title: Anthropometric Measurement and Analysis of the External Nasal Soft Tissue in 119 Young Han Chinese Adults

abstract: A random sample of 119 young, healthy Han Chinese adults (56 men and 63 women) between the age of 18 and 25 years (mean, 22.7 y) in PR China was obtained for this study. By the guidance of standard methods, based on Farkas's anthropometric measurements in craniofacial region, 12 nasal soft tissue landmarks and 12 linear and 3 angular measurements were chosen. The linear measurements were taken directly, whereas the angular measurements were taken by photogrammetric method. Eight nasal proportion indices were calculated according to the linear measurements. The application of the independent-samples t-test showed sex dimorphism in most parameters of the nasal region. All the linear measurements were larger in men than in women, whereas all the angular measurements were smaller in men than in women. The significant differences in partial parameters between men and women have been proved. Ten of 12 linear measurements, 1 of 3 angular measurements, and 3 of 8 nasal proportion indices showed significant sexual dimorphism (P < 0.01). Compared with other racial/ethnic groups, the nasal anthropometric measurements and proportion indices of Han Chinese adults were different, to some extent. This study could provide credible and objective reference material for plastic and maxillofacial surgeons for the external nasal soft tissue evaluation and planning of the cosmetic nasal surgery. Besides, these results could be a useful guidance for preoperative and postoperative evaluations of secondary rhinoplasty in nasal deformity associated with cleft lip and palate.


title: Scalable Object Retrieval with Compact Image Representation from Generic Object Regions

abstract: In content-based visual object retrieval, image representation is one of the fundamental issues in improving retrieval performance. Existing works adopt either local SIFT-like features or holistic features, and may suffer sensitivity to noise or poor discrimination power. In this article, we propose a compact representation for scalable object retrieval from few generic object regions. The regions are identified with a general object detector and are described with a fusion of learning-based features and aggregated SIFT features. Further, we compress feature representation in large-scale image retrieval scenarios. We evaluate the performance of the proposed method on two public ground-truth datasets, with promising results. Experimental results on a million-scale image database demonstrate superior retrieval accuracy with efficiency gain in both computation and memory usage.


title: Design and Realization of a Novel Compact Fluxgate Current Sensor

abstract: A new closed-loop magnetic current sensor is presented in this paper. The sensor consists of two toroidal magnetic cores. One core works in fluxgate principle for the measurement of dc and low-frequency ac, and the other one is used as a current transformer for higher frequency application. Based on the simulation results, a prototype was designed, and the test results have a good agreement with the simulation results. The closed-loop configuration with a magnetic core and a feedback winding in the sensor improved the sensitivity of the sensor, eliminated the offset and drift related to temperature, and greatly reduced the error caused by magnetic hysteresis phenomenon. It can measure currents up to 20 A, with an accuracy of 0.5%, and a 50 kHz small signal bandwidth.


title: Highly Loaded Evanescent Cavities for Widely Tunable High-Q Filters

abstract: In the present work, a widely tunable high-Q air filled evanescent cavity bandpass filter is created in an LTCC substrate. A low loss Rogers Duroidreg flexible substrate forms the top of the filter, acting as a membrane for a tunable parasitic capacitor that allows variable frequency loading. A commercially available piezoelectric actuator is mounted on the Duroidreg substrate for precise electrical tuning of the filter center frequency. The filter is tuned from 2.71 to 4.03 GHz, with insertion losses ranging from 1.3 to 2.4 dB across the range for a 2.5% bandwidth filter. Secondarily, an exceptionally narrow band filter is fabricated to show the potential for using the actuators to fine tune the response to compensate for fabrication tolerances. While most traditional machining techniques would not allow for such narrow band filtering, the high-Q and the sensitive tuning combine to allow for near channel selection for a front-end receiver. For further analysis, a widely tunable resonator is also created with a 100% tunable frequency range, from 2.3 to 4.6 GHz. The resonator analysis gives unloaded quality factors ranging from 360 to 700 with a maximum frequency loading of 89%. This technique shows a lot of promise for tunable RF filtering applications.


title: Brain network analysis: a data mining perspective

abstract: Following the recent advances in neuroimaging technology, the research on brain network analysis becomes an emerging area in data mining community. Brain network data pose many unique challenges for data mining research. For example, in brain networks, the nodes (i.e., the brain regions) and edges (i.e., relationships between brain regions) are usually not given, but should be derived from the neuroimaging data. The network structure can be very noisy and uncertain. Therefore, innovative methods are required for brain network analysis. Many research efforts have been devoted to this area. They have achieved great success in various applications, such as brain network extraction, graph mining, neuroimaging data analysis. In this paper, we review some recent data mining methods which are used in the literature for mining brain network data.


title: Soft-errors induced by terrestrial neutrons and natural alpha-particle emitters in advanced memory circuits at ground level

abstract: Abstract This review covers our recent (2005–2010) experiments and modeling-simulation work dedicated to the evaluation of natural radiation-induced soft errors in advanced static memory (SRAM) technologies. The impact on the chip soft-error rate (SER) of both terrestrial neutrons induced by cosmic rays and alpha-particle emitters, generated from traces of radioactive contaminants in CMOS process or packaging materials, has been experimentally investigated by life (i.e. real-time) testing performed at ground level on the Altitude Single-event Effect Test European Platform (ASTEP) and underground at the underground laboratory of modane (LSM). The paper describes these two test platforms and surveys the characterization results obtained for two SRAM technology nodes (130 nm and 65 nm). Experimental results concerning the characterization of the natural radiation environment are also reported.


title: Introducing a New Generation of Governance Indicators

abstract: This Report is about the state of governance. Few would doubt that the conditions of governance have changed — and continue to change — as the early 21st century seems to enter a period of profound uncertainty. Yet, at the same time, the world seems alive with a cacophony of approaches — old and new — on how to improve governance and, ultimately, policy outcomes. This Report — the first in a series of annual editions produced by the Hertie School of Governance — seeks to address the implications of the current state of the world in terms of "good governance", that is, the effective, efficient, and reliable set of legitimate institutions and actors dedicated to dealing with matters of public concern, be it in the field of financial markets, health care, security, or migration, and across local, national, and international levels. Following an introduction that offers a framework of basic concepts and models, The Governance Report 2013 then goes on to explore a number of global challenges and the reasons behind seemingly lacklustre responses and highlight the need for responsible sovereignty; examine in depth the challenges of financial and fiscal governance with a focus on the trade-offs and ways to address them; analyse key governance innovations and their potential for success; and assess existing indicators of governance, while proposing a new framework for collecting, interpreting, and applying governance-related information. The findings lead to a set of concrete proposals on the way ahead.(About the Book)


title: Improving crop productivity and resource use efficiency to ensure food security and environmental quality in China

abstract: In recent years, agricultural growth in China has accelerated remarkably, but most of this growth has been driven by increased yield per unit area rather than by expansion of the cultivated area. Looking towards 2030, to meet the demand for grain and to feed a growing population on the available arable land, it is suggested that annual crop production should be increased to around 580 Mt and that yield should increase by at least 2% annually. Crop production will become more difficult with climate change, resource scarcity (e.g. land, water, energy, and nutrients) and environmental degradation (e.g. declining soil quality, increased greenhouse gas emissions, and surface water eutrophication). To pursue the fastest and most practical route to improved yield, the near-term strategy is application and extension of existing agricultural technologies. This would lead to substantial improvement in crop and soil management practices, which are currently suboptimal. Two pivotal components are required if we are to follow new trajectories. First, the disciplines of soil management and agronomy need to be given increased emphasis in research and teaching, as part of a grand food security challenge. Second, continued genetic improvement in crop varieties will be vital. However, our view is that the biggest gains from improved technology will come most immediately from combinations of improved crops and improved agronomical practices. The objectives of this paper are to summarize the historical trend of crop production in China and to examine the main constraints to the further increase of crop productivity. The paper provides a perspective on the challenge faced by science and technology in agriculture which must be met both in terms of increased crop productivity but also in increased resource use efficiency and the protection of environmental quality.


title: Reliability in the utility computing era: Towards reliable Fog computing

abstract: This paper considers current paradigms in computing and outlines the most important aspects concerning their reliability. The Fog computing paradigm as a non-trivial extension of the Cloud is considered and the reliability of the networks of smart devices are discussed. Combining the reliability requirements of grid and cloud paradigms with the reliability requirements of networks of sensor and actuators it follows that designing a reliable Fog computing platform is feasible.


title: Simple model of spiking neurons

abstract: A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC.


title: Geometric and discrete path planning for interactive virtual worlds

abstract: Path planning and navigation are indispensable components for controlling autonomous agents in interactive virtual worlds. Given the growing demands on the size and complexity of modern virtual worlds, a number of new techniques have been developed for achieving intelligent navigation for the next generation of interactive multi-agent simulations. This course reviews the evolution of the several related techniques, from classical planning algorithms and computational geometry techniques to more advanced topics with focus on recent developments from the work of the authors. The covered topics range from discrete search and geometric representations to planning under different types of constraints and harnessing the power of graphics hardware in order to address Euclidean shortest paths and discrete search for multiple agents under limited time budgets. The use of planning algorithms beyond path planning is also discussed in the areas of crowd animation and whole-body motion planning for virtual characters.


title: A frog-inspired swimming robot based on dielectric elastomer actuators

abstract: Frogs are capable of multiple locomotion modes including jumping and swimming, which enables them to adapt to various environmental conditions. This paper demonstrates a frog-inspired robot, which can mimic the swimming motion of a natural frog. The robot is developed based on dielectric elastomer actuators, which exhibits muscle-like behavior such as large voltage-induced deformation, high energy density, fast response and low weight. Inspired by the webbed feet of a frog, the foot actuator of the swimming robot is able to increase its projected area by 66% when subject to high voltage. Actuation of the foot actuators can significantly improve the averaged peak thrust by 34.5%. The total mass of the two dielectric elastomer actuators is 14g which only accounts for 13% of its total mass of 108g. The measured average swimming speed for a square wave voltage of 5kV and 0.25Hz is 19mm/s for the swimming robot. Future work of the project includes optimal design and control of this soft robot.


On Attempting to Model the Mathematical Mind.


title: A Game-Theoretic Framework for Resilient and Distributed Generation Control of Renewable Energies in Microgrids

abstract: The integration of microgrids that depend on the renewable distributed energy resources with the current power systems is a critical issue in the smart grid. In this paper, we propose a non-cooperative game-theoretic framework to study the strategic behavior of distributed microgrids that generate renewable energies and characterize the power generation solutions by using the Nash equilibrium concept. Our framework not only incorporates economic factors but also takes into account the stability and efficiency of the microgrids, including the power flow constraints and voltage angle regulations. We develop two decentralized update schemes for microgrids and show their convergence to a unique Nash equilibrium. Also, we propose a novel fully distributed PMU-enabled algorithm which only needs the information of voltage angle at the bus. To show the resiliency of the distributed algorithm, we introduce two failure models of the smart grid. Case studies based on the IEEE 14-bus system are used to corroborate the effectiveness and resiliency of the proposed algorithms.


title: Design of Capacitive Power Transfer Using a Class-E Resonant Inverter

abstract: This paper presents a capacitive power transfer (CPT) system using a Class-E resonant inverter. A Class-E resonant inverter is chosen because of its ability to perform DC-to-AC inversion efficiently while significantly reducing switching losses. The proposed CPT system consists of an efficient Class-E resonant inverter and capacitive coupling formed by two flat rectangular transmitter and receiver plates. To understand CPT behavior, we study the effects of various coupling distances on output power performance. The proposed design is verified through lab experiments with a nominal operating frequency of 1 ㎒ and 0.25 ㎜ coupling gap. An efficiency of 96.3% is achieved. A simple frequency tracking unit is also proposed to tune the operating frequency in response to changes in the coupling gap. With this resonant frequency tracking unit, the efficiency of the proposed CPT system can be maintained within 96.3%–91% for the coupling gap range of 0.25–2 ㎜.


title: Sparse Additive Subspace Clustering

abstract: In this paper, we introduce and investigate a sparse additive model for subspace clustering problems. Our approach, named SASC (Sparse Additive Subspace Clustering), is essentially a functional extension of the Sparse Subspace Clustering (SSC) of Elhamifar & Vidal [7] to the additive nonparametric setting. To make our model computationally tractable, we express SASC in terms of a finite set of basis functions, and thus the formulated model can be estimated via solving a sequence of grouped Lasso optimization problems. We provide theoretical guarantees on the subspace recovery performance of our model. Empirical results on synthetic and real data demonstrate the effectiveness of SASC for clustering noisy data points into their original subspaces.


title: Modelling the Appearance and Behaviour of Urban Spaces

abstract: Urban spaces consist of a complex collection of buildings, parcels, blocks and neighbourhoods interconnected by streets. Accurately modelling both the appearance and the behaviour of dense urban spaces is a significant challenge. The recent surge in urban data and its availability via the Internet has fomented a significant amount of research in computer graphics and in a number of applications in urban planning, emergency management and visualization. In this paper, we seek to provide an overview of methods spanning computer graphics and related fields involved in this goal. Our paper reports the most prominent methods in urban modelling and rendering, urban visualization and urban simulation models. A reader will be well versed in the key problems and current solution methods.


title: REAL TIME HAND GESTURE RECOGNITION USING SIFT

abstract: The objective of the gesture recognition is to identify and distinguish the human gestures and utilizes these identified gestures for applications in specific domain. In this paper we propose a new approach to build a real time system to identify the standard gesture given by American Sign Language, or ASL, the dominant sign language of Deaf Americans, including deaf communities in the United States, in the English-speaking parts of Canada, and in some regions of Mexico. We propose a new method of improvised scale invariant feature transform (SIFT) and use the same to extract the features. The objective of the paper is to decode a gesture video into the appropriate alphabets.


title: Implementation of image fusion algorithm using MATLAB (LAPLACIAN PYRAMID)

abstract: This paper represents a approach to implement image fusion algorithm ie LAPLACIAN PYRAMID. In this technique implements a pattern selective approach to image fusion. The basic idea is to perform a pyramid decomposition on each source image and finally reconstruct the fused image by performing an inverse pyramid transform. It offers benefits like resolution, S/N ratio and pixel size. The aim of image fusion, apart from reducing the amount of data, is to create new images that are more suitable for the purposes of human/machine perception, and for further image-processing tasks such as segmentation, object detection or target recognition in applications such as remote sensing and medical imaging Based on this technique finally it reconstructs the fused image from the fused pyramid.


title: Minimally-Supervised Attribute Fusion for Data Lakes

abstract: Aggregate analysis, such as comparing country-wise sales versus global market share across product categories, is often complicated by the unavailability of common join attributes, e.g., category, across diverse datasets from different geographies or retail chains, even after disparate data is technically ingested into a common data lake. Sometimes this is a missing data issue, while in other cases it may be inherent, e.g., the records in different geographical databases may actually describe different product 'SKUs', or follow different norms for categorization. Record linkage techniques can be used to automatically map products in different data sources to a common set of global attributes, thereby enabling federated aggregation joins to be performed. Traditional record-linkage techniques are typically unsupervised, relying textual similarity features across attributes to estimate matches. In this paper, we present an ensemble model combining minimal supervision using Bayesian network models together with unsupervised textual matching for automating such 'attribute fusion'. We present results of our approach on a large volume of real-life data from a market-research scenario and compare with a standard record matching algorithm. Finally we illustrate how attribute fusion using machine learning could be included as a data-lake management feature, especially as our approach also provides confidence values for matches, enabling human intervention, if required.


title: Psychophysical elements of wearability

abstract: Wearable technology presents a wealth of new HCI issues. In particular, this paper addresses the impact of the physical interaction between the user's body and the device's physical form on the user's mental representation of self and cognitive abilities, a blend of HCI and ergonomics that is unique to wearable computing. We explore the human sensory mechanisms that facilitate perception of worn objects and the elements of sensation that influence the comfort of worn objects, and discuss the psychological elements that may cause worn objects to be forgotten or detected, wearable or not. We discuss the implications of un-wearability on attention and cognitive capability.


title: Optimizing fishbone aisles for dual-command operations in a warehouse

abstract: Unit-load warehouses store and retrieve unit-loads, typically pallets. When storage and retrieval operations are not coordinated, travel is from a pickup and deposit (P&D) point to a pallet location and back again. In some facilities, workers interleave storage and retrieval operations to form a dual-command cycle. Two new aisle designs proposed by Gue and Meller (“Improving the unit-load warehouse.” In Progress in Material Handling Research: 2006. Material Handling Industry of America, Charlotte, NC, 2006) use diagonal aisles to reduce the travel distance to a single pallet location by approximately 10 and 20[percnt] for the two designs, respectively. We develop analytical expressions for travel between pallet locations for one of these—the fishbone design. We then compare fishbone warehouses that have been optimized for dual-command to traditional warehouses that have been optimized in the same manner, and show that an optimal fishbone design reduces dual-command travel by 10–15%. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 54: 389–403, 2009


title: PerfEnforce Demonstration: Data Analytics with Performance Guarantees

abstract: We demonstrate PerfEnforce, a dynamic scaling engine for analytics services. PerfEnforce automatically scales a cluster of virtual machines in order to minimize costs while probabilistically meeting the query runtime guarantees offered by a performance-oriented service level agreement (SLA). The demonstration will show three families of dynamic scaling algorithms --feedback control, reinforcement learning, and online machine learning--and will enable attendees to change tuning parameters, performance thresholds, and workloads to compare and contrast the algorithms in different settings.


title: Approximate undirected maximum flows in o ( m polylog( n )) time

abstract: We give the first O(mpolylog(n)) time algorithms for approximating maximum flows in undirected graphs and constructing polylog(n)-quality cut-approximating hierarchical tree decompositions. Our algorithm invokes existing algorithms for these two problems recursively while gradually incorporating size reductions. These size reductions are in turn obtained via ultra-sparsifiers, which are key tools in solvers for symmetric diagonally dominant (SDD) linear systems.


title: Domain Specific Named Entity Recognition Referring to the Real World by Deep Neural Networks

abstract: In this paper, we propose a method for referring to the real world to improve named entity recognition (NER) specialized for a domain. Our method adds a stacked autoencoder to a text-based deep neural network for NER. We first train the stacked auto-encoder only from the real world information, then the entire deep neural network from sentences annotated with NEs and accompanied by real world information. In our experiments, we took Japanese chess as the example. The dataset consists of pairs of a game state and commentary sentences about it annotated with gamespecific NE tags. We conducted NER experiments and showed that referring to the real world improves the NER accuracy.


title: Robust Real-Time Multi-View Eye Tracking

abstract: Despite significant advances in improving the gaze tracking accuracy under controlled conditions, the tracking robustness under real-world conditions, such as large head pose and movements, use of eyeglasses, illumination and eye type variations, remains a major challenge in eye tracking. In this paper, we revisit this challenge and introduce a real-time multi-camera eye tracking framework to improve the tracking robustness. First, differently from previous work, we design a multi-view tracking setup that allows for acquiring multiple eye appearances simultaneously. Leveraging multi-view appearances enables to more reliably detect gaze features under challenging conditions, particularly when they are obstructed in conventional single-view appearance due to large head movements or eyewear effects. The features extracted on various appearances are then used for estimating multiple gaze outputs. Second, we propose to combine estimated gaze outputs through an adaptive fusion mechanism to compute user's overall point of regard. The proposed mechanism firstly determines the estimation reliability of each gaze output according to user's momentary head pose and predicted gazing behavior, and then performs a reliability-based weighted fusion. We demonstrate the efficacy of our framework with extensive simulations and user experiments on a collected dataset featuring 20 subjects. Our results show that in comparison with state-of-the-art eye trackers, the proposed framework provides not only a significant enhancement in accuracy but also a notable robustness. Our prototype system runs at 30 frames-per-second (fps) and achieves 1 degree accuracy under challenging experimental scenarios, which makes it suitable for applications demanding high accuracy and robustness.


title: Healthcare Data Gateways: Found Healthcare Intelligence on Blockchain with Novel Privacy Risk Control

abstract: Healthcare data are a valuable source of healthcare intelligence. Sharing of healthcare data is one essential step to make healthcare system smarter and improve the quality of healthcare service. Healthcare data, one personal asset of patient, should be owned and controlled by patient, instead of being scattered in different healthcare systems, which prevents data sharing and puts patient privacy at risks. Blockchain is demonstrated in the financial field that trusted, auditable computing is possible using a decentralized network of peers accompanied by a public ledger. In this paper, we proposed an App (called Healthcare Data Gateway (HGD)) architecture based on blockchain to enable patient to own, control and share their own data easily and securely without violating privacy, which provides a new potential way to improve the intelligence of healthcare systems while keeping patient data private. Our proposed purpose-centric access model ensures patient own and control their healthcare data; simple unified Indicator-Centric Schema (ICS) makes it possible to organize all kinds of personal healthcare data practically and easily. We also point out that MPC (Secure Multi-Party Computing) is one promising solution to enable untrusted third-party to conduct computation over patient data without violating privacy.


title: The Seven-Stage Crisis Intervention Model: A Road Map to Goal Attainment, Problem Solving, and Crisis Resolution

abstract: This article explicates a systematic and structured conceptual model for crisis assessment and intervention that facilitates planning for effective brief treatment in outpatient psychiatric clinics, community mental health centers, counseling centers, or crisis intervention settings. Application of Roberts’ seven-stage crisis intervention model can facilitate the clinician’s effective intervening by emphasizing rapid assessment of the client’s problem and resources, collaborating on goal selection and attainment, finding alternative coping methods, developing a working alliance, and building upon the client’s strengths. Limitations on treatment time by insurance companies and managed care organizations have made evidence-based crisis intervention a critical necessity for millions of persons presenting to mental health clinics and hospital-based programs in the midst of acute crisis episodes. Having a crisis intervention protocol facilitates treatment planning and intervention. The authors clarify the distinct differences between disaster management and crisis intervention and when each is critically needed. Also, noted is the importance of built-in evaluations, outcome measures, and performance indicators for all crisis intervention services and programs. We are recommending that the Roberts’ crisis intervention tool be used for time-limited response to persons in acute crisis. [Brief Treatment and Crisis Intervention 5:329–339 (2005)]


title: A social network-aware top-N recommender system using GPU

abstract: A book recommender system is very useful for a digital library. Good book recommender systems can effectively help users find interesting and relevant books from the massive resources, by providing individual recommendation book list for each end-user. By now, a variety of collaborative filtering algorithms have been invented, which are the cores of most recommender systems. However, because of the explosion of information, especially in the Internet, the improvement of the efficiency of the collaborative filting (CF) algorithm becomes more and more important. In this paper, we first propose a parallel Top-N recommendation algorithm in CUDA (Compute Unified Device Architecture) which combines the collaborative filtering and trust-based approach to deal with the cold-start user problem. Then based on this algorithm, we present a parallel book recommender system on a GPU (Graphics Processor unit) for CADAL digital library platform. Our experimental results show our algorithm is very efficient to process the large-scale datasets with good accuracy, and we report the impact of different values of parameters on the recommendation performance.


title: The CRAY-1 computer system

abstract: This paper describes the CRAY-1, discusses the evolution of its architecture, and gives an account of some of the problems that were overcome during its manufacture. The CRAY-1 is the only computer to have been built to date that satisfies ERDA's Class VI requirement (a computer capable of processing from 20 to 60 million floating point operations per second) [1]. The CRAY-1's Fortran compiler (CFT) is designed to give the scientific user immediate access to the benefits of the CRAY-1's vector processing architecture. An optimizing compiler, CFT, “vectorizes” innermost DO loops. Compatible with the ANSI 1966 Fortran Standard and with many commonly supported Fortran extensions, CFT does not require any source program modifications or the use of additional nonstandard Fortran statements to achieve vectorization. Thus the user's investment of hundreds of man months of effort to develop Fortran programs for other contemporary computers is protected.


title: CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios

abstract: Navigation is an essential capability for mobile robots. In this paper, we propose a generalized yet effective 3M (i.e., multi-robot, multi-scenario, and multi-stage) training framework. We optimize a mapless navigation policy with a robust policy gradient algorithm. Our method enables different types of mobile platforms to navigate safely in complex and highly dynamic environments, such as pedestrian crowds. To demonstrate the superiority of our method, we test our methods with four kinds of mobile platforms in four scenarios. Videos are available at https://sites.google.com/view/crowdmove.


A Genetic Algorithm Approach for Creating Neural-Network Ensembles


title: Calibration and Spectral Reconstruction for CRISATEL: an Art Painting Multispectral Acquisition System

abstract: The CRISATEL multispectral acquisition system is dedicated to the digital archiving of fine art paintings. It is composed of a dynamic lighting system and of a high resolution camera equipped with a CCD linear array, 13 interference filters and several built-in electronically controlled mechanisms. A custom calibration procedure has been designed and implemented. It allows us to select the parameters to be used for the raw image acquisition and to collect experimental data, which will be used in the post processing stage to correct the obtained multispectral images. Various techniques have been tested and compared in order to reconstruct the spectral reflectance curve of the painting surface imaged in each pixel. Realistic color rendering under any illuminant can then be obtained from this spectral reconstruction. The results obtained with the CRISATEL acquisition system and the associated multispectral image processing are shown on two art painting examples.


title: Micropropagation of Ilex dumosa (Aquifoliaceae) from nodal segments in a tissue culture system.

abstract: Micropropagation of Ilex dumosa var. dumosa R. ("yerba señorita") from nodal segments containing one axillary bud was investigated. Shoot regeneration from explants of six-year-old plants was readily achieved in 1/4 strength Murashige and Skoog medium (1/4 MS) plus 30 gr x L(-1) sucrose and supplemented with 4.4 microM BA. Further multiplication and elongation of the regenerated shoots were obtained by subculture in a fresh medium of similar composition with 1.5 gr x L(-1) sucrose. Rooting induction from shoots were achieved in two steps: 1) 7 days in 1/4 MS (30 gr x L(-1) sucrose, 0.25% Phytagel) with 7.3 microM IBA and 2) 21 days in the same medium without IBA and 20 microM of cadaverine added. Regenerated plants were successfully transferred to soil. This micropropagation schedule can be implemented in breeding programs of Ilex dumosa.


title: Virality and Susceptibility in Information Diffusions

abstract: Viral diffusion allows a piece of information to widely and quickly spread within the network of users through word-ofmouth. In this paper, we study the problem of modeling both item and user factors that contribute to viral diffusion in Twitter network. We identify three behaviorial factors, namely user virality, user susceptibility and item virality, that contribute to viral diffusion. Instead of modeling these factors independently as done in previous research, we propose a model that measures all the factors simultaneously considering their mutual dependencies. The model has been evaluated on both synthetic and real datasets. The experiments show that our model outperforms the existing ones for synthetic data with ground truth labels. Our model also performs well for predicting the hashtags that have higher retweet likelihood. We finally present case examples that illustrate how the models differ from one another.


title: Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

abstract: The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD) method to extract intrinsic mode functions (IMFs). The entropy features, namely, average Shannon entropy (ShEnAvg), average Renyi’s entropy (RenEnAvg ), average approximate entropy (ApEnAvg), average sample entropy (SpEnAvg) and average phase entropies (S1Avg and S2Avg), are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM) classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct.


title: Online contribution practices in countries that engage in internet blocking and censorship

abstract: In this article we describe people's online contribution practices in contexts in which the government actively blocks access to or censors the Internet. We argue that people experience blocking as confusing, as a motivation for self-censorship online, as a cause of impoverishment of available content and as a real threat of personal persecution. Challenging ideas of blocking as a monolithic, abstract policy, we discuss five strategies with which Internet users navigate blocking: self-censorship, cultivating technical savvy, reliance on social ties to relay blocked content, use of already blocked sites for content production as a form of protection and practiced transparency. We also discuss strategies that forum owners and blogging platform providers employ to deal with and to avoid blocking. We conclude by advocating for more research that acknowledges the complexity of the contexts in which all Internet users contribute to the Internet and social media.


title: Optimal local topology knowledge for energy efficient geographical routing in sensor networks

abstract: Since sensor networks can be composed of a very large number of nodes, the developed protocols for these networks must be scalable. Moreover, these protocols must be designed to prolong the battery lifetime of the nodes. Typical existing routing techniques for ad hoc networks are known not to scale well. On the other hand, the so-called geographical routing algorithms are known to be scalable but their energy efficiency has never been extensively and comparatively studied. For this reason, a novel analytical framework is introduced. In a geographical routing algorithm, the packets are forwarded by a node to its neighbor based on their respective positions. The proposed framework allows to analyze the relationship between the energy efficiency of the routing tasks and the extension of the range of the topology knowledge for each node. The leading forwarding rules for geographical routing are compared in this framework, and the energy efficiency of each of them is studied. Moreover partial topology knowledge forwarding, a new forwarding scheme, is introduced. A wider topology knowledge can improve the energy efficiency of the routing tasks but can increase the cost of topology information due to signaling packets that each node must transmit and receive to acquire this information, especially in networks with high mobility. The problem of determining the optimal knowledge range for each node to make energy efficient geographical routing decisions is tackled by integer linear programming. It is demonstrated that the problem is intrinsically localized, i.e., a limited knowledge of the topology is sufficient to take energy efficient forwarding decisions, and that the proposed forwarding scheme outperforms the others in typical application scenarios. For online solution of the problem, a probe-based distributed protocol which allows each node to efficiently select its topology knowledge, is introduced and shown to converge to a near-optimal solution very fast


title: Using Ground Reaction Forces from Gait Analysis: Body Mass as a Weak Biometric

abstract: Ground reaction forces generated during normal walking have recently been used to identify and/or classify individuals based upon the pattern of the forces observed over time. One feature that can be extracted from vertical ground reaction forces is body mass. This single feature has identifying power comparable to other studies that use multiple and more complex features. This study contributes to understanding the role of body mass in identification by (1) quantifying the accuracy and precision with which body mass can be obtained using vertical ground reaction forces, (2) quantifying the distribution of body mass across a population larger than has previously been studied in relation to gait analysis, and (3) quantifying the expected identification capabilities of systems using body mass as a weak biometric. Our results show that body mass can be measured in a fraction of a second with less than a 1 kilogram standard deviation of error.


title: Joint Extraction of Multiple Relations and Entities by Using a Hybrid Neural Network

abstract: This paper proposes a novel end-to-end neural model to jointly extract entities and relations in a sentence. Unlike most existing approaches, the proposed model uses a hybrid neural network to automatically learn sentence features and does not rely on any Natural Language Processing (NLP) tools, such as dependency parser. Our model is further capable of modeling multiple relations and their corresponding entity pairs simultaneously. Experiments on the CoNLL04 dataset demonstrate that our model using only word embeddings as input features achieves state-of-the-art performance.


title: Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research

abstract: The brain contains more than 100 billion neurons that communicate with each other via axons for the formation of complex neural networks. The structural mapping of such networks during health and disease states is essential for understanding brain function. However, our understanding of brain structural connectivity is surprisingly limited, due in part to the lack of noninvasive methodologies to study axonal anatomy. Diffusion tensor imaging (DTI) is a recently developed MRI technique that can measure macroscopic axonal organization in nervous system tissues. In this article, the principles of DTI methodologies are explained, and several applications introduced, including visualization of axonal tracts in myelin and axonal injuries as well as human brain and mouse embryonic development. The strengths and limitations of DTI and key areas for future research and development are also discussed.


title: Named Entity Recognition for Novel Types by Transfer Learning

abstract: In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly. In this paper, we propose a method where, given training data in a related domain with similar (but not identical) named entity (NE) types and a small amount of in-domain training data, we use transfer learning to learn a domain-specific NE model. That is, the novelty in the task setup is that we assume not just domain mismatch, but also label mismatch.


title: Mining and Leveraging Background Knowledge for Improving Named Entity Linking

abstract: Knowledge-rich Information Extraction (IE) methods aspire towards combining classical IE with background knowledge obtained from third-party resources. Linked Open Data repositories that encode billions of machine readable facts from sources such as Wikipedia play a pivotal role in this development. The recent growth of Linked Data adoption for Information Extraction tasks has shed light on many data quality issues in these data sources that seriously challenge their usefulness such as completeness, timeliness and semantic correctness. Information Extraction methods are, therefore, faced with problems such as name variance and type confusability. If multiple linked data sources are used in parallel, additional concerns regarding link stability and entity mappings emerge. This paper develops methods for integrating Linked Data into Named Entity Linking methods and addresses challenges in regard to mining knowledge from Linked Data, mitigating data quality issues, and adapting algorithms to leverage this knowledge. Finally, we apply these methods to Recognyze, a graph-based Named Entity Linking (NEL) system, and provide a comprehensive evaluation which compares its performance to other well-known NEL systems, demonstrating the impact of the suggested methods on its own entity linking performance.


title: What's in it for Me? Conceptualizing the Perceived Value of Knowledge Sharing

abstract: Social theories suggest that individuals expect something in return when they decide to share knowledge. These expectations of benefits vary from tangible incentives provided by organizations to positive feelings aroused by altruistic behavior. The theory of consumption values (TCV) allows a specification of distinct types of benefits (functional, social, emotional, epistemic and conditional) which individuals consider before they decide to share knowledge. Furthermore, the perceived costs of sharing can reduce the perceived value of knowledge sharing. These costs are non-monetary sacrifices (effort and risks) reducing the perceived value of sharing. In this work we develop the perceived value of knowledge sharing as a multidimensional construct, grounded in assumptions of social exchange theory, consumer research and knowledge sharing literature. This conceptualization is intended to serve as a basis for the operationalization of perceived knowledge value in a future study on knowledge sharing intentions.


title: Deeper Depth Prediction with Fully Convolutional Residual Networks

abstract: This paper addresses the problem of estimating the depth map of a scene given a single RGB image. To model the ambiguous mapping between monocular images and depth maps, we leverage on deep learning capabilities and present a fully convolutional architecture encompassing residual learning. The proposed model is deeper than the current state of the art, but contains fewer parameters and requires less training data, while still outperforming all current CNN approaches aimed at the same task. We further present a novel way to efficiently learn feature map up-sampling within the network. For optimization we introduce the reverse Huber loss, particularly suited for the task at hand and driven by the value distributions commonly present in depth maps. The predictions are given by a single architecture, trained end-to-end, that does not rely on post-processing techniques, such as CRFs or other additional refinement steps.


title: A useful method for the detection of ethylenediaminetetraacetic acid- and cold agglutinin-dependent pseudothrombocytopenia.

abstract: INTRODUCTION ::: Pseudothrombocytopenia (PTCP), caused by platelet (PLT) aggregation, is usually associated with ethylenediaminetetraacetic acid (EDTA)-dependent antibodies and cold aggluti-nins against PLT antigens. The aim of this study was to identify the PTCP and discover the most practical method to distinguish it from real thrombocytopenia. ::: ::: ::: METHODS ::: This study included 85 patients without hemorrhagic abnormalities and suspected PTCP. Blood samples containing EDTA, citrate and EDTA-kanamycin (KN) were analyzed at room temperature and 37°C. ::: ::: ::: RESULTS ::: PTCP was detected in 24 of 85 patients. In 23 of 24 patients, EDTA-dependent pseudothrombocytopenia (EDTA-PTCP) was detected; 5 of whom had also the cold agglutinin-dependent PTCP. In only 1 of 24 patients, the cold agglu-tinin-dependent PTCP was found. In this study, no significant difference was observed in leukocyte counts comparing EDTA and citrate blood samples in cases with EDTA-PTCP. ::: ::: ::: CONCLUSION ::: In clinical laboratories, a significant portion of the cases with low PLT counts was attributable to EDTA-PTCP and, therefore, did not require treatment. Even if these cases can be detected by bringing the blood samples containing EDTA to 37°C or by adding KN to blood samples containing EDTA, the use of blood samples containing citrate taken for erythrocyte sedimentation rate analysis is a more practical priority method.


title: Fast Neural Architecture Construction using EnvelopeNets

abstract: Fast Neural Architecture Construction (NAC) is a method to construct deep network architectures by pruning and expansion of a base network. In recent years, several automated search methods for neural network architectures have been proposed using methods such as evolutionary algorithms and reinforcement learning. These methods use a single scalar objective function (usually accuracy) that is evaluated after a full training and evaluation cycle. In contrast NAC directly compares the utility of different filters using statistics derived from filter featuremaps reach a state where the utility of different filters within a network can be compared and hence can be used to construct networks. The training epochs needed for filters within a network to reach this state is much less than the training epochs needed for the accuracy of a network to stabilize. NAC exploits this finding to construct convolutional neural nets (CNNs) with close to state of the art accuracy, in<1 GPU day, faster than most of the current neural architecture search methods. The constructed networks show close to state of the art performance on the image classification problem on well known datasets (CIFAR-10, ImageNet) and consistently show better performance than hand constructed and randomly generated networks of the same depth, operators and approximately the same number of parameters.


title: Personality Based E-Recruitment System

abstract: Nodes in Mobile Ad Hoc Networks (MANETs) are limited battery powered. That’s why energy efficient routing has become an important optimization criterion in MANETs. The conventional routing protocols do not consider energy of the nodes while selecting routes which leads to early exhaustion of nodes and partitioning of the network. This paper attempts to provide an energy aware routing algorithm. The proposed algorithm finds the transmission energy between the nodes relative to the distance and the performance of the algorithm is analyzed between two metrics Total Transmission energy of a route and Maximum Number of Hops. The proposed algorithm shows efficient energy utilization and increased network lifetime with total transmission energy metric.


title: Towards Secure and Privacy-Preserving Data Sharing in e-Health Systems via Consortium Blockchain

abstract: Electronic health record sharing can help to improve the accuracy of diagnosis, where security and privacy preservation are critical issues in the systems. In recent years, blockchain has been proposed to be a promising solution to achieve personal health information (PHI) sharing with security and privacy preservation due to its advantages of immutability. This work proposes a blockchain-based secure and privacy-preserving PHI sharing (BSPP) scheme for diagnosis improvements in e-Health systems. Firstly, two kinds of blockchains, private blockchain and consortium blockchain, are constructed by devising their data structures, and consensus mechanisms. The private blockchain is responsible for storing the PHI while the consortium blockchain keeps records of the secure indexes of the PHI. In order to achieve data security, access control, privacy preservation and secure search, all the data including the PHI, keywords and the patients' identity are public key encrypted with keyword search. Furthermore, the block generators are required to provide proof of conformance for adding new blocks to the blockchains, which guarantees the system availability. Security analysis demonstrates that the proposed protocol can meet with the security goals. Furthermor, we implement the proposed scheme on JUICE to evaluate the performance.


title: Improving Web Search Ranking by Incorporating User Behavior Information

abstract: Disclosed herein is a carburetor for internal combustion engines having an automatic choke control mechanism including a vacuum diaphragm assembly operatively connected to a choke lever for opening a spring-biased closed choke valve and holding it open at idling and lower engine speeds and a linkage arrangement interconnecting a throttle valve lever and the choke lever for holding the choke valve open at higher engine speeds independently of the vacuum diaphragm assembly. The linkage arrangement includes a spring-biased choke pull-out lever which is held in a locked position during engine idling and lower engine speeds by a link having one end slidably received in a slot in the choke pull-out lever and the other end connected to the throttle lever. As the throttle lever is moved past a predetermined advance speed position, the link is moved to a released position in the choke pull-out lever slot wherein the choke pull-out lever is urged into abutting engagement with the choke lever to hold the choke valve open. As the throttle lever is returned toward the engine idling position past the predetermined advance speed setting, the link returns the choke pull-out lever to the locked position and the choke lever is again controlled by the vacuum diaphragm assembly.


title: Simulating sensorimotor metaphors: Novel metaphors influence sensory judgments

abstract: Abstract Embodied cognition theory proposes that individuals’ abstract concepts can be associated with sensorimotor processes. The authors examined the effects of teaching participants novel embodied metaphors, not based in prior physical experience, and found evidence suggesting that they lead to embodied simulation, suggesting refinements to current models of embodied cognition. Creating novel embodiments of abstract concepts in the laboratory may be a useful method for examining mechanisms of embodied cognition.


title: Working Paper 28/04Forecasting Time-Series with Multiple Seasonal Patterns

abstract: A new approach is proposed for forecasting a time series with multiple seasonal patterns. A state space model is developed for the series using the innovations approach which enables us to develop explicit models for both additive and multiplicative seasonality. Parameter estimates may be obtained using methods from exponential smoothing. The proposed model is used to examine hourly and daily patterns in hourly data for both utility loads and traffic flows. Our formulation provides a model for several existing seasonal methods and also provides new options, which result in superior forecasting performance over a range of prediction horizons. In particular, seasonal components can be updated more frequently than once during a seasonal cycle. The approach is likely to be useful in a wide range of applications involving both high and low frequency data, and it handles missing values in a straightforward manner.


title: Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping

abstract: BackgroundRecent studies have shown that brain-machine interfaces (BMIs) offer great potential for restoring upper limb function. However, grasping objects is a complicated task and the signals extracted from the brain may not always be capable of driving these movements reliably. Vision-guided robotic assistance is one possible way to improve BMI performance. We describe a method of shared control where the user controls a prosthetic arm using a BMI and receives assistance with positioning the hand when it approaches an object.MethodsTwo human subjects with tetraplegia used a robotic arm to complete object transport tasks with and without shared control. The shared control system was designed to provide a balance between BMI-derived intention and computer assistance. An autonomous robotic grasping system identified and tracked objects and defined stable grasp positions for these objects. The system identified when the user intended to interact with an object based on the BMI-controlled movements of the robotic arm. Using shared control, BMI controlled movements and autonomous grasping commands were blended to ensure secure grasps.ResultsBoth subjects were more successful on object transfer tasks when using shared control compared to BMI control alone. Movements made using shared control were more accurate, more efficient, and less difficult. One participant attempted a task with multiple objects and successfully lifted one of two closely spaced objects in 92 % of trials, demonstrating the potential for users to accurately execute their intention while using shared control.ConclusionsIntegration of BMI control with vision-guided robotic assistance led to improved performance on object transfer tasks. Providing assistance while maintaining generalizability will make BMI systems more attractive to potential users.Trial registrationNCT01364480 and NCT01894802.


title: Modelling and Control of a Quad-Rotor Robot

abstract: To date, most quad-rotor aerial robots have been based on flying toys. Although such systems can be used as prototypes, they are not sufficiently robust to serve as experimental robotics platforms. We have developed the X-4 Flyer, a quad-rotor robot using custom-built chassis and avionics with off-the-shelf motors and batteries, to be a highly reliable experimental platform. The vehicle uses tuned plant dynamics with an onboard embedded attitude controller to stabilise flight. A linear SISO controller was designed to regulate flyer attitude.


Capacitance and Power Modeling at Logic-Level


title: SAR automatic target recognition via non-negative matrix approximations

abstract: The set of orthogonal eigen-vectors built via principal component analysis (PCA), while very effective for com- pression, can often lead to loss of crucial discriminative information in signals. In this work, we build a new basis set using synthetic aperture radar (SAR) target images via non-negative matrix approximations (NNMAs). Owing to the underlying physics, we expect a non-negative basis and an accompanying non-negative coecient set to be a more accurate generative model for SAR proles than the PCA basis which lacks direct physical interpretation. The NNMA basis vectors while not orthogonal capture discriminative local components of SAR target images. We test the merits of the NNMA basis representation for the problem of automatic target recognition using SAR images with a support vector machine (SVM) classier. Experiments on the benchmark MSTAR database reveal the merits of basis selection techniques that can model imaging physics more closely and can capture inter-class variability, in addition to identifying a trade-off between classication performance and availability of training.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.


title: Comparing the pulses of categorical hot events in Twitter and Weibo

abstract: The fragility and interconnectivity of the planet argue compellingly for a greater understanding of how different communities make sense of their world. One of such critical demands relies on comparing the Chinese and the rest of the world (e.g., Americans), where communities' ideological and cultural backgrounds can be significantly different. While traditional studies aim to learn the similarities and differences between these communities via high-cost user studies, in this paper we propose a much more efficient method to compare different communities by utilizing social media. Specifically, Weibo and Twitter, the two largest microblogging systems, are employed to represent the target communities, i.e. China and the Western world (mainly United States), respectively. Meanwhile, through the analysis of the Wikipedia page-click log, we identify a set of categorical `hot events' for one month in 2012 and search those hot events in Weibo and Twitter corpora along with timestamps via information retrieval methods. We further quantitatively and qualitatively compare users' responses to those events in Twitter and Weibo in terms of three aspects: popularity, temporal dynamic, and information diffusion. The comparative results show that although the popularity ranking of those events are very similar, the patterns of temporal dynamics and information diffusion can be quite different.


title: Background removal in image indexing and retrieval

abstract: This paper presents our research in image content-based indexing and retrieval, a key technology in digital image libraries. In most of the existing image content-based techniques, image features used for indexing and retrieval are global, features computed over the entire image. The major problem with the global image feature-based retrieval methods is that background features can be easily mistaken as object features. When a user attempts to retrieve images using color features, he/she usually means the color feature of objects of interests contained in the image. The approach we describe in this paper utilizes color clusters for image background analysis. Once the background regions are identified, they are removed from the image indexing procedure, and therefore no longer interfere with the meaningful image content during the retrieval process.


title: Event related potentials to emotional adjectives during reading

abstract: We investigated to what extent emotional connotation influences cortical potentials during reading. To this end, event-related potentials (ERPs) were recorded during reading of high arousal pleasant and unpleasant and low arousal neutral adjectives that were presented at rates of 1 Hz and 3 Hz. Enhanced processing of both pleasant and unpleasant emotional compared to neutral adjectives was first reflected in an amplified early posterior negativity (EPN) starting from 200 ms after word onset. Later potentials (>300 ms), as analyzed in the slower 1 Hz condition, revealed facilitated processing selectively for pleasant adjectives that were associated with a reduced N400 and an enhanced late positive potential (LPP). Pleasant adjectives were also better remembered in an incidental memory test. Thus, emotionally relevant adjectives are processed spontaneously and selectively. Initially, emotional arousal drives attention capture (EPN). Healthy subjects may have a natural bias toward pleasant information facilitating late ERPs (N400, LPP) to pleasant adjectives as well as their superior recall.


title: Operating characteristic and average sample number functions of truncated sequential probability ratio test

abstract: The operating characteristic (OC) and average sample number (ASN) characterize the performance of the sequential probability ratio test (SPRT). In this paper, the ASN and OC of the truncated SPRT (TSPRT) are examined in a general setting, in which the log-likelihood ratios are not necessarily identically distributed and the bounds for the test can be time varying. Two inductive integral equations are derived for the OC and ASN respectively, which can be solved analytically by backward induction provided the convolution involved can be evaluated analytically. The initial value for backward induction can be readily determined by TSPRT's truncation strategy. Due to the difficulty of analytically evaluating the convolution in the induction process, numerical methods may be necessary. Numerical algorithms based on the system of linear algebraic equation method and the finite element analysis are developed. Examples are provided to illustrate our algorithms.


A different drum: Social media and the communication of sociolinguistic research


title: The consistency of task-based authorization constraints in workflow

abstract: Workflow management systems (WFMSs) have attracteda lot of interest both in academia and the business community.A workflow consists of a collection of tasks that areorganized to facilitate some business process specification.To simplify the complexity of security administration, it iscommon to use role-based access control (RBAC) to grantauthorization to roles and users. Typically, security policiesare expressed as constraints on users, roles, tasks and theworkflow itself. A workflow system can become very complexand involve several organizations or different units ofan organization, thus the number of security policies may bevery large and their interactions very complex. It is clearlyimportant to know whether the existence of such constraintswill prevent certain instances of the workflow from completing.Unfortunately, no existing constraint models have consideredthis problem satisfactorily.In this paper we define a model for constrained workflowsystems that includes local and global cardinality constraints,separation of duty constraints and binding of dutyconstraints. We define the notion of a workflow specificationand of a constrained workflow authorization schema.Our main result is to establish necessary and sufficient conditionsfor the set of constraints that ensure a sound constrainedworkflow authorization schema, that is, for anyuser or any role who are authorized to a task, there is atleast one complete workflow instance when this user or thisrole executes this task.


title: Sleep interrupted: nocturnal care disturbances following hematopoietic stem cell transplantation.

abstract: Sleep is an essential biologic and physiologic process that is vital for maintaining or achieving optimal health outcomes. Care requirements in hospitalized patients frequently result in nocturnal disruptions that impact sleep quality. This exploratory, retrospective study aimed to identify and quantify nocturnal care disruptions in patients undergoing hematopoietic stem cell transplantation. A total of 1,642 nocturnal care interactions occurred during 160 patient nights of data collection. An average of 41 nocturnal care interactions occurred per patient. Most occurred from 12-12:59 am and 4-4:59 am. Most patients (73%) had sleep disturbance recorded in their chart by nurses, but physicians documented sleep disturbances for only 28% of patients. Care practices may be modifiable to promote sleep in the hospital setting.


title: Reliable Patch Trackers: Robust visual tracking by exploiting reliable patches

abstract: Most modern trackers typically employ a bounding box given in the first frame to track visual objects, where their tracking results are often sensitive to the initialization. In this paper, we propose a new tracking method, Reliable Patch Trackers (RPT), which attempts to identify and exploit the reliable patches that can be tracked effectively through the whole tracking process. Specifically, we present a tracking reliability metric to measure how reliably a patch can be tracked, where a probability model is proposed to estimate the distribution of reliable patches under a sequential Monte Carlo framework. As the reliable patches distributed over the image, we exploit the motion trajectories to distinguish them from the background. Therefore, the visual object can be defined as the clustering of homo-trajectory patches, where a Hough voting-like scheme is employed to estimate the target state. Encouraging experimental results on a large set of sequences showed that the proposed approach is very effective and in comparison to the state-of-the-art trackers. The full source code of our implementation will be publicly available.


title: Comparative study of trust and reputation systems for wireless sensor networks

abstract: Wireless sensor networks (WSNs) are emerging as useful technology for information extraction from the surrounding environment by using numerous small-sized sensor nodes that are mostly deployed in sensitive, unattended, and (sometimes) hostile territories. Traditional cryptographic approaches are widely used to provide security in WSN. However, because of unattended and insecure deployment, a sensor node may be physically captured by an adversary who may acquire the underlying secret keys, or a subset thereof, to access the critical data and/or other nodes present in the network. Moreover, a node may not properly operate because of insufficient resources or problems in the network link. In recent years, the basic ideas of trust and reputation have been applied to WSNs to monitor the changing behaviors of nodes in a network. Several trust and reputation monitoring (TRM) systems have been proposed, to integrate the concepts of trust in networks as an additional security measure, and various surveys are conducted on the aforementioned system. However, the existing surveys lack a comprehensive discussion on trust application specific to the WSNs. This survey attempts to provide a thorough understanding of trust and reputation as well as their applications in the context of WSNs. The survey discusses the components required to build a TRM and the trust computation phases explained with a study of various security attacks. The study investigates the recent advances in TRMs and includes a concise comparison of various TRMs. Finally, a discussion on open issues and challenges in the implementation of trust-based systems is also presented. Copyright © 2012 John Wiley & Sons, Ltd.


A Scaling Law for the Validation-Set Training-Set Size Ratio


title: How to distinguish inliers from outliers in visual odometry for high-speed automotive applications

abstract: In this paper, we present an outlier removal scheme for stereo-based visual odometry which is especially suited for improving high-speed pose change estimations in large-scale depth environments. First we investigate the variance of the reprojection error on the 3D position of a feature given a fixed error in pose change to conclude that a detection of outliers based on a fixed threshold on the reprojection error is inappropriate. Then we propose an optical flow dependent feature-adaptive scaling of the reprojection error to reach almost invariance to the 3D position of each feature. This feature-adaptive scaling is derived from an approximation showing the relation between longitudinal pose change of the camera, absolute value of the optical flow, and distance of the feature. Using this scaling, we develop an iterative alternating scheme to guide the separation of inliers from outliers. It optimizes the tradeoff between finding a good criterion to remove outliers based on a given pose change and improving the pose change hypothesis based on the current set of inliers. Including the new outlier removal scheme into a pure two-frame stereo-based visual odometry pipeline without applying bundle adjustment or SLAM-filtering we are currently ranked amongst the top camera-based algorithms and furthermore outperform camera and laser scanner methods in Kitti benchmark's high-speed scenarios.


title: Determination of soil nutrients and pH level using image processing and artificial neural network

abstract: In this study, image processing and artificial neural network was used to efficiently identify the nutrients and pH level of soil with the use of Soil Test Kit (STK) and Rapid Soil Testing (RST) of the Bureau of Soils and Water Management: (1) pH, (2) Nitrogen, (3) Phosphorus, (4) Potassium, (5) Zinc, (6) Calcium, and (7) Magnesium. The composition of the system is made of five sections namely soil testing, image capturing, image processing, training system for neural network, and result. The use of Artificial Neural Network is to hasten the performance of image processing in giving accurate result. The system will base on captured image data, 70% for training, 15% for testing and 15% for validation as default of neural network tool of MATLAB. Based on the result, the program will show the qualitative level of soil nutrients and pH. Overall, this study identifies the soil nutrient and pH level of the soil and was proven accurate.


title: Tangible 3 D : Hand Gesture Interaction for Immersive 3 D Modeling

abstract: Most of all interaction tasks relevant for a general three-dimensional virtual environment can be supported by 6DOF control and grab/select input. Obviously a very efficient method is direct manipulation with bare hands, like in real environment. This paper shows the possibility to perform non-trivial tasks using only a few well-known hand gestures, so that almost no training is necessary to interact with 3D-softwares. Using this gesture interaction we have built an immersive 3D modeling system with 3D model representation based on a mesh library, which is optimized not only for real-time rendering but also accommodates for changes of both vertex positions and mesh connectivity in real-time. For performing the gesture interaction, the user's hand is marked with just four fingertipthimbles made of inexpensive material as simple as white paper. Within our scenario, the recognized hand gestures are used to select, create, manipulate and deform the meshes in a spontaneous and intuitive way. All modeling tasks are performed wirelessly through a camera/vision tracking method for the head and hand interaction.


title: A Tale of Two Graphs: Property Graphs as RDF in Oracle

abstract: Databases are gaining popularity, owing to pervasiveness of graph data in social networks, physical sciences, networking, and web applications. A majority of these databases are based on the property graph model, which is characterized as key/value-based, directed, and multi-relational. In this paper, we consider the problem of supporting property graphs as RDF in Oracle Database. We introduce a property graph to RDF transformation scheme. The main challenge lies in representing the key/value properties of property graph edges in RDF. We propose three models: 1) named graph based, 2) subproperty based, and 3) (extended) reification based, all of which can be supported with RDF capabilities in Oracle Database. These models are evaluated with respect to ease of SPARQL query formulation, join complexities, skewness in generated RDF data, query performance, and storage overhead. An experimental study with a real-life Twitter social network dataset on Oracle Database 12c demonstrates the feasibility of representing property graphs as RDF and presents a quantitative performance comparison of the proposed models.


Rome IV—Functional GI Disorders: Disorders of Gut-Brain Interaction


title: Development of floating microspheres of anti-ulcer drug as a gastroretentivedrug delivery system

abstract: In the present study, a novel floating oral drug delivery system was developed utilizing concept of controlled release in order to obtain a unique drug delivery system which could remain in the stomach and control the drug release for an extended period of time. Nizatidine microspheres were prepared by solvent evaporation and spray drying method by using polymers such as Hydroxy propyl methyl cellulose, Ethyl cellulose. Optimized batch was obtained by the Design of experiment. The prepared formulation was subjected to particle size and shape analysis, percentage drug entrapment efficiency, in-vitro floatability, in vitro drug release studies. The compatibility study of drug and polymer was carried out by IR, DSC, and XRD analysis. The polymer and drug were compatible to each other. The prepared microspheres were discrete, spherical with a mean particle size in the range 448 μm to 645μm for solvent evaporated microspheres, and 1-100μm for spray dried microspheres. Entrapment efficiency was found to be 60- 90% for solvent evaporated microspheres, and 60-80% for spray dried microspheres. In-vitro drug release for all the formulations in 0.1N HCL was diffusion controlled gradually over a period of 8hr and followed by first order kinetics. There was no significant change in physicochemical characteristics of the microspheres stored at room temperature for 1 month. The developed system has the dual advantages of being gastro-retentive, to increase oral bioavailability and releasing drug in a controlled manner, to reduce frequency of administration there by promoting patient compliance.


title: Hereditary Early-Onset Parkinson's Disease Caused by Mutations in PINK1

abstract: Parkinson's disease (PD) is a neurodegenerative disorder characterized by degeneration of dopaminergic neurons in the substantia nigra. We previously mapped a locus for a rare familial form of PD to chromosome 1p36 (PARK6). Here we show that mutations in PINK1 (PTEN-induced kinase 1) are associated with PARK6. We have identified two homozygous mutations affecting the PINK1 kinase domain in three consanguineous PARK6 families: a truncating nonsense mutation and a missense mutation at a highly conserved amino acid. Cell culture studies suggest that PINK1 is mitochondrially located and may exert a protective effect on the cell that is abrogated by the mutations, resulting in increased susceptibility to cellular stress. These data provide a direct molecular link between mitochondria and the pathogenesis of PD.


The Fractal Geometry of the Brain


title: Observer-Based Rejection of Cogging Torque Disturbance for Permanent Magnet Motors

abstract: In order to prove the speed regulation for permanent magnet motor systems, a control method is proposed to reject the disturbance by cogging torque, which arises from the interaction of the rotor magnets with the steel teeth on the stator. Toward this aim, we present a state observer with an internal model which has harmonics of sinusoids, and it follows from the proposed observer that the information of the cogging disturbance is obtained. It shows that the disturbance is efficiently compensated by a suitable additional control input based on the estimated information. Simulation and experimental results with an actual permanent magnet motor verify the effectiveness of the proposed observer-based algorithm.


title: The rate of absorption and relative bioavailability of caffeine administered in chewing gum versus capsules to normal healthy volunteers.

abstract: OBJECTIVE ::: The purpose of this study was to evaluate the rate of absorption and relative bioavailability of caffeine from a Stay Alert chewing gum and capsule formulation. ::: ::: ::: METHODS ::: This was a double blind, parallel, randomized, seven treatment study. The treatment groups were: 50, 100, and 200 mg gum, 50, 100, and 200 mg capsule, and a placebo. Subjects consisted of 84 (n=12 per group); healthy, non-smoking, males who had abstained from caffeine ingestion for at least 20 h prior to dosing and were randomly assigned to the treatment groups. Blood samples were collected pre-dose and at 5, 15, 25, 35, 45, 55, 65, 90 min and 2, 3, 4, 6, 8, 12, 16 and 29 h post administration. Plasma caffeine levels were analyzed by a validated UV-HPLC method. ::: ::: ::: RESULTS ::: Mean Tmax for the gum groups ranged from 44.2 to 80.4 min as compared with 84.0-120.0 min for the capsule groups. The Tmax, for the pooled data was significantly lower (P<0.05) for the gum groups as compared with the capsule groups. Differences in Tmax were significant for the 200 mg capsule versus 200 mg gum (P<0.05). The mean ka values for the gum group ranged from 3.21 to 3.96 h-1 and for the capsule groups ranged from 1.29 to 2.36 h-1. Relative bioavailability of the gum formulation after the 50, 100 and 200 mg dose was 64, 74 and 77%, respectively. When normalized to the total drug released from the gum (85%), the relative bioavailability of the 50, 100 and 200 mg dose were 75, 87, and 90%, respectively. No statistical differences were found for Cmax and AUCinf for comparisons of the gum and capsule formulations at each dose. Within each dose level, there were no significant formulation related differences in Cmax. No significant differences were observed in the elimination of caffeine after the gum or capsule. ::: ::: ::: CONCLUSIONS ::: The results suggest that the rate of drug absorption from the gum formulation was significantly faster and may indicate absorption via the buccal mucosa. In addition, for the 100 and 200 mg groups, the gum and capsule formulations provide near comparable amounts of caffeine to the systemic circulation. These findings suggest that there may be an earlier onset of pharmacological effects of caffeine delivered as the gum formulation, which is advantageous in situations where the rapid reversal of alertness and performance deficits resulting from sleep loss is desirable.


title: Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

abstract: The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.


title: A Miniaturized Marchand Balun Model With Short-End and Capacitive Feeding

abstract: A new equivalent circuit model of an electrical size-reduced coupled line radio frequency Marchand balun is proposed and investigated in this paper. It consists of two parts of coupled lines with significantly reduced electrical length. Compared with the conventional Marchand balun, a short-circuit ending is applied instead of the open-circuit ending, and a capacitive feeding is introduced. The electrical length of the proposed balun is reduced to around 1/3 compared with that of the conventional Marchand balun. Detailed mathematical analysis for this design is included in this paper. Groups of circuit simulation results are shown to verify the conclusions. A sample balun is fabricated in microstrip line type on the Teflon substrate, with low dielectric constant of 2.54. It has a dimension of $0.189\lambda _{g} \times 0.066 \lambda _{g}$ with amplitude imbalance of 0.1 dB and phase imbalance of 179.09° ± 0.14°. The simulation and experiment results are in good agreement.


title: Approximation and Temperature Control System via an Actuator and a Cloud: An Application Based on the IoT for Smart Houses

abstract: This work presents the use of the Internet of things (IoT) applied to a control of an actuator that works through temperature and approximation values. The proposed technology consists of three principal parts: Hardware (Raspberry Pi 3), a virtual machine installed in “the cloud” and a mobile device with a Global Positioning System (GPS) application. A system was developed in the Raspberry Pi 3 that is connected to a temperature sensor which monitors the external temperature and is connected to a mechanical actuator. This part of the system is able to send a message to the cloud concerning the ambient temperature which is stored on a database. The mobile device consists of an Android application based on GPS positions which is able to send a positional message to the cloud that is stored on a database. A cloud development records the latest position and temperature data which are then compared to a predetermined threshold value in development. Through the logical operation AND between the comparisons of the temperature and position values with the threshold values, the decision to switch the actuator on or off is taken. The system performs the storage and comparison of the cloud data each time interval t predetermined in the development. The proposed system was validated through a case study.


title: Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion

abstract: This research analyses the suburban expansion in the metropolitan area of Tehran, Iran. A hybrid model consisting of logistic regression model, Markov chain (MC), and cellular automata (CA) was designed to improve the performance of the standard logistic regression model. Environmental and socio-economic variables dealing with urban sprawl were operationalised to create a probability surface of spatiotemporal states of built-up land use for the years 2006, 2016, and 2026. For validation, the model was evaluated by means of relative operating characteristic values for different sets of variables. The approach was calibrated for 2006 by cross comparing of actual and simulated land use maps. The achieved outcomes represent a match of 89% between simulated and actual maps of 2006, which was satisfactory to approve the calibration process. Thereafter, the calibrated hybrid approach was implemented for forthcoming years. Finally, future land use maps for 2016 and 2026 were predicted by means of this hybrid approach. The simulated maps illustrate a new wave of suburban development in the vicinity of Tehran at the western border of the metropolis during the next decades.


title: Kinect cane: An assistive system for the visually impaired based on three-dimensional object recognition

abstract: This paper proposes a novel assistive system for the visually impaired. The system is composed of a Microsoft Kinect sensor, keypad-type controller, tactile device, laptop computer and so on. The system can recognize three-dimensional objects from depth data generated by the Kinect sensor, and inform visually impaired users not only about the existence of objects, but also about their classes such as chairs and upward stairs. Ordinarily, the system works as a conventional white cane. When a user instructs the system to find the object of a particular class, the system executes the recognition scheme that is designed to find the instructed object. If the object is found in the field of view of the Kinect sensor, the tactile device provides vibration feedback. The recognition schemes are applied to actual scenes. The experimental results indicate that the system is promising as means of helping the visually impaired find the desired objects.


title: Unilateral plantar flexors static-stretching effects on ipsilateral and contralateral jump measures.

abstract: The aim of this study was to evaluate the acute effects of unilateral ankle plantar flexors static-stretching (SS) on the passive range of movement (ROM) of the stretched limb, surface electromyography (sEMG) and single-leg bounce drop jump (SBDJ) performance measures of the ipsilateral stretched and contralateral non-stretched lower limbs. Seventeen young men (24 ± 5 years) performed SBDJ before and after (stretched limb: immediately post-stretch, 10 and 20 minutes and non-stretched limb: immediately post-stretch) unilateral ankle plantar flexor SS (6 sets of 45s/15s, 70-90% point of discomfort). SBDJ performance measures included jump height, impulse, time to reach peak force, contact time as well as the sEMG integral (IEMG) and pre-activation (IEMGpre-activation) of the gastrocnemius lateralis. Ankle dorsiflexion passive ROM increased in the stretched limb after the SS (pre-test: 21 ± 4° and post-test: 26.5 ± 5°, p < 0.001). Post-stretching decreases were observed with peak force (p = 0.029), IEMG (P<0.001), and IEMGpre-activation (p = 0.015) in the stretched limb; as well as impulse (p = 0.03), and jump height (p = 0.032) in the non-stretched limb. In conclusion, SS effectively increased passive ankle ROM of the stretched limb, and transiently (less than 10 minutes) decreased muscle peak force and pre-activation. The decrease of jump height and impulse for the non-stretched limb suggests a SS-induced central nervous system inhibitory effect. Key pointsWhen considering whether or not to SS prior to athletic activities, one must consider the potential positive effects of increased ankle dorsiflexion motion with the potential deleterious effects of power and muscle activity during a simple jumping task or as part of the rehabilitation process.Since decreased jump performance measures can persist for 10 minutes in the stretched leg, the timing of SS prior to performance must be taken into consideration.Athletes, fitness enthusiasts and therapists should also keep in mind that SS one limb has generalized effects upon contralateral limbs as well.


title: A hybrid approach for movie recommendation

abstract: Collaborative and content-based filtering are the major methods in recommender systems that predict new items that users would find interesting. Each method has advantages and shortcomings of its own and is best applied in specific situations. Hybrid approaches use elements of both methods to improve performance and overcome shortcomings. In this paper, we propose a hybrid approach based on content-based and collaborative filtering, implemented in MoRe, a movie recommendation system. We also provide empirical comparison of the hybrid approach to the base methods of collaborative and content-based filtering and draw useful conclusions upon their performance.


title: A Novel Self-Paced Model for Teaching Programming

abstract: The Self-Paced Learning Increases Retention and Capacity (SPARC) project is responding to the well-documented surge in CS enrollment by creating a self-paced learning environment that blends online learning, automated assessment, collaborative practice, and peer-supported learning. SPARC delivers educational material online, encourages students to practice programming in groups, frees them to learn material at their own pace, and allows them to demonstrate proficiency at any time. This model contrasts with traditional course offerings, which impose a single schedule of due dates and exams for all students. SPARC allows students to complete courses faster or slower at a pace tailored to the individual, thereby allowing universities to teach more students with the same or fewer resources. This paper describes the goals and elements of the SPARC model as applied to CS1. We present results so far and discuss the future of the project.


title: GDist-RIA Crawler: A Greedy Distributed Crawler for Rich Internet Applications

abstract: Crawling web applications is important for indexing, accessibility and security assessment. Crawling traditional web applications is an old problem, for which good and efficient solution are known. Crawling Rich Internet Applications (RIA) quickly and efficiently, however, is an open problem. Technologies such as AJAX and partial Document Object Model (DOM) updates only make the problem of crawling RIA more time consuming to the web crawler. One way to reduce the time to crawl a RIA is to crawl a RIA in parallel with multiple computers. Previously published Dist-RIA Crawler presents a distributed breath-first search algorithm to crawl RIAs. This paper expands Dist-RIA Crawler in two ways. First, it introduces an adaptive load-balancing algorithm that enables the crawler to learn about the speed of the nodes and adapt to changes, thus better utilize the resources. Second, it present a distributed greedy algorithm to crawl a RIA in parallel, called GDist-RIA Crawler. The GDist-RIA Crawler uses a server-client architecture where the server dispatched crawling jobs to the crawling clients. This paper illustrates a prototype implementation of the GDist-RIA Crawler, explains some of the techniques used to implement the prototype and inspects empirical performance measurements.


title: An optimization on pictogram identification for the road-sign recognition task using SVMs

abstract: Pattern recognition methods are used in the final stage of a traffic sign detection and recognition system, where the main objective is to categorize a detected sign. Support vector machines have been reported as a good method to achieve this main target due to their ability to provide good accuracy as well as being sparse methods. Nevertheless, for complete data sets of traffic signs the number of operations needed in the test phase is still large, whereas the accuracy needs to be improved. The objectives of this work are to propose pre-processing methods and improvements in support vector machines to increase the accuracy achieved while the number of support vectors, and thus the number of operations needed in the test phase, is reduced. Results show that with the proposed methods the accuracy is increased 3-5% with a reduction in the number of support vectors of 50-70%.


title: Dependency-based Convolutional Neural Networks for Sentence Embedding

abstract: In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies. To exploit both deep learning and linguistic structures, we propose a tree-based convolutional neural network model which exploit various long-distance relationships between words. Our model improves the sequential baselines on all three sentiment and question classification tasks, and achieves the highest published accuracy on TREC.


title: Pinot: Realtime OLAP for 530 Million Users

abstract: Modern users demand analytical features on fresh, real time data. Offering these analytical features to hundreds of millions of users is a relevant problem encountered by many large scale web companies. Relational databases and key-value stores can be scaled to provide point lookups for a large number of users but fall apart at the combination of high ingest rates, high query rates at low latency for analytical queries. Online analytical databases typically rely on bulk data loads and are not typically built to handle nonstop operation in demanding web environments. Offline analytical systems have high throughput but do not offer low query latencies nor can scale to serving tens of thousands of queries per second. We present Pinot, a single system used in production at Linkedin that can serve tens of thousands of analytical queries per second, offers near-realtime data ingestion from streaming data sources, and handles the operational requirements of large web properties. We also provide a performance comparison with Druid, a system similar to Pinot.


title: A fast and accurate multilevel inversion of the radon transform

abstract: A number of imaging technologies reconstruct an image function from its Radon projection using the convolution backprojection method. The convolution is an O(N2 log N) algorithm, where the image consists of N X N pixels, while the backprojection is an O(N3 ) algorithm, thus constituting the major computational burden of the convolution backprojection method. An O(N2 log N) multilevel backprojection method is presented here. When implemented with a Fourier-domain postprocessing technique, also presented here, the resulting image quality is similar or superior to the image quality of the classical backprojection technique.


title: The Foster reactance theorem and quality factor for antennas

abstract: The validity of the Foster reactance theorem for the general lossless or lossy antenna is considered for the straight-wire monopole and half-loop antennas. The finite diameter, lossy or lossless monopole, and half-loop behave as either a series RLC circuit near resonance or a parallel RLC circuit near antiresonance. For the Foster reactance theorem to be valid for the general antenna, the frequency derivative of the antenna's feed point reactance, X'(/spl omega/), must be positive for all values of frequency. This behavior is not consistent with that of a parallel RLC circuit near antiresonance and, therefore, the Foster reactance theorem is not valid for either the lossy or the lossless finite diameter antenna. Simulated and measured reactances of the finite diameter monopole and half-loop are shown to be in good agreement and are shown not to obey the Foster reactance theorem in frequency ranges near antiresonance. It is shown that the commonly used relationship between quality factor and |X'(/spl omega/)| is not valid in frequency ranges near antiresonance.