title: Greening geographical load balancing

abstract: Energy expenditure has become a significant fraction of data center operating costs. Recently, "geographical load balancing" has been proposed to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use. We explore whether the geographical diversity of Internet-scale systems can also provide environmental gains. Specifically, we explore whether geographical load balancing can encourage use of "green" renewable energy and reduce use of "brown" fossil fuel energy. We make two contributions. First, we derive three distributed algorithms for achieving optimal geographical load balancing. Second, we show that if the price of electricity is proportional to the instantaneous fraction of the total energy that is brown, then geographical load balancing significantly reduces brown energy use. However, the benefits depend strongly on dynamic energy pricing and the form of pricing used.


title: Ultra-Wideband $E$-Plane Monopulse Antenna Using Vivaldi Antenna

abstract: An ultra-wideband (UWB) E-plane monopulse antenna working in the frequency range from 3.1 to 10.6 GHz is proposed. The UWB E-plane two-element array is constructed using Vivaldi antennas. By shortening the adjacent tapered lines of the two Vivaldi antennas and using corrugated edges, sum and difference characteristics of the array are improved. The UWB monopulse comparator is a multilayer planar magic T consisting of an UWB 90° directional coupler and two UWB 45° phase shifters in series. By integrating the array and the comparator, the UWB E-plane monopulse antenna is designed and fabricated with the low-cost PCB process. In the frequency range from 3.1 to 10.6 GHz, the sum gain is measured as more than 10.1 dBi, the sidelobe level less than -13.1 dB, and the null depth greater than 22.5 dB. The good sum and difference characteristics obtained make the proposed UWB E-plane monopulse antenna desirable for target detecting and tracking.


Element Detection in Japanese Comic Book Panels


title: Durkheim's "Heroic Suicide" in Military Combat:

abstract: Durkheim's neglected concept, heroic suicide, was explored using Congressional Medal of Honor data. All cases meeting the criteria for heroic altruistic suicide in military combat (n=125) were selected for analysis. Both qualitative and quantitative content analysis procedures were used and the usefulness of the combined approach is demonstrated. This research supports Durkheim's claims that altruistic suicide would be higher among non-commissioned officers than commissioned officers, among elite troops than those that display less cohesion, and among those in "leadership roles" than enlisted men. ("leadership role" is a reformulation; Durkheim used an official military rank designation.) Use of citation data enabled an analysis sensitive to the social context of heroic suicide, including some indications of the feelings involved when choosing to sacrifice one's life for another or others.


title: Classification-based Financial Markets Prediction using Deep Neural Networks

abstract: Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community for their superior predictive properties including robustness to over fitting. However their application to algorithmic trading has not been previously researched, partly because of their computational complexity. This paper describes the application of DNNs to predicting financial market movement directions. In particular we describe the configuration and training approach and then demonstrate their application to back testing a simple trading strategy over 43 different Commodity and FX future mid-prices at 5-minute intervals. All results in this paper are generated using a C implementation on the Intel Xeon Phi co-processor which is 11.4x faster than the serial version and a Python strategy back testing environment both of which are available as open source code written by the authors.


title: Dropout Rates and Response Times of an Occupation Search Tree in a Web Survey

abstract: Abstract Occupation is key in socioeconomic research. As in other survey modes, most web surveys use an open-ended question for occupation, though the absence of interviewers elicits unidentifiable or aggregated responses. Unlike other modes, web surveys can use a search tree with an occupation database. They are hardly ever used, but this may change due to technical advancements. This article evaluates a three-step search tree with 1,700 occupational titles, used in the 2010 multilingual WageIndicator web survey for UK, Belgium and Netherlands (22,990 observations). Dropout rates are high; in Step 1 due to unemployed respondents judging the question not to be adequate, and in Step 3 due to search tree item length. Median response times are substantial due to search tree item length, dropout in the next step and invalid occupations ticked. Overall the validity of the occupation data is rather good, 1.7-7.5% of the respondents completing the search tree have ticked an invalid occupation.


Induced resistance of Staphylococcus aureus to antibiotics.


Assessing the health of the general population in England: how do the three- and five-level versions of EQ-5D compare?


title: Parallel intersection detection in massive sets of cubes

abstract: We present ParCube, which finds the pairwise intersections in a set of millions of congruent cubes. This operation is required when computing boolean combinations of meshes or polyhedra in CAD/CAM and additive manufacturing, and in determining close points in a 3D set. ParCube is very compact because it is uses a uniform grid with a functional programming API. ParCube is very fast; even single threaded it usually beats CGAL's elapsed time, sometimes by a factor of 3. Also because it is FP, ParCube parallelizes very well. On an Nvidia GPU, processing 10M cubes to find 6M intersections, it took 0.33 elapsed seconds, beating CGAL by a factor of 131. ParCube is independent of the specific parallel architecture, whether shared memory multicore Intel Xeon using either OpenMP or TBB, or Nvidia GPUs with thousands of cores. We expect the principles used in ParCube to apply to other computational geometry problems. Efficiently finding all bipartite intersections would be an easy extension.


title: Sensing Technologies for Monitoring Serious Mental Illnesses

abstract: Mental health is an urgent global issue. Around 450 million people suffer from serious mental illnesses worldwide, which results in devastating personal outcomes and huge societal burden. Effective symptom monitoring and personalized interventions can significantly improve mental health care across different populations. However, traditional clinical methods often fall short when it comes to real-time monitoring of symptoms. Sensing technologies can address these issues by enabling granular tracking of behavioral, physiological, and social signals relevant to mental health. In this article, we describe how sensing technologies can be used to diagnose and monitor patient states for numerous serious mental illnesses. We also identify current limitations and potential future directions. We believe that the multimedia community can build on sensing technologies to enable efficient clinical decision-making in mental health care. Specifically, innovative multimedia systems can help identify and visualize personalized early-warning signs from complex multimodal signals, which could lead to effective intervention strategies and better preemptive care.


title: From friendfunding to crowdfunding: Relevance of relationships, social media, and platform activities to crowdfunding performance

abstract: Crowdfunding involves raising small amounts of money from a large number of people, typically via the Internet and social networks, to fund a project. Crowdfunding projects are mainly funded by the project creator’s relatively small network of family and friends. We argue that mobilizing funders outside this close network positively contributes to the success of a crowdfunding success. To study how project creators seek to attract funding from more distant/potential resources (latent ties) in addition to existing networks (strong and weak ties), we examined usage of social media (Facebook and Twitter) and the crowdfunding platform (website). We analyzed 10 cultural projects hosted on the Dutch crowdfunding platform “Voordekunst.” Our results contribute to theorizing on latent tie activation by demonstrating that social media messages and platform updates add economic value to the crowdfunding effort. Our study also explains the moderating effect of these messages on funders of various tie strengths.


title: Neural Models for Reasoning over Multiple Mentions using Coreference

abstract: Many problems in NLP require aggregating information from multiple mentions of the same entity which may be far apart in the text. Existing Recurrent Neural Network (RNN) layers are biased towards short-term dependencies and hence not suited to such tasks. We present a recurrent layer which is instead biased towards coreferent dependencies. The layer uses coreference annotations extracted from an external system to connect entity mentions belonging to the same cluster. Incorporating this layer into a state-of-the-art reading comprehension model improves performance on three datasets -- Wikihop, LAMBADA and the bAbi AI tasks -- with large gains when training data is scarce.


title: FlashMob: distributed adaptive self-assembly

abstract: Autonomous systems need to support dynamic software adaptation in order to handle the complexity and unpredictability of the execution environment, and the changing needs of the end user. Although a number of approaches have been proposed, few address a key issue: that of distribution. In this paper we seek to overcome the limitations of centralised approaches. We build on our previous work on adaptive self-assembly within the three-layer model for autonomous systems to provide a decentralised technique for self-assembly. To achieve this in a fault-tolerant and scalable manner, we use a gossip protocol as a basis. While no central or leader node is aware of the full space of solutions, gossip ensures that agreement on a particular solution - in this case a component configuration - is reached in a logarithmic number of steps with respect to the size of the network.


Animals and Science Education


title: Patterns for enterprise application integration

abstract: The development of Information Technology (IT) demands the integration of systems (legacy or new), so that they can operate in a distributed way and promoting the reuse of design, code and information in a high granularity level. Enterprise Application Integration (EAI) plays a fundamental role in this scenario. It can be conducted by different means, for example, by sharing the access to databases or using Web Services (WS). Both solutions are being adopted by organizations, although they present advantages and disadvantages that should be analysed. Several restrictions can be present in the particular integration scenario, causing problems to be solved before deciding if the integration should be done by data access or WS. So, this work has the objective of documenting these problems and solutions in the pattern format, in order to make available to other developers the knowledge acquired when doing EAI. These solutions can be used to improve reuse in certain integration contexts, depending on the organization business rules.


title: Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting

abstract: Graphical abstractDisplay Omitted HighlightsAn ensemble deep learning method has been proposed for load demand forecasting.The hybrid method composes of Empirical Mode Decomposition and Deep Belief Network.Empirical Mode Decomposition based methods outperform the single structure models.Deep learning shows more advantages when the forecasting horizon increases. Load demand forecasting is a critical process in the planning of electric utilities. An ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and deep learning approach is presented in this work. For this purpose, the load demand series were first decomposed into several intrinsic mode functions (IMFs). Then a Deep Belief Network (DBN) including two restricted Boltzmann machines (RBMs) was used to model each of the extracted IMFs, so that the tendencies of these IMFs can be accurately predicted. Finally, the prediction results of all IMFs can be combined by either unbiased or weighted summation to obtain an aggregated output for load demand. The electricity load demand data sets from Australian Energy Market Operator (AEMO) are used to test the effectiveness of the proposed EMD-based DBN approach. Simulation results demonstrated attractiveness of the proposed method compared with nine forecasting methods.


title: Adaptive finite state machine based visual autonomous navigation system

abstract: In this paper we present an original approach applied to autonomous mobile robots navigation integrating localization and navigation using a topological map based on the proposed AFSM (adaptive finite state machine) technique. In this approach, the environment is mapped as a graph, and each possible path is represented by a sequence of states controlled by a FSM-finite state machine. An ANN (artificial neural network) is trained to recognize patterns on input data, where each pattern is associated to specific environment features or properties, consequently representing the present context/state of the FSM. When a new input pattern is recognized by the ANN (changing the current context), this allows the FSM to change to the next state and its associated action/behavior. The input features are related to specific local properties of the environment (obtained from sensors data), as for example, straight path, right and left turns, and intersections. This way, the FSM is integrated to a previously trained ANN, which acts as a key component recognizing and indicating the present state and the state changes, allowing the AFSM to select the current/correct action (local reactive behaviors) for each situation. The AFSM allows the mobile robot to autonomously follow a sequence of states/behaviors in order to reach a destination, first choosing an adequate local reactive behavior for each current state, and second detecting the changes in the current context/state, following a sequence of states/actions that codes the topological (global) path into the FSM (sequence of states/actions). The ANN is also a very important component of this system, since it can be trained/adapted to recognize a complex set of situations and state changes. In order to demonstrate the robustness of the proposed approach to different situations and sensors configurations, we evaluated the proposed approach for both indoor and outdoor environments, using a Pioneer P3-AT robot equipped with Kinect sensor for indoor environments, and an automated vehicle equipped with a standard RGB camera for urban roads environments. The proposed method was tested in different situations with success and demonstrated to be a promising approach to autonomous mobile robots control and navigation.


title: TDoA based UAV Localization using Dual-EKF Algorithm

abstract: Most UAV(unmaned aerial vehicle) systems use GPS signals only to locate the emitter’s position. However GPS signals contain unwanted information contaminated by environmental components and many interference signals. In this paper, to obtain TDoA signal, we use two UAVs which are equipped with embedded wireless sensors. Under the real geolocation circumstance, it is very difficult to estimate the emitter’s position exactly due to environmental noise. In this paper we use the dual-EKF algorithm to obtain the optimal estimation of state values and unknown parameters. The dual-EKF algorithm overcomes the weakness of EKF algorithm which has been widely used in geolocation problem. The performance of our proposed algorithm will be demonstrated through some simulations of UAVs.


title: Best practices in teaching online or hybrid courses: a synthesis of principles

abstract: This paper examines both principles and best practices in designing and teaching large online or hybrid courses (more than 60 students) for undergraduate students. A model of best practices in online or hybrid course design and conduct is created based on the widely-accepted seven principles for quality undergraduate education and our extensive review of literature related to online teaching and learning. This model can guide universities of similar settings with their online teaching or training.


title: Trajectory tracking control of a car{trailer system

abstract: This paper presents the experimental results of the tracking control of a car-trailer system. The proposed scheme involves three steps: 1) generate a path off-line using a path space iterative algorithm; 2) linearize the kinematic model about a trajectory which is constructed using the path; and 3) apply a time-varying linear quadratic regulator to track the trajectory. Experiments presented include parallel parking a car, docking a tractor-trailer vehicle, and parallel parking a double tractor-trailer vehicle.


title: Phonetics embedding learning with side information

abstract: We show that it is possible to learn an efficient acoustic model using only a small amount of easily available word-level similarity annotations. In contrast to the detailed phonetic labeling required by classical speech recognition technologies, the only information our method requires are pairs of speech excerpts which are known to be similar (same word) and pairs of speech excerpts which are known to be different (different words). An acoustic model is obtained by training shallow and deep neural networks, using an architecture and a cost function well-adapted to the nature of the provided information. The resulting model is evaluated in an ABX minimal-pair discrimination task and is shown to perform much better (11.8% ABX error rate) than raw speech features (19.6%), not far from a fully supervised baseline (best neural network: 9.2%, HMM-GMM: 11%).


title: Giving feedback in clinical settings.

abstract: Think about a clinical teaching session that you supervised recently. How much feedback did you provide? How useful do you think your feedback was?


FPGA HIGH-LEVEL DESIGN METHODOLOGY COMES INTO ITS OWN


title: Endovascular Treatment of Complications of Femoral Arterial Access

abstract: Endovascular repair of femoral arterial access complications is nowadays the treatment of choice in a group of patients who cannot tolerate vascular reconstruction and bleeding due to advanced cardiovascular disease. Endovascular procedures can be performed under local anesthesia, are well tolerated by the patient, and are associated with a short hospitalization time. Ninitinol stent technology allows for safe stent and stent-graft extension at the common femoral artery (CFA) level, due to increased resistance to external compression and bending stress. Active pelvic bleeding can be insidious, and prompt placement of a stent-graft at the site of leakage is a lifesaving procedure. Percutaneous thrombin injection under US guidance is the treatment of choice for femoral pseudoaneurysms (PAs); this can theoretically be safer with simultaneous balloon occlusion across the entry site of a PA without a neck or with a short and wide neck. In a few cases with thrombin failure due to a large arterial defect or accompanying arteriovenous fistula (AVF), a stent-graft can be deployed. The vast majority of catheter-induced AVFs can be treated effectively with stent-graft implantation even if they are located very close to the femoral bifurcation. Obstructive dissection flaps localized in the CFA are usually treated with prolonged balloon inflation; however, in more extensive dissections involving iliac arteries, self-expanding stents should be deployed. Iliofemoral thrombosis can be treated effectively with catheter-directed thrombolysis (CDT) followed by prolonged balloon inflation or stent placement. Balloon angioplasty and CDT can occasionally be used to treat stenoses and occlusions complicating the use of percutaneous closure devices.


title: Lower life satisfaction related to materialism in children frequently exposed to advertising.

abstract: OBJECTIVE ::: Research among adults suggests that materialism and life satisfaction negatively influence each other, causing a downward spiral. So far, cross-sectional research among children has indicated that materialistic children are less happy, but causality remains uncertain. This study adds to the literature by investigating the longitudinal relation between materialism and life satisfaction. We also investigated whether their relation depended on children's level of exposure to advertising. ::: ::: ::: METHODS ::: A sample of 466 children (aged 8-11; 55% girls) participated in a 2-wave online survey with a 1-year interval. We asked children questions about material possessions, life satisfaction, and advertising. We used structural equation modeling to study the relationship between these variables. ::: ::: ::: RESULTS ::: For the children in our sample, no effect of materialism on life satisfaction was observed. However, life satisfaction did have a negative effect on materialism. Exposure to advertising facilitated this effect: We only found an effect of life satisfaction on materialism for children who were frequently exposed to advertising. ::: ::: ::: CONCLUSIONS ::: Among 8- to 11-year-old children, life satisfaction leads to decreased materialism and not the other way around. However, this effect only holds for children who are frequently exposed to television advertising. It is plausible that the material values portrayed in advertising teach children that material possessions are a way to cope with decreased life satisfaction. It is important to reduce this effect, because findings among adults suggest that materialistic children may become less happy later in life. Various intervention strategies are discussed.


TR 0707 A Fixed-Point Continuation Method for ` 1-Regularized Minimization with Applications to Compressed Sensing


title: Power and information technology: a review using metatriangulation

abstract: This study uses a metatriangulation approach to explore the relationships between power and information technology impacts, development or deployment, and management or use in a sample of 82 articles from 12 management and MIS journals published between 1980 and 1999. We explore the multiple paradigms underlying this research by applying two sets of lenses to examine the major findings from our sample. The technological imperative, organizational imperative, and emergent perspectives (Markus and Robey 1988) are used as one set of lenses to better understand researchers' views regarding the causal structure between IT and organizational power. A second set of lenses, which includes the rational, pluralist, interpretive, and radical perspectives (Bradshaw-Camball and Murray 1991), is used to focus on researchers' views of the role of power and different IT outcomes. We apply each lens separately to describe patterns emerging from the previous power and IT studies. In addition, we discuss the similarities and differences that occur when the two sets of lenses are simultaneously applied. We draw from this discussion to develop metaconjectures, (i.e., propositions that can be interpreted from multiple perspectives), and to suggest guidelines for studying power in future research.


title: Effective Multi-step Temporal-Difference Learning for Non-Linear Function Approximation

abstract: Multi-step temporal-difference (TD) learning, where the update targets contain information from multiple time steps ahead, is one of the most popular forms of TD learning for linear function approximation. The reason is that multi-step methods often yield substantially better performance than their single-step counter-parts, due to a lower bias of the update targets. For non-linear function approximation, however, single-step methods appear to be the norm. Part of the reason could be that on many domains the popular multi-step methods TD($\lambda$) and Sarsa($\lambda$) do not perform well when combined with non-linear function approximation. In particular, they are very susceptible to divergence of value estimates. In this paper, we identify the reason behind this. Furthermore, based on our analysis, we propose a new multi-step TD method for non-linear function approximation that addresses this issue. We confirm the effectiveness of our method using two benchmark tasks with neural networks as function approximation.


title: Multi-Doppler resolution automotive radar

abstract: Automotive radars operate in challenging environments that include objects with similar position and speed as well as objects with diverse positions and velocities. Automated driving requires the radar to discriminate close objects and also to accurately estimate the position of the objects in the field-of-view. Doppler filtering is essential to fulfill this goal. In conventional automotive radars the Doppler processing has a fixed and predetermined filtering integration time and hence a fixed Doppler resolution. However, in this case, setting the Doppler resolution is a tradeoff between high resolution that enables discrimination of close objects and accurate estimation of their position. In this paper we develop a multi-resolution Doppler processing method that resolves this tradeoff. The performance advantages of multi-resolution Doppler processing compared to the conventional fixed Doppler resolution are evaluated in an automotive scenario. It is shown that the multi-resolution Doppler processing attains better discrimination of close objects as well as more accurate position estimation of the objects.


title: Team leadership in the intensive care unit: the perspective of specialists

abstract: Objectives: To identify the behaviors senior physicians (e.g., specialists, staff attendings) report using to lead multidisciplinary teams in the intensive care unit. Design: Semistructured interviews focusing on team leadership, crisis management, and development of an environment that enable effective team performance in the intensive care unit. Setting: Seven general intensive care units based in National Health Service hospitals in the United Kingdom. Participants: Twenty-five senior intensive care medicine physicians. Measurements and Main Results: Responses to a semistructured interview were transcribed and subjected to “content” analysis. The interview analysis focused on references to the “functional” behaviors used by leaders to manage team performance and the “team development behaviors” used to build the conditions that enable effective team performance. Seven of the interviews were coded by a second psychologist to measure inter-rater reliability. Inter-rater reliability (Cohen's κ) was acceptable for both scales (κ = 0.72 and κ = 0.75). In total, 702 functional leadership behaviors (behaviors for information gathering, planning and decision-making, managing team members) were coded as being used to manage the intensive care unit, along with 216 team development behaviors (for providing team direction and establishing team norms). These behaviors were grouped together in a theoretically driven framework of intensive care unit team leadership. Conclusions: Intensive care unit senior physicians report using a variety of leadership behaviors to ensure high levels of team performance. The data described in this study provide insight into the team leadership behaviors used by intensive care unit team leaders and have implications for the development of team leadership training and assessment tools.


title: Channel Pruning for Accelerating Very Deep Neural Networks

abstract: In this paper, we introduce a new channel pruning method to accelerate very deep convolutional neural networks.Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel selection and least square reconstruction. We further generalize this algorithm to multi-layer and multi-branch cases. Our method reduces the accumulated error and enhance the compatibility with various architectures. Our pruned VGG-16 achieves the state-of-the-art results by 5x speed-up along with only 0.3% increase of error. More importantly, our method is able to accelerate modern networks like ResNet, Xception and suffers only 1.4%, 1.0% accuracy loss under 2x speed-up respectively, which is significant. Code has been made publicly available.


title: An approach to improve impedance plethysmography on the wrist by using adaptive feedback control

abstract: The growing market of wearable devices made ambulant vital sign monitoring popular in recent years. Impedance plethysmography (IPG) provides a promising low power solution for detecting pulse waves and respiration by measuring the impedance of blood vessels at the wrist. However, the measurement principle makes high demands on the analog front end, due to low signal-to-noise ratio (SNR) and unknown measurement conditions. In this paper an approach to apply impedance plethysmography is presented. The proposed circuit is based on the impedance measurement IC AD5933 and an adaptive feedback control to regulate current injection and measuring amplification in order to adjust the optimal operating point. Experimental studies demonstrate the potential of the system as promising technology for continuous vital sign monitoring on the wrist.


title: A multiple view stereo benchmark for satellite imagery

abstract: The availability of public multiple view stereo benchmark datasets has been instrumental in enabling research to advance the state of the art in the field and to apply and customize methods to real-world problems. Until now, no public multiple view stereo benchmark dataset has been available for satellite imaging applications. In this work, we describe a public benchmark dataset for multiple view stereo applied to three-dimensional outdoor scene mapping using commercial satellite imagery. This dataset includes fifty Digital Globe WorldView-3 panchromatic and multispectral images of a 100 square kilometer area near San Fernando, Argentina. We also provide high-resolution airborne lidar ground truth data for a 20 square kilometer subset of this area and performance analysis software to assess accuracy and completeness metrics. We report initial results from available solutions using this benchmark data and encourage continued research by making this benchmark dataset publicly available to the research community.


title: Throughput and delay evaluation framework integrating SDN and IEEE 802.11s WMN

abstract: Software defined Networks (SDN) is a paradigm that promises to facilitate the management of all types of networks, including the Wireless Mesh Networks (WMN). This integration enables an environment with enough resources and coverage to meet the demands of users, and thanks to a centralized network, resources can be managed in a better way. There are multiple simulators/emulators available, integrating SDN and WMN, but most of them do not have IEEE802.11s support. This paper proposes a framework that integrates NS-3, Open Vswitch, and Linux network namespaces, to create an environment that allows emulating IEEE802.11s SDN-WMN scenarios. The framework is used to evaluate the throughput and delay of a real-time streaming video application.


Early Breast Cancer Detection using Mammogram Images: A Review of Image Processing Techniques


title: Building a highly consumable semantic model for smarter cities

abstract: Despite enormous progress made in reconciling and simplifying semantic standards, industry-strength models are still few. In this work, we describe our challenges and approach in building semantic models for consumption in assembling large IT solutions in cyber-physical (Smarter Cities) domains. The result, called SCRIBE, is a large-scale semantic model for Smarter Cities based on data gathered from cities worldwide. We will discuss the design tradeoffs, designer/user experience, and some of the pragmatic challenges related to a model that integrates large, complex, heterogeneous data sources.


title: Bayesian Differential Privacy on Correlated Data

abstract: Differential privacy provides a rigorous standard for evaluating the privacy of perturbation algorithms. It has widely been regarded that differential privacy is a universal definition that deals with both independent and correlated data and a differentially private algorithm can protect privacy against arbitrary adversaries. However, recent research indicates that differential privacy may not guarantee privacy against arbitrary adversaries if the data are correlated. In this paper, we focus on the private perturbation algorithms on correlated data. We investigate the following three problems: (1) the influence of data correlations on privacy; (2) the influence of adversary prior knowledge on privacy; and (3) a general perturbation algorithm that is private for prior knowledge of any subset of tuples in the data when the data are correlated. We propose a Pufferfish definition of privacy, called Bayesian differential privacy, by which the privacy level of a probabilistic perturbation algorithm can be evaluated even when the data are correlated and when the prior knowledge is incomplete. We present a Gaussian correlation model to accurately describe the structure of data correlations and analyze the Bayesian differential privacy of the perturbation algorithm on the basis of this model. Our results show that privacy is poorest for an adversary who has the least prior knowledge. We further extend this model to a more general one that considers uncertain prior knowledge.


[The effect of synthesis cofactors and nucleic acid precursors on the contractile function of the heart in experimental myocardial infarct].


title: An arm-mounted inertial controller for 6DOF input: Design and evaluation

abstract: We designed a low-cost arm-mounted wearable 3D input device that uses inertial measurement units. The device is an alternative to tracking systems requiring fixed frames of reference. The device employs two inertial sensors mounted on the arm to derive a 3D cursor position through natural arm movement. We also explore three methods of selection, one entirely software based (dwell, holding the cursor in the target), one using a twist gesture, and one using a button. To address the paucity of research reporting human performance metrics, we quantify the performance of the device through a point-select experiment. Results indicate that throughput was 1.05 to 1.12 bits/s. In contrast, similar studies using conventional 3D trackers (e.g., NaturalPoint OptiTrack) report throughput ranging from 2.5 to 3.5 bits/s. However, error rates for the wearable input device were lower than with the OptiTrack system at 6.8% vs. 13.5%, respectively. A detailed analysis of system performance issues is provided along with design suggestions for future gyro-based input devices.


title: Surviving Violence in Everyday Life: A Communicative Approach to Homelessness.

abstract: In this narrative review, the author synthesizes the literature on homelessness across various disciplines (e.g., public health, social work, sociology, and communication) to demonstrate how the experiences of homelessness can be created, maintained, and reinforced through communication, including interpersonal interactions and public discourse. By conceptualizing homelessness as a culturally constructed and socially situated phenomenon, the author examines (a) the complex conceptualization of homelessness, (b) everyday violence faced by people who are homeless, and (c) coping strategies of people who are homeless. In summary, homelessness is a complex social phenomenon, involving tensions between individuals, families, and social systems, all of which are situated in the larger sociocultural and sociopolitical contexts of a specific time and place.


title: Model-based vision: a program to see a walking person☆

abstract: Abstract For a machine to be able to ‘see’, it must know something about the object it is ‘looking’ at. A common method in machine vision is to provide the machine with general rather than specific knowledge about the object. An alternative technique, and the one used in this paper, is a model-based approach in which particulars about the object are given and this drives the analysis. The computer program described here, the WALKER model, maps images into a description in which a person is represented by the series of hierarchical levels, i.e. a person has an arm which has a lower-arm which has a hand. The performance of the program is illustrated by superimposing the machine-generated picture over the original photographic images.


title: Design of Robust, Energy-Efficient Full Adders for Deep-Submicrometer Design Using Hybrid-CMOS Logic Style

abstract: We present a new design for a 1-b full adder featuring hybrid-CMOS design style. The quest to achieve a good-drivability, noise-robustness, and low-energy operations for deep submicrometer guided our research to explore hybrid-CMOS style design. Hybrid-CMOS design style utilizes various CMOS logic style circuits to build new full adders with desired performance. This provides the designer a higher degree of design freedom to target a wide range of applications, thus significantly reducing design efforts. We also classify hybrid-CMOS full adders into three broad categories based upon their structure. Using this categorization, many full-adder designs can be conceived. We will present a new full-adder design belonging to one of the proposed categories. The new full adder is based on a novel xor-xnor circuit that generates xor and xnor full-swing outputs simultaneously. This circuit outperforms its counterparts showing 5%-37% improvement in the power-delay product (PDP). A novel hybrid-CMOS output stage that exploits the simultaneous xor-xnor signals is also proposed. This output stage provides good driving capability enabling cascading of adders without the need of buffer insertion between cascaded stages. There is approximately a 40% reduction in PDP when compared to its best counterpart. During our experimentations, we found out that many of the previously reported adders suffered from the problems of low swing and high noise when operated at low supply voltages. The proposed full adder is energy efficient and outperforms several standard full adders without trading off driving capability and reliability. The new full-adder circuit successfully operates at low voltages with excellent signal integrity and driving capability. To evaluate the performance of the new full adder in a real circuit, we embedded it in a 4- and 8-b, 4-operand carry-save array adder with final carry-propagate adder. The new adder displayed better performance as compared to the standard full adders


Science in the imported meat industry.


title: Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization

abstract: In multilabel learning, each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, this problem is addressed in the way that a neural network algorithm named BP-MLL, i.e., backpropagation for multilabel learning, is proposed. It is derived from the popular backpropagation algorithm through employing a novel error function capturing the characteristics of multilabel learning, i.e., the labels belonging to an instance should be ranked higher than those not belonging to that instance. Applications to two real-world multilabel learning problems, i.e., functional genomics and text categorization, show that the performance of BP-MLL is superior to that of some well-established multilabel learning algorithms


title: PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents

abstract: In recent years, deep convolutional neural networks have achieved state of the art performance in various computer vision task such as classification, detection or segmentation. Due to their outstanding performance, CNNs are more and more used in the field of document image analysis as well. In this work, we present a CNN architecture that is trained with the recently proposed PHOC representation. We show empirically that our CNN architecture is able to outperform state of the art results for various word spotting benchmarks while exhibiting short training and test times.


title: Broadband transition from dielectric-filled to air-filled Substrate Integrated Waveguide for low loss and high power handling millimeter-wave Substrate Integrated Circuits

abstract: Air-filled Substrate Integrated Waveguide (SIW) based on multilayer Printed Circuit Board (PCB) process is proposed in this paper for millimeter-wave applications that require low cost, high performances and compactness. This air-filled SIW allows for substantial loss reduction and power handling enhancement. Its fabrication involves three layers. The top and bottom substrates can consist of a low cost standard substrate such as FR-4 on which base-band or digital circuits can be implemented so to obtain a very compact, high performance and low cost millimeter-wave system. At Ka-band, it is shown that air-filled SIW compared to dielectric-filled SIW based on Rogers RT/Duroid 5880 and 6002 reduces losses by a mean value of 0.054 dB/cm and 0.11 dB/cm and increases average power handling capability by 6 dB and 8 dB, respectively. To allow interconnects of the proposed air-filled SIW with dielectric-filled SIW, a broadband transition is presented. The design steps of this transition are detailed. For demonstration purposes, a back-to-back transition operating over the Ka-band is fabricated. It achieves a matching of better than -15 dB and an insertion loss of 0.6 ±0.2 dB (0.3 ±0.1 dB for the transition) from 27 to 40 GHz.


title: Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

abstract: Developing classification algorithms that are fair with respect to sensitive attributes of the data is an important problem due to the increased deployment of classification algorithms in societal contexts. Several recent works have focused on studying classification with respect to specific fairness metrics, modeled the corresponding fair classification problem as constrained optimization problems, and developed tailored algorithms to solve them. Despite this, there still remain important metrics for which there are no fair classifiers with theoretical guarantees; primarily because the resulting optimization problem is non-convex. The main contribution of this paper is a meta-algorithm for classification that can take as input a general class of fairness constraints with respect to multiple non-disjoint and multi-valued sensitive attributes, and which comes with provable guarantees. In particular, our algorithm can handle non-convex "linear fractional" constraints (which includes fairness constraints such as predictive parity) for which no prior algorithm was known. Key to our results is an algorithm for a family of classification problems with convex constraints along with a reduction from classification problems with linear fractional constraints to this family. Empirically, we observe that our algorithm is fast, can achieve near-perfect fairness with respect to various fairness metrics, and the loss in accuracy due to the imposed fairness constraints is often small.


title: Design and implement of QPSK modem based on FPGA

abstract: This paper presents a method to designs QPSK modulator and demodulator of a spread spectrum system, which use field programmable device. The method uses the tool of Quartus II of American Altera Co. The whole system is divided into several small models based on top-down design method, and using VHDL hardware description language to design each model. The direct digital synthesis (DDS) principle is briefly presented and used to design orthogonal cosine signal module. In demodulator, we use the low pass FIR filtering to filter high frequency component. The QPSK module is ultimately implemented on the FPGA device. The whole system has been simulated in the Quartus II7.2 simulation environment and successfully downloaded to the chip of the Cyclone II EP2C5F256C6. The operating results and the theoretical results are consistent, so it's verified the correctness of this design. The results showed that the proposed method can greatly improve the developing efficiency, shorten developing period and reduce costs.


title: Empirical evaluation for finger input properties in multi-touch interaction

abstract: Current multi-touch interaction techniques typically only use the x-y coordinates of the human finger's contact with the screen. However, when fingers contact a touch-sensitive surface, they usually approach at an angle and cover a relatively large 2D area instead of a precise single point. In this paper, a Frustrated Total Internal Reflection (FTIR) based multi-touch device is used to collect the finger imprint data. We designed a series of experiments to explore human finger input properties and identified several useful properties such as contact area, contact shape and contact orientation which can be exploited to improve the performance of multi-touch selecting and pointing tasks. Based on the experimental results, we discuss some implications for the design of human finger input interfaces and propose several design prototypes which incorporate these implications. A set of raw data and several concrete recommendations which are useful for the research community are also presented.


title: Graphflow: An Active Graph Database

abstract: Many applications detect the emergence or deletion of certain subgraphs in their input graphs continuously. In order to evaluate such continuous subgraph queries, these applications resort to inefficient or highly specialized solutions because existing graph databases are passive systems that only support one-time subgraph queries. We demonstrate Graphflow, a prototype active graph data-base that evaluates general one-time and continuous subgraph queries. Graphflow supports the property graph data model and the Cypher++ query language, which extends Neo4j's declarative Cypher language with subgraph-condition-action triggers. At the core of Graphflow's query processor are two worst-case optimal join algorithms called Generic Join and our new Delta Generic Join algorithm for one-time and continuous subgraph queries, respectively.


title: WADaR: Joint Wrapper and Data Repair

abstract: Web scraping (or wrapping) is a popular means for acquiring data from the web. Recent advancements have made scalable wrapper-generation possible and enabled data acquisition processes involving thousands of sources. This makes wrapper analysis and maintenance both needed and challenging as no scalable tools exists that support these tasks. ::: ::: We demonstrate WADaR, a scalable and highly automated tool for joint wrapper and data repair. WADaR uses off-the-shelf entity recognisers to locate target entities in wrapper-generated data. Markov chains are used to determine structural repairs, that are then encoded into suitable repairs for both the data and corresponding wrappers. ::: ::: We show that WADaR is able to increase the quality of wrapper-generated relations between 15% and 60%, and to fully repair the corresponding wrapper without any knowledge of the original website in more than 50% of the cases.


title: Predicting Mental Health Status on Social Media

abstract: The rapid development of social media brings about vast user generated content. Computational cyber-psychology, an interdisciplinary subject area, employs machine learning approaches to explore underlying psychological patterns. Our research aims at identifying users’ mental health status through their social media behavior. We collected both users’ social media data and mental health data from the most popular Chinses microblog service provider, Sina Weibo. By extracting linguistic and behavior features, and applying machine learning algorithms, we made preliminary exploration to identify users’ mental health status automaticly, which previously is mainly measured by well-designed psychological questionnaire. Our classification model achieves the accuracy of 72%, and the continous predicting model achieved correlation of 0.3 with questionnaire based score.


title: Globally optimal direction fields

abstract: We present a method for constructing smooth n-direction fields (line fields, cross fields, etc.) on surfaces that is an order of magnitude faster than state-of-the-art methods, while still producing fields of equal or better quality. Fields produced by the method are globally optimal in the sense that they minimize a simple, well-defined quadratic smoothness energy over all possible configurations of singularities (number, location, and index). The method is fully automatic and can optionally produce fields aligned with a given guidance field such as principal curvature directions. Computationally the smoothest field is found via a sparse eigenvalue problem involving a matrix similar to the cotan-Laplacian. When a guidance field is present, finding the optimal field amounts to solving a single linear system.


title: The next generation of scenarios for climate change research and assessment

abstract: Advances in the science and observation of climate change are providing a clearer understanding of the inherent variability of Earth's climate system and its likely response to human and natural influences. The implications of climate change for the environment and society will depend not only on the response of the Earth system to changes in radiative forcings, but also on how humankind responds through changes in technology, economies, lifestyle and policy. Extensive uncertainties exist in future forcings of and responses to climate change, necessitating the use of scenarios of the future to explore the potential consequences of different response options. To date, such scenarios have not adequately examined crucial possibilities, such as climate change mitigation and adaptation, and have relied on research processes that slowed the exchange of information among physical, biological and social scientists. Here we describe a new process for creating plausible scenarios to investigate some of the most challenging and important questions about climate change confronting the global community.


title: Market Orientation and R&D Effectiveness in High-Technology Firms: An Empirical Investigation in the Biotechnology Industry

abstract: There seems to be lack of consensus among informed scholars about the importance a of market orientation for high-technology firms. This paper gives a comprehensive review of existing empirical studies on the relationship between market orientation and innovation performance and pinpoints two limitations in this research stream that might be at the origin of such controversy. First, extant research often overlooked key innovation outcomes for high-technology firms, such as those related to research and development (R&D) performance. Second, organizational conditions that can ensure an optimal integration of market knowledge in the innovation process have been less analyzed in the case of these firms. Against this background, the present study contributes to the literature by providing a test of the effect of market orientation on R&D effectiveness and the moderating role of knowledge integration in this relationship, using a sample of Italian biotechnology firms. The study's objectives are addressed in two steps. The first one consists of an in-depth qualitative study based on semistructured interviews in five biotechnology firms. The second step consists of a follow-up survey of 50 biotechnology firms. Results from hierarchical multiple regression analysis show that the different dimensions of a market orientation have diverse effects on R&D effectiveness of high-technology firms: whereas interfunctional coordination has a positive main effect, the effect of customer orientation is moderated by knowledge integration, and competitor orientation has no effect on R&D effectiveness. Post hoc analyses also show two additional results involving a broader set of dependent variables. First, R&D effectiveness mediates the effects of customer orientation and interfunctional coordination on organizational performance. Second, market orientation does not appear to significantly affect R&D efficiency. The present study contributes to current literature in two main respects. First, it adds to previous work on market orientation and innovation by proposing a new dependent variable—R&D effectiveness—which offers a better perspective to understand the impact of market orientation on innovation performance in high-technology contexts. Second, while part of the current debate on the role of market orientation in high-tech markets seems to be polarized by positions that sustain its potential drawbacks or, on the contrary, its advantages, this study's findings on the moderating role of knowledge integration shed light on important contingency factors, such as organizational capabilities. The authors discuss the study's limitations and provide directions for future research.


title: Robust Aerial Robot Swarms Without Collision Avoidance

abstract: One of the main challenges in robot swarming arises from the need to design controllers that guarantee safety and motion planners that guarantee collision avoidance. This translates into sensors that measure proximity to neighbors and algorithms that scale exponentially with the number of robots. In many cases, these problems are solved very differently in nature, especially at smaller scales. The penalty due to collisions is small at these scales and sensors and controllers are not precise enough to guarantee collision-free trajectories. We explore a similar approach with small flying robots. We present a design that is capable of sustaining collisions, controllers that are able to recover from collisions, and simple motion planners that allow the robots to navigate without complete knowledge of the environment. We illustrate the potential of bioinspired swarms using simulations and experimental results.


title: A 2.5-Gb/s 15-mW clock recovery circuit

abstract: This paper describes the design of a 2.5-Gb/s 15-mW clock recovery circuit based on the quadricorrelator architecture. Employing both phase and frequency detection, the circuit combines high-speed operations such as differentiation, full-wave rectification, and mixing in one stage to lower the power dissipation. In addition, a two-stage voltage-controlled oscillator is utilized that incorporates both phase shift elements to provide a wide tuning range and isolation techniques to suppress the feedthrough due to input data transitions. Fabricated in a 20-GHz 1-/spl mu/m BiCMOS technology, the circuit exhibits an rms jitter of 9.5 ps and a capture range of 300 MHz.


title: Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis

abstract: In this paper, we present a general framework for understanding the role of artificial neural networks (ANNs) in bankruptcy prediction. We give a comprehensive review of neural network applications in this area and illustrate the link between neural networks and traditional Bayesian classification theory. The method of cross-validation is used to examine the between-sample variation of neural networks for bankruptcy prediction. Based on a matched sample of 220 firms, our findings indicate that neural networks are significantly better than logistic regression models in prediction as well as classification rate estimation. In addition, neural networks are robust to sampling variations in overall classification performance.


title: Unconstrained Impedance Control Using a Compact Series Elastic Actuator

abstract: Series elastic actuators offer high fidelity force control with inherent low impedance above their controllable frequency bandwidth. As a result they well suited for interaction with humans, in which safety is paramount. Their high force fidelity suggests that they may be a suitable for impedance control, in which the interaction between motion and force is controlled. The authors model the unconstrained actuator, and perform tests to evaluate the ability of series elastic actuators to mimic different stiffness. Series elastic actuators are found to be adequate for unconstrained impedance control at input frequencies below 3 Hz.


title: A Simple Flood Forecasting Scheme Using Wireless Sensor Networks

abstract: This paper presents a forecasting model designed using WSNs( Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.


title: Media-portrayed idealized images, body shame, and appearance anxiety

abstract: Objective: This study was designed to determine the effects of media-portrayed idealized images on young women’s body shame and appearance anxiety, and to establish whether the effects depend on advertisement type and on participant self-objectification. Method: Participants were 39 female university students. Twenty-four magazine advertisements comprised 12 body-related and 12 non–body-related products, one half of each with, and the other one half without, idealized images. Preexposure and post exposure body shame and appearance anxiety measures were recorded. Results: Appearance anxiety increased after viewing advertisements featuring idealized images. There was also a significant interaction between selfobjectification level and idealized body (presence vs. absence). No differences emerged for body-related compared with non–body-related product advertisements. The only result for body shame was a main effect for time. Participants’ body shame increased after exposure to idealized images, irrespective of advertisement type. Discussion: Although our findings reveal that media-portrayed idealized images detrimentally affect the body image of young women, they highlight the individual differences in vulnerability and the different effects for different components of body image. These results are discussed in terms of their implications for the prevention and early intervention of body image and dieting-related disorders. a 2005 by Wiley Periodicals, Inc.


title: On-Board-Unit Data: A Big Data Platform for Scalable storage and Processing

abstract: This paper proposes and assesses a Big Data Platform for effective storage and analysis of On Board Unit (OBU) data related to the mobility of trucks in Belgium. The large volume and the streaming nature of the OBU data requires the setup of a big data platform for an efficient collection, storage and analysis. The solution relies on i) the Hadoop Distributed File System (HDFS) to store data, ii) the Apache Parquet format for data compression and columnar storage, and iii) Spark for parallel and streaming processing of data. Data replication, compression and columnar storage ensure robustness to node failure, data distribution, and faster access to data.


title: Differentiation mechanisms for IEEE 802.11

abstract: The IETF is currently working on service differentiation in the Internet. However, in wireless environments where bandwidth is scarce and channel conditions are variable, IP differentiated services are sub-optimal without lower layers' support. We present three service differentiation schemes for IEEE 802.11. The first one is based on scaling the contention window according to the priority of each flow or user. The second one assigns different inter-frame spacings to different users. Finally, the last one uses different maximum frame lengths for different users. We simulate and analyze the performance of each scheme with TCP and UDP flows.


Monads for Functional Programming


Rationale for Multiple British Columbia Instream Flow Standards to Maintain Ecosystem Function and Biodiversity


title: A realistic military scenario and emulation environment for experimenting with tactical communications and heterogeneous networks

abstract: Emulation environments are an effective approach to experimenting with and evaluating network protocols, algorithms, and components. This paper describes a joint effort by the NATO Science & Technology Organization's IST-124 task group to develop and distribute an emulation environment and scenario. The most significant contribution is a vetted and militarily-realistic scenario that provides a rich combination of elements for experimentation. The scenario includes detailed mobility patterns for a battalion-sized operation over the course of two hours, which has been developed by military experts in planning and performing real exercises. The mobility patterns are used to drive the network emulation.


title: Almost periodic solutions for impulsive neural networks with delay

abstract: In the present paper the problems of existence and uniqueness of almost periodic solutions for impulsive cellular neural networks with delay are considered.


title: Inpainting of sparse occlusion in face recognition

abstract: Facial occlusion is a critical issue in many face recognition applications. Existing approaches of face recognition under occlusion conditions mainly focus on the conventional facial accessories (such as sunglasses and scarf) and thus presume that the occluded region is dense and contiguous. Yet due to the wide variety of natural sources which can occlude a human face in uncontrolled environments, methods based on the dense assumption are not robust to thin and randomly distributed occlusions. This paper presents the solution to a newly identified facial occlusion problem - sparse occlusion in the context of face biometrics in video surveillance. We show that the occluded pixels can be detected in the low-rank structure of a canonical face set under the Robust-PCA framework; and the occluded part can be inpainted solely based on the nonoccluded part and a Fields-of-Experts prior via spatial inference. Experiments demonstrate that the proposed approach significantly improve various face recognition algorithms in presence of complex sparse occlusions.


title: Raven-II: An Open Platform for Surgical Robotics Research

abstract: The Raven-II is a platform for collaborative research on advances in surgical robotics. Seven universities have begun research using this platform. The Raven-II system has two 3-DOF spherical positioning mechanisms capable of attaching interchangeable four DOF instruments. The Raven-II software is based on open standards such as Linux and ROS to maximally facilitate software development. The mechanism is robust enough for repeated experiments and animal surgery experiments, but is not engineered to sufficient safety standards for human use. Mechanisms in place for interaction among the user community and dissemination of results include an electronic forum, an online software SVN repository, and meetings and workshops at major robotics conferences.


title: Using Scale Space Filtering to Make Thinning Algorithms Robust Against Noise in Sketch Images

abstract: We apply scale space filtering to thinning of binary sketch images by introducing a framework for making thinning algorithms robust against noise. Our framework derives multiple representations of an input image within multiple scales of filtering. Then, the filtering scale that gives the best trade-off between noise removal and shape distortion is selected. The scale selection is done using a performance measure that detects extra artifacts (redundant branches and lines) caused by noise and shape distortions introduced by high amount of filtering. In other words, our contribution is an adaptive preprocessing, in which various thinning algorithms can be used, and which task is to estimate automatically the optimal amount of filtering to deliver a neat thinning result. Experiments using five state-of-the-art thinning algorithms, as the framework’s thinning stage, show that robustness against various types of noise was achieved. They are mainly contour noise, scratch, and dithers. In addition, application of the framework in sketch matching shows its usefulness as a preprocessing and normalization step that improves matching performances.


title: Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition

abstract: Domain adaptation aims to correct the mismatch in statistical properties between the source domain on which a classifier is trained and the target domain to which the classifier is to be applied. In this paper, we address the challenging scenario of unsupervised domain adaptation, where the target domain does not provide any annotated data to assist in adapting the classifier. Our strategy is to learn robust features which are resilient to the mismatch across domains and then use them to construct classifiers that will perform well on the target domain. To this end, we propose novel kernel learning approaches to infer such features for adaptation. Concretely, we explore two closely related directions. In the first direction, we propose unsupervised learning of a geodesic flow kernel (GFK). The GFK summarizes the inner products in an infinite sequence of feature subspaces that smoothly interpolates between the source and target domains. In the second direction, we propose supervised learning of a kernel that discriminatively combines multiple base GFKs. Those base kernels model the source and the target domains at fine-grained granularities. In particular, each base kernel pivots on a different set of landmarks--the most useful data instances that reveal the similarity between the source and the target domains, thus bridging them to achieve adaptation. Our approaches are computationally convenient, automatically infer important hyper-parameters, and are capable of learning features and classifiers discriminatively without demanding labeled data from the target domain. In extensive empirical studies on standard benchmark recognition datasets, our appraches yield state-of-the-art results compared to a variety of competing methods.


title: Smart (in-home) power scheduling for demand response on the smart grid

abstract: This paper proposes a power scheduling-based communication protocol for in-home appliances connected over home area network and receiving real-time electricity prices via a smart meter. Specifically, a joint media access and appliance scheduling approach is developed to allow appliances to coordinate power usage so that total demand for the home is kept below a target value. Two types of appliances are considered: 1) “real-time” which consume power as they desire; and 2) “schedulable” which can be turned on at a later time. Simulation results indicate that for an appropriate target total power consumption, our scheme leads to a reduced peak demand for the home and produces a demand that is more level over time.


title: Adaptive Regularization of the NL-Means: Application to Image and Video Denoising

abstract: Image denoising is a central problem in image processing and it is often a necessary step prior to higher level analysis such as segmentation, reconstruction, or super-resolution. The nonlocal means (NL-means) perform denoising by exploiting the natural redundancy of patterns inside an image; they perform a weighted average of pixels whose neighborhoods (patches) are close to each other. This reduces significantly the noise while preserving most of the image content. While it performs well on flat areas and textures, it suffers from two opposite drawbacks: it might over-smooth low-contrasted areas or leave a residual noise around edges and singular structures. Denoising can also be performed by total variation minimization-the Rudin, Osher and Fatemi model-which leads to restore regular images, but it is prone to over-smooth textures, staircasing effects, and contrast losses. We introduce in this paper a variational approach that corrects the over-smoothing and reduces the residual noise of the NL-means by adaptively regularizing nonlocal methods with the total variation. The proposed regularized NL-means algorithm combines these methods and reduces both of their respective defaults by minimizing an adaptive total variation with a nonlocal data fidelity term. Besides, this model adapts to different noise statistics and a fast solution can be obtained in the general case of the exponential family. We develop this model for image denoising and we adapt it to video denoising with 3D patches.


title: Factors affecting technology integration in K-12 classrooms: a path model

abstract: The purpose of this study was to examine the direct and indirect effects of teachers’ individual characteristics and perceptions of environmental factors that influence their technology integration in the classroom. A research-based path model was developed to explain causal relationships between these factors and was tested based on data gathered from 1,382 Tennessee public school teachers. The results provided significant evidence that the developed model is useful in explaining factors affecting technology integration and the relationships between the factors.


title: A missense mutation in ALDH18A1, encoding Δ1-pyrroline-5-carboxylate synthase (P5CS), causes an autosomal recessive neurocutaneous syndrome

abstract: There are several rare syndromes combining wrinkled, redundant skin and neurological abnormalities. Although phenotypic overlap between conditions has suggested that some might be allelic to one another, the aetiology for many of them remains unknown. A consanguineous New Zealand Maori family has been characterised that segregates an autosomal recessive connective tissue disorder (joint dislocations, lax skin) associated with neurological abnormalities (severe global developmental delay, choreoathetosis) without metabolic abnormalities in four affected children. A genome-screen performed under a hypothesis of homozygosity by descent for an ancestral mutation, identified a locus at 10q23 (Z = 3.63). One gene within the candidate interval, ALDH18A1, encoding Delta1-pyrroline-5-carboxylate synthase (P5CS), was considered a plausible disease gene since a missense mutation had previously been shown to cause progressive neurodegeneration, cataracts, skin laxity, joint dislocations and metabolic derangement in a consanguineous Algerian family. A missense mutation, 2350C>T, was identified in ALDH18A1, which predicts the substitution H784Y. H784 is invariant across all phyla and lies within a previously unrecognised, conserved C-terminal motif in P5CS. In an in vivo assay of flux through this metabolic pathway using dermal fibroblasts obtained from an affected individual, proline and ornithine biosynthetic activity of P5CS was not affected by the H784Y substitution. These data suggest that P5CS may possess additional uncharacterised functions that affect connective tissue and central nervous system function.


title: Image Representations and New Domains in Neural Image Captioning

abstract: We examine the possibility that recent promising results in automatic caption generation are due primarily to language models. By varying image representation quality produced by a convolutional neural network, we find that a state-of-the-art neural captioning algorithm is able to produce quality captions even when provided with surprisingly poor image representations. We replicate this result in a new, fine-grained, transfer learned captioning domain, consisting of 66K recipe image/title pairs. We also provide some experiments regarding the appropriateness of datasets for automatic captioning, and find that having multiple captions per image is beneficial, but not an absolute requirement.


title: The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire.

abstract: We examine and refine the Fagerström Tolerance Questionnaire (FTQ: Fagerström, 1978). The relation between each FTQ item and biochemical measures of heaviness of smoking was examined in 254 smokers. We found that the nicotine rating item and the inhalation item were unrelated to any of our biochemical measures and these two items were primary contributors to psychometric deficiencies in the FTQ. We also found that a revised scoring of time to the first cigarette of the day (TTF) and number of cigarettes smoked per day (CPD) improved the scale. We present a revision of the FTQ: the Fagerström Test for Nicotine Dependence (FTND).


title: A Design Pattern for Parallel Programming of Games

abstract: We present a new design pattern, Sayl, for parallel programming of games and real-time simulation applications to make effective use of the multiple cores of CPUs which are becoming commonplace in today's host machines. Sayl is a specialization of the more generic Task Parallelism pattern. The specialization addresses three important requirements of programs in this domain -- heterogeneity of tasks, data flow type dependency amongst tasks, and dynamically changing set of active tasks in different frames of the game. Our main claim is that use of Sayl involves less programmer work. Further, by enabling the creation of dynamic task graphs, Sayl can enable parallel execution of tasks both within and across frames; the latter is one significant advantage of Sayl which seems not possible in most other parallelization approaches targeted to this domain. Sayl was used in a case study to convert the serial code of a small game into a parallel version and compared with an-other game parallelization framework. The Sayl version was simpler to code and had better run-time performance.


title: Adaptive Video Content Manipulation for OLED Display Power Management

abstract: Emerged as a choice of display for vivid colours and power efficiency in expensive Smartphones, in recent days Organic Light Emitting Diode (OLED) displays are increasingly replacing Liquid Crystal Displays (LCD) and Plasma in Smartphones and large screen Televisions. Yet, OLED displays are one of the most energy consuming modules in modern Smartphones. Power consumption of OLED displays have strong correlation with the colours illuminated by its pixels. Improvements in pixel density by manufacturers makes the displays more power hungry. In this paper, we present Human Visual System (HVS) aware adaptive colour transformation and darkening methods to create power-efficient videos for OLED displays while preserving visual fidelity of the videos. Our measurements and subjective evaluations show that we can save up to 60% of energy consumption with minimum distortion to visual quality of the video. As our colour transformation process uses pre-computed colour-map and the darkening process uses the gamma-correction function which is already implemented in most of the display hardware, runtime computation overheads are minimum and hence, our method can be used to manipulate realtime/live video streams as well.


title: Multi-Story Indoor Floor Plan Reconstruction via Mobile Crowdsensing

abstract: The lack of floor plans is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this paper, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size, and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes, and shapes. It also identifies different types of connection areas (e.g., escalators and stairs) between stories, and employs a refinement algorithm to correct detection errors. Our experiments on three stories of two large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about $1\sim 2m$ and $5\sim 9^\circ$ , while the hallway connectivity and connection areas between stories are 100 percent correct.


title: Reconfigurable Skyrmion Logic Gates

abstract: Magnetic skyrmion, a nanosized spin texture with topological property, has become an area of significant interest due to the scientific insight that it can provide and also its potential impact on applications such as ultra-low-energy and ultra-high-density logic gates. In the quest for the reconfiguration of single logic device and the implementation of the complete logic functions, a novel reconfigurable skyrmion logic (RSL) is proposed and verified by micromagnetic simulations. Logic functions including AND, OR, NOT, NAND, NOR, XOR, and XNOR are implemented in the ferromagnetic (FM) nanotrack by virtue of various effects including spin orbit torque, skyrmion Hall effect, skyrmion–edge repulsions, and skyrmion–skyrmion collision. Different logic functions can be selected in an RSL by applying voltage to specific region(s) of the device, changing the local anisotropy energy of FM film. Material properties and geometrical scaling studies suggest RSL gates fit for energy-efficient computing as well as prov...


title: Translating Questions to SQL Queries with Generative Parsers Discriminatively Reranked

abstract: In this paper, we define models for automatically translating a factoid question in natural language to an SQL query that retrieves the correct answer from a target relational database (DB). We exploit the DB structure to generate a set of candidate SQL queries, which we rerank with an SVM-ranker based on tree kernels. In particular, in the generation phase, we use (i) lexical dependencies in the question and (ii) the DB metadata, to build a set of plausible SELECT, WHERE and FROM clauses enriched with meaningful joins. We combine the clauses by means of rules and a heuristic weighting scheme, which allows for generating a ranked list of candidate SQL queries. This approach can be recursively applied to deal with complex questions, requiring nested SELECT instructions. Finally, we apply the reranker to reorder the list of question and SQL candidate pairs, whose members are represented as syntactic trees. The F1 of our model derived on standard benchmarks, 87% on the first question, is in line with the best models using external and expensive hand-crafted resources such as the question meaning interpretation. Moreover, our system shows a Recall of the correct answer of about 94% and 98% on the first 2 and 5 candidates, respectively. This is an interesting outcome considering that we only need pairs of questions and answers concerning a target DB (no SQL query is needed) to train our model.


Monitoring of slope-instabilities and deformations with Micro-Electro-Mechanical-Systems (MEMS) in wireless ad-hoc Sensor Networks


title: Student's Attitudes towards Online Assignment Submission at College of Health Sciences, University of Bahrain

abstract: Technology has made a significant impact on assessment, including its use to automate assignment submission. All processes in the workflow - submission of assignments, assessment, and feedback given are performed using electronic files. Online assignment submission was introduced at the College of Health Sciences (CHS), University of Bahrain (UoB) in 2008 through the Learning Management System. This research attempts to answer two questions: Firstly, what are the perceptions of the CHS instructors and students about the use of online assignment submission? Secondly, what challenges do instructors and students encounter in using online assignment submission at the CHS? One hundred and forty (140) students from different programs at the College have participated in this study. Ninety six (96%) of the CHS students had very good computer skills and e-learning experience and 63% of the students reported familiarity with online assignments during their study period at the CHS. Eighty percent (80%) of students prefer to submit and to get their assignments grades online. Also, eighty three percent (83%) think that online assignment submission is quicker than the hard copy submission. Eight (8) teaching faculty from the Integrated Sciences Department participated in the interviews. The interview results revealed that although most teaching faculty have good skills in using computers and its applications, they prefer to use traditional teaching methods with increased dependence on computer applications. They also believe that the Web based programs with supporting materials such as online assignments, animations, and videos support the teaching process and make learning more enjoyable.


title: Back up your Stance: Recognizing Arguments in Online Discussions

abstract: In online discussions, users often back up their stance with arguments. Their arguments are often vague, implicit, and poorly worded, yet they provide valuable insights into reasons underpinning users’ opinions. In this paper, we make a first step towards argument-based opinion mining from online discussions and introduce a new task of argument recognition. We match usercreated comments to a set of predefined topic-based arguments, which can be either attacked or supported in the comment. We present a manually-annotated corpus for argument recognition in online discussions. We describe a supervised model based on comment-argument similarity and entailment features. Depending on problem formulation, model performance ranges from 70.5% to 81.8% F1-score, and decreases only marginally when applied to an unseen topic.


title: Art, dance, and music therapy

abstract: At the beginning of the twenty-first century, the creative arts therapies are firmly established as an important part of complementary medicine for psychologic and physiologic illnesses. The arts therapies are present in every facet of medical practice in hospitals, hospices, other health care institutions, and private practice. Art, dance, and music therapy, as they now are practiced, were formally organized in the twentieth century. Before that time, art, dance, and music played an informal although continuous role in eastern and western medicine. Through the centuries, the healing nature of these creative therapies has been primarily reported in anecdotes that describe a way of restoring wholeness to a person struggling with either mind or body illness. Since the 1950s, however, there has been a trend toward descriptive and experimental research in all three fields [1]. Although art, dance, and music therapy are present in all areas of health care, the literature presently shows relatively few strictly controlled research studies. The three arts therapies will be discussed in this article primarily in the context of interventions for brain injury, cognitive dysfunction, pain, and musculoskeletal injuries. As a registered practitioner, a therapist may work in private practice or with a medical team in a hospital or institutional setting. The advent of these trained practitioners into medical facilities has changed the nature of health care in the past century by contributing to the esthetic ambience of facilities, patient and staff well-being, and encouragement of patients to participate in the artistic process. The National Center of Complementary and Alternative


title: FPGA Implementations of the AES Masked Against Power Analysis Attacks

abstract: Power analysis attacks are a serious treat for implementations of modern cryptographic algorithms. Masking is a particularly appealing countermeasure against such attacks since it increases the security to a well quantifiable level and can be implemented without modifying the underlying technology. Its main drawback is the performance overhead it implies. For example, due to prohibitive memory costs, the straightforward application of masking to the AES algorithm, with precomputed tables, is hardly practical. In this paper, we exploit both the increased size of state-of-the-art reconfigurable hardware devices and previous optimization techniques to minimize the memory occupation of software S-boxes, in order to provide an efficient FPGA implementation of the AES algorithm, masked against side-channel attacks. We describe two high throughput architectures, based on 32-bit and 128-bit datapaths that are suitable for Xilinx Virtex-5 devices. In this way, we demonstrate the possibility to efficiently combine technological advances with algorithmic optimizations in this context.


title: A geometric study of v-style pop-ups: theories and algorithms

abstract: Pop-up books are a fascinating form of paper art with intriguing geometric properties. In this paper, we present a systematic study of a simple but common class of pop-ups consisting of patches falling into four parallel groups, which we call v-style pop-ups. We give sufficient conditions for a v-style paper structure to be pop-uppable. That is, it can be closed flat while maintaining the rigidity of the patches, the closing and opening do not need extra force besides holding two patches and are free of intersections, and the closed paper is contained within the page border. These conditions allow us to identify novel mechanisms for making pop-ups. Based on the theory and mechanisms, we developed an interactive tool for designing v-style pop-ups and an automated construction algorithm from a given geometry, both of which guaranteeing the pop-uppability of the results.


title: Induction heating inverter with simultaneous dual-frequency output

abstract: Induction surface hardening of parts with nonuniform cylindrical shape requires a multi-frequency process in order to obtain a uniform surface hardened depth. This paper presents an induction heating high power supply composed by a single inverter circuit and a specially designed output resonant circuit. The whole circuit supplies simultaneously both medium and high frequency power signals to the heating inductor.


title: NLIDB Templates for Semantic Parsing

abstract: In this paper, we present our work in building a template-based system for translating English sentences into SQL queries for a relational database system. The input sentences are syntactically parsed using the Link Parser, and semantically parsed through the use of domain-specific templates. The system is composed of a pre-processor and a run-time module. The pre-processor builds a conceptual knowledge base from the database schema using WordNet. The knowledge base is then used at run-time to semantically parse the input and create the SQL query. The system is meant to be domain independent and has been tested with the Cindi database that contains information on a virtual library.


Modeling multi-way data with linearly dependent loadings y


title: Decentralizing execution of composite web services

abstract: Distributed enterprise applications today are increasingly being built from services available over the web. A unit of functionality in this framework is a web service, a software application that exposes a set of "typed'' connections that can be accessed over the web using standard protocols. These units can then be composed into a composite web service. BPEL (Business Process Execution Language) is a high-level distributed programming language for creating composite web services. Although a BPEL program invokes services distributed over several servers, the orchestration of these services is typically under centralized control. Because performance and throughput are major concerns in enterprise applications, it is important to remove the inefficiencies introduced by the centralized control. In a distributed, or decentralized orchestration, the BPEL program is partitioned into independent sub-programs that interact with each other without any centralized control. Decentralization can increase parallelism and reduce the amount of network traffic required for an application. This paper presents a technique to partition a composite web service written as a single BPEL program into an equivalent set of decentralized processes. It gives a new code partitioning algorithm to partition a BPEL program represented as a program dependence graph, with the goal of minimizing communication costs and maximizing the throughput of multiple concurrent instances of the input program. In contrast, much of the past work on dependence-based partitioning and scheduling seeks to minimize the completion time of a single instance of a program running in isolation. The paper also gives a cost model to estimate the throughput of a given code partition.


title: Biped walking pattern generation by using preview control of zero-moment point

abstract: We introduce a new method of a biped walking pattern generation by using a preview control of the zero-moment point (ZMP). First, the dynamics of a biped robot is modeled as a running cart on a table which gives a convenient representation to treat ZMP. After reviewing conventional methods of ZMP based pattern generation, we formalize the problem as the design of a ZMP tracking servo controller. It is shown that we can realize such controller by adopting the preview control theory that uses the future reference. It is also shown that a preview controller can be used to compensate the ZMP error caused by the difference between a simple model and the precise multibody model. The effectiveness of the proposed method is demonstrated by a simulation of walking on spiral stairs.


title: Dense Optical Flow Prediction from a Static Image

abstract: Given a scene, what is going to move, and in what direction will it move? Such a question could be considered a non-semantic form of action prediction. In this work, we present a convolutional neural network (CNN) based approach for motion prediction. Given a static image, this CNN predicts the future motion of each and every pixel in the image in terms of optical flow. Our CNN model leverages the data in tens of thousands of realistic videos to train our model. Our method relies on absolutely no human labeling and is able to predict motion based on the context of the scene. Because our CNN model makes no assumptions about the underlying scene, it can predict future optical flow on a diverse set of scenarios. We outperform all previous approaches by large margins.


Human Activity Recognition Based on Wearable Sensor Data : A Standardization of the State-ofthe-Art


title: Proactive Maintenance of Railway Switches

abstract: Railway switches operate in harsh environmental conditions; still, their reliability requirements are high due to safety and economic factors. Once deployed, their maintenance depends on the data collected on their status, and the decisions on due corrective actions. The more regular this data collection and decision cycle is, the better confidence their operator has on effective and proper service. Proactive maintenance, in general, targets exactly this: rather than scheduling maintenance actions based on operating hours or servicing volume, actions should be taken when it is really needed. This requires the effective combination of data collection, analysis, presentation and decision making processes. The MANTIS project proposes a reference architecture for proactive maintenance, supported by the concept of cyber-physical systems. Besides suggesting data collection, processing and presentation methods and tools for this, the results were applied in various domains - including production systems, the energy grid, utility vehicles, or railways. The current paper presents data collection, analysis and presentation concepts related to proactive maintenance, applied on railway switches.


title: Collective Emotions in Conflict Situations: Societal Implications

abstract: It is well established today that emotions are an important part of most societal dynamics. The current article focuses on the role of different collective emotional elements in creating, preserving, and resolving conflicts. The main premise is that collective emotions play a pivotal role in shaping individual and societal responses to conflicting events and in contributing to the evolvement of a social context that maintains the emotional climate and collective emotional orientation that have developed. The first part of the article provides a conceptual framework to discuss the relations between conflict, context, and collective emotions. The second part uses the conceptual framework to discuss the societal implications of the articles presented in this issue. Taken together, the parts create a platform for future research on the role of collective emotions in conflict resolution and the construction of cultures of peace.


title: A Time-Triggered Ethernet (TTE) Switch

abstract: This paper presents the design of a Time-Triggered Ethernet (TTE) Switch, which is one of the core units of the Time-Triggered Ethernet system. Time-triggered Ethernet is a communication architecture intended to support event-triggered and time-triggered traffic in a single communication system. The TTE Switch distinguishes between two classes of traffic. The Event-Triggered (ET) traffic is handled in conformance with the existing Ethernet standard, while the Time-Triggered (TT) traffic is transmitted with temporal guarantees. A TTE Switch is used in the Time-Triggered Ethernet system for exchanging time-triggered messages in a time-predictable way while continuing the support of standard Ethernet traffic in order to use existing networking protocols such as IP, UDP or IPX without any modifications. In this paper we present the mechanisms the TTE Switch uses to guarantee a constant transmission delay for time-triggered traffic. Also an experimental validation of these mechanisms is given.


title: Community Specific Temporal Topic Discovery from Social Media

abstract: Studying temporal dynamics of topics in social media is very useful to understand online user behaviors. Most of the existing work on this subject usually monitors the global trends, ignoring variation among communities. Since users from different communities tend to have varying tastes and interests, capturing communitylevel temporal change can improve the understanding and management of social content. Additionally, it can further facilitate the applications such as community discovery, temporal prediction and online marketing. However, this kind of extraction becomes challenging due to the intricate interactions between community and topic, and intractable computational complexity. In this paper, we take a unified solution towards the communitylevel topic dynamic extraction. A probabilistic model, CosTot (Community Specific Topics-over-Time) is proposed to uncover the hidden topics and communities, as well as capture community-specific temporal dynamics. Specifically, CosTot considers text, time, and network information simultaneously, and well discovers the interactions between community and topic over time. We then discuss the approximate inference implementation to enable scalable computation of model parameters, especially for large social data. Based on this, the application layer support for multi-scale temporal analysis and community exploration is also investigated. We conduct extensive experimental studies on a large real microblog dataset, and demonstrate the superiority of proposed model on tasks of time stamp prediction, link prediction and topic perplexity.


title: An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation

abstract: This paper presents a novel electromyography (EMG)-driven hand exoskeleton for bilateral rehabilitation of grasping in stroke. The developed hand exoskeleton was designed with two distinctive features: (a) kinematics with intrinsic adaptability to patient’s hand size, and (b) free-palm and free-fingertip design, preserving the residual sensory perceptual capability of touch during assistance in grasping of real objects. In the envisaged bilateral training strategy, the patient’s non paretic hand acted as guidance for the paretic hand in grasping tasks. Grasping force exerted by the non paretic hand was estimated in real-time from EMG signals, and then replicated as robotic assistance for the paretic hand by means of the hand-exoskeleton. Estimation of the grasping force through EMG allowed to perform rehabilitation exercises with any, non sensorized, graspable objects. This paper presents the system design, development, and experimental evaluation. Experiments were performed within a group of six healthy subjects and two chronic stroke patients, executing robotic-assisted grasping tasks. Results related to performance in estimation and modulation of the robotic assistance, and to the outcomes of the pilot rehabilitation sessions with stroke patients, positively support validity of the proposed approach for application in stroke rehabilitation.


A framework for authorship identification of online messages: Writing-style features and classification techniques