Multiplicative Update Research Articles

Current Approaches in the Allocation of Liver Transplantation.

In recent decades, important advances have been made in the field of liver transplantation. One of the major problems remaining in this area is the small number of donors. Thus, recent data bring multiple updates of the indications and contraindications of this therapeutic method. The main goal is to increase the number of patients who can benefit from liver transplantation, a therapeutic method that can improve life expectancy and the quality of life of patients with end-stage liver disease. Another goal in the management of these patients is represented by the optimal care of those on the waiting list during that period. A multidisciplinary team approach is necessary to obtain the best results for both the donor and the recipient.

Communicating Uncertainty Information in a Dynamic Decision Environment

Abstract The likelihood of threatening events is often simplified for members of the public and presented as risk categories such as the “watches” and “warnings” currently issued by National Weather Service in the United States. However, research (e.g., Joslyn and LeClerc) suggests that explicit numeric uncertainty information—for example, 30%—improves people’s understanding as well as their decisions. Whether this benefit extends to dynamic situations in which users must process multiple forecast updates is as yet unknown. It may be that other likelihood expressions, such as color coding, are required under those circumstances. The experimental study reported here compared the effect of the categorical expressions “watches” and “warnings” with both color-coded and numeric percent chance expressions of the likelihood of a tornado in a situation with multiple updates. Participants decided whether and when to take shelter to protect themselves from a tornado on each of 40 trials, each with seven updated tornado forecasts. Understanding, decision quality, and trust were highest in conditions that provided percent chance information. Color-coded likelihood information inspired the least trust and led to the greatest overestimation of likelihood and confusion with severity information of all expressions.

Exploring the Performance of Ensemble Smoothers to Calibrate Urban Drainage Models

AbstractUrban drainage models (UDMs) are often used to manage urban flooding. However, these models generally involve many parameters to represent the underlying complex hydrodynamic processes. This results in significant challenges to achieving effective and robust model calibration especially with frequently limited observations, leading to unreliable model predictions. This paper makes the first attempt at UDM calibration using the Bayesian‐based Ensemble Smoother (ES) method. Three ES variants are considered, that is, the primary ES, the versions with multiple data assimilation (ES‐MDA) and iterative local update (ES‐ILU). Two synthetic cases and one real‐world application with up to 5,236 calibration parameters are tested. Results obtained show that: (a) both ES‐MDA and ES‐ILU can produce effective model calibration with ES‐ILU outperforming ES‐MDA in terms of both accuracy and uncertainty while ES exhibits limited performance; (b) for the real‐world case, both the ES‐MDA and ES‐ILU methods provide better calibration results than the best‐known solution manually obtained, (c) a minimum number of observations are required to enable an overall accurate model calibration (e.g., four and ten more monitoring sites are needed in the two synthetic cases); and (d) the model calibrated using an intense rainfall event is generally robust to make reliable predictions across different rainfall events while the model calibrated using less intense rainfall event does not perform well for more intense rainfall events. It was also found that ubiquitous parameter equifinality significantly hinders unique parameter identification even when overall accurate state estimates are obtained. This should be clearly understood in practical applications.

Nonnegative Latent Factor Analysis-Incorporated and Feature-Weighted Fuzzy Double $c$-Means Clustering for Incomplete Data

Fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> -means (FCM) clustering is a promising method to handle uncertainties in data clustering. However, the traditional FCM and most of its variants cannot address incomplete inputs. To this aim, a novel fuzzy clustering framework is put forward to perform highly accurate clustering on incomplete data. It adopts twofold ideas: 1) Utilizing a nonnegative latent factor model to prefill the missing data in the inputs by rigidly extracting involved entities’ latent features, where the principle of a minibatch gradient descent algorithm is incorporated into a single latent factor-dependent, nonnegative and multiplicative update algorithm to accelerate the convergence rate; and 2) integrating the distribution of inputs and the weights of local features into the objective function through sparse self-representation and weighting allocation to focus on crucial features. In this way, a NLF analysis-incorporated and feature-weighted fuzzy double <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> -means clustering (NF <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> D) method is achieved, where the data distribution and instance correlation are simultaneously considered with care. Experiments on 12 real-world datasets including both data and images with different missing rates show that the proposed NF <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> D method has a significant superiority over state-of-the-art fuzzy clustering methods.

A speech enhancement algorithm based on a non-negative hidden Markov model and Kullback-Leibler divergence

In this paper, we propose a supervised single-channel speech enhancement method that combines Kullback-Leibler (KL) divergence-based non-negative matrix factorization (NMF) and a hidden Markov model (NMF-HMM). With the integration of the HMM, the temporal dynamics information of speech signals can be taken into account. This method includes a training stage and an enhancement stage. In the training stage, the sum of the Poisson distribution, leading to the KL divergence measure, is used as the observation model for each state of the HMM. This ensures that a computationally efficient multiplicative update can be used for the parameter update of this model. In the online enhancement stage, a novel minimum mean square error estimator is proposed for the NMF-HMM. This estimator can be implemented using parallel computing, reducing the time complexity. Moreover, compared to the traditional NMF-based speech enhancement methods, the experimental results show that our proposed algorithm improved the short-time objective intelligibility and perceptual evaluation of speech quality by 5% and 0.18, respectively.

Online Multipath Routing via Multiplicative Weight Update

The multiplicative weight update (MWU) schema is a powerful tool to design online routing algorithms. However, even the latest research still employs or incorporates the inefficient online single-path routing (OSR) algorithm. In this article, we design an online multipath routing (OMR) algorithm via the MWU schema in order to exploit the network path diversity. Compared to OSR and comparative multipath solutions, OMR has the same worst-case performance guarantee in theory and also has significant performance advantage in practice.

An updated lymphohematopoietic and bladder cancers risk evaluation for occupational and environmental exposures to 1,3-butadiene

EPA designated 1,3-butadiene (BD) as a high priority chemical in December 2019 and is presently performing an evaluation under the Toxic Substances Control Act (TSCA). EPA's cancer dose-response assessment for BD was published in 2002 and was primarily based on a study on workers exposed to BD in the North American synthetic Styrene-Butadiene Rubber (SBR) Industry developed by the University of Alabama at Birmingham (UAB). EPA relied upon a Poisson regression of leukemia mortality data from this cohort (hereinafter referred to as the SBR study) to estimate the cancer potency of BD. At the time, the SBR cohort included more than 15,000 male workers that were followed up through 1991. The SBR cohort has undergone multiple updates over the past two decades. Most recently, Sathiakumar et al. (2021a, b) published an update, with 18 more years of follow up in addition to approximately 5,000 female workers and updated exposure concentration estimates. Recent EPA assessments (e.g., for ethylene oxide, USEPA 2016) based on epidemiological studies use Cox proportional hazards models because they offer better control of the effect of age in cancer development and are less restrictive than Poisson regression models. Here, we develop exposure-response models using standard Cox proportional hazards regression. We explore the relationship between six endpoints (all leukemia, lymphoid leukemia, myeloid leukemia, multiple myeloma, non-Hodgkin's lymphoma, and bladder cancer) and exposures to BD using the most recent exposure metrics and the most recent update of the SBR study. After adjusting for statistically significant covariates, an upper 95% confidence level on the cancer potency based on leukemia derived herein is 0.000086 per ppm, which is approximately 1,000-fold less than EPA's (2002) estimate of 0.08 per ppm and about 10-fold less than TCEQ's (2008) estimate of 0.0011 per ppm.

RegCOVID: Tracking publications of registered COVID-19 studies

BackgroundIn response to the COVID-19 pandemic many clinical studies have been initiated leading to the need for efficient ways to track and analyze study results. We expanded our previous project that tracked registered COVID-19 clinical studies to also track result articles generated from these studies. Our objective was to develop a data science approach to identify and analyze all publications linked to COVID-19 clinical studies and generate a prioritized list of publications for efficient understanding of the state of COVID-19 clinical research.MethodsWe conducted searches of ClinicalTrials.gov and PubMed to identify articles linked to COVID-19 studies, and developed criteria based on the trial phase, intervention, location, and record recency to develop a prioritized list of result publications.ResultsThe performed searchers resulted in 1 022 articles linked to 565 interventional trials (17.8% of all 3 167 COVID-19 interventional trials as of 31 January 2022). 609 publications were identified via abstract-link in PubMed and 413 via registry-link in ClinicalTrials.gov, with 27 articles linked from both sources. Of the 565 trials publishing at least one article, 197 (34.9%) had multiple linked publications. An attention score was assigned to each publication to develop a prioritized list of all publications linked to COVID-19 trials and 83 publications were identified that are result articles from late phase (Phase 3) trials with at least one US site and multiple study record updates. For COVID-19 vaccine trials, 108 linked result articles for 64 trials (14.7% of 436 total COVID-19 vaccine trials) were found.ConclusionsOur method allows for the efficient identification of important COVID-19 articles that report results of registered clinical trials and are connected via a structured article-trial link. Our data science methodology also allows for consistent and as needed data updates and is generalizable to other conditions of interest.

Deployment and Association of Multiple UAVs in UAV-Assisted Cellular Networks With the Knowledge of Statistical User Position

Exploiting unmanned aerial vehicles (UAVs) as flying relays is becoming an indispensable strategy to assist terrestrial cellular networks to enhance coverage. One challenging problem for UAV-integrated cellular networks is how to design their deployment and association schemes to provide on-demand coverage with minimum network power consumption. In this paper, the uplink transmission in a UAV-assisted cellular network is studied with the objective of minimizing the transmit power consumption of users and UAVs through designing proper UAV deployment and association schemes. To avoid the computational complexity caused by the estimation of instantaneous position of users, we investigate UAV deployment and association schemes based on the statistical user position. By discretizing the space where UAV can be located, we build a centralized multi-agent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm, with which multiple UAVs update their positions in a joint manner. In the training process of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm, a reward function is built based on the optimal association scheme and its corresponding power consumption. By adopting the optimal transport theory, the existence of the unique optimal association scheme for given statistical user distribution and UAVs’ state is proved. Simulation results demonstrate that the proposed designs considerably outperform the similar existing algorithms. Comparisons with the benchmark scheme show that the proposed scheme can bring about 85% energy efficiency improvement under the same simulation setups.

An α-β-Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences.

To quantify user-item preferences, a recommender system (RS) commonly adopts a high-dimensional and sparse (HiDS) matrix. Such a matrix can be represented by a non-negative latent factor analysis model relying on a single latent factor (LF)-dependent, non-negative, and multiplicative update algorithm. However, existing models' representative abilities are limited due to their specialized learning objective. To address this issue, this study proposes an α- β -divergence-generalized model that enjoys fast convergence. Its ideas are three-fold: 1) generalizing its learning objective with α- β -divergence to achieve highly accurate representation of HiDS data; 2) incorporating a generalized momentum method into parameter learning for fast convergence; and 3) implementing self-adaptation of controllable hyperparameters for excellent practicability. Empirical studies on six HiDS matrices from real RSs demonstrate that compared with state-of-the-art LF models, the proposed one achieves significant accuracy and efficiency gain to estimate huge missing data in an HiDS matrix.

Enhanced sooty tern optimization algorithm using multiple search guidance strategies and multiple position update modes for solving optimization problems

Enhanced sooty tern optimization algorithm using multiple search guidance strategies and multiple position update modes for solving optimization problems

Continuous Network Update With Consistency Guaranteed in Software-Defined Networks

Network update enables Software-Defined Networks (SDNs) to optimize the data plane performance. The single update focuses on processing one update event at a time, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , updating a set of flows from their initial routes to target routes, but it fails to handle continuously arriving update events in time incurred by high-frequency network changes. On the contrary, the continuous update proposed in “Update Algebra” can handle multiple update events concurrently and respond to the network condition changes at all times. However, “Update Algebra” only guarantees the blackhole-free and loop-free update. The congestion-free property cannot be respected. In this paper, we propose Coeus to achieve the continuous update while maintaining consistency, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , ensuring the blackhole-free, loop-free, and congestion-free properties simultaneously. Firstly, we establish the continuous update model based on the update operations in update events. With the update model, we dynamically reconstruct the operation dependency graph (ODG) to capture the relationship between update operations and link utilization variations. Then, we develop a composition algorithm to eliminate redundant operations in update events. To further speed up the update procedure, we present a partition algorithm to split the operation nodes of the ODG into a series of suboperation nodes that can be executed independently. The partition algorithm is proven to be optimal. Finally, extensive evaluations show that Coeus can improve the update speed by at least 179% and reduce redundant operations by at least 52% compared with state-of-the-art approaches when the arrival rate of update events equals three times per second.

Semi-blind hyperspectral and multispectral image fusion based on a non-factorization model

Fusion of low-resolution hyperspectral images (LR-HSI) with high-resolution multispectral images (HR-MSI) is currently the most cost-effective method for acquiring high-resolution hyperspectral images (HR-HSI). The existing fusion models like spectral unmixing, matrix-factorization, and tensor-factorization require decomposing the target HR-HSI into multiple factor matrices or tensors before fusion. Usually requires a large number of variable optimization processes. So, the above approaches do not apply to the arguably real-time scenario. Moreover, the miscellaneous intermediate estimates render poor fusion stability. This paper proposes a novel non-factorization model to address the above problems. The model firstly gets access to a rough estimate of HR-HSI through the Moore–Penrose inverse operation. Then, we acquire the further HR-HSI forecast according to the correlation between the target HR-HSI and LR-HSI singular values through the coupled singular value decomposition (SVD) of the rough HR-HSI and LR-HIS. Finally, we obtain the precise HR-HSI estimates via the multiplicative update rule. Our proposed model directly estimates the target HR-HSI, reduces the estimation process of intermediate variables, effectively saves calculation time, and enhances the stability of the fusion. We conduct experiments on remote sensing, ground-based, and real datasets to validate our proposed model’s real-time and stability.

Fast multiplicative algorithms for symmetric nonnegative tensor factorization

Symmetric nonnegative tensor factorization (SNTF) is an important tool for clustering analysis. To date, most of algorithms for SNTF are based on multiplicative update rules, which have many attractive properties, e.g., they are often simple to implement and can enforce nonnegativity without extra projection steps. However, the existing multiplicative algorithms often converge slowly due to the conservative multiplicative learning steps or the use of low-level BLAS (basic linear algebra subprograms) in their implementation. In this paper, three new multiplicative algorithms are proposed for SNTF to overcome the drawback of slow convergence. First, a parallel multiplicative algorithm, which can be implemented with high-level BLAS, is derived by auxiliary optimization. To further accelerate the convergence, two new parallel multiplicative algorithms, which enable larger learning steps for improving efficiency, are developed based on weighted geometric mean and weighted arithmetic mean, respectively. Finally, we apply the proposed algorithms to multiway probabilistic clustering, where a new hyper-stochastic normalization scheme based on Euclidean distance is developed for better data preprocessing. The experiment results on both synthetic and real-world data show that the proposed SNTF algorithms converge faster than the state-of-the-art algorithms.

Information freshness optimization of multiple status update streams in Internet of things: Generation rate control and service rate reservation

The Internet of things (IoT) has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications. However, there are limited sensing and communication resources in IoT devices, which significantly affects the timeliness and freshness of the updated status. This work proposes two schemes, namely, the generation rate control and service rate reservation schemes, to improve the overall information freshness of multiple status update streams at the receiver. Specifically, using the recently proposed Age of Information (AoI) as the metric for evaluating information freshness, we characterized the overall information freshness, i.e., the overall average AoI at the receiver for both schemes, by considering the urgency difference of status update and streams. Both schemes for status updates and streams, respectively, were formulated as two optimization problems. We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm. Moreover, we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases. Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions, guiding the design and deployment of IoT.

A Load-Aware Multistripe Concurrent Update Scheme in Erasure-Coded Storage System

Erasure coding has been widely deployed in today’s data centers for it can significantly reduce extra storage costs while providing high storage reliability. However, erasure coding introduced more network traffic and computational overhead in the data update process. How to improve the efficiency and mitigate the system imbalance during the update process in erasure coding is still a challenging problem. Recently, most of the existing update schemes of erasure codes only focused on the single stripe update scenario and ignored the heterogeneity of the node and network status which cannot sufficiently deal with the problems of low update efficiency and load imbalance caused by the multistripe concurrent update. To solve this problem, this paper proposes a Load-Aware Multistripe concurrent Update (LAMU) scheme in erasure-coded storage systems. Notably, LAMU introduces the Software-Defined Network (SDN) mechanism to measure the node loads and network status in real time. It selects nonduplicated nodes with better performance such as CPU utilization, remaining memory, and I/O load as the computing nodes for multiple update stripes. Then, a multiattribute decision-making method is used to schedule the network traffic generated in the update process. This mechanism can improve the transmission efficiency of update traffic and make LAMU adapt to the multistripe concurrent update scenarios in heterogeneous network environments. Finally, we designed a prototype system of multistripe concurrent updates. The extensive experimental results show that LAMU could improve the update efficiency and provide better system load-balancing performance.

High-Resolution ISAR Imaging Based on Plug-and-Play 2D ADMM-Net

We propose a deep learning architecture, dubbed Plug-and-play 2D ADMM-Net (PAN), by combining model-driven deep networks and data-driven deep networks for effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging with various signal-to-noise ratios (SNR) and incomplete data scenarios. First, a sparse observation model of 2D ISAR imaging is established, and a 2D ADMM algorithm is presented. On this basis, using the plug and play (PnP) technique, PnP 2D ADMM is proposed, by combining the 2D ADMM algorithm and the deep denoising network DnCNN. Then, we unroll and generalize the PnP 2D ADMM to the PAN architecture, in which all adjustable parameters in the reconstruction layers, denoising layers, and multiplier update layers are learned by end-to-end training through back-propagation. Experimental results showed that the PAN with a single parameter set can achieve noise-robust ISAR imaging with superior reconstruction performance on incomplete simulated and measured data under different SNRs.

FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features

We introduce a novel federated learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters. Conventional federated learning approaches are inefficient for cross-silo problems because they require the exchange of messages for gradient updates at every iteration, and raise security concerns over sharing such messages during learning. We propose a <i>Federated Stochastic Block Coordinate Descent (FedBCD)</i> algorithm, allowing each party to conduct multiple local updates before each communication to effectively reduce communication overhead. Under a practical security model, we show that parties cannot infer others&#x0027; exact raw data (<i>&#x201C;deep leakage&#x201D;</i>) from collections of messages exchanged in our framework, regardless of the number of communication to be performed. Further, we provide convergence guarantees and empirical evaluations on a variety of tasks and datasets, demonstrating significant improvement inefficiency.

Decentralized Federated Learning: Balancing Communication and Computing Costs

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL). The performance of decentralized SGD is jointly influenced by inter-node communications and local updates. In this paper, we propose a general DFL framework, which implements both multiple local updates and multiple inter-node communications periodically, to strike a balance between communication efficiency and model consensus. It can provide a general decentralized SGD analytical framework. We establish strong convergence guarantees for the proposed DFL algorithm without the assumption of convex objectives. The convergence rate of DFL can be optimized to achieve the balance of communication and computing costs under constrained resources. For improving communication efficiency of DFL, compressed communication is further introduced to the proposed DFL as a new scheme, named DFL with compressed communication (C-DFL). The proposed C-DFL exhibits linear convergence for strongly convex objectives. Experiment results based on MNIST and CIFAR-10 datasets illustrate the superiority of DFL over traditional decentralized SGD methods and show that C-DFL further enhances communication efficiency.

Multiple system atrophy therapeutic development update

Multiple system atrophy therapeutic development update