arXiv subjects

Anton Ivanov

Publications and source records attributed to Anton Ivanov.

10 recordsLinked to original sources

arXiv2026

Comment on the preprint: First Limits on Axion Dark Matter from a DALI Prototype arXiv:2603.21951

We report on an independent assessment of the sensitivity of the DALI setup to axion-like particle (ALP) dark matter, as described in [1]. Our analysys is based on the information provided in the preprint and using the formalism developed within the MADMAX collaboration. We do not identify any mode below 12 GHz with significant coupling to the axion field to account for the reported sensitivity. We provide recommendations for assessing the axion coupling of electromagnetic modes in dielectric haloscope experiments.

hep-ex↗

arXiv2026

Asteroid Mining to Sustain a Mars Colony: A Logistics Point of View

Asteroid mining can become an enabling technology to establish a sustainable manned colony on Mars, which requires metallic materials more often than they are readily available in shipments from Earth. This paper describes a feasibility study of a supply chain that delivers metals extracted from metallic asteroids to Mars. The asteroids are selected to respect the $\Delta V$ limits imposed by up-to-date spacecraft. The study is conducted with reference to the state of the art in space transportation technologies and in-situ resource utilization. A possibility for mining on carbonaceous asteroids to produce the propellant required for return trips is also taken into account. Different supply chains are computed through a multi-objective optimization routine that considers the mission $\Delta V$, the mass of extracted metals and the mass of propellant produced on the asteroids. Schedules to visit the asteroids within reach are obtained and the total mass of the delivered material is evaluated for various mining rates. Finally, the use of the metallic material to build habitats and rovers on the Martian soil through additive manufacturing is discussed.

astro-ph.IM↗

arXiv2023

Just-in-Time Code Duplicates Extraction

Refactoring is a critical task in software maintenance, and is usually performed to enforce better design and coding practices, while coping with design defects. The Extract Method refactoring is widely used for merging duplicate code fragments into a single new method. Several studies attempted to recommend Extract Method refactoring opportunities using different techniques, including program slicing, program dependency graph analysis, change history analysis, structural similarity, and feature extraction. However, irrespective of the method, most of the existing approaches interfere with the developer's workflow: they require the developer to stop coding and analyze the suggested opportunities, and also consider all refactoring suggestions in the entire project without focusing on the development context. To increase the adoption of the Extract Method refactoring, in this paper, we aim to investigate the effectiveness of machine learning and deep learning algorithms for its recommendation while maintaining the workflow of the developer. The proposed approach relies on mining prior applied Extract Method refactorings and extracting their features to train a deep learning classifier that detects them in the user's code. We implemented our approach as a plugin for IntelliJ IDEA called AntiCopyPaster. To develop our approach, we trained and evaluated various popular models on a dataset of 18,942 code fragments from 13 Open Source Apache projects. The results show that the best model is the Convolutional Neural Network (CNN), which recommends appropriate Extract Method refactorings with an F-measure of 0.82. We also conducted a qualitative study with 72 developers to evaluate the usefulness of the developed plugin. The results show that developers tend to appreciate the idea of the approach and are satisfied with various aspects of the plugin's operation.

cs.SE↗

arXiv2022

AntiCopyPaster: Extracting Code Duplicates As Soon As They Are Introduced in the IDE

We developed a plugin for IntelliJ IDEA called AntiCopyPaster, which tracks the pasting of code fragments inside the IDE and suggests the appropriate Extract Method refactoring to combat the propagation of duplicates. Unlike the existing approaches, our tool is integrated with the developer's workflow, and pro-actively recommends refactorings. Since not all code fragments need to be extracted, we develop a classification model to make this decision. When a developer copies and pastes a code fragment, the plugin searches for duplicates in the currently opened file, waits for a short period of time to allow the developer to edit the code, and finally inferences the refactoring decision based on a number of features. Our experimental study on a large dataset of 18,942 code fragments mined from 13 Apache projects shows that AntiCopyPaster correctly recommends Extract Method refactorings with an F-score of 0.82. Furthermore, our survey of 59 developers reflects their satisfaction with the developed plugin's operation. The plugin and its source code are publicly available on GitHub at https://github.com/JetBrains-Research/anti-copy-paster. The demonstration video can be found on YouTube: https://youtu.be/_wwHg-qFjJY.

cs.SE↗

arXiv2018

Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching

End-to-end deep-learning networks recently demonstrated extremely good perfor- mance for stereo matching. However, existing networks are difficult to use for practical applications since (1) they are memory-hungry and unable to process even modest-size images, (2) they have to be trained for a given disparity range. The Practical Deep Stereo (PDS) network that we propose addresses both issues: First, its architecture relies on novel bottleneck modules that drastically reduce the memory footprint in inference, and additional design choices allow to handle greater image size during training. This results in a model that leverages large image context to resolve matching ambiguities. Second, a novel sub-pixel cross- entropy loss combined with a MAP estimator make this network less sensitive to ambiguous matches, and applicable to any disparity range without re-training. We compare PDS to state-of-the-art methods published over the recent months, and demonstrate its superior performance on FlyingThings3D and KITTI sets.

cs.CV↗

arXiv2017

Geometric calibration of Colour and Stereo Surface Imaging System of ESA's Trace Gas Orbiter

There are many geometric calibration methods for "standard" cameras. These methods, however, cannot be used for the calibration of telescopes with large focal lengths and complex off-axis optics. Moreover, specialized calibration methods for the telescopes are scarce in literature. We describe the calibration method that we developed for the Colour and Stereo Surface Imaging System (CaSSIS) telescope, on board of the ExoMars Trace Gas Orbiter (TGO). Although our method is described in the context of CaSSIS, with camera-specific experiments, it is general and can be applied to other telescopes. We further encourage re-use of the proposed method by making our calibration code and data available on-line.

astro-ph.IM↗

arXiv2017

Extension of the Nakajima-Zwanzig approach to multitime correlation functions of open systems

We extend the Nakajima-Zwanzig projection operator technique to the determination of multitime correlation functions of open quantum systems. The correlation functions are expressed in terms of certain multitime homogeneous and inhomogeneous memory kernels for which suitable equations of motion are derived. We show that under the condition of finite memory times these equations can be used to determine the memory kernels by employing an exact stochastic unraveling of the full system-environment dynamics. The approach thus allows to combine exact stochastic methods, feasible for short times, with long-time master equation simulations. The applicability of the method is demonstrated by numerical simulations of 2D-spectra for a donor-acceptor model, and by comparison of the results with those obtained from the reduced hierarchy equations of motion. We further show that the formalism is also applicable to the time evolution of a periodically driven two-level system initially in equilibrium with its environment.

quant-ph↗

arXiv2017

Quantum corrections of the truncated Wigner approximation applied to an exciton transport model

We modify the path integral representation of exciton transport in open quantum systems such that an exact description of the quantum fluctuations around the classical evolution of the system is possible. As a consequence, the time evolution of the system observables is obtained by calculating the average of a stochastic difference equation which is weighted with a product of pseudo-probability density functions. From the exact equation of motion one can clearly identify the terms that are also present if we apply the truncated Wigner approximation. This description of the problem is used as a basis for the derivation of a new approximation, whose validity goes beyond the truncated Wigner approximation. To demonstrate this we apply the formalism to a donor-acceptor transport model.

quant-ph↗

arXiv2016

Semi-supervised learning of deep metrics for stereo reconstruction

Deep-learning metrics have recently demonstrated extremely good performance to match image patches for stereo reconstruction. However, training such metrics requires large amount of labeled stereo images, which can be difficult or costly to collect for certain applications. The main contribution of our work is a new semi-supervised method for learning deep metrics from unlabeled stereo images, given coarse information about the scenes and the optical system. Our method alternatively optimizes the metric with a standard stochastic gradient descent, and applies stereo constraints to regularize its prediction. Experiments on reference data-sets show that, for a given network architecture, training with this new method without ground-truth produces a metric with performance as good as state-of-the-art baselines trained with the said ground-truth. This work has three practical implications. Firstly, it helps to overcome limitations of training sets, in particular noisy ground truth. Secondly it allows to use much more training data during learning. Thirdly, it allows to tune deep metric for a particular stereo system, even if ground truth is not available.

cs.CV↗

arXiv2013

Bosonic transport through a chain of quantum dots

The particle transport through a chain of quantum dots coupled to two bosonic reservoirs is studied. For the case of reservoirs of non-interacting bosonic particles, we derive an exact set of stochastic differential equations, whose memory kernels and driving noise are characterised entirely by the properties of the reservoirs. Going to the Markovian limit an analytically solvable case is presented. The effect of interparticle interactions on the transient behaviour of the system, when both reservoirs are instantaneously coupled to an empty chain of quantum dots, is approximated by a semiclassical method, known as the Truncated Wigner approximation. The steady-state particle flow through the chain and the mean particle occupations are explained via the spectral properties of the interacting system.

cond-mat.mes-hall↗