Ergodicity breaking of an inorganic glass in aging near Tg{T_g} probed by elasticity relaxation

We performed a series of aging experiments of an inorganic glass (As${_2}$Se${_3}$) at a temperature ${T_2}$ near the glass transition point ${T_g}$ by first relaxing it at ${T_1}$. The relaxation of Young's modulus was monitored, which was(almost if not ideally) exponential with a ${T_1}$-dependent relaxation time ${\tau(T_1)}$. We demostrate the Kovacs' paradox for the first time in an inorganic glasses. Associated with the divergence of ${\tau}$, the quasi-equilibrated Young's modulus ${E_\infty}$ does not converge either. An elastic model of relaxation time and a Mori-Tanaka analysis of ${E_\infty}$ lead to a similar estimate of the persistent memory of the history, ergodicity breaking within the accessible experimental time. Experiments with different ${T_2}$ exhibits a critical temperature ${T_p \sim T_g}$, i.e., when ${T_2 > T_p}$, both ${\tau}$ and ${E_\infty}$ converge.Comment: 7 pages, 5 figure

Semi-sparsity Priors for Image Structure Analysis and Extraction

Image structure-texture decomposition is a long-standing and fundamental problem in both image processing and computer vision fields. In this paper, we propose a generalized semi-sparse regularization framework for image structural analysis and extraction, which allows us to decouple the underlying image structures from complicated textural backgrounds. Combining with different textural analysis models, such a regularization receives favorable properties differing from many traditional methods. We demonstrate that it is not only capable of preserving image structures without introducing notorious staircase artifacts in polynomial-smoothing surfaces but is also applicable for decomposing image textures with strong oscillatory patterns. Moreover, we also introduce an efficient numerical solution based on an alternating direction method of multipliers (ADMM) algorithm, which gives rise to a simple and maneuverable way for image structure-texture decomposition. The versatility of the proposed method is finally verified by a series of experimental results with the capability of producing comparable or superior image decomposition results against cutting-edge methods.Comment: 18 page

Semi-Sparsity for Smoothing Filters

In this paper, we propose an interesting semi-sparsity smoothing algorithm based on a novel sparsity-inducing optimization framework. This method is derived from the multiple observations, that is, semi-sparsity prior knowledge is more universally applicable, especially in areas where sparsity is not fully admitted, such as polynomial-smoothing surfaces. We illustrate that this semi-sparsity can be identified into a generalized $L_0$-norm minimization in higher-order gradient domains, thereby giving rise to a new "feature-aware" filtering method with a powerful simultaneous-fitting ability in both sparse features (singularities and sharpening edges) and non-sparse regions (polynomial-smoothing surfaces). Notice that a direct solver is always unavailable due to the non-convexity and combinatorial nature of $L_0$-norm minimization. Instead, we solve the model based on an efficient half-quadratic splitting minimization with fast Fourier transforms (FFTs) for acceleration. We finally demonstrate its versatility and many benefits to a series of signal/image processing and computer vision applications

Intrinsic Image Transfer for Illumination Manipulation

This paper presents a novel intrinsic image transfer (IIT) algorithm for illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework consisting of three photo-realistic losses defined on the sub-layers factorized by an intrinsic image decomposition. We illustrate that all losses can be reduced without the necessity of taking an intrinsic image decomposition under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge. Moreover, with a series of relaxations, all of them can be directly defined on images, giving a closed-form solution for image illumination manipulation. This new paradigm differs from the prevailing Retinex-based algorithms, as it provides an implicit way to deal with the per-pixel image illumination. We finally demonstrate its versatility and benefits to the illumination-related tasks such as illumination compensation, image enhancement, and high dynamic range (HDR) image compression, and show the high-quality results on natural image datasets

MXene-based Membranes for Drinking Water Production

The soaring development of industry exacerbates the shortage of fresh water, making drinking water production an urgent demand. Membrane techniques feature the merits of high efficiency, low energy consumption, and easy operation, deemed as the most potential technology to purify water. Recently, a new type of two-dimensional materials, MXenes as the transition metal carbides or nitrides in the shape of nanosheets, have attracted enormous interest in water purification due to their extraordinary properties such as adjustable hydrophilicity, easy processibility, antifouling resistance, mechanical strength, and light-to-heat transformation capability. In pioneering studies, MXene-based membranes have been evaluated in the past decade for drinking water production including the separation of bacteria, dyes, salts, and heavy metals. This review focuses on the recent advancement of MXene-based membranes for drinking water production. A brief introduction of MXenes is given first, followed by descriptions of their unique properties. Then, the preparation methods of MXene membranes are summarized. The various applications of MXene membranes in water treatment and the corresponding separation mechanisms are discussed in detail. Finally, the challenges and prospects of MXene membranes are presented with the hope to provide insightful guidance on the future design and fabrication of high-performance MXene membranes

Decentralized Federated Learning with Asynchronous Parameter Sharing for Large-scale IoT Networks

Federated learning (FL) enables wireless terminals to collaboratively learn a shared parameter model while keeping all the training data on devices per se. Parameter sharing consists of synchronous and asynchronous ways: the former transmits parameters as blocks or frames and waits until all transmissions finish, whereas the latter provides messages about the status of pending and failed parameter transmission requests. Whatever synchronous or asynchronous parameter sharing is applied, the learning model shall adapt to distinct network architectures as an improper learning model will deteriorate learning performance and, even worse, lead to model divergence for the asynchronous transmission in resource-limited large-scale Internet-of-Things (IoT) networks. This paper proposes a decentralized learning model and develops an asynchronous parameter-sharing algorithm for resource-limited distributed IoT networks. This decentralized learning model approaches a convex function as the number of nodes increases, and its learning process converges to a global stationary point with a higher probability than the centralized FL model. Moreover, by jointly accounting for the convergence bound of federated learning and the transmission delay of wireless communications, we develop a node scheduling and bandwidth allocation algorithm to minimize the transmission delay. Extensive simulation results corroborate the effectiveness of the distributed algorithm in terms of fast learning model convergence and low transmission delay.Comment: 17 pages, 8 figures, to appear in IEEE Internet of Things Journa

Shearing Liquid-Crystalline MXene into Lamellar Membranes with Super-Aligned Nanochannels for Ion Sieving

Ion-selective membranes are crucial in various chemical and physiological processes. Numerous studies have demonstrated progress in separating monovalent/multivalent ions, but efficient monovalent/monovalent ion sieving remains a great challenge due to their same valence and similar radii. Here, this work reports a two-dimensional (2D) MXene membrane with super-aligned slit-shaped nanochannels with ultrahigh monovalent ion selectivity. The MXene membrane is prepared by applying shear forces to a liquid-crystalline (LC) MXene dispersion, which is conducive to the highly-ordered stacking of the MXene nanosheets. The obtained LC MXene membrane (LCMM) exhibits ultrahigh selectivities toward Li+/Na+, Li+/K+, and Li+/Rb+ separation (≈45, ≈49, and ≈59), combined with a fast Li+ transport with a permeation rate of ≈0.35 mol m−2 h−1, outperforming the state-of-the-art membranes. Theoretical calculations indicate that in MXene nanochannels, the hydrated Li+ with a tetrahedral shape has the smallest diameter among the monovalent ions, contributing to the highest mobility. Besides, the weakest interaction is found between hydrated Li+ and MXene channels which also contributes to the ultrafast permeation of Li+ through the super-aligned MXene channels. This work demonstrates the capability of MXene membranes in monovalent ion separation, which also provides a facile and general strategy to fabricate lamellar membranes in a large scale

Effects of electrode materials and potential gradient on electro-osmotic consolidation for marine clayey soils

This study conducted experimental investigations into the effects of electrode material and potential gradient on the effectiveness of electro-osmotic consolidation (EO) in strengthening soft soils. Seven laboratory tests were conducted on high-water-content marine clayey soils through EO. In these experimental tests, four different types of electrodes made of steel, copper, aluminum, and composite carbon fiber (CCF) were employed in four tests each to evaluate the consolidation effectiveness. Additionally, four tests, one was the comparitive study for different eletrode materials, were carried out to determine the optimal gradient for the EO using CCF electrode. Several critical properties of the tested soils were examined and evaluated in this study, including the effective voltage utilization, potential distribution, water discharge, discharge rate, energy consumption, and soil bearing capacity. The test results indicated that the CCF electrode had superior performance in water discharge, discharge rate, and average soil water content compared to metal electrodes. Furthermore, CCF led to uniform enhancement of soil strength, with treated soil bearing capacities 6.3 to 12 times higher than initial values, and 1.9 to 2.5 times higher than those attained with metal electrodes. Additionally, an effective potential gradient of 1 V/cm was identified for the EO with the CCF electrode, providing a higher discharge rate and a larger soil strength in a uniform distribution. Moreover, the use of CCF electrode significantly reduced corrosion compared to metal electrodes during the consolidation process, further contributing to improved consolidation efficiency. This study offers valuable insights and recommendations for the utilization of CCF in marine clayey soils, effectively addressing the challenges posed by electrode corrosion and high energy consumption in EO applications