Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk's bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.Comment: 13 pages, 1 colum

Effect of Dzyaloshinskii–Moriya interaction on Heisenberg antiferromagnetic spin chain in a longitudinal magnetic field

Using functional integral method for the Heisenberg antiferromagnetic spin chain with the added Dzyaloshinskii-Moriya Interaction in the presence of the longitudinal magnetic field, we find out expression for free energy of the spin chain via spin fluctuations, from which quantities characterize the antiferromagnetic order and phase transition such as staggered and total magnetizations derived. From that, we deduce the significant effect of the Dzyaloshinskii-Moriya interaction on the reduction of the antiferromagnetic order and show that the total magnetization can be deviated from the initial one under the influence of canting of the spins due to a combination of the Dzyaloshinskii-Moriya interaction and the magnetic field. Besides, the remarkable role of the transverse spin fluctuations due to the above factors on the antiferromagnetic behaviours of the spin chain is also indicated. &nbsp

Airy-based equilibrium mesh-free method for static limit analysis of plane problems

This paper presents a numerical procedure for lower bound limit analysis of plane problems governed by von Mises yield criterion. The stress fields are calculated based on the Airy function which is approximated using the moving least squares technique. With the use of the Airy-based equilibrium mesh-free method, equilibrium equations are ensured to be automatically satisfied a priori, and the size of the resulting optimization problem is reduced significantly. Various plane strain and plane stress with arbitrary geometries and boundary conditions are examined to illustrate the performance of the proposed procedure

Ordered Mesoporous Carbons as Novel and Efficient Adsorbent for Dye Removal from Aqueous Solution

Ordered mesoporous carbons (OMCs) were successfully synthesized by using hard template and soft template methods. These materials were characterized by XRD, TEM, and N2 adsorption-desorption Brunauer-Emmett-Teller (BET). From the obtained results, it is revealed that the obtained OMCs samples showed high surface area (>1000 m2/g) with high pore volume, mainly mesopore volume (1.2–2.4 cm3/g). Moreover, OMCs samples had similar structure of the SBA-15 silica and exhibited high MB adsorption capacity with qm of 398 mg·g−1 for OMCs synthesis with hard template and 476 mg·g−1 for OMCs synthesis with soft template, respectively. From kinetics investigation, it is confirmed that MB adsorption from aqueous solution obeys the pseudo-second-order kinetic equation

Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.

Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization

Non-Independent and Identically Distributed (non- IID) data distribution among clients is considered as the key factor that degrades the performance of federated learning (FL). Several approaches to handle non-IID data such as personalized FL and federated multi-task learning (FMTL) are of great interest to research communities. In this work, first, we formulate the FMTL problem using Laplacian regularization to explicitly leverage the relationships among the models of clients for multi-task learning. Then, we introduce a new view of the FMTL problem, which in the first time shows that the formulated FMTL problem can be used for conventional FL and personalized FL. We also propose two algorithms FedU and dFedU to solve the formulated FMTL problem in communication-centralized and decentralized schemes, respectively. Theoretically, we prove that the convergence rates of both algorithms achieve linear speedup for strongly convex and sublinear speedup of order 1/2 for nonconvex objectives. Experimentally, we show that our algorithms outperform the conventional algorithm FedAvg in FL settings, MOCHA in FMTL settings, as well as pFedMe and Per-FedAvg in personalized FL settings

On the Generalization of Wasserstein Robust Federated Learning

In federated learning, participating clients typically possess non-i.i.d. data, posing a significant challenge to generalization to unseen distributions. To address this, we propose a Wasserstein distributionally robust optimization scheme called WAFL. Leveraging its duality, we frame WAFL as an empirical surrogate risk minimization problem, and solve it using a local SGD-based algorithm with convergence guarantees. We show that the robustness of WAFL is more general than related approaches, and the generalization bound is robust to all adversarial distributions inside the Wasserstein ball (ambiguity set). Since the center location and radius of the Wasserstein ball can be suitably modified, WAFL shows its applicability not only in robustness but also in domain adaptation. Through empirical evaluation, we demonstrate that WAFL generalizes better than the vanilla FedAvg in non-i.i.d. settings, and is more robust than other related methods in distribution shift settings. Further, using benchmark datasets we show that WAFL is capable of generalizing to unseen target domains