In the paper, the study of the carbon footprint (CF) assessment in the frozen vegetable production processes is shown in order to receive low-carbon products. Three methods of clusterization have been chosen for the p...
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The aim of the current paper is to apply Bambi’s method (Bambi, 2015) to a source which contains two or more simultaneous triads of variability components. The joint χ2 variable that can be composed in this case, un...
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Federated learning is a machine learning method that allows models to be trained on decentralised data without participants sharing it with a central server. Federated learning can be modelled using Petri nets, mathem...
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ISBN:
(数字)9798350350159
ISBN:
(纸本)9798350350166
Federated learning is a machine learning method that allows models to be trained on decentralised data without participants sharing it with a central server. Federated learning can be modelled using Petri nets, mathematical models for sequence sequences. Devices train on their own data and send model updates to a central server. The central server averages the updates and returns the global model to the devices. The devices then update their local models using the global model.
MSC Codes 83C57 (Primary) 81P45 (Secondary)A general structure of unitary evolution (evaporation) of the black hole, respecting causality imposed by the event horizon (semicausality), has been derived and presented in...
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Novae were discovered to emit transient γ rays during the period of several days to a few weeks after initial explosion, indicating presence of acceleration processes of particles in their expanding shells. In the ca...
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The present study investigates the usage of a novel natural dye derived from red algae of Morocco in dye-sensitized solar cells (DSSCs) for the first time. The main pigments responsible for sensitizing the semiconduct...
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It is important to understand the dynamics of red blood cells (RBCs) in blood flow. This requires the formulation of coarse-grained RBC models that reproduce the hydrodynamic properties of blood accurately. One of the...
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We propose a new technique for creating a space-efficient index for large repetitive text collections, such as pangenomic databases containing sequences of many individuals from the same species. We combine two recent...
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We propose a new technique for creating a space-efficient index for large repetitive text collections, such as pangenomic databases containing sequences of many individuals from the same species. We combine two recent techniques from this area: Wheeler graphs (Gagie et al., 2017) and prefix-free parsing (PFP, Boucher et al., 2019). Wheeler graphs are a general framework encompassing several indexes based on the Burrows-Wheeler transform (BWT), such as the FM-index. Wheeler graphs admit a succinct representation which can be further compacted by employing the idea of tunnelling, which exploits redundancies in the form of parallel, equally-labelled paths called blocks that can be merged into a single path. The problem of finding the optimal set of blocks for tunnelling, i.e. the one that minimizes the size of the resulting Wheeler graph, is known to be NP-complete and remains the most computationally challenging part of the tunnelling process. To find an adequate set of blocks in less time, we propose a new method based on the prefix-free parsing (PFP). The idea of PFP is to divide the input text into phrases of roughly equal sizes that overlap by a fixed number of characters. The phrases are then sorted lexicographically. The original text is represented by a sequence of phrase ranks (the parse) and a list of all used phrases (the dictionary). In repetitive texts, the PFP representation of the text is generally much shorter than the original since individual phrases are used many times in the parse, thus reducing the size of the dictionary. To speed up the block selection for tunnelling, we apply the PFP to obtain the parse and the dictionary of the original text, tunnel the Wheeler graph of the parse using existing heuristics and subsequently use this tunnelled parse to construct a compact Wheeler graph of the original text. Compared with constructing a Wheeler graph from the original text without PFP, our method is much faster and uses less memory on collections of
We analyze recent approaches to quantum Markovianity and how they relate to the proper definition of quantum memory. We show that the well-known criterion of information backflow does not report a distinction between ...
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We analyze recent approaches to quantum Markovianity and how they relate to the proper definition of quantum memory. We show that the well-known criterion of information backflow does not report a distinction between the quantum or classical character of observed memory effects. Therefore, as a complement to the current description, we propose a definition of a quantum backflow based on the concept of component dynamical maps and convexity arguments. In particular, for a natural choice of component maps, we provide a practical characterization of quantum memory in terms of the new concept of quantum memory witness. In particular, we give a full description of such witnesses in a qubit case. Finally, we discuss a potential generalization of component maps that should capture certain classical features of information backflow.
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