On the trace theorem to Volterra-type equations with local or non-local derivatives

This paper considers traces at the initial time for solutions of evolution equations with local or non-local derivatives in vector-valued $A_p$ weighted $L_p$ spaces. To achieve this, we begin by introducing a generalized real interpolation method. Within the framework of generalized interpolation theory, we make use of stochastic process theory and two-weight Hardy's inequality to derive our trace and extension theorems. Our results encompass findings applicable to time-fractional equations with broad temporal weight functions

Characterizations of weighted Besov and Triebel-Lizorkin spaces with variable smoothness

In this paper, we study different types of weighted Besov and Triebel-Lizorkin spaces with variable smoothness. The function spaces can be defined by means of the Littlewood-Paley theory in the field of Fourier analysis, while there are other norms arising in the theory of partial differential equations such as Sobolev-Slobodeckij spaces. It is known that two norms are equivalent when one considers constant regularity function spaces without weights. We show that the equivalence still holds for variable smoothness and weights, which is accomplished by making use of shifted maximal functions, Peetre's maximal functions, and the reverse H\"older inequality. Moreover, we obtain a weighted regularity estimate for time-fractional evolution equations and a generalized Sobolev embedding theorem without weights.Comment: 36 page

Efficient and Privacy Preserving Group Signature for Federated Learning

Federated Learning (FL) is a Machine Learning (ML) technique that aims to reduce the threats to user data privacy. Training is done using the raw data on the users' device, called clients, and only the training results, called gradients, are sent to the server to be aggregated and generate an updated model. However, we cannot assume that the server can be trusted with private information, such as metadata related to the owner or source of the data. So, hiding the client information from the server helps reduce privacy-related attacks. Therefore, the privacy of the client's identity, along with the privacy of the client's data, is necessary to make such attacks more difficult. This paper proposes an efficient and privacy-preserving protocol for FL based on group signature. A new group signature for federated learning, called GSFL, is designed to not only protect the privacy of the client's data and identity but also significantly reduce the computation and communication costs considering the iterative process of federated learning. We show that GSFL outperforms existing approaches in terms of computation, communication, and signaling costs. Also, we show that the proposed protocol can handle various security attacks in the federated learning environment

Boosting thermal conductivity by surface plasmon polaritons propagating along a thin Ti film

We experimentally demonstrate a boosted in-plane thermal conduction by surface plasmon polaritons (SPPs) propagating along a thin Ti film on a glass substrate. Owing to a lossy nature of metal, SPPs can propagate over centimeter-scale distance even with a supported metal film, and resulting ballistic heat conduction can be quantitatively validated. Further, for a 100-nm-thick Ti film on glass substrate, a significant enhancement of in-plane thermal conductivity compared to bulk value ($\sim 35\%$) is experimentally shown. This study will provide a new avenue to employ SPPs for heat dissipation along a supported thin film, which can be readily applied to mitigate hot-spot issues in microelectronics.Comment: 3 figure

Chipped Pharmaceuticals from Production to in VIVO (in body) Drug Delivery Becoming Reality

AbstractAdvances in medical technology rely heavily on the collection and analysis of measured data to facilitate patient diagnosis and business decisions. The healthcare industry, particularly pharmaceuticals and diagnostic processes, has an ongoing need to improve item tracking and data collection to improve the quality of care while reducing cost. The remote, non-invasive characteristics of RFID can facilitate the information needs of healthcare without imposing additional burden onto the patient or staff. Properly deployed RFID enabled devices can provide convenient and accurate data for disease diagnosis, evaluation of prescription non-compliance and identification of medication dosage errors. This paper describes an all-encompassing RFID tracking system that begins with compliance documentation from the drug manufacturer through confirmation of patient compliance by capsule extraction from the bottle, into a pill case and ultimately ingested or inserted into the body. This RFID system can provide data for decision-making and facilitate compliance with FDA proposed e-pedigree requirements. This transcript provides an introduction to healthcare trends in order to motivate the need for a biocompatible RFID system. An approach to research as well as an in vitro tabletop test method is presented in light of pending research. The overall goal of the pending research is to develop biocompatible RFID tag components for use with systems beginning with the manufacturer and continuing through distribution to the point of interest within the patients body. Keywords RFID; e-pedigree; pharmaceuticals; trackin

Data of methylome and transcriptome derived from human dilated cardiomyopathy

AbstractAlterations in DNA methylation and gene expression have been implicated in the development of human dilated cardiomyopathy (DCM). Differentially methylated probes (DMPs) and differentially expressed genes (DEGs) were identified between the left ventricle (LV, a pathological locus for DCM) and the right ventricle (RV, a proxy for normal hearts). The data in this DiB are for supporting our report entitled “Methylome analysis reveals alterations in DNA methylation in the regulatory regions of left ventricle development genes in human dilated cardiomyopathy” (Bong-Seok Jo, In-Uk Koh, Jae-Bum Bae, Ho-Yeong Yu, Eun-Seok Jeon, Hae-Young Lee, Jae-Joong Kim, Murim Choi, Sun Shim Choi, 2016) [1]