Selective Update Research Articles

Kathryn S. Freeman, <i>A Guide to the Cosmology of William Blake</i>

Kathryn S. Freeman’s A Guide to the Cosmology of William Blake, like S. Foster Damon’s A Blake Dictionary, is an encyclopedia of terms, works, characters, and figures relevant to Blake’s corpus, one especially useful to newcomers to Blake’s works who are trying to find their way through the labyrinth of his mythology. Supplements have been published since Damon’s time, including Alexander Gourlay’s “A Glossary of Terms, Names, and Concepts in Blake” in The Cambridge Companion to William Blake (2003), which he republished in expanded form on the William Blake Archive website, but nothing has appeared until now on the scale of the 181 entries Freeman has written. Her guide is neither as comprehensive as Damon’s dictionary nor limited only to entries strictly relevant to Blake’s cosmology, so it is perhaps best understood as her own selective updating of Damon’s entries and a correction of some omissions in Damon, such as a much-needed separate entry for Catherine Blake.

Nonlinear Adaptive Filtering With Kernel Set-Membership Approach

This paper develops nonlinear kernel adaptive filtering algorithms based on the set-membership filtering (SMF) framework. The set-membership-based filtering approach is distinct from the conventional adaptive filtering approaches in that it aims for the filtering error being bounded in magnitude, as opposed to seeking to minimize the time average or ensemble average of the squared errors. The proposed kernel SMF algorithms feature selective updates of their parameter estimates by making discerning use of the input data, and selective increase of the dimension in the kernel expansion. These result in less computational cost and faster tracking without compromising the mean-squared error performance. We show, through convergence analysis, that the sequences of parameter estimates of our proposed algorithms are convergent, and the filtering error is asymptotically upper bounded in magnitude. Simulations are performed which show clearly the advantages of the proposed algorithms in terms of lower computational complexity, reduced dictionary size, and steady-state mean-squared errors comparable to existing algorithms.

2017 Kidney Disease: Improving Global Outcomes (KDIGO) Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Guideline Update Implementation: Asia Summit Conference Report

The Kidney DiseaseImproving Global Outcomes (KDIGO) Clinical Practice Guideline on Chronic Kidney Disease–Mineral and Bone Disorder (CKD–MBD) 2009 provided recommendations on the detection, evaluation, and treatment of CKD-MBD in patients CKD who are and are not undergoing dialysis. Because of the accumulation of evidence since this initial publication, the CKD-MBD Guideline underwent a selective update in 2017. In April 2018, KDIGO convened a CKD-MBD Guideline Implementation Summit in Japan with the key objective to discuss various barriers to the uptake and implementation of the CKD-MBD Guideline in 8 Asian countries/regions. These countries/regions were comparable according to their high-to-middle economic ranking assigned by the World Bank. The discussion took into account the availability of CKD-MBD medication therapies and government health policies that may influence reimbursement and practice patterns in the region. Most importantly, Summit participants developed a framework of multifaceted strategies aimed at overcoming barriers to guideline implementation. The Summit attendees suggested a shared decision-making approach between clinicians and patients in CKD-MBD management, as well as individualized care based on the treatment risk-benefit ratio. The Summit participants also discussed how KDIGO, as a guideline development organization, may work in partnership with local and national nephrology societies to provide education and facilitate implementation of the guideline by clinicians. The conclusions drawn from this Summit in Asia may serve as an important guide for other regions to follow.

Deep discriminative correlation tracking based on adaptive feature fusion

Discriminative Correlation Filter (DCF) is a fashionable method to treat with the challenges in visual tracking tasks. Recently, the integration of DCF and Convolutional Neural Network (CNN) obtains a favorable tracking performance. However, how to make full use of the CNN features under the DCF tracking framework is still a challenging problem. In this paper, we designed an uncertainty measurement of the correlation response map, and based on this measurement, we proposed an adaptive feature fusion strategy to fuse the correlation response maps of different CNN features. Finally, a deep discriminative correlation tracking (DDCT) algorithm based on this adaptive feature fusion strategy is proposed. Meanwhile, the separate scale correlation filters based on scale pyramid and HOG feature are also introduced to further treat with the scale estimation. Moreover, in order to solve the problem of tracking drifts due to the severe occlusion or serious appearance changes of the target, we present a new adaptive and selective update mechanism to update the discriminative correlation filters and scale correlation filters effectively. Extensive experimental results on OTB2013, OTB2015 and Temple Color 128 benchmark datasets show that the proposed algorithm achieves a better overall performance compared with the state-of-the-art methods.

Power efficient low latency architecture for decoder: Breaking the ACS bottleneck

SummaryViterbi decoder (VD) is the majority used decoder for convolutional codes which play a role in WLAN and WSN applications. The trellis in VD needs proper analysis to calculate the metric at each stage to obtain a shortest path from every state to the next state. Existing techniques in VD design are namely (a) pipelined architecture, (b) modular ACS and buffers technique, and (c) quasi cyclic trellis technique. The key challenge of the VD trellis circuit is to attain high throughput and better latency performance with low power consumption without affecting hardware complexity of VD. This paper presents several of conventional methods used in VD. We also proposed a new method for VD with K the constraint length as multiple of M, the radix in trellis to calculate the shortest survival path to travel in trellis. The proposed VD is simulated using Xilinx. Use of a trace back approach in the proposed Viterbi decoder with capacitive and resistive feedback yields better throughput, latency, and power consumption with respect to other techniques. The static power outputs obtained in RE, shift update, and selective update methods using Libero IDE are also compared.

Low-Complexity Matrix-Based Conjugate Gradient Channel Estimation for Cooperative Wireless Sensor Networks

In this paper, we consider a general cooperative wireless sensor network (WSN) with multiple hops and the problem of channel estimation. A matrix-based conjugate gradient (CG) algorithm is first developed, which is then incorporated with the set-membership filtering framework to improve the performance of complex channel estimation for WSNs. Due to selective update, the proposed set-membership matrix-based CG (SM-MCG) algorithm can reduce the computational complexity significantly and extend the lifetime of WSN. In addition, by introducing an exponentially decaying bound and combining with SM-MCG algorithm, we design an updating criterion to accurately track the optimal maximum iteration number of each data window while reducing complexity further. Simulation results show good performance of our proposed algorithms in terms of convergence speed, steady state mean-square error, and indicate reduced complexity.

Variable Threshold-Based Selective Updating Algorithms in Feed-Forward Active Noise Control Systems

Selective updating (SU)-based adaptive algorithms are proposed for secondary path estimation and noise control in active noise control systems. Use of the proposed variable threshold (VT) for noise control filter aids in reducing the computational complexity of filtered-x LMS-Newton algorithm to achieve fast convergence and improved noise reduction irrespective of the eigen-spread of input signal correlation matrix. A VT-SU least mean square algorithm is presented for the online estimation of the secondary path that reduces computational cost while maintaining estimation accuracy and convergence speed. A power scheduling scheme is presented that requires only one tunable parameter and discontinues auxiliary noise for power level below a predefined limit to reduce the residual error signal. The proposed VT-SU algorithms allow frequent updates in the transition phase and fewer updates in the steady state. Simulations are performed under benchmark conditions to validate the improved performance of the proposed method in comparison with established state-of-the-art methods in terms of estimation accuracy, steady-state residual error, convergence speed, number of tunable parameters, tracking capability, and computational complexity.

Real-Time Dense Monocular SLAM With Online Adapted Depth Prediction Network

Considerable advances have been achieved in estimating the depth map from a single image via convolutional neural networks (CNNs) during the past few years. Combining depth prediction from CNNs with conventional monocular simultaneous localization and mapping (SLAM) is promising for accurate and dense monocular reconstruction, in particular addressing the two long-standing challenges in conventional monocular SLAM: low map completeness and scale ambiguity. However, depth estimated by pretrained CNNs usually fails to achieve sufficient accuracy for environments of different types from the training data, which are common for certain applications such as obstacle avoidance of drones in unknown scenes. Additionally, inaccurate depth prediction of CNN could yield large tracking errors in monocular SLAM. In this paper, we present a real-time dense monocular SLAM system, which effectively fuses direct monocular SLAM with an online-adapted depth prediction network for achieving accurate depth prediction of scenes of different types from the training data and providing absolute scale information for tracking and mapping. Specifically, on one hand, tracking pose (i.e., translation and rotation) from direct SLAM is used for selecting a small set of highly effective and reliable training images, which acts as ground truth for tuning the depth prediction network on-the-fly toward better generalization ability for scenes of different types. A stage-wise Stochastic Gradient Descent algorithm with a selective update strategy is introduced for efficient convergence of the tuning process. On the other hand, the dense map produced by the adapted network is applied to address scale ambiguity of direct monocular SLAM which in turn improves the accuracy of both tracking and overall reconstruction. The system with assistance of both CPUs and GPUs, can achieve real-time performance with progressively improved reconstruction accuracy. Experimental results on public datasets and live application to obstacle avoidance of drones demonstrate that our method outperforms the state-of-the-art methods with greater map completeness and accuracy, and a smaller tracking error.

A mathematical analysis of improved EigenAnt algorithm

ABSTRACTAs a variant of Ant Colony Optimization, the EigenAnt algorithm finds the shortest path between a source node and a destination node based on negative feedback in the form of selective pheromone removal that occurs only on the path which is actually chosen for each trip. EigenAnt algorithm also could change quickly to reflect to the dynamic variety of initial pheromone concentrations and path length etc. However, in general, the solution of EigenAnt algorithm is not always convergent. In this paper, we propose an improved EigenAnt (iEigenAnt) algorithm in terms of both negative and positive feedback; that is, selective pheromone updates are decided by smart ants or stupid ones, which depends whether the amount of the pheromone at the selected path increases or not. The system modelled by our algorithm has a unique equilibrium as the shortest path. Besides, using mathematical analysis, we demonstrate that the equilibrium is global asymptotically stable, i.e., stable and convergent. Finally, we also implement the iEigenAnt algorithm under four different cases and apply it on travelling salesman problem problem, the simulation result shows that our iEigenAnt algorithm is faster convergent and more effective compared to the original EigenAnt algorithm, and some combinatorial optimisation problems can be effectively solved based on our iEigenAnt algorithm.

Diagnosis, Evaluation, Prevention, and Treatment of Chronic Kidney Disease-Mineral and Bone Disorder: Synopsis of the Kidney Disease: Improving Global Outcomes 2017 Clinical Practice Guideline Update.

The Kidney Disease: Improving Global Outcomes (KDIGO) 2017 Clinical Practice Guideline Update for the Diagnosis, Evaluation, Prevention, and Treatment of Chronic Kidney Disease-Mineral and Bone Disorder (CKD-MBD) is a selective update of the prior CKD-MBD guideline published in 2009. The guideline update and the original publication are intended to assist practitioners caring for adults with CKD and those receiving long-term dialysis. Development of the guideline update followed an explicit process of evidence review and appraisal. The approach adopted by the Work Group and the evidence review team was based on systematic reviews of relevant trials, appraisal of the quality of the evidence, and rating of the strength of recommendations according to the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. Searches of the English-language literature were conducted through September 2015 and were supplemented with targeted searches through February 2017. Final modification of the guidelines was informed by a public review process involving numerous stakeholders, including patients, subject matter experts, and industry and national organizations. The update process resulted in the revision of 15 recommendations. This synopsis focuses primarily on recommendations for diagnosis of and testing for CKD-MBD and treatment of CKD-MBD that emphasizes decreasing phosphate levels, maintaining calcium levels, and addressing elevated parathyroid hormone levels in adults with CKD stage G3a to G5 and those receiving dialysis. Key elements include basing treatment on trends in laboratory values rather than a single abnormal result and being cautious to avoid hypercalcemia when treating secondary hyperparathyroidism.

Online Scale Adaptive Visual Tracking Based on Multilayer Convolutional Features.

Convolutional neural networks can efficiently exploit sophisticated hierarchical features which have different properties for visual tracking problem. In this paper, by using multilayer convolutional features jointly and constructing a scale pyramid, we propose an online scale adaptive tracking method. We construct two separate correlation filters for translation and scale estimations. The translation filters improve the accuracy of target localization by a weighted fusion of multiple convolutional layers. Meanwhile, the separate scale filters achieve the optimal and fast scale estimation by a scale pyramid. This design decreases the mutual errors of translation and scale estimations, and reduces computational complexity efficiently. Moreover, in order to solve the problem of tracking drifts due to the severe occlusion or serious appearance changes of the target, we present a new adaptive and selective update mechanism to update the translation filters effectively. Extensive experimental results show that our proposed method achieves the excellent overall performance compared with the state-of-the-art methods.

Explicit time integration of eddy current problems using a selective matrix update strategy

PurposeDiscretizing the magnetic vector potential formulation of eddy current problems in space results in an infinitely stiff differential algebraic equation system that is integrated in time using implicit time integration methods. Applying a generalized Schur complement to the differential algebraic equation system yields an ordinary differential equation (ODE) system. This ODE system can be integrated in time using explicit time integration schemes by which the solution of high-dimensional nonlinear algebraic systems of equations is avoided. The purpose of this paper is to further investigate the explicit time integration of eddy current problems.Design/methodology/approachThe resulting magnetoquasistatic Schur complement ODE system is integrated in time using the explicit Euler method taking into account the Courant–Friedrich–Levy (CFL) stability criterion. The maximum stable CFL time step can be rather small for magnetoquasistatic field problems owing to its proportionality to the smallest edge length in the mesh. Ferromagnetic materials require updating the reluctivity matrix in nonlinear material in every time step. Because of the small time-step size, it is proposed to only selectively update the reluctivity matrix, keeping it constant for as many time steps as possible.FindingsNumerical simulations of the TEAM 10 benchmark problem show that the proposed selective update strategy decreases computation time while maintaining good accuracy for different dynamics of the source current excitation.Originality/valueThe explicit time integration of the Schur complement vector potential formulation of the eddy current problem is accelerated by updating the reluctivity matrix selectively. A strategy for this is proposed and investigated.

Selective updating of sentences: Introducing a new measure of verbal working memory

ABSTRACTWorking memory (WM) is one of the most studied cognitive constructs in psychology, because of its relevance to human performance, including language processing. When measuring verbal WM for sentences, the reading span task is the most widely used WM measure for this purpose. However, comparable sentence-level updating tasks are missing. Hence, we sought to develop a WM updating task, which we termed the selective updating of sentences (SUS) task, which taps the ability to constantly update sentences. In two experiments with Finnish-speaking young adults, we examined the internal consistency and concurrent validity of the SUS task. It exhibited adequate internal consistency and correlated positively with well-established working memory measures. Moreover, the SUS task also showed positive correlations with verbal episodic memory tasks employing sentences and paragraphs. These results indicate that the SUS task is a promising new task for psycholinguistic studies addressing verbal WM updating.

Executive summary of the 2017 KDIGO Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Guideline Update: what’s changed and why it matters

The KDIGO 2017 Clinical Practice Guideline Update for the Diagnosis, Evaluation, Prevention, and Treatment of CKD-MBD represents a selective update of the prior CKD-MBD Guideline published in 2009. This update, along with the 2009 publication, is intended to assist the practitioner caring for adults and children with chronic kidney disease (CKD), those on chronic dialysis therapy, or individuals with a kidney transplant. This review highlights key aspects of the 2017 CKD-MBD Guideline Update, with an emphasis on the rationale for the changes made to the original guideline document. Topic areas encompassing updated recommendations include diagnosis of bone abnormalities in CKD-mineral and bone disorder (MBD), treatment of CKD-MBD by targeting phosphate lowering and calcium maintenance, treatment of abnormalities in parathyroid hormone in CKD-MBD, treatment of bone abnormalities by antiresorptives and other osteoporosis therapies, and evaluation and treatment of kidney transplant bone disease.

Robust Set-Membership Filtering Techniques on GPS Sensor Jamming Mitigation

The global positioning system (GPS) is a satellite-based navigation sensor that supplies the consumer/military users with the proper equipment access to accurate positioning and timing information anywhere in the world. Interference has a significant influence on the acquisition and tracking of satellite signals from a GPS sensor. An adaptive BEACON filter based on optimal bounding ellipsoid (OBE) criterion is proposed for GPS interference mitigation applications. The OBE algorithm that provides the low computational efficiency and highly selective update mechanism is adopted to adjust the weights and thereby track swiftly the stationary and non-stationary frequency modulation (FM) waveforms. According to the GPS signal specification, a time-varying error bound mechanism is derived and incorporated into the BEACON method. The remarkable properties of the proposed BEACON canceller are their rapider convergence rate and favorable lower complexity burden. Simulation results demonstrate that its SNR improvement factor exceeds the factors of conventional NLMS, SM-NLMS, and SM-APA filters under the single-tone CWI, multi-tone CWI, pulse CWI, and FM jamming environments, respectively.

Robust Struck tracker via color Haar-like feature and selective updating

Recently, Struck—a tracker based on structured support vector machine, received great attention as a consequence of its superior performance on many challenging scenes. In this work, we present an improved Struck tracker by using color Haar-like features and effective selective updating. First, we integrate color information into Haar-like features in a simple way, which models the spatial and color information simultaneously without increasing the computational complexity. Second, we make selective model updates according to the tracking status of the object. This prevents inferior patterns resulted by occlusions, abrupt appearance or illumination changes from being added to object model, which decreases the risk of model drift problem. The experimental results indicate that the proposed tracking algorithm outperforms the original Struck by a remarkable margin in precision and accuracy, and it is competitive with other state-of-the-art trackers on a tracking benchmark of 50 challenging sequences.

An overlapping Domain Decomposition preconditioning method for monolithic solution of shear bands

Metals subjected to high strain rate impact often exhibit a sudden and profound drop in the material’s load bearing capability, a ductile failure phenomenon known as shear banding. Shear bands, characterized as material instabilities, are driven by shear heating and described as narrow regions that have sustained intense plastic deformation and high temperature rise. This coupled thermo-mechanical localization problem can be formulated as a nonlinear system with two balance equations, momentum and energy, and two constitutive laws for elasticity and plasticity.In our formulation, mixed finite elements are used to discretize the equations in space and an implicit finite difference scheme is used to advance the system in time, where at every time step, a Newton type method is used to solve the system monolithically. To that end, a block Jacobian matrix is formed analytically using Gâteaux derivatives. The resulting Jacobian is sparse, nonsymmetric and its sparsity pattern and eigenvalue content vary with the different stages of shear bands formation, consequently posing a significant challenge to iterative solvers.To address this issue, an overlapping Domain Decomposition preconditioner that takes into account the physics of shear bands and the domain in which it forms, is proposed. The key idea is to resolve the shear band domain accurately while only solving the remaining domain approximately, with selective updates when necessary. The preconditioner is formed on the basis of an additive Schwartz method that is applied to a Schur complement partition of the system.The proposed preconditioner is implemented in parallel and tested on three different benchmark problems as compared against off-the-shelf solvers. We study the effect of h- and k-refinement that are obtained from Isogeometric discretizations of the system, the overlapping strategy and its parallel performance. Excellent performance is demonstrated in serial and parallel on all benchmark examples.

Data-driven background representation method to video surveillance.

Background subtraction is the fundamental step of moving object detection. As current methods use specific data-based or data learning methods to represent background, this will induce the problem of model distortion while the scenarios cannot meet their assumptions or model conditions. A novel data-driven framework is proposed to represent the background using the intrinsic characteristics of the background. In the framework, the model-free adaptive control method is used as an instance to analyze the background by its status of the nearest time instants, and linearize them dynamically from a pure data perspective. To overcome the occlusion of foreground objects, the selective update method is employed to satisfy the background update. Experiments are carried out under different video conditions to compare algorithm performance with state-of-the-art background models. The results show that the proposed method has reached over 95% in F-measure and percentage of correct classification in most cases, which is better than other state-of-the-art methods. Furthermore, the proposed method shows better robustness in severe video conditions, including bad weather and night cases, and its simplified data-driven control laws make it suitable for outdoor video surveillance.

Research on Updating of Body Schema Using AR Limb and Measurement of the Updated Value

The body schema, an internal model in the brain used to recall the body position, is known to be updated by adapting to the changes in the body figure due to the body growth. Furthermore, the recent research has reported the active updating of the body schema by misunderstanding one's body position using the illusion phenomenon. However, the current method of actively updating the body schema has limited effects and does not handle selective updating of each body part. Therefore, in this paper, a new method using an augmented reality (AR) limb is proposed for the selective updating of unitary parts of the body image in a short period of time. The AR limb is the virtual limb composed by actively extendable and retractable parts, projected by head-mounted display. The basic concept is that the body schema is updated to match the extended and retracted part of the AR limb based on the body schema learning mechanism in the sensory integration of visual and somatic information. The potential effectiveness of the method was verified by actual experiment applying the body schema update to the right forearm.

Pipelined set-membership approach to adaptive Volterra filtering

Due to the high computational complexity required by Volterra filter, some of its practical implementations consider pipelined adaptive Volterra filter architecture with two layers structure. However, its main challenges are the poor robustness against impulsive noise, slow convergence and high computational complexity for long memory high order expansion on each module. In this paper, we firstly extend the pipelined second-order adaptive Volterra filter to its high order version. Then, to reduce the computational complexity and improve the robust performance of pipelined adaptive Volterra filter architecture, the pipelined adaptive Volterra set-membership (PAVF-SM) algorithm and its robust version (PAVF-RSM) are proposed, which are derived from the least-perturbation property and adaptive approximation principle. Due to the inherent variable step size and nonlinear selective update mechanisms, the proposed PAVF-SM and PAVF-RSM algorithms achieve lower complexity and improved convergence performance. Simulations also verify the improved performance of the PAVF-SM and PAVF-RSM algorithms under Gaussian noise and impulsive noise environments.