Zobrazeno 1 - 10
of 17 375
pro vyhledávání: '"Ivan, V."'
Autor:
Besaga, Vira R., Lopushenko, Ivan V., Sieryi, Oleksii, Bykov, Alexander, Setzpfandt, Frank, Meglinski, Igor
We explore quantum-based optical polarimetry as a potential diagnostic tool for biological tissues by developing a theoretical and experimental framework to understand polarization-entangled photon behavior in scattering media. We investigate the mat
Externí odkaz:
http://arxiv.org/abs/2411.06134
Autor:
Dudinets, Ivan V., Lychkovskiy, Oleg
We study a quantum system that consists of two fermionic chains coupled by a driven quantum point contact (QPC). The QPC contains a bond with a periodically varying tunneling amplitude. Initially the left chain is packed with fermions while the right
Externí odkaz:
http://arxiv.org/abs/2411.04982
Autor:
Vikhorev, Alexandr V., Rempel, Michael M., Polesskaya, Oksana O., Savelev, Ivan V., Myakishev-Rempel, Max V.
Transposable elements (TEs) constitute a significant portion of eukaryotic genomes, yet their role in chromatin organization remains poorly understood. This study investigates the distribution patterns of TEs around chromatin ligation points (LPs) id
Externí odkaz:
http://arxiv.org/abs/2408.11079
Autor:
Hadizadeh, Hadi, Bajić, Ivan V.
Autonomous driving sensors generate an enormous amount of data. In this paper, we explore learned multimodal compression for autonomous driving, specifically targeted at 3D object detection. We focus on camera and LiDAR modalities and explore several
Externí odkaz:
http://arxiv.org/abs/2408.08211
Optimal transport has been used to define bijective nonlinear transforms and different transport-related metrics for discriminating data and signals. Here we briefly describe the advances in this topic with the main applications and properties in eac
Externí odkaz:
http://arxiv.org/abs/2406.15503
Autor:
Ulhaq, Mateen, Bajić, Ivan V.
The entropy bottleneck introduced by Ball\'e et al. is a common component used in many learned compression models. It encodes a transformed latent representation using a static distribution whose parameters are learned during training. However, the a
Externí odkaz:
http://arxiv.org/abs/2406.13059
We review the formalism of center-of-mass tomograms that allows us to describe quantum states in terms of probability distribution functions. We introduce the concept of separable and entangled probability distributions for the center-of-mass tomogra
Externí odkaz:
http://arxiv.org/abs/2406.06778
Recent advancements in decentralized learning, such as Federated Learning (FL), Split Learning (SL), and Split Federated Learning (SplitFed), have expanded the potentials of machine learning. SplitFed aims to minimize the computational burden on indi
Externí odkaz:
http://arxiv.org/abs/2405.19453
In recent years, there has been a significant increase in applications of multimodal signal processing and analysis, largely driven by the increased availability of multimodal datasets and the rapid progress in multimodal learning systems. Well-known
Externí odkaz:
http://arxiv.org/abs/2405.12456
Autor:
Alvar, Saeed Ranjbar, Bajić, Ivan V.
Deep models produce a number of features in each internal layer. A key problem in applications such as feature compression for remote inference is determining how important each feature is for the task(s) performed by the model. The problem is especi
Externí odkaz:
http://arxiv.org/abs/2405.09077