This paper proposes an image-to-painting translation method that generates vivid and realistic painting artworks with controllable styles. Different from previous image-to-image translation methods that formulate the translation as pixel-wise prediction, we deal with such an artistic creation process in a vectorized environment and produce a sequence of physically meaningful stroke parameters that can be further used for rendering… Since a typical vector render is not differentiable, we design a novel neural renderer which imitates the behavior of the vector renderer […]

Deep learned models are now largely adopted in different fields, and they generally provide superior performances with respect to classical signal-based approaches. Notwithstanding this, their actual reliability when working in an unprotected environment is far enough to be proven… In this work, we consider a novel deep neural network architecture, named Neural Ordinary Differential Equations (N-ODE), that is getting particular attention due to an attractive property — a test-time tunable trade-off between accuracy and efficiency. This paper analyzes the robustness […]

Prostate radiotherapy is a well established curative oncology modality, which in future will use Magnetic Resonance Imaging (MRI)-based radiotherapy for daily adaptive radiotherapy target definition. However the time needed to delineate the prostate from MRI data accurately is a time consuming process… Deep learning has been identified as a potential new technology for the delivery of precision radiotherapy in prostate cancer, where accurate prostate segmentation helps in cancer detection and therapy. However, the trained models can be limited in their […]

This work describes a concise algorithm for the generation of triangular meshes with the help of standard adaptive finite element methods. We demonstrate that a generic adaptive finite element solver can be repurposed into a triangular mesh generator if a robust mesh smoothing algorithm is applied between the mesh refinement steps… We present an implementation of the mesh generator and demonstrate the resulting meshes via several examples. (read more) PDF Abstract  

Computational load imbalance due to direct integration of chemical kinetics is a well-known performance issue in parallel reacting flow simulations. We introduce an open-source dynamic load balancing model to address this problem within OpenFOAM, an open-source C++ library for Computational Fluid Dynamics (CFD)… Due to the commonly applied operator splitting practice in reactive flow solvers, chemistry can be treated as an independent stiff ordinary differential equation (ODE) system within each computational cell. As a result of highly non-linear characteristics of […]

Optimization methods (optimizers) get special attention for the efficient training of neural networks in the field of deep learning. In literature there are many papers that compare neural models trained with the use of different optimizers… Each paper demonstrates that for a particular problem an optimizer is better than the others but as the problem changes this type of result is no longer valid and we have to start from scratch. In our paper we propose to use the combination […]

This paper provides a survey of methods and tools for automated code-reuse exploit generation. Such exploits use code that is already contained in a vulnerable program… The code-reuse approach allows one to exploit vulnerabilities in the presence of operating system protection that prohibits data memory execution. This paper contains a description of various code-reuse methods: return-to-libc attack, return-oriented programming, jump-oriented programming, and others. We define fundamental terms: gadget, gadget frame, gadget catalog. Moreover, we show that, in fact, a gadget […]

This paper is dedicated to simulation of so-called trapping-effect observed in Troitsk nu-mass experiment. The effect is caused by magnetic trapping of decay electrons in the windowless gaseous tritium source and the gradual of evaporation of those electrons… As a result, alongside regular tritium beta-spectrum electrons, we see additional electrons that are initially trapped in the source and escape it with changed energy. The spectrum of evaporated electrons is quite peculiar and could not be directly measured in the experiment. […]

We introduce a randomized algorithm, namely {tt rchol}, to construct an approximate Cholesky factorization for a given sparse Laplacian matrix (a.k.a., graph Laplacian). The (exact) Cholesky factorization for the matrix introduces a clique in the associated graph after eliminating every row/column… By randomization, {tt rchol} samples a subset of the edges in the clique. We prove {tt rchol} is breakdown free and apply it to solving linear systems with symmetric diagonally-dominant matrices. In addition, we parallelize {tt rchol} based on […]

The recently introduced locally orderless tensor network (LoTeNet) for supervised image classification uses matrix product state (MPS) operations on grids of transformed image patches. The resulting patch representations are combined back together into the image space and aggregated hierarchically using multiple MPS blocks per layer to obtain the final decision rules… In this work, we propose a non-patch based modification to LoTeNet that performs one MPS operation per layer, instead of several patch-level operations. The spatial information in the input […]