Update Algorithm Research Articles

Reduced-Complexity RS Codes Recognizer Based on Spectra Update Algorithm

In non-cooperative communication systems, the effective recognition by intercepted bitstream at the 3rd-party receiver is an important mission for military or security purposes. However, the existing recognition schemes lead to long latency and significant hardware due to the large number of traversal operations. Hence, the spectra update algorithm (SUA) is proposed by analyzing the differences between the adjacent reconstructed matrices. Compared with the spectra calculations available like the Galois field Fourier transform (GFFT) and simplified-GFFT (S-GFFT), the presented algorithm reduces the computational complexity to 2/ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> and 2/λ without performance loss, respectively, where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> is the code length and λ is the cardinality of the conjugacy class of the corresponding Galois fields. Besides, a reduced-complexity RS recognizer architecture is designed and analyzed to show the effectiveness of the proposed SUA.

Adaptive Threshold-Based ZUPT for Single IMU-Enabled Wearable Pedestrian Localization

Without dependence on external anchors, the Micro-Electro-Mechanical System Inertia Measurement Unit (MIMU) allowing autonomous localization has promised great potential in wearable IoT applications, including kinematic analysis in sports, medical treatment, elderly care, and disaster rescue. However, the miniaturization of devices for unobtrusive sensing, algorithms minimizing the inherent accumulative errors of inertial devices, and the adaptivity of algorithms for various motion modalities are the key challenges. The removal of accumulative error in the continuous gait cycles with adaptive algorithms is a critical issue regarding localization accuracy, especially for low-cost devices. This investigation proposes an adaptive threshold-based Zero-velocity Update (ZUPT) algorithm to separate the timing of gait cycle phases and compensate for the residual velocity with a linear fitting approximation. The key contributions include: (1) a lightweight threshold-based zero-velocity detection algorithm to split the gait cycle phases of continuous walking; (2) a quaternion-based Extended Kalman Filter (EKF) algorithm to reduce the errors of the non-linear operations for attitude prediction; (3) a linear fitting method for compensating the residual velocity in each gait cycle of continuous walking; (4) the design of a miniature single-MIMU based foot-mountable wearable device and the corresponding experimental studies to verify the proposed methods. Results show that the relative error is less than 3.0% for 2D and 3D trajectories, and the tests with different locomotion patterns demonstrate the adaptivity of the proposed algorithm compared to its peers. The results show that the presented techniques are capable of handling accumulative errors for low-cost MIMU-based systems with good adaptivity.

A Network-on-Chip-Based Annealing Processing Architecture for Large-Scale Fully Connected Ising Model

Combinatorial optimization problems are prevalent in many different fields. Most of these problems are NP-hard and challenging for computers with conventional Von-Neumann architecture. Ising machines with a number of spins have the potential to solve these problems by emulating the natural annealing process of solid matter. Recent research has explored some hardware implementation methods of Ising machines to accelerate the convergence process of such problems at room temperature. However, most of them are suffering from low scalability and low parallel processing capability due to the huge hardware cost and high complexity. In this paper, a novel network-on-chip-based annealing processing architecture (NoCAPA) for a large-scale Ising processor is described to address these issues with a NoC computing paradigm, a distributed storage scheme, and a fully pipelined structure design. Several techniques are developed to further increase convergence speed and reduce hardware resource consumption, including a dynamic multithread parallel update algorithm, a router with merge and deflection abilities, and a unique multiply-accumulate operation. The prototype is implemented in FPGA with the maximum operation frequency of 200MHz, achieving up to 120.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> faster than conventional simulated annealing method when solving the max-cut problem while supporting high scalability.

Deep Multiplicative Update Algorithm for Nonnegative Matrix Factorization and Its Application to Audio Signals

Deep Multiplicative Update Algorithm for Nonnegative Matrix Factorization and Its Application to Audio Signals

Intelligent cache and buffer optimization for mobile VR adaptive transmission in 5G edge computing networks

Virtual Reality (VR) is a key industry for the development of the digital economy in the future. Mobile VR has advantages in terms of mobility, lightweight and cost-effectiveness, which has gradually become the mainstream implementation of VR. In this paper, a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing (MEC)-equipped 5G networks is proposed, aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive transmission. To support VR content proactive caching and intelligent buffer management, users' behavioral similarity and head movement trajectory are jointly used for viewpoint prediction. The tile-based content is proactively cached in the MEC nodes based on the popularity of the VR content. Second, a hierarchical buffer-based adaptive update algorithm is presented, which jointly considers bandwidth, buffer, and predicted viewpoint status to update the tile chunk in client buffer. Then, according to the decomposition of the problem, the buffer update problem is modeled as an optimization problem, and the corresponding solution algorithms are presented. Finally, the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations, and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.

Soybean-MVS: Annotated Three-Dimensional Model Dataset of Whole Growth Period Soybeans for 3D Plant Organ Segmentation

The study of plant phenotypes based on 3D models has become an important research direction for automatic plant phenotype acquisition. Building a labeled three-dimensional dataset of the whole growth period can help the development of 3D crop plant models in point cloud segmentation. Therefore, the demand for 3D whole plant growth period model datasets with organ-level markers is growing rapidly. In this study, five different soybean varieties were selected, and three-dimensional reconstruction was carried out for the whole growth period (13 stages) of soybean using multiple-view stereo technology (MVS). Leaves, main stems, and stems of the obtained three-dimensional model were manually labeled. Finally, two-point cloud semantic segmentation models, RandLA-Net and BAAF-Net, were used for training. In this paper, 102 soybean stereoscopic plant models were obtained. A dataset with original point clouds was constructed and the subsequent analysis confirmed that the number of plant point clouds was consistent with corresponding real plant development. At the same time, a 3D dataset named Soybean-MVS with labels for the whole soybean growth period was constructed. The test result of mAccs at 88.52% and 87.45% verified the availability of this dataset. In order to further promote the study of point cloud segmentation and phenotype acquisition of soybean plants, this paper proposed an annotated three-dimensional model dataset for the whole growth period of soybean for 3D plant organ segmentation. The release of the dataset can provide an important basis for proposing an updated, highly accurate, and efficient 3D crop model segmentation algorithm. In the future, this dataset will provide important and usable basic data support for the development of three-dimensional point cloud segmentation and phenotype automatic acquisition technology of soybeans.

DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning

In recent years, multi-agent reinforcement learning (MARL) has presented impressive performance in various applications. However, physical limitations, budget restrictions, and many other factors usually impose constraints on a multi-agent system (MAS), which cannot be handled by traditional MARL frameworks. Specifically, this paper focuses on constrained MASes where agents work cooperatively to maximize the expected team-average return under various constraints on expected team-average costs, and develops a constrained cooperative MARL framework, named DeCOM, for such MASes. In particular, DeCOM decomposes the policy of each agent into two modules, which empowers information sharing among agents to achieve better cooperation. In addition, with such modularization, the training algorithm of DeCOM separates the original constrained optimization into an unconstrained optimization on reward and a constraints satisfaction problem on costs. DeCOM then iteratively solves these problems in a computationally efficient manner, which makes DeCOM highly scalable. We also provide theoretical guarantees on the convergence of DeCOM's policy update algorithm. Finally, we conduct extensive experiments to show the effectiveness of DeCOM with various types of costs in both moderate-scale and large-scale (with 500 agents) environments that originate from real-world applications.

Target-Aware Tracking with Long-Term Context Attention

Most deep trackers still follow the guidance of the siamese paradigms and use a template that contains only the target without any contextual information, which makes it difficult for the tracker to cope with large appearance changes, rapid target movement, and attraction from similar objects. To alleviate the above problem, we propose a long-term context attention (LCA) module that can perform extensive information fusion on the target and its context from long-term frames, and calculate the target correlation while enhancing target features. The complete contextual information contains the location of the target as well as the state around the target. LCA uses the target state from the previous frame to exclude the interference of similar objects and complex backgrounds, thus accurately locating the target and enabling the tracker to obtain higher robustness and regression accuracy. By embedding the LCA module in Transformer, we build a powerful online tracker with a target-aware backbone, termed as TATrack. In addition, we propose a dynamic online update algorithm based on the classification confidence of historical information without additional calculation burden. Our tracker achieves state-of-the-art performance on multiple benchmarks, with 71.1% AUC, 89.3% NP, and 73.0% AO on LaSOT, TrackingNet, and GOT-10k. The code and trained models are available on https://github.com/hekaijie123/TATrack.

Unsupervised feature selection based on variance–covariance subspace distance

Subspace distance is an invaluable tool exploited in a wide range of feature selection methods. The power of subspace distance is that it can identify a representative subspace, including a group of features that can efficiently approximate the space of original features. On the other hand, employing intrinsic statistical information of data can play a significant role in a feature selection process. Nevertheless, most of the existing feature selection methods founded on the subspace distance are limited in properly fulfilling this objective. To pursue this void, we propose a framework that takes a subspace distance into account which is called “Variance–Covariance subspace distance”. The approach gains advantages from the correlation of information included in the features of data, thus determines all the feature subsets whose corresponding Variance–Covariance matrix has the minimum norm property. Consequently, a novel, yet efficient unsupervised feature selection framework is introduced based on the Variance–Covariance distance to handle both the dimensionality reduction and subspace learning tasks. The proposed framework has the ability to exclude those features that have the least variance from the original feature set. Moreover, an efficient update algorithm is provided along with its associated convergence analysis to solve the optimization side of the proposed approach. An extensive number of experiments on nine benchmark datasets are also conducted to assess the performance of our method from which the results demonstrate its superiority over a variety of state-of-the-art unsupervised feature selection methods. The source code is available at https://github.com/SaeedKarami/VCSDFS.

Blockchain-based fake news traceability and verification mechanism

The rapid development of the Internet and Internet of Things has rapidly introduced human society into the information age, and the way of fake news production has been updated, which has greatly affected the normal life of human beings. In order to identify worthless fake news and trace massive fake news data from unknown sources, and share valuable news data to fully disseminate effective real news, news owners usually store news data in cloud. Users of IoT terminals can access news data on demand without storing it locally. However, the authenticity of the fictive newspaper numbers source, which is easy to destroy, and the social media platform. Besides, when massive news data is saved on cloud server, the news owners have to at the risk of lose physical control over news data and it will face the risk of fake news being disseminated and real news being falsified. Thus, this paper proposes a novel mechanism for secure storage of news data using blockchain technology. Firstly, traceability and verification of fake news data is improved by the cooperative storage model on and off the chain. Secondly due to the inability of past polynomial commitment to update the commitment, we will be a hindrance to use polynomial commitment to build a secure authentication protocol. Therefore, in this paper, we design the update algorithm for polynomial commitment in order to be able to guarantee the consistency of on-chain and blockchain database news data.

Geopolitical risk, economic uncertainty, and militarization: Significant agents of energy consumption and environmental quality

This study estimates the unattended linkages from geopolitical risk (GPRI), economic uncertainty (EUI), and militarization (GMI) to environmental quality through production-based CO2 emissions, primary energy consumption, and GHG emissions. For this purpose, the study consumes data from Brazil, China, India, Russia, and South Africa (BRICS) between 2000 and 2021. Using the updated econometric algorithms, the continuously updated and fully modified (CupFM) and continuously updated and bias-corrected (CupBC), the results infer that both GPRI and GMI are degrading the environmental quality. In contrast, economic uncertainty tends to decline environmental degradation, energy usage, and greenhouse gas emissions in the long run. Similarly, economic growth and population both have negative responses to environmental degradation. The causal dimensions have explored that GPRI and EUI have bidirectional while GMI has unidirectionally Granger causes the environment. Similarly, EUI is bidirectionally, while GMI unidirectionally causes energy consumption. The study results discussed various policies based on GPRI, EUI, and GMI outcomes to improve the environmental quality for sustainable development.

Interpolation of irregularly sampled seismic data via non-convex regularization

Seismic data interpolation is necessary for high-resolution imaging when the field data are not adequate or when some traces are missing. Sparse inversion is an important interpolation method for seismic data, which requires sparse transformation to build an inversion model. Most existing sparsity-promoting interpolation methods are generally based on convex function regularization, especially the L1 norm. Some studies suggest that non-convex regularization with non-convex functions can achieve better sparsity than L1 norm, and produce faster and better results. But this approach is only suitable for noise-free data interpolation, as it is less stable than convex regularization. To overcome the noisy data interpolation problem and improve the stability of non-convex regularization, we propose a novel non-convex function, called arc-tangent function, to construct the inversion model. We also present a gradient-based prediction-projection method to solve the proposed models. We used a prediction-projection method to solve the proposed models. In each iteration, we first obtained a predictive solution by gradient update algorithm and then projected it onto a convex set to update the iterative solution, and the predictive solution instead of the projection solution was output as the final results. Tests on synthetic and field data demonstrated that our non-convex regularization method performed well for both noise-free and noisy data interpolation.

Management of newborn screening for severe combined immunodeficiency at a quaternary referral centre—an updated algorithm

Severe combined immunodeficiency (SCID) is caused by critical genetic defects affecting the immune system. Early diagnosis and intervention are essential for preventing life-threatening infections, end-organ damage, and complications. Newborn screening for SCID is currently performed in many provinces and territories across Canada. The SickKids Newborn Screening Centre in Toronto, Ontario, is a quaternary referral centre that has evaluated SCID newborn screen-positive infants since the program’s introduction in 2013. Here, we provide updated algorithms for clinical investigation and follow-up of infants with an initial positive screen. Statement of novelty: We provide algorithms for the clinical follow-up of SCID newborn screen-positive infants at a quaternary referral centre.

Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed; and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l₁-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance.

Privacy-Preserving Shortest Distance Query for Dynamic Large-Scale Road Network

The road network plays an important role in guiding people's daily travel. In the road network, the shortest distance query is one of the most basic query operations. With the scale of the road network continuously expanding, people choose to outsource the road network to the cloud server in the form of an encrypted graph. To the best of authors' knowledge, the existing privacy-preserving shortest distance query schemes over large-scale encrypted graphs are all designed for the static graph. Nonetheless, the graph of road network is often updated over time. These static privacy-preserving shortest distance query schemes cannot work well in this practical application. In order to address this problem, we explore how to implement the privacy-preserving shortest distance query in an incremental large-scale road network. We use the 2-hop cover label index as the basic secure index structure, which can realize the shortest distance fast query. In order to update the 2-hop cover label index safely and efficiently, we combine a special adjacency index as an auxiliary index structure with homomorphic encryption to implement a dynamic incremental update algorithm for the encrypted graph. We prove that the proposed scheme achieves adaptive semantic security and verify its practicality through experiments.

Improving National Blend of Models Probabilistic Precipitation Forecasts Using Long Time Series of Reforecasts and Precipitation Reanalyses. Part I: Methods

Abstract This article describes proposed revised methods for the statistical postprocessing of precipitation amount intended for the NOAA’s National Blend of Models using the Global Ensemble Forecast System version 12 data (GEFSv12). The procedure updates the previously established procedure of quantile mapping, weighting of sorted members, and dressing of the ensemble. The revised method leverages the long reforecast training dataset that has become available to improve quantile mapping of GEFSv12 data by eliminating the use of supplemental locations, that is, training data from other grid points. It establishes improved definitions of cumulative distributions through a spline-fitting approach. It provides updated algorithms for the weighting of sorted members based on closest-member histogram statistics, and it establishes an objective method for the dressing of the quantile-mapped, weighted ensemble. Verification statistics and case studies are provided in the accompanying article (Part II).

2023 National Heart Center/Saudi Heart Association Focused Update of the 2019 Saudi Heart Association Guidelines for the Management of Heart Failure.

The burden of cardiovascular diseases is undeniable in local populations, who have high mortality rates and a young age of disease onset. A systematic review of emerging evidence and update of the Saudi Heart Association (SHA) 2019 heart failure (HF) guidelines was therefore undertaken. A panel of expert cardiologists reviewed recommendations of the 2019 guidelines following the Saudi Heart Association methodology for guideline recommendations. When needed, the panel provided updated and new recommendations endorsed by the national heart council that are appropriate for clinical practice and local resources in Saudi Arabia. The focused update describes the appropriate use of clinical assessment as well as invasive and non-invasive modalities for the classification and diagnosis of HF. The prevention of HF was emphasized by expanding on both primary and secondary prevention approaches. Pharmacological treatment of HF was supplemented with recommendations on newer therapies, such as SGLT-2 inhibitors. Recommendations were also provided on the management of patients with cardiovascular and non-cardiovascular co-morbidities, with a focus on cardio-oncology and pregnancy. Updated clinical algorithms were included in support of HF management in both the acute and chronic settings. The implementation of this focused update on HF management in clinical practice is expected to lead to improved patient outcomes by providing evidence-based comprehensive guidance for practitioners in Saudi Arabia.

An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data

Validated methods of identifying childhood maltreatment (CM) in primary and secondary care data are needed. We aimed to create the first externally validated algorithm for identifying maltreatment using routinely collected healthcare data. Comprehensive code lists were created for use within GP and hospital admissions datasets in the SAIL Databank at Swansea University working with safeguarding clinicians and academics. These code lists build on and refine those previously published to include an exhaustive set of codes. Sensitivity, specificity and positive predictive value of previously published lists and the new algorithm were estimated against a clinically assessed cohort of CM cases from a child protection service secondary care-based setting—‘the gold standard’. We conducted sensitivity analyses to examine the utility of wider codes indicating Possible CM. Trends over time from 2004 to 2020 were calculated using Poisson regression modelling. Our algorithm outperformed previously published lists identifying 43–72% of cases in primary care with a specificity ≥ 85%. Sensitivity of algorithms for identifying maltreatment in hospital admissions data was lower identifying between 9 and 28% of cases with high specificity (> 96%). Manual searching of records for those cases identified by the external dataset but not recorded in primary care suggest that this code list is exhaustive. Exploration of missed cases shows that hospital admissions data is often focused on the injury being treated rather than recording the presence of maltreatment. The absence of child protection or social care codes in hospital admissions data poses a limitation for identifying maltreatment in admissions data. Linking across GP and hospital admissions maximises the number of cases of maltreatment that can be accurately identified. Incidence of maltreatment in primary care using these code lists has increased over time. The updated algorithm has improved our ability to detect CM in routinely collected healthcare data. It is important to recognize the limitations of identifying maltreatment in individual healthcare datasets. The inclusion of child protection codes in primary care data makes this an important setting for identifying CM, whereas hospital admissions data is often focused on injuries with CM codes often absent. Implications and utility of algorithms for future research are discussed.

Characterization of a robust mouse model of heart failure with preserved ejection fraction.

Heart failure (HF) is a leading cause of morbidity and mortality particularly in older adults and patients with multiple metabolic comorbidities. Heart failure with preserved ejection fraction (HFpEF) is a clinical syndrome with multisystem organ dysfunction in which patients develop symptoms of HF as a result of high left ventricular (LV) diastolic pressure in the context of normal or near normal LV ejection fraction (LVEF; ≥50%). Challenges to create and reproduce a robust rodent phenotype that recapitulates the multiple comorbidities that exist in this syndrome explain the presence of various animal models that fail to satisfy all the criteria of HFpEF. Using a continuous infusion of angiotensin II and phenylephrine (ANG II/PE), we demonstrate a strong HFpEF phenotype satisfying major clinically relevant manifestations and criteria of this pathology, including exercise intolerance, pulmonary edema, concentric myocardial hypertrophy, diastolic dysfunction, histological signs of microvascular impairment, and fibrosis. Conventional echocardiographic analysis of diastolic dysfunction identified early stages of HFpEF development and speckle tracking echocardiography analysis including the left atrium (LA) identified strain abnormalities indicative of contraction-relaxation cycle impairment. Diastolic dysfunction was validated by retrograde cardiac catheterization and analysis of LV end-diastolic pressure (LVEDP). Among mice that developed HFpEF, two major subgroups were identified with predominantly perivascular fibrosis and interstitial myocardial fibrosis. In addition to major phenotypic criteria of HFpEF that were evident at early stages of this model (3 and 10 days), accompanying RNAseq data demonstrate activation of pathways associated with myocardial metabolic changes, inflammation, activation of extracellular matrix (ECM) deposition, microvascular rarefaction, and pressure- and volume-related myocardial stress.NEW & NOTEWORTHY Heart failure with preserved ejection fraction (HFpEF) is an emerging epidemic affecting up to half of patients with heart failure. Here we used a chronic angiotensin II/phenylephrine (ANG II/PE) infusion model and instituted an updated algorithm for HFpEF assessment. Given the simplicity in generating this model, it may become a useful tool for investigating pathogenic mechanisms, identification of diagnostic markers, and for drug discovery aimed at both prevention and treatment of HFpEF.

Practical Post-Kohn-Sham Methods for Time-Reversal Symmetry Breaking References.

The applicability of reduced scaling algorithms based on auxiliary subspace methods for the correlation energy from the random phase approximation (RPA) as well as the correlation part of the self-energy obtained from the GW method is outlined for time-reversal symmetry breaking Kohn-Sham (KS) references. The updated algorithms allow for an efficient evaluation of RPA energies and GW quasiparticle energies for molecular systems with KS references that break time-reversal symmetry. The latter occur, for example, in magnetic fields. Furthermore, KS references for relativistic open-shell molecules also break time-reversal symmetry due to the single determinant ansatz used. Errors of the updated reduced-scaling algorithms are shown to be negligible compared to reference implementations, while the overall computational scaling is reduced by 2 orders of magnitude. Ionization energies obtained from the GW approximation are shown to be robust even for the electronically complicated group of trivalent lanthanoid ions. Starting from GW quasiparticle energies, it is subsequently shown that light-matter interactions of these systems can be calculated using the Bethe-Salpeter equation (BSE). Using the combined GW-BSE method, the absorption and emission spectra of a molecular europium(III) complex can be obtained including spin-orbit coupling.