title: Collaborative Task Execution in Mobile Cloud Computing Under a Stochastic Wireless Channel

abstract: This paper investigates collaborative task execution between a mobile device and a cloud clone for mobile applications under a stochastic wireless channel. A mobile application is modeled as a sequence of tasks that can be executed on the mobile device or on the cloud clone. We aim to minimize the energy consumption on the mobile device while meeting a time deadline, by strategically offloading tasks to the cloud. We formulate the collaborative task execution as a constrained shortest path problem. We derive a one-climb policy by characterizing the optimal solution and then propose an enumeration algorithm for the collaborative task execution in polynomial time. Further, we apply the LARAC algorithm to solving the optimization problem approximately, which has lower complexity than the enumeration algorithm. Simulation results show that the approximate solution of the LARAC algorithm is close to the optimal solution of the enumeration algorithm. In addition, we consider a probabilistic time deadline, which is transformed to hard deadline by Markov inequality. Moreover, compared to the local execution and the remote execution, the collaborative task execution can significantly save the energy consumption on the mobile device, prolonging its battery life.


title: Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices

abstract: Indoor localization is one of the key enablers for various application and service areas that rely on precise locations of people, goods, and assets, ranging from home automation and assisted living to increased automation of production and logistic processes and wireless network optimization. Existing solutions provide various levels of precision, which also depends on the complexity of the indoor radio environment. In this paper, we propose two methods for reducing the localization error in indoor non-line-of-sight (NLoS) conditions using raw channel impulse response (CIR) information obtained from ultra-wide band radios requiring no prior knowledge about the radio environment. The methods are based on NLoS channel classification and ranging error regression models, both using convolutional neural networks (CNNs) and implemented in the TensorFlow computational framework. We first show that NLoS channel classification using raw CIR data outperforms existing approaches that are based on derived input signal features. We further demonstrate that the predicted NLoS channel state and predicted ranging error information, used in combination with least squares (LS) and weighted LS location estimation algorithms, significantly improve indoor localization performance. We also evaluate the computational performance and suitability of the proposed CNN-based algorithms on various computing platforms with a wide range of different capabilities and show that in a distributed localization system, they can also be used on computationally restricted devices.


title: Zero-shot Sim-to-Real Transfer with Modular Priors

abstract: Current end-to-end Reinforcement Learning (RL) approaches are severely limited by restrictively large search spaces and are prone to overfitting to their training environment. This is because in end-to-end RL perception, decision-making and low-level control are all being learned jointly from very sparse reward signals, with little capability of incorporating prior knowledge or existing algorithms. In this work, we propose a novel framework that effectively decouples RL for high-level decision making from low-level perception and control. This allows us to transfer a learned policy from a highly abstract simulation to a real robot without requiring any transfer learning. We therefore coin our approach zero-shot sim-to-real transfer. We successfully demonstrate our approach on the robot manipulation task of object sorting. A key component of our approach is a deep sets encoder that enables us to reinforcement learn the high-level policy based on the variable-length output of a pre-trained object detector, instead of learning from raw pixels. We show that this method can learn effective policies within mere minutes of highly simplified simulation. The learned policies can be directly deployed on a robot without further training, and generalize to variations of the task unseen during training.


title: Neurocognitive control in dance perception and performance

abstract: Dance is a rich source of material for researchers interested in the integration of movement and cognition. The multiple aspects of embodied cognition involved in performing and perceiving dance have inspired scientists to use dance as a means for studying motor control, expertise, and action-perception links. The aim of this review is to present basic research on cognitive and neural processes implicated in the execution, expression, and observation of dance, and to bring into relief contemporary issues and open research questions. The review addresses six topics: 1) dancers' exemplary motor control, in terms of postural control, equilibrium maintenance, and stabilization; 2) how dancers' timing and on-line synchronization are influenced by attention demands and motor experience; 3) the critical roles played by sequence learning and memory; 4) how dancers make strategic use of visual and motor imagery; 5) the insights into the neural coupling between action and perception yielded through exploration of the brain architecture mediating dance observation; and 6) a neuroesthetics perspective that sheds new light on the way audiences perceive and evaluate dance expression. Current and emerging issues are presented regarding future directions that will facilitate the ongoing dialog between science and dance.


Smart System for Utility Tunnel Management 2


title: MPPT Method for PV Systems Under Partially Shaded Conditions by Approximating I–V Curve

abstract: Partially shaded (PS) photovoltaic (PV) arrays have multiple peaks at their P–V characteristic. Although conventional maximum power point tracking (MPPT) algorithms are successful when PV arrays are under uniform irradiance conditions (UICs), their tracking speeds are low and may fail to track global maximum power point (GMPP) for PS arrays. Several MPPT algorithms have been proposed for PS arrays. Most of them require numerous samplings which decreases MPPT speed and increases energy loss. The proposed method in this paper gets the GMPP deterministically and very fast. It intelligently takes some samples from the array's P–V curve and divides the search voltage range into small subregions. Then, it approximates the I–V curve of each subregion with a simple curve, and accordingly estimates an upper limit for the array power in that subregion. Next, by comparing the measured real power values with the estimated upper limits, the search region of GMPP is limited, and based on some defined criteria, the vicinity of GMPP is determined. Simulation and experimental results and comparisons are presented to highlight the performance and superiority of the proposed approach.


title: An FPGA Implementation of Elliptic Curve Cryptography for Future Secure Web Transaction

abstract: Elliptic curve cryptography (ECC) is an alternative to traditional techniques for public key cryptography. It offers smaller key size without sacrificing security level. In a typical elliptic curve cryptosystem, elliptic curve point multiplication is the most computationally expensive component. So it would be more attractive to implement this unit using hardware than using software. In this paper, we propose an efficient FPGA implementation of the elliptic curve point multiplication in GF(2). We have designed and synthesized the elliptic curve point multiplication with Xilinx’s FPGA. Experimental results demonstrate that the FPGA implementation can speedup the point multiplication by 31.6 times compared to a software based implementation.


title: A Nonlinear Unified State-Space Model for Ship Maneuvering and Control in a Seaway

abstract: This article presents a unified state-space model for ship maneuvering, station-keeping, and control in a seaway. The frequency-dependent potential and viscous damping terms, which in classic theory results in a convolution integral not suited for real-time simulation, is compactly represented by using a state-space formulation. The separation of the vessel model into a low-frequency model (represented by zero-frequency added mass and damping) and a wave-frequency model (represented by motion transfer functions or RAOs), which is commonly used for simulation, is hence made superfluous.


title: How to represent scenes for classification?

abstract: Object-based scene image representations can effectively capture the semantic meanings of a scene. However, they usually neglect a scene's structure information. In this paper, we propose a novel and effective detector-based scene representation method for scene classification. In particular, we extract object features by object detectors. By sensible principal component analysis, we obtain a compact representation vector of objects in a scene image. To capture the scene layout, we then train lots of deformable part models to form a scene response vector. By concatenating these two vectors we use a linear support vector machine for scene classification. When combining with DeCAF [1] in a special way, our method is even more powerful on complex scene categorization. Experimental results on the MIT indoor database show that our approach achieves state-of-the-art performance on scene classification compared with several popular methods.


title: Spatially Aware Melanoma Segmentation Using Hybrid Deep Learning Techniques

abstract: In this paper, we proposed using a hybrid method that utilises deep convolutional and recurrent neural networks for accurate delineation of skin lesion of images supplied with ISBI 2017 lesion segmentation challenge. The proposed method was trained using 1800 images and tested on 150 images from ISBI 2017 challenge.


title: Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications

abstract: This paper compares three candidate large-scale propagation path loss models for use over the entire microwave and millimeter-wave (mmWave) radio spectrum: the alpha-betagamma (ABG) model, the close-in (CI) free-space reference distance model, and the CI model with a frequency-weighted path loss exponent (CIF). Each of these models has been recently studied for use in standards bodies such as 3rd Generation Partnership Project (3GPP) and for use in the design of fifth-generation wireless systems in urban macrocell, urban microcell, and indoor office and shopping mall scenarios. Here, we compare the accuracy and sensitivity of these models using measured data from 30 propagation measurement data sets from 2 to 73 GHz over distances ranging from 4 to 1238 m. A series of sensitivity analyses of the three models shows that the physically based two-parameter CI model and three-parameter CIF model offer computational simplicity, have very similar goodness of fit (i.e., the shadow fading standard deviation), exhibit more stable model parameter behavior across frequencies and distances, and yield smaller prediction error in sensitivity tests across distances and frequencies, when compared to the four-parameter ABG model. Results show the CI model with a 1-m reference distance is suitable for outdoor environments, while the CIF model is more appropriate for indoor modeling. The CI and CIF models are easily implemented in existing 3GPP models by making a very subtle modification — by replacing a floating non-physically based constant with a frequency-dependent constant that represents free-space path loss in the first meter of propagation. This paper shows this subtle change does not change the mathematical form of existing ITU/3GPP models and offers much easier analysis, intuitive appeal, better model parameter stability, and better accuracy in sensitivity tests over a vast range of microwave and mmWave frequencies, scenarios, and distances, while using a simpler model with fewer parameters. This material is based upon work supported by the NYU WIRELESS Industrial Affiliates: ATT tsr@nyu.edu). T. A. Thomas and A. Ghosh are with Nokia, Arlington Heights, IL 60004, USA (e-mail: timothy.thomas@nokia.com; amitava.ghosh@nokia.com). H. Nguyen and I. Rodriguez are with Aalborg University, Aalborg 9220, Denmark (e-mail: huan.nguyen.ext@nokia.com; irl@es.aau.dk). I. Z. Kovacs is with Nokia, Aalborg 9220, Denmark (e-mail: istvan.kovacs@nokia.com). O. Koymen and A. Partyka are with Qualcomm RD apartyka@qti.qualcomm.com).


title: A unified hypothesis on the lineage of neural stem cells

abstract: For many years, it was assumed that neurons and glia in the central nervous system were produced from two distinct precursor pools that diverged early during embryonic development. This theory was partially based on the idea that neurogenesis and gliogenesis occurred during different periods of development, and that neurogenesis ceased perinatally. However, there is now abundant evidence that neural stem cells persist in the adult brain and support ongoing neurogenesis in restricted regions of the central nervous system. Surprisingly, these stem cells have the characteristics of fully differentiated glia. Neuroepithelial stem cells in the embryonic neural tube do not show glial characteristics, raising questions about the putative lineage from embryonic to adult stem cells. In the developing brain, radial glia have long been known to produce cortical astrocytes, but recent data indicate that radial glia might also divide asymmetrically to produce cortical neurons. Here we review these new developments and propose that the stem cells in the central nervous system are contained within the neuroepithelial --> radial glia --> astrocyte lineage.


title: Acute dissection of the descending aorta: noncommunicating versus communicating forms.

abstract: BACKGROUND ::: Noncommunicating dissecting intramural hematoma is an aortic dissection variant, characterized by absent flow within the false lumen. Noncommunicating dissecting intramural hematoma is thought to be more stable than communicating dissection when beginning in the descending aorta. This study assessed clinical characteristics, anatomic characteristics, and 1-year outcomes in acute descending noncommunicating dissecting intramural hematoma versus communicating dissection. ::: ::: ::: METHODS ::: Retrospective database review identified patients who underwent magnetic resonance or computed tomography imaging revealing acute descending noncommunicating dissecting intramural hematoma or communicating dissection. Comparisons of clinical and anatomic characteristics and 1-year outcomes were performed. ::: ::: ::: RESULTS ::: Twenty-four noncommunicating dissecting intramural hematoma and 36 communicating dissection cases were identified. Patients with noncommunicating dissecting intramural hematoma were older (68.5 +/- 8.8 versus 61.8 +/- 11.6 years; p < 0.05). Although noncommunicating dissecting intramural hematoma often showed abdominal aorta extension (50%), the infrarenal level was spared. Communicating dissection characteristically extended beyond the diaphragm (89%), including into the infrarenal aorta (28%). There was no significant difference in rates of adverse clinical events for noncommunicating dissecting intramural hematoma versus communicating dissection (13% versus 30%; 0.10 > p > 0.05). By follow-up imaging (87% of population), aortic deterioration was more frequent in noncommunicating dissecting intramural hematoma versus communicating dissection cases (60% versus 15%; p < 0.005). ::: ::: ::: CONCLUSIONS ::: Acute descending noncommunicating dissecting intramural hematoma and communicating dissection represent two variants, with differing clinical and anatomic characteristics, but comparable levels of 1-year morbidity.


title: Platforms in healthcare innovation ecosystems: The lens of an innovation intermediary

abstract: Healthcare innovation has made progressive strides. Innovative solutions now tend to incorporate device integration, data collection and data analysis linked across a diverse range of actors building platform-centric healthcare ecosystems. The interconnectedness and inter-disciplinarity of the ecosystems bring with it a number of vital issues around how to strategically manage such a complex system. This paper highlights the importance of innovation intermediaries particularly in a platform-centric ecosystem such as the healthcare industry. It serves as a reminder of why it is important for healthcare technologists to consider proactive ways to contribute to the innovation ecosystem by creating devices with the platform perspective in mind.


title: BTI impact on SRAM sense amplifier

abstract: Bias Temperature Instability (BTI) - Negative BTI in PMOS and Positive BTI in NMOS transistors-has become a key reliability bottleneck in the nano-scaled era. This paper presents BTI impact on SRAM's sense amplifier of different technologies, a robust sense amplifier has a lower sensing delay and higher sensing voltage. The results show that as technology scales down (i.e., from 90nm to 65nm, and 45nm), BTI impact on sensing delay increases, while that on the sensing voltage decreases, causing the sense amplifier memory, hence to be less robust and reliable. In addition, the paper also investigate the use of supply voltage to reduce the BTI degradation. The result show that increasing the power supply can reduce the sense amplifier BTI degradadtion with 33% for sensing voltage and with 18% for sensing delay; leading to clear tradeoff engineering question between power and robustness.


title: A Theoretical Framework on the Ideal Number of Classifiers for Online Ensembles in Data Streams

abstract: A priori determining the ideal number of component classifiers of an ensemble is an important problem. The volume and velocity of big data streams make this even more crucial in terms of prediction accuracies and resource requirements. There is a limited number of studies addressing this problem for batch mode and none for online environments. Our theoretical framework shows that using the same number of independent component classifiers as class labels gives the highest accuracy. We prove the existence of an ideal number of classifiers for an ensemble, using the weighted majority voting aggregation rule. In our experiments, we use two state-of-the-art online ensemble classifiers with six synthetic and six real-world data streams. The violation of providing independent component classifiers for our theoretical framework makes determining the exact ideal number of classifiers nearly impossible. We suggest upper bounds for the number of classifiers that gives the highest accuracy. An important implication of our study is that comparing online ensemble classifiers should be done based on these ideal values, since comparing based on a fixed number of classifiers can be misleading.


title: Overview of PAN 2018. Author identification, author profiling, and author obfuscation.

abstract: PAN 2018 explores several authorship analysis tasks enabling a systematic comparison of competitive approaches and advancing research in digital text forensics. More specifically, this edition of PAN introduces a shared task in cross-domain authorship attribution, where texts of known and unknown authorship belong to distinct domains, and another task in style change detection that distinguishes between single-author and multi-author texts. In addition, a shared task in multimodal author profiling examines, for the first time, a combination of information from both texts and images posted by social media users to estimate their gender. Finally, the author obfuscation task studies how a text by a certain author can be paraphrased so that existing author identification tools are confused and cannot recognize the similarity with other texts of the same author. New corpora have been built to support these shared tasks. A relatively large number of software submissions (41 in total) was received and evaluated. Best paradigms are highlighted while baselines indicate the pros and cons of submitted approaches.


title: Learning Monocular Depth by Distilling Cross-domain Stereo Networks

abstract: Monocular depth estimation aims at estimating a pixelwise depth map for a single image, which has wide applications in scene understanding and autonomous driving. Existing supervised and unsupervised methods face great challenges. Supervised methods require large amounts of depth measurement data, which are generally difficult to obtain, while unsupervised methods are usually limited in estimation accuracy. Synthetic data generated by graphics engines provide a possible solution for collecting large amounts of depth data. However, the large domain gaps between synthetic and realistic data make directly training with them challenging. In this paper, we propose to use the stereo matching network as a proxy to learn depth from synthetic data and use predicted stereo disparity maps for supervising the monocular depth estimation network. Cross-domain synthetic data could be fully utilized in this novel framework. Different strategies are proposed to ensure learned depth perception capability well transferred across different domains. Our extensive experiments show state-of-the-art results of monocular depth estimation on KITTI dataset.


title: Trust Management for the Semantic Web

abstract: Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user maintains trusts in a small number of other users. We then compose these trusts into trust values for all other users. The result of our computation is not an agglomerate "trustworthiness" of each user. Instead, each user receives a personalized set of trusts, which may vary widely from person to person. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise.


title: A Two-Mode Control Scheme With Input Voltage Feed-Forward for the Two-Switch Buck-Boost DC–DC Converter

abstract: The two-switch buck-boost (TSBB) converter is suitable for wide input voltage applications. In order to achieve high efficiency over the entire input voltage range, the TSBB converter is operated in buck mode at high input voltage and boost mode at low input voltage. Such operation is called the two-mode control scheme. The objective of this paper is to propose an input voltage feed-forward (IVFF) method to reduce the influence of the input voltage disturbance on the output voltage. The small-signal models of the TSBB converter are built, and based on which, the IVFF functions under different operating modes of the TSBB converter are derived. The IVFF function in boost mode is simplified for easy implementation. The two-mode control scheme with IVFF compensation is then proposed for the TSBB converter, which realizes automatic selections of operating modes and the corresponding IVFF functions. Besides, nearly smooth switching between buck and boost modes is also guaranteed. For exhibiting the advantages of the proposed control scheme clearly, comparisons between the two-mode control with and without IVFF compensation have been presented in this paper, including the output signal of the voltage regulator and input-to-output voltage transfer function. Finally, a 250-500-V input, 360-V output, and 6-kW-rated power prototype is fabricated to validate the effectiveness of the proposed control scheme in the laboratory, and the experimental results show that the TSBB converter has an improved input transient response and high efficiency over the entire input voltage range with this proposed control scheme.


title: Knowledge, management, and knowledge management in business operations

abstract: Purpose – The purpose of this research is to help knowledge managers systematically grasp ‘‘knowledge about management knowledge’’ and get a ‘‘deep and full’’ understanding of the nature, scope and methodologies of knowledge management. Design/methodology/approach – Through presenting a variety of perspectives on knowledge, management, and knowledge management, the article explores the essence of knowledge management in organizations from a perspective of critical systems thinking. Findings – Knowledge management in business organizations has the task of managing the activities of knowledge workers or the transformation and interaction of organizational ‘‘static substance knowledge’’ and ‘‘dynamic process knowledge’’ for ‘‘products, services, and practical process innovation’’ and, at the same time, ‘‘creating new or justifying existing organizational systematic knowledge’’. Knowledge management is not simply about recording and manipulating explicit knowledge, but needs to address that which is implicit, and from which benefit can therefore be derived only through process rather than content. Originality/value – The comprehensive review and classification of various management theories will expand both knowledge managers’ and knowledge workers’ understanding of the subject and provide a foundation for building a knowledge management toolkit in practice.


title: An integrated socio-environmental framework for glacier hazard management and climate change adaptation: lessons from Lake 513, Cordillera Blanca, Peru

abstract: Glacier hazards threaten societies in mountain regions worldwide. Glacial lake outburst floods (GLOFs) pose risks to exposed and vulnerable populations and can be linked in part to long-term post-Little Ice Age climate change because precariously dammed glacial lakes sometimes formed as glaciers generally retreated after the mid-1800s. This paper provides an interdisciplinary and historical analysis of 40 years of glacier hazard management on Mount Hualcan, at glacial Lake 513, and in the city of Carhuaz in Peru’s Cordillera Blanca mountain range. The case study examines attempted hazard zoning, glacial lake evolution and monitoring, and emergency engineering projects to drain Lake 513. It also analyzes the 11 April 2010 Hualcan rock-ice avalanche that triggered a Lake 513 GLOF; we offer both a scientific assessment of the possible role of temperature on slope stability and a GIS spatial analysis of human impacts. Qualitative historical analysis of glacier hazard management since 1970 allows us to identify and explain why certain actions and policies to reduce risk were implemented or omitted. We extrapolate these case-specific variables to generate a broader socio-environmental framework identifying factors that can facilitate or impede disaster risk reduction and climate change adaptation. Facilitating factors are technical capacity, disaster events with visible hazards, institutional support, committed individuals, and international involvement. Impediments include divergent risk perceptions, imposed government policies, institutional instability, knowledge disparities, and invisible hazards. This framework emerges from an empirical analysis of a coupled social-ecological system and offers a holistic approach for integrating disaster risk reduction and climate change adaptation.


title: SPGrid: a sparse paged grid structure applied to adaptive smoke simulation

abstract: We introduce a new method for fluid simulation on high-resolution adaptive grids which rivals the throughput and parallelism potential of methods based on uniform grids. Our enabling contribution is SPGrid, a new data structure for compact storage and efficient stream processing of sparsely populated uniform Cartesian grids. SPGrid leverages the extensive hardware acceleration mechanisms inherent in the x86 Virtual Memory Management system to deliver sequential and stencil access bandwidth comparable to dense uniform grids. Second, we eschew tree-based adaptive data structures in favor of storing simulation variables in a pyramid of sparsely populated uniform grids, thus avoiding the cost of indirect memory access associated with pointer-based representations. We show how the costliest algorithmic kernels of fluid simulation can be implemented as a composition of two kernel types: (a) stencil operations on a single sparse uniform grid, and (b) structured data transfers between adjacent levels of resolution, even when modeling non-graded octrees. Finally, we demonstrate an adaptive multigrid-preconditioned Conjugate Gradient solver that achieves resolution-independent convergence rates while admitting a lightweight implementation with a modest memory footprint. Our method is complemented by a new interpolation scheme that reduces dissipative effects and simplifies dynamic grid adaptation. We demonstrate the efficacy of our method in end-to-end simulations of smoke flow.


title: A Dual-Wideband and High Gain Magneto-Electric Dipole Antenna and Its 3D MIMO System With Metasurface for 5G/WiMAX/WLAN/X-Band Applications

abstract: A substrate integrated magneto-electric (ME) dipole antenna with metasurface is proposed for the 5G/WiMAX/WLAN/X-band MIMO applications. In order to provide a low profile, the radiated electric dipoles integrated with shorted wall are used in the multi-layer substrates at different heights. Owing to the coordination of the metasurface and the ME dipole, dual wideband and high gain have been obtained. As a result of the 3-D hexagonal structure, good envelope correlation coefficient and mean effective gain performance can be achieved by the MIMO antenna system. The antenna element can provide an impedance bandwidth of 66.7% (3.1–6.2 GHz) with a stable gain of 7.6±1.5 dBi and an impedance bandwidth of 20.3% (7.1–8.7 GHz) with a gain of 7.4±1.8 dBi for the lower and upper bands, respectively. The overall size of the element is $60\times 60\times 7.92$ mm3. Hence, it is well-suited for the future 5G/WiMAX/WLAN/X-band MIMO communications.


title: Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks

abstract: Recent developments in deep reinforcement learning have enabled the creation of agents for solving a large variety of games given a visual input. These methods have been proven successful for 2D games, like the Atari games, or for simple tasks, like navigating in mazes. It is still an open question, how to address more complex environments, in which the reward is sparse and the state space is huge. In this paper we propose a divide and conquer deep reinforcement learning solution and we test our agent in the first person shooter (FPS) game of Doom. Our work is based on previous works in deep reinforcement learning and in Doom agents. We also present how our agent is able to perform better in unknown environments compared to a state of the art reinforcement learning algorithm.


title: Furan analysis for liquid power transformers

abstract: This article attempts to capture and summarize the known technical information and recommendations for analysis of furan test results. It will also provide the technical basis for continued gathering and evaluation of furan data for liquid power transformers, and provide a recommended structure for collecting that data.


title: Bat Algorithm and Cuckoo Search: A Tutorial

abstract: Nature-inspired metaheuristic algorithms have attracted much attention in the last decade, and new algorithms have emerged almost every year with a vast, ever-expanding literature. In this chapter, we briefly review two latest metaheuristics: bat algorithm and cuckoo search for global optimization. Bat algorithm was proposed by Xin-She Yang in 2010, inspired by the echolocation of microbats, while cuckoo search was developed by Xin-She Yang and Suash Deb in 2009, inspired by the brood parasitism of some cuckoo species. Both algorithms have shown superiority over many other metaheuristics over a wide range of applications.


title: Neural Citation Network for Context-Aware Citation Recommendation

abstract: The accelerating rate of scientific publications makes it difficult to find relevant citations or related work. Context-aware citation recommendation aims to solve this problem by providing a curated list of high-quality candidates given a short passage of text. Existing literature adopts bag-of-word representations leading to the loss of valuable semantics and lacks the ability to integrate metadata or generalize to unseen manuscripts in the training set. We propose a flexible encoder-decoder architecture called Neural Citation Network (NCN), embodying a robust representation of the citation context with a max time delay neural network, further augmented with an attention mechanism and author networks. The recurrent neural network decoder consults this representation when determining the optimal paper to recommend based solely on its title. Quantitative results on the large-scale CiteSeer dataset reveal NCN cultivates a significant improvement over competitive baselines. Qualitative evidence highlights the effectiveness of the proposed end-to-end neural network revealing a promising research direction for citation recommendation.


title: DRIVE: Dynamic Routing of Independent VEhicles

abstract: We present DRIVE (Dynamic Routing of Independent VEhicles), a planning module to be incorporated in a decision support system for the direct transportation at Van Gend and Loos BV. Van Gend and Loos BV is the largest company providing road transportation in the Benelux, with about 1400 vehicles transporting 160,000 packages from thousands of senders to tens of thousands of addressees per day. The heart of DRIVE is a branch-and-price algorithm. Approximation and incomplete optimization techniques as well as a sophisticated column management scheme have been employed to create the right balance between solution speed and solution quality. DRIVE has been tested by simulating a dynamic planning environment with real-life data and has produced very encouraging results.


title: Waveform optimization using trapezoidal pulses for 5G random access with short message support

abstract: It is well-known that sporadic traffic will be an increasing demand in the 5G market. Obviously, bulky synchronization procedure of the current 4G LTE cellular systems is not a viable option. Transmission of control signaling and payload “in one shot” in physical layer random access channel is an interesting direction. A major challenge is to cope with highly asynchronous access as well as high carrier frequency offsets of low-cost IoT devices. We address this challenge by using a waveform design approach based on a (tail-biting) bi-orthogonal frequency division multiplexing approach (at non-critical density). More specifically, we introduce a 2-parameter trapezoidal pulse family and investigate their properties both by analysis and simulations. In particular, we reveal surprising favorable properties like excellent time domain localization measured in terms of their L 1 -norm and relate this to “extreme” cases like the triangle pulse and the sinc pulse which appear as special cases of the trapezoidal family.


title: Position and speed control of a low-cost two-wheeled, self-balancing inverted pendulum vehicle

abstract: In this paper we present a low-cost prototype of a two-wheeled, self-balancing inverted pendulum realized at the mechatronics laboratory of the University of Trento. This vehicle lends itself to represent a class of control problems that arise in many educational robotic devices custom built and assembled with low-end market components. We provide a detailed description of the equipment and describe the strategies adopted to suitably address backlash of the low-cost DC motors and disturbances arising from the voltage controlled hardware. Then we propose a nested control paradigm where an inner loop provides regulation of a virtual control requested by an outer loop that uses the pitch angle to regulate the longitudinal displacement and speed. Three saturation-based control laws are proposed for the outer loop, all of them allowing the designer to adjust the maximum pitch angle. Experimental results illustrate the effectiveness of the proposed control laws and the operation of the developed prototype.


title: Learning effective human pose estimation from inaccurate annotation

abstract: The task of 2-D articulated human pose estimation in natural images is extremely challenging due to the high level of variation in human appearance. These variations arise from different clothing, anatomy, imaging conditions and the large number of poses it is possible for a human body to take. Recent work has shown state-of-the-art results by partitioning the pose space and using strong nonlinear classifiers such that the pose dependence and multi-modal nature of body part appearance can be captured. We propose to extend these methods to handle much larger quantities of training data, an order of magnitude larger than current datasets, and show how to utilize Amazon Mechanical Turk and a latent annotation update scheme to achieve high quality annotations at low cost. We demonstrate a significant increase in pose estimation accuracy, while simultaneously reducing computational expense by a factor of 10, and contribute a dataset of 10,000 highly articulated poses.


title: Analysis of charge-pump phase-locked loops

abstract: In this paper, we present an exact analysis for third-order charge-pump phase-locked loops using state equations. Both the large-signal lock acquisition process and the small-signal linear tracking behavior are described using this analysis. The nonlinear state equations are linearized for the small-signal condition and the z-domain noise transfer functions are derived. A comparison to some of the existing analysis methods such as the impulse-invariant transformation and s-domain analysis is provided. The effect of the loop parameters and the reference frequency on the loop phase margin and stability is analyzed. The analysis is verified using behavioral simulations in MATLAB and SPECTRE.


title: Joint Acoustic and Modulation Frequency

abstract: There is a considerable evidence that our perception of sound uses important features which are related to underlying signal modulations. This topic has been studied extensively via perceptual experiments, yet there are few, if any, well-developed signal processing methods which capitalize on or model these effects. We begin by summarizing evidence of the importance of modulation representations from psychophysical, physiological, and other sources. The concept of a two-dimensional joint acoustic and modulation frequency representation is proposed. A simple single sinusoidal amplitude modulator of a sinusoidal carrier is then used to illustrate properties of an unconstrained and ideal joint representation. Added constraints are required to remove or reduce undesired interference terms and to provide invertibility. It is then noted that the constraints would be also applied to more general and complex cases of broader modulation and carriers. Applications in single-channel speaker separation and in audio coding are used to illustrate the applicability of this joint representation. Other applications in signal analysis and filtering are suggested.


title: Edge-preserving Smoothing and Mean-shift Segmentation of Video Streams

abstract: Video streams are ubiquitous in applications such as surveillance, games, and live broadcast. Processing and analyzing these data is challenging because algorithms have to be efficient in order to process the data on the fly. From a theoretical standpoint, video streams have their own specificities --- they mix spatial and temporal dimensions, and compared to standard video sequences, half of the information is missing, i.e. the future is unknown. The theoretical part of our work is motivated by the ubiquitous use of the Gaussian kernel in tools such as bilateral filtering and mean-shift segmentation. We formally derive its equivalent for video streams as well as a dedicated expression of isotropic diffusion. Building upon this theoretical ground, we adapt a number of classical algorithms to video streams: bilateral filtering, mean-shift segmentation, and anisotropic diffusion.


title: The Effectiveness of Advertising in Relation to the Personality of the Consumer

abstract: Advertising creates a communication relationship between the producer or the provider of a service and the target group or the consumers. The main task of psychology of advertising is to motivate the consumer to take the desired action, to buy the promoted product or service. That is why more and more sophisticated forms and tools for reaching the goal set by the advertisment creator are used. The aim of the presented study was to determine whether an individual's temperament plays a major role in favouring a particular advertising spot and to identify which type of advertising corresponds with the type of a consumer’s personality temperament. The research sample consisted of 126 respondents with different types of temperaments, aged between 22 to 68 years, who filled out two methodologies: Assessment of the television advertising in relation to personality typologies and the EPI – Eysenck Personality Inventory . By means of this research, the existence of a relationship between temperament of individuals and their perception of various types of advertising spots (funny, calm, nostalgic, dynamic) was recorded. A successful advertisement should be aimed properly at a given group of individuals whom it should effectively reach. For that reason, psychology becomes an essential part in marketing and advertising strategies.


title: TrendMiner: Large-scale analysis of political attitudes in public facebook messages

abstract: This paper presents the methods and results of a project that collects and analyses public comments written in response to political posts on Facebook using natural language processing and social psychological methods in order to explore emotional attitudes and social behavior.


title: The operation and mechanization of the hemispherical resonator gyroscope

abstract: The Hemispherical Resonator Gyro (HRG) is unique in the area of high accuracy inertial sensors. One of its very first selected applications was on the Cassini mission to Saturn, its Rings, and Moons. Since that time it has been the chosen gyro technology for precision space missions both military and commercial. For these missions the HRG has been mechanized to operate in the Whole Angle (WA) mode when operation under high dynamic rates is required. However when high sensitivity and very precise angular measurements are at a premium the same HRG is switched to a Force To Rebalance (FTR) mode. In this FTR mode the gyro operates at low rates thus sacrificing the inherent high rate capability of the HRG. It should be noted that a third mechanization can be implemented that gives the same high rate capability as the WA mode and at the same time capable of the sensitivity that is inherent in the FTR mode. This mode is known as the Whole Angle Tracking (WAT) mode. To implement this mode changes to electronics, that are responsible for the readout and control of the gyro, are required. This paper will outline these mechanization changes and also show that a reduction in the electronics that support the HRG is possible. Because electronic noise characteristics plays a very large role in noise sensitivity of the gyro, a reduction of control electronics should lead to an improvement of the noise characteristics of the complete HRG sensor. Mechanization of this WAT mode is presented together with simulation of the mechanization that demonstrates both the high rate and high precision sensitivity of the gyroscope. It should be noted that the WAT mode actually has been implemented and tested when exposed to laboratory and extreme environmental conditions. Finally, D.D Lynch plainly demonstrated in one of his paper Ref (2) that “size does matter” when considering the performance of the HRG. In the quest for high precision using HRG technology accordingly this paper will outline geometry of a type HRG and mechanization changes will give precision performance that is very competitive with that used in HRG applications today, but at greatly reduced production costs. A low cost technique proposes “glass blowing” and, or “glass moldings” which are being actively pursued today in many establishments with excellent results. Incorporating these techniques is discussed in the design of a new HRG to meet both space and terrestrial applications. The introduction of “Glass blowing” allows geometry changes to be made so that the HRG is not the only geometric candidate. Accordingly the term Shell Resonator Gyroscopes, SRG, is introduced to cover these other geometries.


title: Ultra-Wideband GaN MMIC Chip Set and High Power Amplifier Module for Multi-Function Defense AESA Applications

abstract: This paper presents measurement results of a monolithic microwave integrated circuit (MMIC) chip set and of an ultra-wideband high power amplifier (HPA) transmit module for multi-functional next-generation active electronically scanned antenna radar/electronic warfare/communication applications targeting the frequency range from 6 to 18 GHz. The reported chip set consists of a driver amplifier (DA) MMIC and an HPA MMIC on a high-power gallium-nitride process with high electronic-mobility transistors. The DA reaches a power gain of 11 dB and maximum output power of 2 W, which is sufficient to drive a final stage in a balanced configuration. The HPA reaches a typical output power of 12.5 and 10.6 W in pulsed and continuous wave (CW) operation, respectively. Measurements on the module level indicate 18.5-W typical output power in both pulsed and CW operation.


title: Secure systems development based on the common criteria: the PalME project

abstract: Security is a very important issue in information processing, especially in open network environments like the Internet. The Common Criteria (CC) is the standard requirements catalogue for the evaluation of security critical systems. Using the CC, a large number of security requirements on the system itself and on the system development can be defined. However, the CC does not give methodological support.In this paper, we show how integrate security aspects into the software engineering process. The activities and documents from the Common Criteria are tightly intertwined with the system development, which improves the quality of the developed system and reduces the additional cost and effort due to high security requirements. For modelling and verification of critical parts of the system, we use formal description techniques and model checking (supported by the graphical CASE tool AUTOFOCUS), which increases both the understanding of the system specification and the system's reliability. We demonstrate our ideas by means of a case-study, the PalME project--an electronic purse application for Palm handhelds.


title: Meaning in life and intuition.

abstract: Three correlational studies and 2 experiments examined the association between meaning in life (MIL) and reliance on intuitive information processing. In Studies 1–3 (total N = 5,079), Faith in Intuition (FI) scale and MIL were correlated positively, controlling for religiosity, positive mood, self-esteem, basic need satisfaction, and need for cognition. Two experiments manipulated processing style. In Study 4 (N = 614), participants were randomly assigned to complete the Cognitive Reflection Task (CRT; Fredrick, 2005) either immediately before (reflective/low intuitive mindset condition) or immediately after (control condition) rating MIL. Condition did not affect MIL. However, low MIL rated before the CRT predicted superior performance and greater time spent on the task. The association between reflection and MIL was curvilinear, such that MIL was strongly negatively related to CRT performance particularly at low levels of MIL. In Study 5 (N = 804), intuitive or reflective mindsets were induced and FI and MIL were measured. Induced processing style study did not affect MIL. However, those high in MIL were more responsive to the intuitive mindset induction. The relationship between FI and MIL was curvilinear (in this and the correlational studies), with intuitive processing being strongly positively related to MIL particularly at higher levels of MIL. Although often considered in the context of conscious reflection, MIL shares a positive relationship with reliance on gut feelings, and high MIL may facilitate reliance on those feelings. (PsycINFO Database Record (c) 2016 APA, all rights reserved)


title: Feature Warping for Robust Speaker Verification

abstract: We propose a novel feature mapping approach that is robust to channel mismatch, additive noise and to some extent, non-linear effects attributed to handset transducers. These adverse effects can distort the short-term distribution of the speech features. Some methods have addressed this issue by conditioning the variance of the distribution, but not to the extent of conforming the speech statistics to a target distribution. The proposed target mapping method warps the distribution of a cepstral feature stream to a standardised distribution over a specified time interval. ::: We evaluate a number of the enhancement methods for speaker verification, and compare them against a Gaussian target mapping implementation. Results indicate improvements of the warping technique over a number of methods such as Cepstral Mean Subtraction (CMS), modulation spectrum processing, and short-term windowed CMS and variance normalisation.


title: Speech Emotion Recognition: A Review

abstract: Field of emotional content recognition of speech signals has been gaining increasing interest during recent years. Several emotion recognition systems have been constructed by different researchers for recognition of human emotions in spoken utterances. This paper describes speech emotion recognition based on the previous technologies which uses different methods of feature extraction and different classifiers for the emotion recognition are reviewed. The database for the speech emotion recognition system is the emotional speech samples and the features extracted from these speech samples are the energy, pitch, linear prediction cepstrum coefficient (LPCC), Mel frequency cepstrum coefficient (MFCC). Different wavelet decomposition structures can also used for feature vector extraction. The classifiers are used to differentiate emotions such as anger, happiness, sadness, surprise, fear, neutral state, etc. The classification performance is based on extracted features. Conclusions drawn from performance and limitations of speech emotion recognition system based on different methodologies are also discussed.


title: Volumetric rejuvenation of the tear trough with repo and Ristow.

abstract: Abstract The authors report the results of their approach for the treatment of the tear trough deformity, by lower eyelid blepharoplasty with fat repositioning and fat transfer to Ristow's space and the deep medial fat compartment. One hundred fourteen eyes of 57 patients underwent lower eyelid blepharoplasty, fat repositioning, and fat transfer between 2010 and 2012. No major complications were observed in any of the patients in this series. In all cases, there was significant improvement in the lower eyelid contour, tear trough, and blending of the transition at the eyelid-cheek junction noted by both patients and physicians. Despite the availability of multiple procedures, effacement of the tear trough region remains a challenge. The addition of fat transfer to Ristow's space and the deep medial fat compartment to traditional fat repositioning lower blepharoplasty are effective methods for volumizing the tear trough and may synergistically improve outcomes.


title: A Critical Analysis of Decision Support Systems Research Revisited: The Rise of Design Science

abstract: In 2005 the Journal of Information Technology article ‘A critical analysis of decision support systems research’ analyzed 1020 decision support systems (DSS) articles from 1990 to 2003. Since 2003 business intelligence (BI) and business analytics have gained popularity in practice. In theory and research the period since 2003 has seen a change in the decision-making theory orthodoxy and the codification and acceptance of design science. To investigate the changes in the DSS field, a number of expectations were derived from previous literature analyses. These expectations were assessed using bibliometric content analysis. The article sample to 2010 now includes 1466 articles from 16 journals. The analysis of the expectations yields mixed results for the DSS field. On the negative side, there has been an overall decline in DSS publishing, the relevance of DSS research published in journals to IT professionals has declined, and the rigor of DSS research designs has not improved. On the positive side, there has been improvement in relevance to managers, grant funding of DSS research has increased, there has been a positive shift in judgment and decision-making foundations, BI publishing has increased, and group support systems publishing has reduced to a more balanced level. An important result from the analysis of the last 7 years of DSS research is the significant increase in DSS design-science research (DSR) to almost half of published articles. It is clear from the analysis that DSS is undergoing a transition from a field based on statistical hypothesis testing and conceptual studies to one where DSR is the most popular method.


title: Improved Bounds on the Epidemic Threshold of Exact SIS Models on Complex Networks

abstract: The SIS (susceptible-infected-susceptible) epidemic model on an arbitrary network, without making approximations, is a $2^n$-state Markov chain with a unique absorbing state (the all-healthy state). This makes analysis of the SIS model and, in particular, determining the threshold of epidemic spread quite challenging. It has been shown that the exact marginal probabilities of infection can be upper bounded by an $n$-dimensional linear time-invariant system, a consequence of which is that the Markov chain is"fast-mixing"when the LTI system is stable, i.e. when $\frac{\beta}{\delta}<\frac{1}{\lambda_{\max}(A)}$ (where $\beta$ is the infection rate per link, $\delta$ is the recovery rate, and $\lambda_{\max}(A)$ is the largest eigenvalue of the network's adjacency matrix). This well-known threshold has been recently shown not to be tight in several cases, such as in a star network. In this paper, We provide tighter upper bounds on the exact marginal probabilities of infection, by also taking pairwise infection probabilities into account. Based on this improved bound, we derive tighter eigenvalue conditions that guarantee fast mixing (i.e., logarithmic mixing time) of the chain. We demonstrate the improvement of the threshold condition by comparing the new bound with the known one on various networks and epidemic parameters.


title: Design considerations for a contactless electric vehicle battery charger

abstract: This paper overviews theoretical and practical design issues related to inductive power transfer systems and verifies the developed theory using a practical electric vehicle battery charger. The design focuses on the necessary approaches to ensure power transfer over the complete operating range of the system. As such, a new approach to the design of the primary resonant circuit is proposed, whereby deviations from design expectations due to phase or frequency shift are minimized. Of particular interest are systems that are neither loosely nor tightly coupled. The developed solution depends on the selected primary and secondary resonant topologies, the magnetic coupling coefficient, and the secondary quality factor.


title: Multicast Routing for Multimedia Communications in the Internet of Things

abstract: Multicast routing that meets multiple quality of service constraints is important for supporting multimedia communications in the Internet of Things (IoT). Existing multicast routing technologies for IoT mainly focus on ad hoc sensor networking scenarios; thus, are not responsive and robust enough for supporting multimedia applications in an IoT environment. In order to tackle the challenging problem of multicast routing for multimedia communications in IoT, in this paper, we propose two algorithms for the establishing multicast routing tree for multimedia data transmissions. The proposed algorithms leverage an entropy-based process to aggregate all weights into a comprehensive metric, and then uses it to search a multicast tree on the basis of the spanning tree and shortest path tree algorithms. We conduct theoretical analysis and extensive simulations for evaluating the proposed algorithms. Both analytical and experimental results demonstrate that one of the proposed algorithms is more efficient than a representative multiconstrained multicast routing algorithm in terms of both speed and accuracy; thus, is able to support multimedia communications in an IoT environment. We believe that our results are able to provide in-depth insight into the multicast routing algorithm design for multimedia communications in IoT.


Dynamic Pricing with Model Uncertainty : Learning & Earning ( tutorial proposal at ACM EC 2015 )


title: Wideband planar horn antenna using substrate integrated waveguide technique

abstract: A substrate-integrated waveguide (SIW) H-plane sectoral horn antenna, with significantly improved bandwidth, is presented. A tapered ridge, consists of a simple arrangement of vias on the side flared wall within the multilayer substrate, is introduced to enlarge the operational bandwidth. A simple feed configuration is suggested to provide the propagating wave for the antenna structure. The proposed antenna is simulated by two well-known full wave packages, the Ansoft HFSS and the CST microwave studio. Close agreement between both simulation results is obtained. The designed antenna shows good radiation characteristics and low VSWR, lower than 2.5, for the whole frequency range of 18– 40 GHz.


title: A forensic cloud environment to address the big data challenge in digital forensics

abstract: The advancement of technologies such as the Internet, artificial intelligence and so on has given rise to Big Data. Also as a result of technological evolution, crimes committed within the digital domain are rising. Subsequently the number of forensic collection has increased and this has significantly affected digital forensics. Thus, the current forensic tools cannot cope with the demand of Big Data analysis. In this paper, we realise the urgent need to propose a generic Forensic Cloud Environment (FCE) that facilitates forensic analysis of Big Data cases. The FCE introduces new features that improves the turnaround time of digital forensic investigation.


title: Design Issues in Mobile Agent Programming Systems

abstract: The article discusses system-level issues and agent programming requirements that arise in the design of mobile agent systems. The authors describe several mobile agent systems to illustrate different approaches designers have taken in addressing these challenges. The following areas are discussed: agent mobility, naming, security issues, privacy and integrity, authentication, authorization and access control, metering and charging mechanisms, programming primitives, agent communication and synchronization primitives, agent monitoring and control primitives, and fault tolerance primitives.


title: The emergent algebraic structure of RNNs and embeddings in NLP

abstract: We examine the algebraic and geometric properties of a uni-directional GRU and word embeddings trained end-to-end on a text classification task. A hyperparameter search over word embedding dimension, GRU hidden dimension, and a linear combination of the GRU outputs is performed. We conclude that words naturally embed themselves in a Lie group and that RNNs form a nonlinear representation of the group. Appealing to these results, we propose a novel class of recurrent-like neural networks and a word embedding scheme.


title: A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer

abstract: Abstract Survival analysis has been developed and applied in the number of areas including manufacturing, finance, economics and healthcare. In healthcare domain, usually clinical data are high-dimensional, sparse and complex and sometimes there exists few amount of time-to-event (labeled) instances. Therefore building an accurate survival model from electronic health records is challenging. With this motivation, we address this issue and provide a new survival analysis framework using deep learning and active learning with a novel sampling strategy. First, our approach provides better representation with lower dimensions from clinical features using labeled (time-to-event) and unlabeled (censored) instances and then actively trains the survival model by labeling the censored data using an oracle. As a clinical assistive tool, we introduce a simple effective treatment recommendation approach based on our survival model. In the experimental study, we apply our approach on SEER-Medicare data related to prostate cancer among African–Americans and white patients. The results indicate that our approach outperforms significantly than baseline models.


title: Machine Learning for Efficient Assessment and Prediction of Human Performance in Collaborative Learning Environments

abstract: The objective of this work is to propose a machine learning-based methodology system architecture and algorithms to find patterns of learning, interaction, and relationship and effective assessment for a complex system involving massive data that could be obtained from a proposed collaborative learning environment (CLE). Collaborative learning may take place between dyads or larger team members to find solutions for real-time events or problems, and to discuss concepts or interactions during situational judgment tasks (SJT). Modeling a collaborative, networked system that involves multimodal data presents many challenges. This paper focuses on proposing a Machine Learning - (ML)-based system architecture to promote understanding of the behaviors, group dynamics, and interactions in the CLE. Our framework integrates techniques from computational psychometrics (CP) and deep learning models that include the utilization of convolutional neural networks (CNNs) for feature extraction, skill identification, and pattern recognition. Our framework also identifies the behavioral components at a micro level, and can help us model behaviors of a group involved in learning.


Self-adaptation Challenges for Cloud-based Applications : A Control Theoretic Perspective


title: Topic Modeling Using Distributed Word Embeddings

abstract: We propose a new algorithm for topic modeling, Vec2Topic, that identifies the main topics in a corpus using semantic information captured via high-dimensional distributed word embeddings. Our technique is unsupervised and generates a list of topics ranked with respect to importance. We find that it works better than existing topic modeling techniques such as Latent Dirichlet Allocation for identifying key topics in user-generated content, such as emails, chats, etc., where topics are diffused across the corpus. We also find that Vec2Topic works equally well for non-user generated content, such as papers, reports, etc., and for small corpora such as a single-document.


title: CMOS 2.45GHz RF power amplifier for RFID reader

abstract: A high linearity CMOS RF (radio frequency) power amplifier, which has the advantage of being low-cost and easily integrated on chip has become the technology of choice for RFID readers. This work demonstrates a 3.3V single voltage self-biased 2.4GHz--2.5GHz high linearity RF power amplifier for RFID applications.


title: Simulation System of Production Scheduling Multi-agent-Based

abstract: This paper researches the production scheduling system, and presents a simulation model of the manufacturing system which is based on MAS (Multi Agent Simulation) and HLA (High Level Architecture).According to the model, a related simulation system is developed with Jade and Jess as developing tools.Specific scheduling algorithm or rule is turned into conducting rules of Agent. The system can describe the manufacturing system and to help find a satisfactory scheduling scheme.


title: A decade of reconfigurable computing: a visionary retrospective

abstract: The paper surveys a decade of R&D on coarse grain reconfigurable hardware and related CAD, points out why this emerging discipline is heading toward a dichotomy of computing science, and advocates the introduction of a new soft machine paradigm to replace CAD by compilation.


title: Wideband to multi sub-band antenna with different bandwiths

abstract: In this paper, a wideband to multi sub-band antenna with different bandwidths is presented. This antenna is designed on a microstrip technology, printed in two sides of the substrate of the size of 32 × 40 mm2. By controlling the length of resonator with using ideal switches, the re-configurability of the antenna can be achieved. This antenna offers a high frequency band-width tuning from wideband (4–10 GHz) to a narrow sub-band (5.7–7 GHz). This covers several wireless communication systems, including C-band weather Radar, ISM and Satellite uplinks communications. The simulation results show that the antenna has a return loss better than 15 dB through the entire tuning bandwidths. The prototypes of the antennas with ideal switches are fabricated and measured.


title: Adolescents on the net: Internet use and well-being.

abstract: With the growing popularity of Internet communication applications among adolescents, the Internet has become an important social context for their development. This paper examined the relationship between adolescent online activity and well-being. Participants included 156 adoleseents between 15 to 18.4 years of age who were surveyed about their access to and use of the Internet. Participants also completed measures of loneliness and perceived soeial support. An ANOVA suggested that loneliness was not related to the total time spent onhne, nor to the time spent on e-mail, but was related to participants' gender. Regression analyses suggested that gender and participants' perceptions regarding their online relationships were the only variables that predicted lonehness. Adoleseents who felt that their relationship with online partners was one that they could turn to in times of need were more lonely. However, perceived support from significant others was not related to time spent online, time on e-mail, participants' relationships with onhne partners, and to their perceptions about these relationships. The implications of our results for researchers, parents, and other lay persons are discussed.


title: Evaluating Alternatives for Better Deaf Accessibility to Selected Web-Based Multimedia

abstract: The proliferation of video and audio media on the Internet has created a distinct disadvantage for deaf Internet users. Despite technological and legislative milestones in recent decades in making television and movies more accessible, there has been less progress with online access. A major obstacle to providing captions for Internet media is the high cost of captioning and transcribing services. This paper reports on two studies that focused on multimedia accessibility for Internet users who were born deaf or became deaf at an early age. An initial study attempted to identify priorities for deaf accessibility improvement. A total of 20 deaf and hard-of-hearing participants were interviewed via videophone about their Internet usage and the issues that were the most frustrating. The most common theme was concern over a lack of accessibility for online news. In the second study, a total of 95 deaf and hard-of-hearing participants evaluated different caption styles, some of which were generated through automatic speech recognition. Results from the second study confirm that captioning online videos makes the Internet more accessible to the deaf users, even when the captions are automatically generated. However color-coded captions used to highlight confidence levels were found neither to be beneficial nor detrimental; yet when asked directly about the benefit of color-coding, participants strongly favored the concept.


title: TarMAC: Targeted Multi-Agent Communication

abstract: We propose a targeted communication architecture for multi-agent reinforcement learning, where agents learn both what messages to send and whom to address them to while performing cooperative tasks in partially-observable environments. This targeting behavior is learnt solely from downstream task-specific reward without any communication supervision. We additionally augment this with a multi-round communication approach where agents coordinate via multiple rounds of communication before taking actions in the environment. We evaluate our approach on a diverse set of cooperative multi-agent tasks, of varying difficulties, with varying number of agents, in a variety of environments ranging from 2D grid layouts of shapes and simulated traffic junctions to 3D indoor environments, and demonstrate the benefits of targeted and multi-round communication. Moreover, we show that the targeted communication strategies learned by agents are interpretable and intuitive. Finally, we show that our architecture can be easily extended to mixed and competitive environments, leading to improved performance and sample complexity over recent state-of-the-art approaches.


title: Micropower energy harvesting

abstract: More than a decade of research in the field of thermal, motion, vibration and electromagnetic radiation energy harvesting has yielded increasing power output and smaller embodiments. Power management circuits for rectification and DC-DC conversion are becoming able to efficiently convert the power from these energy harvesters. This paper summarizes recent energy harvesting results and their power management circuits.


title: Effect of Balance Board Training with Tactile Stimulation on Affected Leg in Hemiplegic Patient

abstract: Objective: The purpose of this study was to determine the effect of balance board training combined with tactile stimulation on the affected leg of hemiplegic patients in improving the ability of balance control. Method: Thirty hemiplegic patients participated. In the study group, two pieces of adhesive tapes were attached on the skin of affected lower leg. And then, they performed balance training on a balance board. The training was performed for 4 weeks. The control group received conventional gait training program for the same period. Subjects in both groups were tested for their balance control abilities using Balance Master before and after the training period. Results: In the study group, there were statistically significant improvements in the abilities of the weight bearing, body sway control, and rhythmic weight shift (p＜0.05) after balance board training. After the training, there were statistically significant differences in the abilities of weight bearing and rhythmic weight shift (p＜0.05) between the two groups. Conclusion: The training with balance board combined with tactile stimulation to the affected leg and foot was proved to be effective for the treatment of balance control abilities in hemiplegic patients.


title: l2, 1 Regularized correntropy for robust feature selection

abstract: In this paper, we study the problem of robust feature extraction based on l 2, 1 regularized correntropy in both theoretical and algorithmic manner. In theoretical part, we point out that an l 2, 1 -norm minimization can be justified from the viewpoint of half-quadratic (HQ) optimization, which facilitates convergence study and algorithmic development. In particular, a general formulation is accordingly proposed to unify l 1 -norm and l 2, 1 -norm minimization within a common framework. In algorithmic part, we propose an l 2, 1 regularized correntropy algorithm to extract informative features meanwhile to remove outliers from training data. A new alternate minimization algorithm is also developed to optimize the non-convex correntropy objective. In terms of face recognition, we apply the proposed method to obtain an appearance-based model, called Sparse-Fisherfaces. Extensive experiments show that our method can select robust and sparse features, and outperforms several state-of-the-art subspace methods on large-scale and open face recognition datasets.


title: Hepatitis B virus X protein stimulates viral genome replication via a DDB1-dependent pathway distinct from that leading to cell death.

abstract: The hepatitis B virus (HBV) X protein (HBx) is essential for virus infection and has been implicated in the development of liver cancer associated with chronic infection. HBx can interact with a number of cellular proteins, and in cell culture, it exhibits pleiotropic activities, among which is its ability to interfere with cell viability and stimulate HBV replication. Previous work has demonstrated that HBx affects cell viability by a mechanism that requires its binding to DDB1, a highly conserved protein implicated in DNA repair and cell cycle regulation. We now show that an interaction with DDB1 is also needed for HBx to stimulate HBV genome replication. Thus, HBx point mutants defective for DDB1 binding fail to complement the low level of replication of an HBx-deficient HBV genome when provided in trans, and one such mutant regains activity when directly fused to DDB1. Furthermore, DDB1 depletion by RNA interference specifically compromises replication of wild-type HBV, indicating that HBx produced from the viral genome also functions in a DDB1-dependent fashion. We also show that HBx in association with DDB1 acts in the nucleus and stimulates HBV replication mainly by enhancing viral mRNA levels, regardless of whether the protein is expressed from the HBV genome itself or supplied in trans. Interestingly, whereas HBx induces cell death in both HepG2 and Huh-7 hepatoma cell lines, it enhances HBV replication only in HepG2 cells, suggesting that the two activities involve distinct DDB1-dependent pathways.


title: Classify or Select: Neural Architectures for Extractive Document Summarization

abstract: We present two novel and contrasting Recurrent Neural Network (RNN) based architectures for extractive summarization of documents. The Classifier based architecture sequentially accepts or rejects each sentence in the original document order for its membership in the summary. The Selector architecture, on the other hand, is free to pick one sentence at a time in any arbitrary order to generate the extractive summary. ::: ::: Our models under both architectures jointly capture the notions of salience and redundancy of sentences. In addition, these models have the advantage of being very interpretable, since they allow visualization of their predictions broken up by abstract features such as information content, salience and redundancy. ::: ::: We show that our models reach or outperform state-of-the-art supervised models on two different corpora. We also recommend the conditions under which one architecture is superior to the other based on experimental evidence.


title: Developing a pediatric pain service

abstract: Pediatric pain services were first established in larger pediatric centers over two decades ago. Children's acute pain was poorly managed at the time owing to misconceptions, safety concerns, and variability in practice. While many larger pediatric centers now have acute pain services, there remains a need for better pain management in facilities and geographic locations with fewer resources. Institutional acknowledgement and desire to change, appropriate staffing, and funding are major obstacles. Better recognition and assessment as well safer and more efficacious treatment of pain are the principal objectives when establishing a pain service. It is important to determine whether the proposed service intends to treat acute, chronic, procedural, and/or cancer and palliative pain as each requires different skills and resources. An ideal and comprehensive pediatric pain service should be equipped to diagnose and treat acute, persistent (chronic), procedural, and cancer/palliative pain. It is not feasible or necessary for every hospital to manage all. Establishing the scope of practice (based on case mix and caseload) in any given hospital will determine which resources are desired. Country-specific standards, local staffing, and fiscal constraints will influence which resources are available.


title: Color associations for days and letters across different languages

abstract: While colors are commonplace in everyday metaphors, relatively little is known about implicit color associations to linguistic or semantic concepts in a general population. In this study, we test color associations for ordered linguistic concepts (letters and days). The culture and language specificity of these effects was examined in a large group (457) of Dutch-speaking participants, 92 English-speaking participants, and 49 Hindi-speaking participants. Non-random distributions of color choices were revealed; consistencies were found across the three language groups in color preferences for both days and letters. Interestingly, while the Hindi-speaking participants were presented with letter stimuli matched on phonology, their pattern of letter-to-color preferences still showed similarities with Dutch- and English-speaking participants. Furthermore, we found that that the color preferences corresponded between participants indicating to have conscious color experiences with letters or days (putative synesthetes) and participants who do not (non-synesthetes). We also explored possible mechanisms underlying the color preferences. There were a few specific associations, including red for “A”, red for “Monday”, and white for “Sunday”. We also explored more general mechanisms, such as overall color preferences shown by Simner et al (2005). While certainly not all variation can be explained or predicted, the results show that regularities are present in color-to-letter or color-to-day preferences in both putative synesthetes and non-synesthetes across languages. Both letter-to-color and day-to-color preferences were influenced by multiple factors. The findings support a notion of abstract concepts (such as days and letters) that are not represented in isolation, but are connected to perceptual representational systems. Interestingly, at least some of these connections to color representations are shared across different language/cultural groups.


title: Beauty and the Beast: Diverting Modern Web Browsers to Build Unique Browser Fingerprints

abstract: Worldwide, the number of people and the time spent browsing the web keeps increasing. Accordingly, the technologies to enrich the user experience are evolving at an amazing pace. Many of these evolutions provide for a more interactive web (e.g., boom of JavaScript libraries, weekly innovations in HTML5), a more available web (e.g., explosion of mobile devices), a more secure web (e.g., Flash is disappearing, NPAPI plugins are being deprecated), and a more private web (e.g., increased legislation against cookies, huge success of extensions such as Ghostery and AdBlock). Nevertheless, modern browser technologies, which provide the beauty and power of the web, also provide a darker side, a rich ecosystem of exploitable data that can be used to build unique browser fingerprints. Our work explores the validity of browser fingerprinting in today's environment. Over the past year, we have collected 118,934 fingerprints composed of 17 attributes gathered thanks to the most recent web technologies. We show that innovations in HTML5 provide access to highly discriminating attributes, notably with the use of the Canvas API which relies on multiple layers of the user's system. In addition, we show that browser fingerprinting is as effective on mobile devices as it is on desktops and laptops, albeit for radically different reasons due to their more constrained hardware and software environments. We also evaluate how browser fingerprinting could stop being a threat to user privacy if some technological evolutions continue (e.g., disappearance of plugins) or are embraced by browser vendors (e.g., standard HTTP headers).


title: Face liveness detection by exploring multiple scenic clues

abstract: Liveness detection is an indispensable guarantee for reliable face recognition, which has recently received enormous attention. In this paper we propose three scenic clues, which are non-rigid motion, face-background consistency and imaging banding effect, to conduct accurate and efficient face liveness detection. Non-rigid motion clue indicates the facial motions that a genuine face can exhibit such as blinking, and a low rank matrix decomposition based image alignment approach is designed to extract this non-rigid motion. Face-background consistency clue believes that the motion of face and background has high consistency for fake facial photos while low consistency for genuine faces, and this consistency can serve as an efficient liveness clue which is explored by GMM based motion detection method. Image banding effect reflects the imaging quality defects introduced in the fake face reproduction, which can be detected by wavelet decomposition. By fusing these three clues, we thoroughly explore sufficient clues for liveness detection. The proposed face liveness detection method achieves 100% accuracy on Idiap print-attack database and the best performance on self-collected face anti-spoofing database.


ROBUST MULTIMODAL HAND- AND HEAD GESTURE RECOGNITION FOR CONTROLLING AUTOMOTIVE INFOTAINMENT SYSTEMS


title: Soft-Switching Solid-State Transformer (S4T)

abstract: This paper presents a new topology for a fully bidirectional soft-switching solid-state transformer (S4T). The minimal topology, featuring 12 main devices and a high-frequency transformer, does not use an intermediate dc voltage link, and provides sinusoidal input and output voltages. The S4T can be configured to interface with two- or multiterminal dc, single- or multiphase ac systems. An auxiliary resonant circuit creates zero-voltage-switching conditions for main devices from no-load to full-load, and helps manage interactions with circuit parasitic elements. The modularized structure allows series and/or parallel stacking of converter cells for high-voltage and high-power applications.


title: The Everywhere Displays Projector: A Device to Create Ubiquitous Graphical Interfaces

abstract: This paper introduces the Everywhere Displays projector, a device that uses a rotating mirror to steer the light from an LCD/DLP projector onto different surfaces of an environment. Issues of brightness, oblique projection distortion, focus, obstruction, and display resolution are examined. Solutions to some of these problems are described, together with a plan to use a video camera to allow device-free interaction with the projected images. The ED-projector is a practical way to create ubiquitous graphical interfaces to access computational power and networked data. In particular, it is envisioned as an alternative to the carrying of laptops and to the installation of displays in furniture, objects, and walls. In addition, the use of ED-projectors to augment reality without the use of goggles is examined and illustrated with examples.


title: Provably Fair Representations

abstract: Machine learning systems are increasingly used to make decisions about people's lives, such as whether to give someone a loan or whether to interview someone for a job. This has led to considerable interest in making such machine learning systems fair. One approach is to transform the input data used by the algorithm. This can be achieved by passing each input data point through a representation function prior to its use in training or testing. Techniques for learning such representation functions from data have been successful empirically, but typically lack theoretical fairness guarantees. We show that it is possible to prove that a representation function is fair according to common measures of both group and individual fairness, as well as useful with respect to a target task. These provable properties can be used in a governance model involving a data producer, a data user and a data regulator, where there is a separation of concerns between fairness and target task utility to ensure transparency and prevent perverse incentives. We formally define the 'cost of mistrust' of using this model compared to the setting where there is a single trusted party, and provide bounds on this cost in particular cases. We present a practical approach to learning fair representation functions and apply it to financial and criminal justice datasets. We evaluate the fairness and utility of these representation functions using measures motivated by our theoretical results.


Does Self-Criticism Mediate the Relationship between Self-Compassion and Well-Being ? Does Self-Criticism Mediate the Relationship between Self-Compassion and Well-Being ?


title: Unsupervised Relation Extraction of In-Domain Data from Focused Crawls

abstract: This thesis proposal approaches unsupervised relation extraction from web data, which is collected by crawling only those parts of the web that are from the same domain as a relatively small reference corpus. The first part of this proposal is concerned with the efficient discovery of web documents for a particular domain and in a particular language. We create a combined, focused web crawling system that automatically collects relevant documents and minimizes the amount of irrelevant web content. The collected web data is semantically processed in order to acquire rich in-domain knowledge. Here, we focus on fully unsupervised relation extraction by employing the extended distributional hypothesis. We use distributional similarities between two pairs of nominals based on dependency paths as context and vice versa for identifying relational structure. We apply our system for the domain of educational sciences by focusing primarily on crawling scientific educational publications in the web. We are able to produce promising initial results on relation identification and we will discuss future directions.


title: Femicomfort in the Treatment of Premenstrual Syndromes: A Double-Blind, Randomized and Placebo Controlled Trial

abstract: Premenstrual syndromes (PMS) are among the most common health problems reported by women, affecting 20–40% of women of reproductive age. Premenstrual dysphoric disorder (PMDD) is a severe subtype of PMS that occurs in 3–8% of women of reproductive age.(1) It is characterized by severe mood and behavioral changes. The hallmark of PMDD is its cyclical luteal-phase-related nature.(2) The etiology of the syndrome is multifactorial and not fully defined. Initially, a great role in the etiology was attributed to decreased levels of progesterone in the luteal phase. An American telephone survey suggested that up to 80% of self-medicating sufferers use complementary remedies.(3) It has been reported that herbal medicine and vitamin B6 and Vitamin E are useful in relieving PMS symptoms. Evening primrose (Oenothera biennis) is a commonly used alternative therapy for PMS and is a rich source of omega-6 essential fatty acids.(3) The aim of this double blind and placebo controlled trial was to investigate whether femicofort (a supplement) which contains Vitamin B6, Vitamin E and evening primrose oil could relieve PMS symptoms.


title: Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature

abstract: Interdisciplinary scientific research (IDR) extends and challenges the study of science on a number of fronts, including creating output science and engineering (S&E) indicators. This literature review began with a narrow search for quantitative measures of the output of IDR that could contribute to indicators, but the authors expanded the scope of the review as it became clear that differing definitions, assessment tools, evaluation processes, and measures all shed light on different aspects of IDR. Key among these broader aspects is (a) the importance of incorporating the concept of knowledge integration, and (b) recognizing that integration can occur within a single mind as well as among a team. Existing output measures alone cannot adequately capture this process. Among the quantitative measures considered, bibliometrics (co-authorships, co-inventors, collaborations, references, citations and co-citations) are the most developed, but leave considerable gaps in understanding of the social dynamics that lead to knowledge integration. Emerging measures in network dynamics (particularly betweenness centrality and diversity), and entropy are promising as indicators, but their use requires sophisticated interpretations. Combinations of quantitative measures and qualitative assessments being applied within evaluation studies appear to reveal IDR processes but carry burdens of expense, intrusion, and lack of reproducibility year-upon-year. This review is a first step toward providing a more holistic view of measuring IDR, although research and development is needed before metrics can adequately reflect the actual phenomenon of IDR.


title: NeNMF: An Optimal Gradient Method for Nonnegative Matrix Factorization

abstract: Nonnegative matrix factorization (NMF) is a powerful matrix decomposition technique that approximates a nonnegative matrix by the product of two low-rank nonnegative matrix factors. It has been widely applied to signal processing, computer vision, and data mining. Traditional NMF solvers include the multiplicative update rule (MUR), the projected gradient method (PG), the projected nonnegative least squares (PNLS), and the active set method (AS). However, they suffer from one or some of the following three problems: slow convergence rate, numerical instability and nonconvergence. In this paper, we present a new efficient NeNMF solver to simultaneously overcome the aforementioned problems. It applies Nesterov's optimal gradient method to alternatively optimize one factor with another fixed. In particular, at each iteration round, the matrix factor is updated by using the PG method performed on a smartly chosen search point, where the step size is determined by the Lipschitz constant. Since NeNMF does not use the time consuming line search and converges optimally at rate in optimizing each matrix factor, it is superior to MUR and PG in terms of efficiency as well as approximation accuracy. Compared to PNLS and AS that suffer from numerical instability problem in the worst case, NeNMF overcomes this deficiency. In addition, NeNMF can be used to solve -norm, -norm and manifold regularized NMF with the optimal convergence rate. Numerical experiments on both synthetic and real-world datasets show the efficiency of NeNMF for NMF and its variants comparing to representative NMF solvers. Extensive experiments on document clustering suggest the effectiveness of NeNMF.


title: Siberian tiger’s recent population bottleneck in the Russian Far East revealed by microsatellite markers

abstract: Because size reduced, or bottlenecked, populations are more prone to adverse events, the detection of genetic bottleneck signatures in wildlife species is highly relevant for conservation. Here we applied 11 microsatellite markers to the endangered Siberian tiger (Panthera tigris altaica) using tissue and blood samples of animals from the Primorsky region of the Russian Far East. Excess heterozygosity and mode shift in allele frequencies tests were positive, while the M-ratio test was negative, indicating the likelihood of a contemporary rather than a historical population bottleneck. The recent genetic population bottleneck could be attributed to the well-documented demographic collapse of the Siberian tiger population in the 1940s, when population size hit bottom with 20–30 surviving animals. The mean effective population size Ne was 14 Siberian tigers (CI95: 12–25 animals), and the effective population size/census size ratio (Ne/N ratio) was 0.028. This is the first molecular evidence of a recent Siberian tiger population bottleneck, which is of great interest for further conservation and management plans of the highly endangered largest felid species, while the worryingly low effective population size challenges the optimism for the recovery of the huge Siberian cat.


title: Letters: A hierarchical intrusion detection model based on the PCA neural networks

abstract: Most of existing intrusion detection (ID) models with a single-level structure can only detect either misuse or anomaly attacks. A hierarchical ID model using principal component analysis (PCA) neural networks is proposed to overcome such shortages. In the proposed model, PCA is applied for classification and neural networks are used for online computing. Experimental results and comparative studies based on the 1998 DARPA evaluation data sets are given, which show the proposed model can classify the network connections with satisfying performance.


title: A Constant Frequency Output-Ripple-Voltage-Based Buck Converter Without Using Large ESR Capacitor

abstract: A constant frequency output-ripple-voltage based CMOS current-mode dc-dc buck converter, providing fast load transient response and reference-tracking speed, is proposed in this paper. Unlike V2 control output-ripple-voltage based buck converter, the proposed buck converter can achieve fast and stable load transient response without relying on ESR value of output capacitor. In addition, the proposed converter has faster reference-tracking speed than V2 the control counterparts by about 25 times.


title: Fault diagnosis and failure prognosis for engineering systems: A global perspective

abstract: Engineering systems, such as aircraft, industrial processes, manufacturing systems, transportation systems, electrical and electronic systems, etc., are becoming more complex and are subjected to failure modes that impact adversely their reliability, availability, safety and maintainability. Such critical assets are required to be available when needed, and maintained on the basis of their current condition rather than on the basis of scheduled or breakdown maintenance practices. Moreover, on-line, real-time fault diagnosis and prognosis can assist the operator to avoid catastrophic events. Recent advances in Condition-Based Maintenance and Prognostics and Health Management (CBM/PHM) have prompted the development of new and innovative algorithms for fault, or incipient failure, diagnosis and failure prognosis aimed at improving the performance of critical systems. This paper introduces an integrated systemsbased framework (architecture) for diagnosis and prognosis that is generic and applicable to a variety of engineering systems. The enabling technologies are based on suitable health monitoring hardware and software, data processing methods that focus on extracting features or condition indicators from raw data via data mining and sensor fusion tools, accurate diagnostic and prognostic algorithms that borrow from Bayesian estimation theory, and specifically particle filtering, fatigue or degradation modeling, and real-time measurements to declare a fault with prescribed confidence and given false alarm rate while predicting accurately and precisely the remaining useful life of the failing component/system. Potential benefits to industry include reduced maintenance costs, improved equipment uptime and safety. The approach is illustrated with examples from the aircraft and industrial domains.


title: Effects of farm management practices on cattle grazing behaviour and the potential for transmission of bovine tuberculosis from badgers to cattle.

abstract: Despite the large body of circumstantial evidence to suggest a link, the means by which bovine tuberculosis is passed from badgers to cattle remains unclear; pasture contamination with the urine, faeces and/or sputum of infectious badgers is believed to be the main route of transmission. Therefore the behaviour of grazing cattle was studied to determine whether they avoided investigating and/or grazing pasture contaminated with badger excreta, and whether different farm management practices enhanced the potential for disease transmission. Active latrines were avoided by most cattle until the sward length in the rest of the field was reduced, after which there was an increasing likelihood that active badger latrines would be grazed. Most of the cattle grazed active badger latrines, but cattle of low rank within the herd grazed latrines more heavily. Farm management practices that reduced the availability of long swards shortened the period of investigative behaviour and greatly enhanced the risk that cattle would graze active badger latrines. Cattle were more likely to graze pasture away from latrines that was contaminated either with badger urine or single faeces. Because bacilli remain viable in the soil for up to 2 years, there is the potential for bacilli to accumulate at active badger latrines, and these could pose a significant risk to cattle, even when the latrine is no longer being used by badgers. Cattle readily grazed the lush sward at disused latrines, during which they could ingest contaminated soil; the amount of soil ingested increases as sward length decreases.