Deep Residual Learning via Large Sample Mean-Field Stochastic Optimization

We study a class of stochastic optimization problems of the mean-field type arising in the optimal training of a deep residual neural network. We consider the sampling problem arising from a continuous layer idealization, and establish the existence of optimal relaxed controls when the training set has finite size. The core of our paper is to prove the Gamma-convergence of the sequence of sampled objective functionals, i.e., show that as the size of the training set grows large, the minimizer of the sampled relaxed problem converges to that of the limiting optimization problem. We connect the limit of the large sampled objective functional to the unique solution, in the trajectory sense, of a nonlinear Fokker-Planck-Kolmogorov (FPK) equation in a random environment. We construct an example to show that, under mild assumptions, the optimal network weights can be numerically computed by solving a second-order differential equation with Neumann boundary conditions in the sense of distributions

A Comparative Study of Physicochemical, Dielectric and Thermal Properties of Pressboard Insulation Impregnated with Natural Ester and Mineral Oil

Natural ester is considered to be a substitute of mineral oil in the future. To apply natural ester in large transformers safely, natural ester impregnated solid insulation should be proved to have comparable dielectric strength and thermal stability to mineral oil impregnated solid insulation. This paper mainly focuses on a comparative study of physicochemical, ac breakdown strength and thermal stability behavior of BIOTEMP natural ester/pressboard insulation and Karamay 25# naphthenic mineral oil/pressboard insulation after long term thermal ageing. The physicochemical and dielectric parameters including moisture, acids and the ac breakdown strength of these two oil/pressboard insulation systems at different ageing status were compared. The permittivity and ac breakdown strength of these two oil/pressboard insulation systems at different temperatures were also investigated. And a comparative result of the thermal stability behavior of these two oil/pressboard insulation systems with different ageing status was provided at last. Results show that though natural ester has higher absolute humidity and acidity during the long ageing period, the lower relative humidity of natural ester helps to keep its ac breakdown strength higher than mineral oil. The pressboard aged in natural ester also has higher ac breakdown strength than that aged in mineral oil. The lower relative permittivity ratio of natural ester impregnated paper to natural ester is beneficial to its dielectric strength. Using natural ester in transformer, the resistance to thermal decomposition of the oil/pressboard insulation system could be also effectively improved

Group Key Agreement for Ad Hoc Networks

Over the last 30 years the study of group key agreement has stimulated much work. And as a result of the increased popularity of ad hoc networks, some approaches for the group key establishment in such networks are proposed. However, they are either only for static group or the memory, computation and communication costs are unacceptable for ad-hoc networks. In this thesis some protocol suites from the literature (2^d-cube, 2^d-octopus, Asokan-Ginzboorg, CLIQUES, STR and TGDH) shall be discussed. We have optimized STR and TGDH by reducing the memory, communication and computation costs. The optimized version are denoted by µSTR and µTGDH respectively. Based on the protocol suites µSTR and µTGDH we present a Tree-based group key agreement Framework for Ad-hoc Networks (TFAN). TFAN is especially suitable for ad-hoc networks with limited bandwidth and devices with limited memory and computation capability. To simulate the protocols, we have implemented TFAN, µSTR and µTGDH with J2ME CDC. The TFAN API will be described in this thesis

The Insulation Properties of Oil-Impregnated Insulation Paper Reinforced with Nano-TiO 2

Oil-impregnated insulation paper has been widely used in transformers because of its low cost and desirable physical and electrical properties. However, research to improve the insulation properties of oil-impregnated insulation paper is rarely found. In this paper, nano-TiO2 was used to stick to the surface of cellulose which was used to make insulation paper. After oil-impregnated insulation paper reinforced by nano-TiO2 was prepared, the tensile strength, breakdown strength, and dielectric properties of the oil-impregnated insulation paper were investigated to determine whether the modified paper had a better insulation performance. The results show that there were no major changes in tensile strength, and the value of the breakdown strength was greatly improved from 51.13 kV/mm to 61.78 kV/mm. Also, the values of the relative dielectric constant, the dielectric loss, and conductivity declined. The discussion reveals that nano-TiO2 plays a major role in the phenomenon. Because of the existence of nano-TiO2, the contact interface of cellulose and oil was changed, and a large number of shallow traps were produced. These shallow traps changed the insulation properties of oil-impregnated insulation paper. The results show that the proposed solution offers a new method to improve the properties of oil-impregnated insulation paper

The Spin of New Black Hole Candidate: MAXI J1803-298 Observed by NuSTAR and NICER

MAXI J1803-298, a newly-discovered Galactic transient and black hole candidate, was first detected by \emph{MAXI}/GSC on May 1st, 2021. In this paper, we present a detailed spectral analysis of MAXI J1803-298. Utilizing the X-ray reflection fitting method, we perform a joint fit to the spectra of MAXI J1803-298, respectively, observed by \emph{NuSTAR} and \emph{NICER}/XTI on the same day over the energy range between 0.7-79.0 keV, and found its spin (and the inclination angle i) can be constrained to be close to an extreme value, 0.991 ($i\sim$ $70 ^{\circ}$), at 68\% confidence interval. The results suggest that MAXI J1803-298 may be a fast-rotating black hole with a large inclination angle.Comment: 6 pages, 5 figure

Authentication using c-VEP evoked in a mild-burdened cognitive task

In recent years, more and more researchers are devoting themselves to the studies about authentication based on biomarkers. Among a wide variety of biomarkers, code-modulated visual evoked potential (c-VEP) has attracted increasing attention due to its significant role in the field of brain-computer interface. In this study, we designed a mild-burdened cognitive task (MBCT), which can check whether participants focus their attention on the visual stimuli that evoke c-VEP. Furthermore, we investigated the authentication based on the c-VEP evoked in the cognitive task by introducing a deep learning method. Seventeen participants were recruited to take part in the MBCT experiments including two sessions, which were carried out on two different days. The c-VEP signals from the first session were extracted to train the authentication deep models. The c-VEP data of the second session were used to verify the models. It achieved a desirable performance, with the average accuracy and F1 score, respectively, of 0.92 and 0.89. These results show that c-VEP carries individual discriminative characteristics and it is feasible to develop a practical authentication system based on c-VEP