A Universal RAM Machine Resistant to Isolated Bursts of Faults

The most natural question of reliable computation, in every computation model and noise model,is whether given a certain level of noise, a machine of that model exists that canperform arbitrarily complex computations under noise of that level. This question has positive answers for circuits, cellular automata, and recently for Turing machines. Here, we raise the question of the existence of a random access machine that---with some moderate slowdown --- can simulate any other random access machine even if the simulator is subjected to constant size bursts of faults separated by a certain minimum number of steps from each other. We will analyze and spell out the problems and difficulties that need to be addressed in such construction

Seismic Design Of Tunnels In Fault Zones

The tunnels due to their restrictions as a infrastructure work often overpass very disturbed tectonic zones. In those zones due to overthrust geological processes the rockquality are extremely poor in one side, and changes abruptly on the other side. These changes impose differential deformation on soil and tunnel linings. Especially for near faults tunnelswhere the directivity pulse and fling step phenomena plays an important role in the characterization of the seismic motion. This article gives the theoretical explanation and design consideration concerning the above mention problems. A numerical simulation whichis indented to study the behavior of the tunnel during this type of seismic events is presented.This example is taken from the design of a tunnel that shall be constructed in Albania

GAN-based matrix factorization for recommender systems

Proposed in 2014, Generative Adversarial Networks (GAN) initiated a fresh interest in generative modelling. They immediately achieved state-of-the-art in image synthesis, image-to-image translation, text-to-image generation, image inpainting and have been used in sciences ranging from medicine to high-energy particle physics. Despite their popularity and ability to learn arbitrary distributions, GAN have not been widely applied in recommender systems (RS). Moreover, only few of the techniques that have introduced GAN in RS have employed them directly as a collaborative filtering (CF) model. In this work we propose a new GAN-based approach that learns user and item latent factors in a matrix factorization setting for the generic top-N recommendation problem. Following the vector-wise GAN training approach for RS introduced by CFGAN, we identify 2 unique issues when utilizing GAN for CF. We propose solutions for both of them by using an autoencoder as discriminator and incorporating an additional loss function for the generator. We evaluate our model, GANMF, through well-known datasets in the RS community and show improvements over traditional CF approaches and GAN-based models. Through an ablation study on the components of GANMF we aim to understand the effects of our architectural choices. Finally, we provide a qualitative evaluation of the matrix factorization performance of GANMF.Comment: Accepted at the 37th ACM/SIGAPP Symposium on Applied Computing (SAC '22