The Deep Weight Prior

Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution. In this work, we propose a new type of prior distributions for convolutional neural networks, deep weight prior (DWP), that exploit generative models to encourage a specific structure of trained convolutional filters e.g., spatial correlations of weights. We define DWP in the form of an implicit distribution and propose a method for variational inference with such type of implicit priors. In experiments, we show that DWP improves the performance of Bayesian neural networks when training data are limited, and initialization of weights with samples from DWP accelerates training of conventional convolutional neural networks.Comment: TL;DR: The deep weight prior learns a generative model for kernels of convolutional neural networks, that acts as a prior distribution while training on new dataset

The radiation problem from a vertical short dipole antenna above flat and lossy ground. Novel formulation in the spectral domain with closed form analytical solution in the high frequency regime

In this paper we consider the problem of radiation from a vertical short Hertzian dipole above flat lossy ground, which represents the well known in the literature Sommerfeld radiation problem. The problem is formulated in a novel spectral domain approach, and by inverse three dimensional Fourier transformation the expressions for the received electric and magnetic field in the physical space are derived as one dimensional integrals over the radial component of wavevector, in cylindrical coordinates. Subsequent use of the Stationary Phase Method in the high frequency regime yields closed form analytical solutions for the received EM field vectors, which coincide with the corresponding reflected EM field originating from the image point. In this way, we conclude that the so called in the literature space wave, i.e. line of sight plus reflected EM field, represents the total solution of the Sommerfeld problem in the high frequency regime, in which case the surface wave can be ignored. Finally, numerical results in the high frequency regime are presented in this paper, in comparison with corresponding numerical results based on Norton solution of the problem, i.e. space and surface waves

Field trials on attractiveness of the synthetic sex pheromone of the four-spotted bean weevil, Callosobruchus maculatus Fabricius (Coleoptera: Bruchidae).: Poster

Quarantine pests of legumes pose a threat to many countries of the world including Russia. Pests that can enter the country as a result of the transportation of regulated articles (by sea, air, road, rail, etc.) pose a particular danger (Shutova, 1970; Dankvert et al., 2009). Monitoring and identification of legume pests is complicated by the fact that small beetles have a hidden mode of life. One of the most dangerous quarantine pest is the fourspotted bean weevil Callosobruchus maculatus Fabricius (Coleoptera: Bruchidae), which is widespread throughout the world and can cause serious economic losses in agriculture of Russia. Research work on the identification, synthesis and laboratory evaluation of the synthetic sex pheromone of Callosobruchus maculatus was carried out at the All-Russia Plant Quarantine Center (Bykovo, Moscow region). Tests have shown that synthesized sex pheromone of C. maculatus has a high attractiveness for males. An effective dose of pheromone that attracts males of the four-spotted bean weevil has been found at the laboratory and is equal to 0.5 µg per dispenser. Thereafter tests have shown that the concentration of pheromone above 2 µg does not cause behavioral response in beetles and doesn’t result in contact with the stimulus. Dispensers with doses of pheromone from 4 to 8 mg have been used with a Delta trap in storage. The use of pheromone traps can help in pest identification, decreasing or complete avoidance of repeated treatments with chemicals at low pest population. The results of this study will be presented and discussed on the basis of laboratory and literature data.Quarantine pests of legumes pose a threat to many countries of the world including Russia. Pests that can enter the country as a result of the transportation of regulated articles (by sea, air, road, rail, etc.) pose a particular danger (Shutova, 1970; Dankvert et al., 2009). Monitoring and identification of legume pests is complicated by the fact that small beetles have a hidden mode of life. One of the most dangerous quarantine pest is the fourspotted bean weevil Callosobruchus maculatus Fabricius (Coleoptera: Bruchidae), which is widespread throughout the world and can cause serious economic losses in agriculture of Russia. Research work on the identification, synthesis and laboratory evaluation of the synthetic sex pheromone of Callosobruchus maculatus was carried out at the All-Russia Plant Quarantine Center (Bykovo, Moscow region). Tests have shown that synthesized sex pheromone of C. maculatus has a high attractiveness for males. An effective dose of pheromone that attracts males of the four-spotted bean weevil has been found at the laboratory and is equal to 0.5 µg per dispenser. Thereafter tests have shown that the concentration of pheromone above 2 µg does not cause behavioral response in beetles and doesn’t result in contact with the stimulus. Dispensers with doses of pheromone from 4 to 8 mg have been used with a Delta trap in storage. The use of pheromone traps can help in pest identification, decreasing or complete avoidance of repeated treatments with chemicals at low pest population. The results of this study will be presented and discussed on the basis of laboratory and literature data

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

The constant emission of polluting gases is causing an urgent need for timely detection of harmful gas mixtures in the atmosphere. A method and algorithm of the determining spectral composition of gas with a gas analyzer using an artificial neural network (ANN) were suggested in the article. A small closed gas dynamic system was designed and used as an experimental bench for collecting and quantifying gas concentrations for testing the proposed method. This device was based on AS7265x and BMP180 sensors connected in parallel to a 3.3 V compatible Arduino Uno board via QWIIC. Experimental tests were conducted with air from the laboratory room, carbon dioxide (CO2), and a mixture of pure oxygen (O2) with nitrogen (N2) in a 9:1 ratio. Three ANNs with one input, one hidden and one output layer were built. The ANN had 5, 10, and 20 hidden neurons, respectively. The dataset was divided into three parts: 70% for training, 15% for validation, and 15% for testing. The mean square error (MSE) error and regression were analyzed during training. Training, testing, and validation error analysis were performed to find the optimal iteration, and the MSE versus training iteration was plotted. The best indicators of training and construction were shown by the ANN with 5 (five) hidden layers, and 16 iterations are enough to train, test and verify this neural network. To test the obtained neural network, the program code was written in the MATLAB. The proposed scheme of the gas analyzer is operable and has a high accuracy of gas detection with a given error of 3%. The results of the study can be used in the development of an industrial gas analyzer for the detection of harmful gas mixtures

The Mu2e crystal calorimeter

The Mu2e Experiment at Fermilab will search for coherent, neutrino-less conversion of negative muons into electrons in the field of an Aluminum nucleus, μ− + Al → e− +Al. Data collection start is planned for the end of 2021. The dynamics of such charged lepton flavour violating (CLFV) process is well modelled by a two-body decay, resulting in a mono-energetic electron with an energy slightly below the muon rest mass. If no events are observed in three years of running, Mu2e will set an upper limit on the ratio between the conversion and the capture rates R_μe = μ− + A(Z,N) → e− + A(Z,N)/μ− + A(Z,N) → ν_μ− + A(Z−1,N) of ≤ 6 × 10^(−17) (@ 90% C.L.). This will improve the current limit of four order of magnitudes with respect to the previous best experiment. Mu2e complements and extends the current search for μ → e γ decay at MEG as well as the direct searches for new physics at the LHC. The observation of such CLFV process could be clear evidence for New Physics beyond the Standard Model. Given its sensitivity, Mu2e will be able to probe New Physics at a scale inaccessible to direct searches at either present or planned high energy colliders. To search for the muon conversion process, a very intense pulsed beam of negative muons (~ 10^(10) μ/sec) is stopped on an Aluminum target inside a very long solenoid where the detector is also located. The Mu2e detector is composed of a straw tube tracker and a CsI crystals electromagnetic calorimeter. An external veto for cosmic rays surrounds the detector solenoid. In 2016, Mu2e has passed the final approval stage from DOE and has started its construction phase. An overview of the physics motivations for Mu2e, the current status of the experiment and the required performances and design details of the calorimeter are presented

Enhancing cryptographic protection, authentication, and authorization in cellular networks: a comprehensive research study

This research article provides an extensive analysis of novel methods of cryptographic protection as well as advancements in authentication and authorization techniques within cellular networks. The aim is to explore recent literature and identify effective authentication and authorization methods, including high-speed data encryption. The significance of this study lies in the growing need for enhanced data security in scientific research. Therefore, the focus is on identifying suitable authentication and authorization schemes, including blockchain-based approaches for distributed mobile cloud computing. The research methodology includes observation, comparison, and abstraction, allowing for a comprehensive examination of advanced encryption schemes and algorithms. Topics covered in this article include multi-factor authentication, continuous authentication, identity-based cryptography for vehicle-to-vehicle (V2V) communication, secure blockchain-based authentication for fog computing, internet of things (IoT) device mutual authentication, authentication for wireless sensor networks based on blockchain, new secure authentication schemes for standard wireless telecommunications networks, and the security aspects of 4G and 5G cellular networks. Additionally, in the paper a differentiated authentication mechanism for heterogeneous 6G networks blockchain-based is discussed. The findings presented in this article hold practical value for organizations involved in scientific research and information security, particularly in encryption and protection of sensitive data