Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory

Split learning is a privacy-preserving distributed learning paradigm in which an ML model (e.g., a neural network) is split into two parts (i.e., an encoder and a decoder). The encoder shares so-called latent representation, rather than raw data, for model training. In mobile-edge computing, network functions (such as traffic forecasting) can be trained via split learning where an encoder resides in a user equipment (UE) and a decoder resides in the edge network. Based on the data processing inequality and the information bottleneck (IB) theory, we present a new framework and training mechanism to enable a dynamic balancing of the transmission resource consumption with the informativeness of the shared latent representations, which directly impacts the predictive performance. The proposed training mechanism offers an encoder-decoder neural network architecture featuring multiple modes of complexity-relevance tradeoffs, enabling tunable performance. The adaptability can accommodate varying real-time network conditions and application requirements, potentially reducing operational expenditure and enhancing network agility. As a proof of concept, we apply the training mechanism to a millimeter-wave (mmWave)-enabled throughput prediction problem. We also offer new insights and highlight some challenges related to recurrent neural networks from the perspective of the IB theory. Interestingly, we find a compression phenomenon across the temporal domain of the sequential model, in addition to the compression phase that occurs with the number of training epochs.Comment: Accepted to Proc. IEEE Globecom 202

Influence of Initial Conditions on Optical Characteristics of Cu Ion-exchanged Glasses

In the present article, we report the results of a study of the optical and spectroscopic properties of Cu ion-exchanged glasses. The well-known ion-exchange method is used and developed for the specific present work. A soda-lime glass plate is doped ionic copper nanoparticles. It is found that the initial temperature and the specific combinations of the glass substrates have essential roles in the physical properties of the produced doped glasses. Therefore, we made two different types of copper-ion-exchanged glasses: green and red colored ones. Each type has its unique optical and spectroscopic properties due to the initial conditions of the ion-exchange process. Using absorption spectroscopy, and surface plasmon resonance studies, we can conclude that the different initial temperature of the samples in the ion-exchange procedure crucially influences the color of the samples, their characteristic index of refraction, photoluminescence, and the reflection spectra. The color of the samples is related to the type and shape of the ionic Cu-clusters, formed in the glass matrix

Evolution of Optoelectronic and Texture Properties

We show a simple room temperature surface functionalization approach using iodine vapour to control a surface phase transition from cubic silver (Ag) of thin films into wurtzite silver-iodid (β-AgI) films. A combination of surface characterization techniques (optical, electronical and structural characterization) reveal distinct physical properties of the new surface phase. We discuss the AgI thin film formation dynamics and related transformation of physical properties by determining the work-function, dielectric constant and pyroelectric behavior together with morphological and structural thin film properties such as layer thickness, grain structure and texture formation. Notable results are: (i) a remarkable increase of the work- function (by 0.9 eV) of the Ag thin layer after short a iodine exposure time (≤60 s), with simultaneous increase of the thin film transparency (by two orders of magnitude), (ii) pinning of the Fermi level at the valance band maximum upon iodine functionalization, (iii) 84% of all crystallites grain were aligned as a result of the evolution of an internal electric field. Realizing a nano-scale layer stack composed of a dielectric AgI layer on top of a metallic thin Ag layer with such a simple method has some technological implications e.g. to realize optical elements such as planar optical waveguides

An introduction to lithography methods and providing a practical method for its optimization

In the first part of the present article, the important and main methods of photolithography are reviewed and discussed. Then we introduce the ways to improve the images created on the photoresist, which is the main material of lithography. Lithography with high-energy particle and soft lithography are then described. In the second part, the contact-photolithography method and its improvement process, which we use in our laboratory, are introduced and described in detail. We used this method for lithography to make diffraction optical elements on a glass substrate, doped by silver nanoparticles, using a helium ion beam. Light diffraction from the created lithography masks prevents access to very small images. To reduce the diffraction influence on the quality of the produced elements, we adapted and optimized the contact-lithography method for our project. Our solution, presented in this article, is practical and available for other researchers in Ira