Cognitive Frailty (CF), characterized by the co-occurrence of Physical Frailty (PF) and Mild Cognitive Impairment (MCI), is increasingly recognized as a critical predictor of adverse health outcomes in aging populations. Despite its clinical significance, early identification of CF remains challenging due to heterogeneous diagnostic criteria and reliance on resource-intensive assessments. This systematic review synthesizes current evidence on sensor-based digital biomarkers for CF detection across eight databases following PRISMA 2020 guidelines. A combined qualitative and quantitative synthesis is conducted based on 20 eligible studies. Key findings reveal that Inertial Measurement Units (IMUs) are the most frequently utilized sensors (n = 6 studies), primarily capturing gait and motor function parameters under various functional tasks. Among digital biomarkers, gait features, particularly Stride Length under single-task conditions and Velocity under dual-task or single-task conditions, show significant associations with CF. Body composition metrics, particularly Appendicular Skeletal Muscle Mass Index (ASM), and physical activity parameters, such as Moderate-to-Vigorous-intensity Physical Activity (MVPA), show consistent negative associations with CF, while cardiovascular indices like Cardio-Ankle Vascular Index (CAVI) reveal positive correlations. Eighteen studies develop a total of 23 CF-related models, of which only four focus on predictive classification. These four studies report ten distinct models, with AUCs ranging from 0.696 to 0.911 and accuracy from 0.480 to 0.863. A quantitative synthesis of six models from three studies yields pooled estimates of sensitivity at 0.81 (95% CI: 0.55-0.94) and specificity at 0.80 (95% CI: 0.49-0.95), suggesting moderate-to-good discriminative performance despite notable heterogeneity in diagnostic definitions, sample sizes, sensor types, and modeling approaches. These results systematically map the evolving landscape of digital biomarkers for CF, emphasizing the need for standardized sensor protocols and rigorous validation to advance clinical translation.

In this paper we develop a simple scheme for a coupled matrix factorization problem arising in the topic of hyperspectral super-resolution (HSR). HSR considers the problem of recovering a super-resolution image from a multispectral image and a hyperspectral image, which have lower spectral and spatial resolutions, respectively, and it is a motivated topic in the domain of remote sensing. The challenge with coupled factorization (COFAC) is that we are required to simultaneously factorize two data matrices, with their factors being interrelated. We adopt a separable COFAC strategy, in which we first factorize one data matrix, and then use the retrieved factors and the coupled factor relationship to help us factorize another matrix; the merit is that it may lead to simple COFAC schemes. Our scheme is based on the simplex-structured factorization model, which is commonly used in HSR, and a sparse factor assumption. In particular, we leverage the coupled factor structure to exploit sparsity in an easy way; we solve simple constrained least squares problems, and we sidestep the need to do sparse optimization. Numerical results show that our proposed scheme works reasonably on both semi-real data and synthetic data, and it runs much faster than some state-of-the-art COFAC schemes.

We propose a novel Federated Edge Network Utility Maximization (FEdg-NUM) architecture for solving a large-scale distributed network utility maximization (NUM) problem. In FEdg-NUM, clients with private utilities communicate to a peer-to-peer network of edge servers. This represents a departure from the classical distributed NUM master-slave configuration and enables distributed computing harnessing local communications. Compared to a solution using cloud synchronization via Ring AllReduce, we prove that our federated edge computing model has shorter run-time in the presence of network congestion, thanks to its configuration and its ability to make progress in the presence of intermittent links. The paper studies its convergence and run-time performance both analytically and numerically, and illustrates several possible networking applications.

In federated learning, models are learned from users’ data that are held private in their edge devices, by aggregating them in the service provider’s “cloud” to obtain a global model. Such global model is of great commercial value in, e.g., improving the customers’ experience. In this paper we focus on two possible areas of improvement of the state of the art. First, we take the difference between user habits into account and propose a quadratic penalty-based formulation, for efficient learning of the global model that allows to personalize local models. Second, we address the latency issue associated with the heterogeneous training time on edge devices, by exploiting a hierarchical structure modeling communication not only between the cloud and edge devices, but also within the cloud. Specifically, we devise a tailored block coordinate descent-based computation scheme, accompanied with communication protocols for both the synchronous and asynchronous cloud settings. We characterize the theoretical convergence rate of the algorithm, and provide a variant that performs empirically better. We also prove that the asynchronous protocol, inspired by multi-agent consensus technique, has the potential for large gains in latency compared to a synchronous setting when the edge-device updates are intermittent. Finally, experimental results are provided that corroborate not only the theory, but also show that the system leads to faster convergence for personalized models on the edge devices, compared to the state of the art.

Hyperspectral unmixing (HU) is a problem of blindly identifying the underlying materials, in form of spectral signatures, in the captured hyperspectral image. HU has received tremendous interest in remote sensing, and fundamentally the problem can be regarded as solving a simplex-structured matrix factorization problem. The majority of HU research has been focused on the linear mixture model. On the other hand, remote sensing research has long indicated that real-life hyperspectral images can exhibit spatial variant and nonlinear effects. In this study we introduce a probabilistic approach for HU under two different models, namely, the normal composition model for modeling spatial endmember variability and the generalized bilinear model for modeling multi-path nonlinear effects. Our approach formulates HU as a maximum-likelihood (ML) model parameter estimation problem, and we demonstrate that the ML formulation can flexibly handle the two models. The ML problem is technical challenging, and we apply a combination of techniques, namely, sample average approximation, block coordinate descent and majorization minimization, to tackle the problem. Our empirical study suggests that the ML method gives competitive recovery performance.

Hyperspectral super-resolution (HSR) is a problem that aims to estimate an image of high spectral and spatial resolutions from a pair of coregistered multispectral (MS) and hyperspectral (HS) images, which have coarser spectral and spatial resolutions, respectively. In this article, we pursue a lowrank matrix estimation approach for HSR. We assume that the spectral-spatial matrices associated with the whole image and the local areas of the image have low-rank structures. The local low-rank assumption, in particular, has the aim of providing a more flexible model for accounting for local variation effects due to endmember variability. We formulate the HSR problem as a global-local rank-regularized least-squares problem. By leveraging on the recent advances in nonconvex large-scale optimization, namely the smooth Schatten-p approximation and the accelerated majorization-minimization method, we develop an efficient algorithm for the global-local low-rank problem. Numerical experiments on synthetic, semi-real, and real data show that the proposed algorithm outperforms a number of benchmark algorithms in terms of recovery performance.

Simplex-structured matrix factorization (SSMF) is a problem of recovering a basis matrix and the corresponding coefficient vectors from data, where the coefficient vectors are constrained to lie in the unit simplex. SSMF has attracted growing attention in recent years, with numerous applications such as hyperspectral unmixing and document clustering. In this work, we develop a maximum-likelihood (ML) approach for SSMF. Specifically, by modeling the coefficient vectors as random variables following a Dirichlet mixture distribution-which allows us to model more complex data distributions in real-life data, a probabilistic model for SSMF is employed. We consider a marginalized likelihood with respect to the coefficient vectors, and use ML estimation to learn the basis matrix and unknown Dirichlet mixture parameters. The marginalized likelihood does not admit a closed form and is non-concave, and this makes the problem challenging to solve. To handle this challenge, an effective algorithm using sample average approximation and block successive upper-bound minimization is proposed. We consider the aforementioned two real-world applications by simulations. Numerical results show that the proposed algorithm delivers appealing performance in both applications.

Hyperspectral super-resolution (HSR) is a problem of recovering a high-spectral-spatial-resolution image from a multispectral measurement and a hyperspectral measurement, which have low spectral and spatial resolutions, respectively. We consider a low-rank structured matrix factorization formulation for HSR, which is a non-convex large-scale optimization problem. Our contributions contain both computational and theoretical aspects. On the computational side, we develop three inexact block coordinate descent (BCD) schemes that are empirically found to run many times faster than a state-of-the-art method, which uses exact BCD. We achieve this by applying concepts in the proximal gradient (PG) and Frank-Wolfe (FW) methods and by exploiting the HSR problem structures. On the theoretical side, we show that these inexact BCD schemes guarantee convergence to a stationary point. In particular, the convergence result for a hybrid PG- FW inexact BCD scheme is new.

Consider a structured matrix factorization model where one factor is restricted to have its columns lying in the unit simplex. This simplex-structured matrix factorization (SSMF) model and the associated factorization techniques have spurred much interest in research topics over different areas, such as hyperspectral unmixing in remote sensing and topic discovery in machine learning, to name a few. In this paper we develop a new theoretical SSMF framework whose idea is to study a maximum volume ellipsoid inscribed in the convex hull of the data points. This maximum volume inscribed ellipsoid (MVIE) idea has not been attempted in prior literature, and we show a sufficient condition under which the MVIE framework guarantees exact recovery of the factors. The sufficient recovery condition we show for MVIE is much more relaxed than that of separable nonnegative matrix factorization (or pure-pixel search); coincidentally, it is also identical to that of minimum volume enclosing simplex, which is known to be a powerful SSMF framework for nonseparable problem instances. We also show that MVIE can be practically implemented by performing facet enumeration and then by solving a convex optimization problem. The potential of the MVIE framework is illustrated by numerical results.

Consider a structured matrix factorizaton (SMF) whose coefficient vectors are constrained to lie in the unit simplex. This kind of simplex SMF (SSMF) has received growing attention and has found many applications such as hyperspectral unmixing in remote sensing, text mining in machine learning, and blind source separation in signal processing. The aim of this paper is to establish a maximum-likelihood (ML) estimation framework for SSMF in the presence of Gaussian noise and outliers, and to demonstrate its potential. Our ML formulation has the coefficient vectors marginalized in accordance with a prescribed probabilistic model, and this leads to a likelihood function that contains multi-dimensional integrals. Unfortunately these integrals do not appear to have analytically tractable solutions, and this makes the ML problem challenging. We tackle the problem by using sample average approximation in stochastic optimization and majorization-minimization. Simulation results show that the resulting ML algorithm significantly outperforms several existing methods when noise and outliers are present.

We propose a depth image denoising and enhancement framework using a light convolutional network. The network contains three layers for high dimension projection, missing data completion and image reconstruction. We jointly use both depth and visual images as inputs. For the gray image, we design a pre-processing procedure to enhance the edges and remove unnecessary detail. For the depth image, we propose a data augmentation strategy to regenerate and increase essential training data. Further, we propose a weighted loss function for network training to adaptively improve the learning efficiency. We tested our algorithm on benchmark data and obtained very promising visual and quantitative results at real-time speed.

In this paper, the use of a Fuzzy Gain Scheduling IMC-PID (FGS+IMC-PID) scheme has been presented based on fuzzy performance degree coefficient n self-adjusting controller for the improvement of IMC-PID control. It is shown that the IMC-PID controller with the Fuzzy PID parameters Gain Scheduler provides satisfactory closed-loop responses with less overshoot and shorter rising times in case of both set point disturbance and the plant/model mismatch. Simulations are given, in which the proposed method is compared to other PID tuning methods (IMC, SPMG). The proposed scheme is suitable to implement in the complex process control system in power generation, since it does not demand significant computing resources. It has already been implemented through Function Code in many typical DCS (EDPU, XDPS, Ovation etc). The industrial applications show that the scheme achieves better performance in specific load variation range.

A new auto-tuning method is presented in this paper for the TITO process with time delay. The first step is to identify the process model and the second step is to design the IMC-PID controller. In the identifying procedure, both the bias relay test and the idea relay test are involved to identify the model. The important feature of the proposed method is that it does not require the prior information about the process and the steady-state process gain can be directly obtained. In the second step, this method is used to design the IMC-PID controller of the TITO control system. The new auto-tuning scheme is implemented through the function code in typical DCSs (EDPF-NT, OVATION, Symphony, etc) and is used in 300MW fossil-fuel units. The industrial applications show that the scheme achieves better performance in specific load variation range.