested it on a Yeast gene functional classification problem with positive results.


Behavioral Game Theory on Online Social Networks: Colonel Blotto is on Facebook


title: Places205-VGGNet Models for Scene Recognition

abstract: VGGNets have turned out to be effective for object recognition in still images. However, it is unable to yield good performance by directly adapting the VGGNet models trained on the ImageNet dataset for scene recognition. This report describes our implementation of training the VGGNets on the large-scale Places205 dataset. Specifically, we train three VGGNet models, namely VGGNet-11, VGGNet-13, and VGGNet-16, by using a Multi-GPU extension of Caffe toolbox with high computational efficiency. We verify the performance of trained Places205-VGGNet models on three datasets: MIT67, SUN397, and Places205. Our trained models achieve the state-of-the-art performance o these datasets and are made public available 1 .


title: Leave a Reply: An Analysis of Weblog Comments

abstract: Access to weblogs, both through commercial services and in academic studies, is usually limited to the content of the weblog posts. This overlooks an important aspect distinguishing weblogs from other web pages: the ability of weblog readers to respond to posts directly, by posting comments. In this paper we present a large-scale study of weblog comments and their relation to the posts. Using a sizable corpus of comments, we estimate the overall volume of comments in the blogosphere; analyze the relation between the weblog popularity and commenting patterns in it; and measure the contribution of comment content to various aspects of weblog access.


title: Nonexercise activity thermogenesis (NEAT): environment and biology

abstract: Nonexercise activity thermogenesis (NEAT) is the energy expended for everything that is not sleeping, eating, or sports-like exercise. It includes the energy expended walking to work, typing, performing yard work, undertaking agricultural tasks, and fidgeting. NEAT can be measured by one of two approaches. The first is to measure or estimate total NEAT. Here, total daily energy expenditure is measured, and from it "basal metabolic rate-plus-thermic effect of food" is subtracted. The second is the factoral approach, whereby the components of NEAT are quantified, and total NEAT is calculated by summing these components. The amount of NEAT that humans perform represents the product of the amount and types of physical activities and the thermogenic cost of each activity. The factors that impact a human's NEAT are readily divisible into environmental factors, such as occupation or dwelling within a "concrete jungle," and biological factors such as weight, gender, and body composition. The combined impact of these factors explains the substantial variance in human NEAT. The variability in NEAT might be viewed as random, but human and animal data contradict this. It appears that changes in NEAT subtly accompany experimentally induced changes in energy balance and are important in the physiology of weight change. Inadequate modulation of NEAT plus a sedentary lifestyle may thus be important in obesity. It then becomes intriguing to dissect mechanistic studies that delineate how NEAT is regulated into neural, peripheral, and humoral factors. A scheme is described in this review in which NEAT corresponds to a carefully regulated "tank" of physical activity that is crucial for weight control.


title: Billund builds music: An engineering education initiative in Danish Kindergartens

abstract: In a recent educational initiative in Billund, Denmark, almost every student between the ages of three to eighteen years participated in Billund Builds Music, a one-week municipality-wide project to explore music and engineering by designing and creating musical instruments. This endeavor was a collaboration between the Tufts University Center for Engineering Education and Outreach (CEEO), the LEGO Foundation, and Billund's Capital of Children, and LEGO Foundation partner which represents children's voices in civic projects. Approximately 4,000 students and 300 educators from 10 schools across Billund took part in the project. This paper presents preliminary trends and findings from two participating Kindergartens. Results show that in this open-ended Danish Kindergarten setting, 3–5 year-old children were able to build musical instruments and play at least one unique sound on them.


title: Power Operation in ISD: Technological Frames Perspectives.

abstract: This paper investigates the power operation in information system development (ISD) processes. Due to the fact that key actors in different departments possess different professional knowledge, their different contexts lead to some employees supporting IS, while others resist it to achieve their goals. We aim to interpret these power operations in ISD from the theory of technological frames. This study is based on qualitative data collected from KaoKang (pseudonym), a port authority in Taiwan. We attempt to understand the situations of different key actors (e.g. top manager, MIS professionals, employees of DP-1 division, consultants of KaoKang, and customers (outside users)) who wield power in ISD in different situations. In this respect, we interpret the data using a technological frame. Finally, we aim to gain fresh insight into power operation in ISD from this perspective.


title: An HMM-based approach for off-line unconstrained handwritten word modeling and recognition

abstract: Describes a hidden Markov model-based approach designed to recognize off-line unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consisting of an alternating sequence of shape-symbols and segmentation-symbols, which are both explicitly modeled. The word model is made up of the concatenation of appropriate letter models consisting of elementary HMMs and an HMM-based interpolation technique is used to optimally combine the two feature sets. Two rejection mechanisms are considered depending on whether or not the word image is guaranteed to belong to the lexicon. Experiments carried out on real-life data show that the proposed approach can be successfully used for handwritten word recognition.


title: Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene

abstract: Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.


title: Modeling Expertise in Assistive Navigation Interfaces for Blind People

abstract: Evaluating the impact of expertise and route knowledge on task performance can guide the design of intelligent and adaptive navigation interfaces. Expertise has been relatively unexplored in the context of assistive indoor navigation interfaces for blind people. To quantify the complex relationship between the user»s walking patterns, route learning, and adaptation to the interface, we conducted a study with 8 blind participants. The participants repeated a set of navigation tasks while using a smartphone-based turn-by-turn navigation guidance app. The results demonstrate the gradual evolution of user skill and knowledge throughout the route repetitions, significantly impacting the task completion time. In addition to the exploratory analysis, we take a step towards tailoring the navigation interface to the user»s needs by proposing a personalized recurrent neural network-based behavior model for expertise level classification.


title: Corporate social responsibility influences, employee commitment and organizational performance

abstract: Research on corporate social responsibility (CSR) majorly describes its effects on corporate financial performance or consumers’ behavior towards CSR. Relatively few studies have focused employee’s sentiments towards CSR. This study analyzes the multifaceted influence of CSR on employee’s organizational commitment and organizational performance. The study uses exploratory approach; primary data is collected from 371 professionals working in different sectors of Pakistan. The study uses structural equation model (SEM) technique to test the hypotheses. The study found significantly positive relationship between CSR actions and employee organizational commitment, CSR and organizational performance and employee organizational commitment and organizational performance. The study discusses important implications regarding uses of CSR for enhancing employee’s organizational commitment and improving organizational performance. ::: ::: ::: ::: Key words: Corporate social responsibility, employee job satisfaction, organizational commitment, Pakistan.


title: On Optimal Multiple Changepoint Algorithms for Large Data

abstract: There is an increasing need for algorithms that can accurately detect changepoints in long time-series, or equivalent, data. Many common approaches to detecting changepoints, for example based on penalised likelihood or minimum description length, can be formulated in terms of minimising a cost over segmentations. Dynamic programming methods exist to solve this minimisation problem exactly, but these tend to scale at least quadratically in the length of the time-series. Algorithms, such as Binary Segmentation, exist that have a computational cost that is close to linear in the length of the time-series, but these are not guaranteed to find the optimal segmentation. Recently pruning ideas have been suggested that can speed up the dynamic programming algorithms, whilst still being guaranteed to find true minimum of the cost function. Here we extend these pruning methods, and introduce two new algorithms for segmenting data, FPOP and SNIP. Empirical results show that FPOP is substantially faster than existing dynamic programming methods, and unlike the existing methods its computational efficiency is robust to the number of changepoints in the data. We evaluate the method at detecting Copy Number Variations and observe that FPOP has a computational cost that is competitive with that of Binary Segmentation.


title: The Dark Triad personality: Attractiveness to women

abstract: It has been suggested that the Dark Triad (DT) personality constellation is an evolved facilitator of men’s short-term mating strategies. However, previous studies have relied on self-report data to consider the sexual success of DT men. To explore the attractiveness of the DT personality to the other sex, 128 women rated created (male) characters designed to capture high DT facets of personality or a control personality. Physicality was held constant. Women rated the high DT character as significantly more attractive. Moreover, this greater attractiveness was not explained by correlated perceptions of Big 5 traits. These findings are considered in light of mating strategies, the evolutionary ‘arms race’ and individual differences.


A Framework of a Mechanical Translation between Japanese and English by Analogy Principle


title: Continuous Blood Pressure Measurement From Invasive to Unobtrusive: Celebration of 200th Birth Anniversary of Carl Ludwig

abstract: The year 2016 marks the 200th birth anniversary of Carl Friedrich Wilhelm Ludwig (1816-1895). As one of the most remarkable scientists, Ludwig invented the kymograph, which for the first time enabled the recording of continuous blood pressure (BP), opening the door to the modern study of physiology. Almost a century later, intra-arterial BP monitoring through an arterial line has been used clinically. Subsequently, arterial tonometry and volume clamp method were developed and applied in continuous BP measurement in a noninvasive way. In the last two decades, additional efforts have been made to transform the method of unobtrusive continuous BP monitoring without the use of a cuff. This review summarizes the key milestones in continuous BP measurement; that is, kymograph, intra-arterial BP monitoring, arterial tonometry, volume clamp method, and cuffless BP technologies. Our emphasis is on recent studies of unobtrusive BP measurements as well as on challenges and future directions.


title: Road markings extraction based on threshold segmentation

abstract: A method for road markings extraction on urban streets acquired by a camera mounted on a moving vehicle is described. This method works in three stages. First, the search for the road markings is reduced to a suitable bird's eye view of road surface by using inverse perspective mapping. Secondly, an integrated approach for image segmentation is presented that combines local adaptive threshold and canny edge detection, which output a binary image by separating desirable foreground objects from the background. Thirdly, a geometrical analysis of the contours extracted from binary image is carried out, which extract candidates for further road markings recognition. This method can be helpful for road markings detection under uneven illumination conditions.


title: Towards Exploring Data-Intensive Scientific Applications at Extreme Scales through Systems and Simulations

abstract: The state-of-the-art storage architecture of high-performance computing systems was designed decades ago, and with today's scale and level of concurrency, it is showing significant limitations. Our recent work proposed a new architecture to address the I/O bottleneck of the conventional wisdom, and the system prototype (FusionFS) demonstrated its effectiveness on up to 16 K nodes—the scale on par with today's largest supercomputers. The main objective of this paper is to investigate FusionFS's scalability towards exascale. Exascale computers are predicted to emerge by 2018, comprising millions of cores and billions of threads. We built an event-driven simulator (FusionSim) according to the FusionFS architecture, and validated it with FusionFS's traces. FusionSim introduced less than 4 percent error between its simulation results and FusionFS traces. With FusionSim we simulated workloads on up to two million nodes and find out almost linear scalability of I/O performance; results justified FusionFS's viability for exascale systems. In addition to the simulation work, this paper extends the FusionFS system prototype in the following perspectives: (1) the fault tolerance of file metadata is supported, (2) the limitations of the current system design is discussed, and (3) a more thorough performance evaluation is conducted, such as N-to-1 metadata write, system efficiency, and more platforms such as Amazon Cloud.


title: Relationship between anterior guidance and mesiodistal inclination of lower incisor in human subject with or without TMJ noise

abstract: The relationship between anterior guidance and the mesiodistal inclination of the lower incisors relative to the existence of temporomandibular joint (TMJ) noise was investigated. One hundred and thirty-four individuals, 21 to 40 years of age (mean age 23·8; 104 men and 30 women), were examined. A highly significant correlation was observed between the mean lower-incisor inclination and the difference in steepness between the right and left anterior guidances in the subject group showing TMJ noise (P<0·001), whereas no significant correlation was observed in the group without TMJ noise.


title: Softassign and EM-ICP on GPU

abstract: In this paper we propose CUDA-based implementations of two 3D point sets registration algorithms: Soft assign and EM-ICP. Both algorithms are known for being time demanding, even on modern multi-core CPUs. Our GPUbased implementations vastly outperform CPU ones. For instance, our CUDA EM-ICP aligns 5000 points in less than 7 seconds on a GeForce 8800GT, while the same implementation in OpenMP on an Intel Core 2 Quad would take 7 minutes.


STT-RAM , SRAM , BRAM , ReRAM , and eDRAM Analysis of Their Advantages and Disadvantages


title: Face Recognition : A Convolutional Neural Network Approach

abstract: We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.


title: A Comparison of Common Users across Instagram and Ask.fm to Better Understand Cyberbullying

abstract: This paper examines users who are common to two popular online social networks, Instagram and Ask.fm, That are often used for cyber bullying. An analysis of the negativity and positivity of word usage in posts by common users of these two social networks is performed. These results are normalized in comparison to a sample of typical users in both networks. We also examine the posting activity of common user profiles and consider its correlation with negativity. Within the Ask.fm social network, which allows anonymous posts, the relationship between anonymity and negativity is further explored.


title: An Integrated Low-Voltage Class AB CMOS OTA

abstract: We present a new circuit topology for a low-voltage class AB amplifier. The circuit shows superior current efficiency in the use of the supply current to charge and discharge the output load. It uses negative feedback rather than component matching to optimize current efficiency and performance, resulting in a current boost ratio exactly equal to one. Measurement results for an example circuit fabricated in a 2-/spl mu/m CMOS process are given. The circuit uses a quiescent supply current of 0.2 /spl mu/A and is able to settle to a 1% error in 1.1 ms for a 0.4-V input step and a load capacitance of 35 pF. The circuit design is straightforward and modular, and the core circuit can be used to replace the differential pair of other op-amp topologies.


title: Housing Discrimination among a Sample of Aboriginal People in Winnipeg and Thompson, Manitoba

abstract: There is a paucity of research on housing discrimination in Canada, generally, and even less research on housing discrimination among Aboriginal people—despite the related critical policy issues. Housing discrimination occurs when a group of people are denied equal access to housing. Typically, this discrimination involves the restriction of choices in renting or owning accommodations. There is a consensus among Canadian researchers that Aboriginal people have experienced sustained and widespread housing discrimination even though there are federal, provincial, and constitutional laws and provisions that protect equal access for all ethnic racial groups (Beavis 1995; Quann 1979). Aboriginal people’s access to housing has historically been a controversial policy issue. However, it has become a more complex issue since the large scale migration of Aboriginal people to urban centres throughout Canada. Unlike the housing issues on reserves, housing in the urban context typically involves far more subtle forms of discrimination. In turn, developing effective policy responses is particularly challenging, especially given the different municipal, provincial, and federal jurisdictional issues. Given that the Aboriginal urban migration trend will continue and that available housing will also continue to be limited and expensive, it is vital to understand the types of housing discrimination that exist and how Aboriginal migrants perceive their attempts to access housing. In effect, government housing policies need to be informed by systematic empirical research concerning the contemporary Aboriginal housing experience. Clatworthy (1996) examined the migration, residential mobility, and housing patterns of Canadian Aboriginal people between 1986 and 1991. He found that Aboriginal people moving to urban centers are more commonly: female; younger; include large numbers of families with small children; and have less formal education than their non-Aboriginal counterparts. In 6


title: Increasing FastSLAM accuracy for radar data by integrating the Doppler information

abstract: This paper presents a modified FastSLAM approach for the specific application of radar sensors using the Doppler information to increase the localization and map accuracy. The developed approach is based on the FastSLAM 2.0 algorithm. It is shown how the FastSLAM 2.0 approach can be significantly improved by taking the Doppler information into account. Therefore, the modelled, so-called expected Doppler, and the measured Doppler are compared for every detection. Both, simulations and experiments on real world data show the increase in accuracy of the modified FastSLAM approach by incorporating the Doppler measurements of automotive radar sensors. The proposed algorithm is compared to the state-of-the-art FastSLAM 2.0 algorithm and the vehicle odometry, whereas profiles of an Automotive Dynamic Motion Analyzer serve as the reference.


title: Query-driven soft interconnection of EMF models

abstract: Model repositories based on the Eclipse Modeling Framework (EMF) play a central role in the model-driven development of complex software-intensive systems by offering means to persist and manipulate models obtained from heterogeneous languages and tools. Complex EMF models can be assembled by interconnecting model fragments by hard links, i.e. regular references, where the target end points to external resources using storage-specific URIs. This approach, in certain application scenarios, may prove to be a too rigid and error prone way of interlinking models. As a flexible alternative, we propose to combine derived features of EMF models with advanced incremental model queries as means for soft interlinking of model elements residing in different model resources. These soft links can be calculated on-demand with graceful handling for temporarily unresolved references. In the background, the interlinks are maintained efficiently and flexibly by using incremental model queries as provided by the EMF-IncQuery framework.


title: Design, implementation, and evaluation of the linear road bnchmark on the stream processing core

abstract: Stream processing applications have recently gained significant attention in the networking and database community. At the core of these applications is a stream processing engine that performs resource allocation and management to support continuous tracking of queries over collections of physically-distributed and rapidly-updating data streams. While numerous stream processing systems exist, there has been little work on understanding the performance characteristics of these applications in a distributed setup. In this paper, we examine the performance bottlenecks of streaming data applications, in particular the Linear Road stream data management benchmark, in achieving good performance in large-scale distributed environments, using the Stream Processing Core (SPC), a stream processing middleware we have developed. First, we present the design and implementation of the Linear Road benchmark on the SPC middleware. SPC has been designed to scale to tens of thousands of processing nodes, while supporting concurrent applications and multiple simultaneous queries. Second, we identify the main performance bottlenecks in the Linear Road application in achieving scalability and low query response latency. Our results show that data locality, buffer capacity, physical allocation of processing elements to infrastructure nodes, and packaging for transporting streamed data are important factors in achieving good application performance. Though we evaluate our system primarily for the Linear Road application, we believe it also provides useful insights into the overall system behavior for supporting other distributed and large-scale continuous streaming data applications. Finally, we examine how SPC can be used and tuned to enable a very efficient implementation of the Linear Road application in a distributed environment.


title: Broadband circularly polarized stacked patch antenna for UHF RFID applications

abstract: A broadband circularly polarized stacked patch antenna for UHF RFID application has been presented and investigated in this paper. The measured results have shown the good antenna performance with gain of more than 6.5 dBic, return loss less than -10 dB, and axial ratio less than 3 dB within the frequency range of 820-985 MHz (18.3 %). Due to the broadband performance covering the whole RFID UHF band, it is a good candidate for a reader antenna with high performance and low cost.


title: Explaining Neural Networks Semantically and Quantitatively

abstract: This paper presents a method to pursue a semantic and quantitative explanation for the knowledge encoded in a convolutional neural network (CNN). The estimation of the specific rationale of each prediction made by the CNN presents a key issue of understanding neural networks, and it is of significant values in real applications. In this study, we propose to distill knowledge from the CNN into an explainable additive model, which explains the CNN prediction quantitatively. We discuss the problem of the biased interpretation of CNN predictions. To overcome the biased interpretation, we develop prior losses to guide the learning of the explainable additive model. Experimental results have demonstrated the effectiveness of our method.


title: Echinococcus canadensis G8 Tapeworm Infection in a Sheep, China, 2018

abstract: We report a sheep infected with Echinococcus canadensis G8 tapeworm in China in 2018. This pathogen was previously detected in moose, elk, muskox, and mule deer in Europe and North America; our findings suggest a wider host range and geographic distribution. Surveillance for the G8 tapeworm should be conducted in China.


title: Video (language) modeling: a baseline for generative models of natural videos

abstract: We propose a strong baseline model for unsupervised feature learning using video data. By learning to predict missing frames or extrapolate future frames from an input video sequence, the model discovers both spatial and temporal correlations which are useful to represent complex deformations and motion patterns. The models we propose are largely borrowed from the language modeling literature, and adapted to the vision domain by quantizing the space of image patches into a large dictionary. We demonstrate the approach on both a filling and a generation task. For the first time, we show that, after training on natural videos, such a model can predict non-trivial motions over short video sequences.


title: Are test smells really harmful? An empirical study

abstract: Bad code smells have been defined as indicators of potential problems in source code. Techniques to identify and mitigate bad code smells have been proposed and studied. Recently bad test code smells (test smells for short) have been put forward as a kind of bad code smell specific to tests such a unit tests. What has been missing is empirical investigation into the prevalence and impact of bad test code smells. Two studies aimed at providing this missing empirical data are presented. The first study finds that there is a high diffusion of test smells in both open source and industrial software systems with 86 % of JUnit tests exhibiting at least one test smell and six tests having six distinct test smells. The second study provides evidence that test smells have a strong negative impact on program comprehension and maintenance. Highlights from this second study include the finding that comprehension is 30 % better in the absence of test smells.


title: Turning Point? The Volatile Geographies of Taxation

abstract: This article argues that the geographies of taxation offer an important but neglected insight into changes taking place in the nature of the contemporary state in the context of globalisation. Following Schumpeter's analysis of the “tax state”, the paper argues that, historically, the theory and practice of fiscal space are fundamental both to state form and to the possibility of political and social institutions. Despite this, the complexity and fluidity inherent in fiscal space has been obscured by the dominant normative conception of “the” fiscal state. As the concept of “fiscal sovereignty” becomes less and less salient in practice in the context of economic globalisation, it remains a powerful ideological concept for state governance. This paper reviews the primary contemporary accounts of fiscal space across a range of disciplinary contexts and scales of governance. Despite the expectation and or desire for some form of “fiscal globalisation” on the part of commentators, in practice what we see is an increased centralisation of state fiscal control coupled with a creeping individuation and privatisation of fiscal responsibility. This radical respatialisation of fiscal space has profound implications both for the state itself and for any prospect of the creation of a global “public domain” founded on a global fisc.


title: On the alleged memory-undermining effects of daydreaming

abstract: In three experiments, we examined the memory-undermining effects of daydreaming for (un)related stimuli. In Experiments 1 and 2, we tested whether daydreaming fosters forgetting of semantically interrelated material and hence, catalyzes false memory production. In Experiment 3, we examined the memory effects of different daydreaming instructions. In Experiment 1, daydreaming did not undermine correct recall of semantically interrelated words, nor did it affect false memories. In Experiment 2, we again failed to find that daydreaming exerted memory-undermining effects a. In Experiment 3, no memory effects were obtained using different daydreaming instructions. Together, our studies fail to show appreciable memory-undermining effects of daydreaming.


Bridging the Reality Gap A Dual Simulator Approach to the Evolution of Whole-Body Motion for the Nao Humanoid Robot.


title: Maximum Entropy Models for Natural Language Ambiguity Resolution

abstract: This thesis demonstrates that several important kinds of natural language ambiguities can be resolved to state-of-the-art accuracies using a single statistical modeling technique based on the principle of maximum entropy. ::: We discuss the problems of sentence boundary detection, part-of-speech tagging, prepositional phrase attachment, natural language parsing, and text categorization under the maximum entropy framework. In practice, we have found that maximum entropy models offer the following advantages: ::: State-of-the-art accuracy. The probability models for all of the tasks discussed perform at or near state-of-the-art accuracies, or outperform competing learning algorithms when trained and tested under similar conditions. Methods which outperform those presented here require much more supervision in the form of additional human involvement or additional supporting resources. ::: Knowledge-poor features. The facts used to model the data, or features, are linguistically very simple, or "knowledge-poor", but yet succeed in approximating complex linguistic relationships. ::: Reusable software technology. The mathematics of the maximum entropy framework are essentially independent of any particular task, and a single software implementation can be used for all of the probability models in this thesis. ::: The experiments in this thesis suggest that experimenters can obtain state-of-the-art accuracies on a wide range of natural language tasks, with little task-specific effort, by using maximum entropy probability models.


title: The liquid biopsy in lung cancer

abstract: The incidence of lung cancer has significantly increased over the last century, largely due to smoking, and remains the most common cause of cancer deaths worldwide. This is often due to lung cancer first presenting at late stages and a lack of curative therapeutic options at these later stages. Delayed diagnoses, inadequate tumor sampling, and lung cancer misdiagnoses are also not uncommon due to the limitations of the tissue biopsy. Our better understanding of the tumor microenvironment and the systemic actions of tumors, combined with the recent advent of the liquid biopsy, may allow molecular diagnostics to be done on circulating tumor markers, particularly circulating tumor DNA. Multiple liquid biopsy molecular methods are presently being examined to determine their efficacy as surrogates to the tumor tissue biopsy. This review will focus on new liquid biopsy technologies and how they may assist in lung cancer detection, diagnosis, and treatment.


title: A More but not Fully Constructed Arena: A Critical Analysis of the AKP’s Policy toward Kurdish Ethno-Cultural Rights (2002-2014)

abstract: Until the very early 2000s, the Kurds had suffered from numerous Turkification policies; however, after the Justice and Development Party (Adalet ve Kalkinma Partisi, AKP) came into power in 2002, a new political process aimed at enhancing Kurdish ethno-cultural rights was initiated. As a result of this process, the historical harm done by the early republican regime, the three military administrations and their successor governments has been reduced. Notwithstanding, there are still some restrictions that can be removed through a new constitutional reform package. By employing the methods of comparative politics and constitutional law, this article gives the following suggestions for the reform package. First, in the name of laying a foundation for bilingual education, the reform package may delete or amend Article 42(9) of the Turkish Constitution, according to which the Kurds are now obliged to receive education in Turkish, hindering them from being taught in their mother tongue. Second, it may revise Article 3(1) of the Constitution, under which Turkish is the sole language of the state. The new version of Article 3(1) may recognise the Kurdish language and its dialects as national or regional official languages. Finally, the constitutional reform package may expand the scope of Article 134, pursuant to which only Turkish cultural, historical and linguistic features can now receive public funding and be protected constitutionally, and it may grant the same privilege to Kurdish characteristics.


title: Stealing Neural Networks via Timing Side Channels

abstract: Deep learning is gaining importance in many applications. However, Neural Networks face several security and privacy threats. This is particularly significant in the scenario where Cloud infrastructures deploy a service with Neural Network model at the back end. Here, an adversary can extract the Neural Network parameters, infer the regularization hyperparameter, identify if a data point was part of the training data, and generate effective transferable adversarial examples to evade classifiers. This paper shows how a Neural Network model is susceptible to timing side channel attack. In this paper, a black box Neural Network extraction attack is proposed by exploiting the timing side channels to infer the depth of the network. Although, constructing an equivalent architecture is a complex search problem, it is shown how Reinforcement Learning with knowledge distillation can effectively reduce the search space to infer a target model. The proposed approach has been tested with VGG architectures on CIFAR10 data set. It is observed that it is possible to reconstruct substitute models with test accuracy close to the target models and the proposed approach is scalable and independent of type of Neural Network architectures.


Measuring the Impact of ISIS Social Media Strategy


title: A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

abstract: In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.


title: The Value of Privacy

abstract: In several experimental auctions, participants put a dollar value on private information before revealing it to a group. An analysis of results show that a trait's desirability in relation to the group played a key role in the amount people demanded to publicize private information. Because people can easily obtain, aggregate, and disperse personal data electronically, privacy is a central concern in the information age. This concern is clear in relation to financial data and genetic information, both of which can lead to identity abuse and discrimination. However, other relatively harmless information can also be abused, including a person's gender, salary, age, marital status, or shopping preferences. What's unclear is whether it's the fear of such abuse that actually causes people's stated hesitance to reveal their data. Our hypothesis - and the motivation for our study - is that people reveal information when they feel that they're somewhat typical or positively atypical compared to the target group. To test this hypothesis, we conducted experiments that elicit the value people place on their private data. We found, with great significance (more than 95 percent statistical confidence) that a linear relationship exists between an individual's belief about a trait and the value he or she places on it. That is, the less desirable the trait, the greater the price a person demands for releasing the information. Furthermore, we found that small deviations in a socially positive direction are associated with a lower asking price.


title: Range Fixes: Interactive Error Resolution for Software Configuration

abstract: To prevent ill-formed configurations, highly configurable software often allows defining constraints over the available options. As these constraints can be complex, fixing a configuration that violates one or more constraints can be challenging. Although several fix-generation approaches exist, their applicability is limited because (1) they typically generate only one fix or a very long fix list, difficult for the user to identify the desirable fix; and (2) they do not fully support non-Boolean constraints, which contain arithmetic, inequality, and string operators. This paper proposes a novel concept, range fix, for software configuration. A range fix specifies the options to change and the ranges of values for these options. We also design an algorithm that automatically generates range fixes for a violated constraint. We have evaluated our approach with three different strategies for handling constraint interactions, on data from nine open source projects over two configuration platforms. The evaluation shows that our notion of range fix leads to mostly simple yet complete sets of fixes, and our algorithm is able to generate fixes within one second for configuration systems with a few thousands options and constraints.


title: Integrating rich information for video recommendation with multi-task rank aggregation

abstract: Video recommendation is an important approach for helping people to access interesting videos. In this paper, we propose a scheme to integrate rich information for video recommendation. We regard video recommendation as a ranking problem and generate multiple ranking lists by exploring different information sources. A multi-task rank aggregation approach is proposed to integrate the ranking lists for different users in a joint manner. Our scheme is flexible and can easily incorporate other methods by adding their generated ranking lists into our multi-task learning algorithm. We conduct experiments with 76 users and more than 10,000 videos. The results demonstrate the feasibility and effectiveness of our approach.


Novel Design of 3D Imaging Pixel On-chip Using A Micro-conjugate Mirror System


title: A 99.1% efficient, 490 W/in3 power density power factor correction front end based on a 7-level flying capacitor multilevel converter

abstract: This paper presents a power factor correction (PFC) front end based on a GaN-Based 7-level flying capacitor multilevel (FCML) boost converter. Compared to the conventional 2-level boost converter, the proposed 7-level FCML converter features the use of low-voltage-rated transistors, reduced voltage stress and high effective switching frequency on the filter inductor. These characteristics of the FCML converter lead to drastic reduction in the filter inductor size while maintaining high efficiency, and therefore significantly improve the power density of the PFC frond end compared to conventional solutions. On the other hand, the small inductance imposes challenges for the PFC control. The dynamics of the 7-level FCML converter has been analyzed and a feedforward control has been implemented to overcome these challenges. A hardware prototype is designed for universal AC input (90 VAC to 265 VAC), 400 V DC output at 1.5 kW power rating. The hardware prototype demonstrates improved efficiency and power density as well as power factor and THD performance compared to existing solutions. A peak efficiency of 99.07% and a power density of 490 W/in3 has been experimentally verified.


State of the Art on 3D Reconstruction with RGB-D Cameras


Adversarial Learning of Portable Student Networks


title: Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

abstract: The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings \(\in \mathbb {R}^d\) and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex \(v_1\) more central than vertex \(v_2\) given centrality c?. We then show that a GNN can be trained to develop a lingua franca of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves \(89\%\) accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained (\(n=128\)). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.


title: Electrophysiological effects of dofetilide in an in vitro model of "border zone" between normal and ischemic/reperfused myocardium.

abstract: BACKGROUND ::: To evaluate both class III activity and antiarrhythmic action of dofetilide at the level of the "border zone," we investigated its electrophysiological effects on guinea pig ventricular strips submitted partly to normoxia (normal zone, NZ) and partly to simulated severe ischemia, then reperfusion (altered zone, AZ). ::: ::: ::: METHODS AND RESULTS ::: Because of the differential class III effects of dofetilide in normal and ischemic regions, the dispersion of the action potential duration at 90% repolarization (APD(90)) between NZ and AZ was reduced by 5 nmol/L of drug during early ischemia (at 10 minutes, APD(90) NZ/APD(90) AZ was 1.68+/-0.22 versus 2.82+/-0.17 in control, P<0.05), whereas 50 nmol/L dofetilide worsened it during late ischemia (at 30 minutes, APD(90) NZ/APD(90) AZ was 4.62+/-0.76 versus 2.57+/-0.21 in control, P<0.05). Concomitantly, dofetilide at 5, 10, and 50 nmol/L abolished the early extrastimulus (ES)-induced arrhythmias, and at 10 and 50 nmol/L, it significantly enhanced the incidence of late spontaneous repetitive responses (in 86% and 75% of preparations treated with 10 and 50 nmol/L, respectively, versus 25% in control, P<0.05). During reperfusion, dofetilide at 5, 10, and 50 nmol/L exhibited concentration-dependent class III effects, as it did in the NZ, and did not modify the incidence of spontaneous arrhythmias. ::: ::: ::: CONCLUSIONS ::: Dofetilide 5 nmol/L decreased APD(90) dispersion between NZ and AZ and reduced the early ES-induced arrhythmias. However, dofetilide 50 nmol/L increased APD(90) dispersion, and at 10 and 50 nmol/L, it increased the late spontaneous arrhythmias.


title: Multi-Modal Models for Concrete and Abstract Concept Meaning

abstract: Multi-modal models that learn semantic representations from both linguistic and perceptual input outperform language-only models on a range of evaluations, and better reflect human concept acquisition. Most perceptual input to such models corresponds to concrete noun concepts and the superiority of the multi-modal approach has only been established when evaluating on such concepts. We therefore investigate which concepts can be effectively learned by multi-modal models. We show that concreteness determines both which linguistic features are most informative and the impact of perceptual input in such models. We then introduce ridge regression as a means of propagating perceptual information from concrete nouns to more abstract concepts that is more robust than previous approaches. Finally, we present weighted gram matrix combination , a means of combining representations from distinct modalities that outperforms alternatives when both modalities are sufficiently rich.


title: Three-stage inversion process for polarimetric SAR interferometry

abstract: The authors provide a new geometrical approach for the inversion of a two-layer coherent scattering model, widely used for the interpretation of polarimetric interferometric SAR data. It has been shown in several recent publications that, by using interferograms in multiple polarisation channels, estimation of vegetation height, underlying ground topography and mean extinction is possible. Furthermore, this can be achieved with a single frequency sensor without the need for a separate reference DEM, other a priori information or the use of data-specific regression formulas. The authors first review the details of this approach and then develop a three-stage inversion procedure to illustrate the steps involved in parameter estimation. They then consider several possible sources of error in the inversion. In particular, they concentrate on the effects of vertical tree structure and on the effects of temporal decorrelation on inversion accuracy. It is shown that the former leads to errors, mainly in the extinction estimation, while the latter does not change the model structure but reduces the available parameter set and increases the variance of the parameter estimates. Finally, the new algorithm is applied to simulated vector coherent SAR data for a random canopy.


Can EU Social NGOs Promote EU Social Policy


title: A reconfigurable wheelchair/bed system for the elderly and handicapped

abstract: A Reconfigurable Wheelchair/Bed System (RWBS) for the elderly and handicapped is designed. The control system of this reconfigurable wheelchair consists of a main control unit, a motion control unit, a posture adjusting unit, a physiological signal acquisition unit, a sensors processing unit, an image processing unit and an intelligent human-machine interaction (HMI) unit. With the human-machine interaction (HMI) unit, the wheelchair can be used as a common powered wheelchair, adjust its posture so that the user can change his/her body to an ideal position or to a flat stationary bed. One of the distinguished points of this reconfigurable wheelchair/bed system is that it can be docked into the bed frame automatically by just push a button with the help of a visual feedback control unit. The three control modes can be changed via the touch screen. Posture stabilization and trajectory tracking methods for the visual servoing system are exploited and, experiments are also carried out to show the effectiveness.


title: PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES The Do Re Mi’s of Everyday Life: The Structure and Personality Correlates of Music Preferences

abstract: The present research examined individual differences in music preferences. A series of 6 studies investigated lay beliefs about music, the structure underlying music preferences, and the links between music preferences and personality. The data indicated that people consider music an important aspect of their lives and listening to music an activity they engaged in frequently. Using multiple samples, methods, and geographic regions, analyses of the music preferences of over 3,500 individuals converged to reveal 4 music-preference dimensions: Reflective and Complex, Intense and Rebellious, Upbeat and Conventional, and Energetic and Rhythmic. Preferences for these music dimensions were related to a wide array of personality dimensions (e.g., Openness), self-views (e.g., political orientation), and cognitive abilities (e.g., verbal IQ).


title: Comparing and evaluating the sentiment on newspaper articles: A preliminary experiment

abstract: Recent years have brought a symbolic growth in the volume of research in Sentiment Analysis, mostly on highly subjective text types like movie or product reviews. The main difference these texts have with news articles is that their target is apparently defined and unique across the text. Thence while dealing with news articles, we performed three subtasks namely identifying the target; separation of good and bad news content from the good and bad sentiment expressed on the target and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. On concluding these tasks, we present our work on mining opinions about three different Indian political parties during elections in the year 2009. We built a Corpus of 689 opinion-rich instances from three different English dailies namely The Hindu, Times of India and Economic Times extracted from 02/ 01/ 2009 to 05/ 01/ 2009 (MM/ DD/ YY). In which (a) we tested the relative suitability of various sentiment analysis methods (both machine learning and lexical based) and (b) we attempted to separate positive or negative opinion from good or bad news. Evaluation includes comparison of three sentiment analysis methods (two machine learning based and one lexical based) and analyzing the choice of certain words used in political text which influence the Sentiments of public in polls. This preliminary experiment will benefit in predicting and forecasting the winning party in forthcoming Indian elections 2014.


title: DeepShape: Deep-Learned Shape Descriptor for 3D Shape Retrieval

abstract: Complex geometric variations of 3D models usually pose great challenges in 3D shape matching and retrieval. In this paper, we propose a novel 3D shape feature learning method to extract high-level shape features that are insensitive to geometric deformations of shapes. Our method uses a discriminative deep auto-encoder to learn deformation-invariant shape features. First, a multiscale shape distribution is computed and used as input to the auto-encoder. We then impose the Fisher discrimination criterion on the neurons in the hidden layer to develop a deep discriminative auto-encoder. Finally, the outputs from the hidden layers of the discriminative auto-encoders at different scales are concatenated to form the shape descriptor. The proposed method is evaluated on four benchmark datasets that contain 3D models with large geometric variations: McGill, SHREC’10 ShapeGoogle, SHREC’14 Human and SHREC’14 Large Scale Comprehensive Retrieval Track Benchmark datasets. Experimental results on the benchmark datasets demonstrate the effectiveness of the proposed method for 3D shape retrieval.


title: A Scalable ADMM Algorithm for Rigid Registration

abstract: A fundamental problem that comes up in computer vision, image processing, manifold learning, and sensor networks is that of registering multiple point sets using rigid transforms. A standard result in this regard is that the least-square formulation of the registration problem admits a closed-form solution for two point sets. However, since the group of rigid transforms is not convex, solving the least-square optimization for multiple point sets is computationally challenging. It was recently demonstrated that the least-square formulation can be relaxed into a tractable semidefinite program, and that the relaxation is provably tight under certain assumptions. The difficulty is that standard solvers for semidefinite programming (e.g., interior-point solvers) cannot be scaled to handle large-sized problems. In this letter, we propose an iterative solver based on variable splitting and the alternating direction method of multipliers. Since each iteration essentially involves an eigendecomposition, the proposed solver can be scaled to problems that are beyond the reach of interior-point solvers. We present results on simulated and real data to demonstrate the potential of the solver.


DISTRIBUTION OF THE SPECIES TELESTES SOUFFIA ( RISSO , 1827 ) ( PISCES : CYPRINIDAE ) IN THE UPPER TISA RIVER AND ITS TRIBUTARIES ( MARAMURE COUNTY-NORTH ROMANIA )


title: FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

abstract: Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large. Due to this, previous neural architecture search (NAS) methods are computationally expensive. ConvNet architecture optimality depends on factors such as input resolution and target devices. However, existing approaches are too resource demanding for case-by-case redesigns. Also, previous work focuses primarily on reducing FLOPs, but FLOP count does not always reflect actual latency. To address these, we propose a differentiable neural architecture search (DNAS) framework that uses gradient-based methods to optimize ConvNet architectures, avoiding enumerating and training individual architectures separately as in previous methods. FBNets (Facebook-Berkeley-Nets), a family of models discovered by DNAS surpass state-of-the-art models both designed manually and generated automatically. FBNet-B achieves 74.1% top-1 accuracy on ImageNet with 295M FLOPs and 23.1 ms latency on a Samsung S8 phone, 2.4x smaller and 1.5x faster than MobileNetV2-1.3 with similar accuracy. Despite higher accuracy and lower latency than MnasNet, we estimate FBNet-B's search cost is 420x smaller than MnasNet's, at only 216 GPU-hours. Searched for different resolutions and channel sizes, FBNets achieve 1.5% to 6.4% higher accuracy than MobileNetV2. The smallest FBNet achieves 50.2% accuracy and 2.9 ms latency (345 frames per second) on a Samsung S8. Over a Samsung-optimized FBNet, the iPhone-X-optimized model achieves a 1.4x speedup on an iPhone X. FBNet models are open-sourced at https://github. com/facebookresearch/mobile-vision.


title: Targeting FOXA1-mediated repression of TGF-&bgr; signaling suppresses castration-resistant prostate cancer progression

abstract: Prostate cancer (PC) progressed to castration resistance (CRPC) is a fatal disease. CRPC tumors develop resistance to new-generation antiandrogen enzalutamide through lineage plasticity, characterized by epithelial-mesenchymal transition (EMT) and a basal-like phenotype. FOXA1 is a transcription factor essential for epithelial lineage differentiation. Here, we demonstrate that FOXA1 loss leads to remarkable upregulation of transforming growth factor beta 3 (TGFB3), which encodes a ligand of the TGF-&bgr; pathway. Mechanistically, this is due to genomic occupancy of FOXA1 on an upstream enhancer of the TGFB3 gene to directly inhibit its transcription. Functionally, FOXA1 downregulation induces TGF-&bgr; signaling, EMT, and cell motility, which is effectively blocked by the TGF-&bgr; receptor I inhibitor galunisertib (LY2157299). Tissue microarray analysis confirmed reduced levels of FOXA1 protein and a concordant increase in TGF-&bgr; signaling, indicated by SMAD2 phosphorylation, in CRPC as compared with primary tumors. Importantly, combinatorial LY2157299 treatment sensitized PC cells to enzalutamide, leading to synergistic effects in inhibiting cell invasion in vitro and xenograft CRPC tumor growth and metastasis in vivo. Therefore, our study establishes FOXA1 as an important regulator of lineage plasticity mediated in part by TGF-&bgr; signaling, and supports a novel therapeutic strategy to control lineage switching and potentially extend clinical response to antiandrogen therapies.


abstract: How Space Guides Interpretation of a Novel Mathematical System David Landy (dlandy@indiana.edu) Department of Computer Science Indiana University, Bloomington, IN 47408 USA Robert L. Goldstone (rgoldsto@indiana.edu) Department of Psychological and Brain Sciences Indiana University, Bloomington, IN 47408 USA Abstract for instance, the c is closer to the exponent than to the b, and b is closer to the c than to the a. This physical relationship maps directly onto the corresponding order of precedence: c should first be squared, then the result multiplied by b, and a should be added to the result. The correspondence is far from perfect, especially when expressions are hand-written (Landy & Goldstone, in press A), but nevertheless, there is a general relationship between physical and syntactic proximity in mathematics, exacerbated by the frequent omission of the multiplication sign in algebra (of four prominent algebra textbooks (McGraw-Hill 1998; Cord Communications 2004; Holt, Rinehart and Winston, 2004; McDougal Littell, 2004), each used at least one times sign convention which was more closely spaced than plus signs, besides omission. None ever did the reverse). People can learn and understand formal rule systems that lack the kind of perceptual-syntactic regularities algebra contains. The question addressed here is whether and in what ways salient perceptual regularities, when present, are used by people learning novel formal structures. This paper investigates how people build interpretations of compound mathematical expressions in a novel formal system. In traditional arithmetic, interpretations are guided by an order of precedence convention (times and division precede addition and subtraction). This order is supported by a spatial convention that supports the order of precedence. In the experiment described here, participants learned computation tables of two simple novel operators, and then were asked to discover a precedence rule. The operators were presented with a physical spacing convention that either aligned with the precedence order, opposed it, or randomly opposed or aligned with the precedence order. Participants were more likely to reach a criterion of successful performance when the order of operations aligned with the precedence order, and did so more quickly than either other group. The results indicate that reasoners integrate salient perceptual cues with formal knowledge following familiar conventions, even on novel systems. Keywords: Mathematical cognition, embodied cognition, formal reasoning, symbolic processing Introduction Background Kirshner (1989) explored the correspondence between spatial proximity and arithmetic syntax by creating a novel but natural set of symbols for the basic operations: M for multiplication, A for addition, E for exponentiation, and so on. Problems expressed in this language were presented with the natural spacing relationships to one group of high-school students, uniformly spaced to another. The participants who solved problems presented in the spaced language made many fewer errors compared to participants solving unspaced problems. Kirshner’s study demonstrates that arithmetic learners represent spacing regularities inherent in standard symbolic notation, and that they rely on the presence of those cues when developing interpretations of symbolic mathematical expressions. This is compatible with other studies that have shown that rule-based behavior uses irrelevant features of exemplars (McNeil & Alibali, 2004). One limitation of this work, however, is its use of standard arithmetic operations in the novel language. Because the stimuli are standard mathematical operations, it is difficult to determine the generality of the visual processes that govern order of operations behaviors. Participants have extensive experience with spacing in standard arithmetic symbology; this experience may drive their behavior when learning novel symbols for familiar operations without implying any general connection between syntax and spacing. The ability to understand abstract formal structures is one of humanity’s most distinctive and powerful cognitive traits. Arithmetical and algebraic notations, formal logic, and natural language syntax all contain underlying structure that at some level is entirely arbitrary and abstract. However, every actual notation has some particular physical presentation, and that notation always contains formally irrelevant physical relations. Often, especially when formal understanding is poor or partial, these relations may be more salient to a reasoner than the formally sanctioned abstract relations. The goal of this work is to explore whether and how people use irrelevant but salient visual information in exploring a novel formal system. This issue has special importance for understanding mathematical reasoning and learning. Although arithmetic notation may be the best-known example of a purely formal symbol system, arithmetic itself contains a variety of non- formal conventions that relate visual aspects of expressions to their formal structure. One of the most striking of these is a correlation between physical proximity and the order of precedence in three common operators: addition, multiplication, and exponentiation. In the typeset expression a + b c 2 ,


title: Design of fuzzy logic control of permanent magnet DC motor under real constraints and disturbances

abstract: This paper presents a design of the fuzzy logic control for a permanent magnet DC motor. The main objective is to achieve a robust controller under disturbances and unmodeled dynamics acting, such as load torque, dead zone, measurement noise and nonlinearities. The whole system contains the DC motor, driver, tachogenerator, external load and microprocessor based system (dSPACE CLP1004). This system is considered such as black box. The fuzzy controller is designed in simulation mode first. The model of whole system was obtained through identification procedure. Then this fuzzy controller is included into a real physical control structure. Control performance of the fuzzy controller in both simulation and experimental modes are compared under mentioned constraints. Also, results obtained by the fuzzy controller are compared with the same obtained by PID controller.


title: A robust hybrid feedback active noise cancellation headset

abstract: This paper investigates the robustness of a hybrid analog/digital feedback active noise cancellation (ANC) headset system. The digital ANC systems with the filtered-x least-mean-square (FXLMS) algorithm require accurate estimation of the secondary path for the stability and convergence of the algorithm. This demands a great challenge for the ANC headset design because the secondary path may fluctuate dramatically such as when the user adjusts the position of the ear-cup. In this paper, we analytically show that adding an analog feedback loop into the digital ANC systems can effectively reduce the plant fluctuation, thus achieving a more robust system. The method for designing the analog controller is highlighted. A practical hybrid analog/digital feedback ANC headset has been built and used to conduct experiments, and the experimental results show that the hybrid headset system is more robust under large plant fluctuation, and has achieved satisfactory noise cancellation for both narrowband and broadband noises.


A Comprehensive Overview of Basic Clustering Algorithms


title: Multi-agent reinforcement learning for traffic signal control

abstract: Optimal control of traffic lights at junctions or traffic signal control (TSC) is essential for reducing the average delay experienced by the road users amidst the rapid increase in the usage of vehicles. In this paper, we formulate the TSC problem as a discounted cost Markov decision process (MDP) and apply multi-agent reinforcement learning (MARL) algorithms to obtain dynamic TSC policies. We model each traffic signal junction as an independent agent. An agent decides the signal duration of its phases in a round-robin (RR) manner using multi-agent Q-learning with either is an element of-greedy or UCB 3] based exploration strategies. It updates its Q-factors based on the cost feedback signal received from its neighbouring agents. This feedback signal can be easily constructed and is shown to be effective in minimizing the average delay of the vehicles in the network. We show through simulations over VISSIM that our algorithms perform significantly better than both the standard fixed signal timing (FST) algorithm and the saturation balancing (SAT) algorithm 15] over two real road networks.


title: Subordination and coordination in syntax, semantics, and discourse: Evidence from the study of connectives

abstract: This article discusses the question whether the distinction between subordination and coordination is parallel in syntax and discourse. Its main thesis is that subordination and coordination, as they are commonly understood in the linguistic literature, are genuinely syntactic concepts. The distinction between hierarchical and non-hierarchical connection in discourse structure, as far as it is defined clearly in the literature, is of a quite different nature. The syntax and semantics of connectives (as the most prominent morphosyntactic means by which subordination and coordination are encoded) offers little evidence to support the assumption of a structural parallelism between syntax and discourse. As a methodological consequence, sentence and discourse structure should not be mixed up in linguistic analysis.