Search results for "Python"
showing 10 items of 160 documents
Left-to-right tree pattern matching
1991
We propose a new technique to construct left-to-right matching automata for trees. Our method is based on the novel concept of prefix unifcation which is used to compute a certain closure of the pattern set. From the closure a kind of deterministic matching automaton can be derived immediately. We also point out how to perform the construction incrementally which makes our approach suitable for applications in which pattern sets change dynamically, such as in the Knuth-Bendix completion algorithm.
Design of Multiresolution Operators Using Statistical Learning Tools: Application to Compression of Signals
2012
Using multiresolution based on Harten's framework [J. Appl. Numer. Math., 12 (1993), pp. 153---192.] we introduce an alternative to construct a prediction operator using Learning statistical theory. This integrates two ideas: generalized wavelets and learning methods, and opens several possibilities in the compressed signal context. We obtain theoretical results which prove that this type of schemes (LMR schemes) are equal to or better than the classical schemes. Finally, we compare traditional methods with the algorithm that we present in this paper.
Suffix array and Lyndon factorization of a text
2014
Abstract The main goal of this paper is to highlight the relationship between the suffix array of a text and its Lyndon factorization. It is proved in [15] that one can obtain the Lyndon factorization of a text from its suffix array. Conversely, here we show a new method for constructing the suffix array of a text that takes advantage of its Lyndon factorization. The surprising consequence of our results is that, in order to construct the suffix array, the local suffixes inside each Lyndon factor can be separately processed, allowing different implementative scenarios, such as online, external and internal memory, or parallel implementations. Based on our results, the algorithm that we prop…
FINDUS: An Open-Source 3D Printable Liquid-Handling Workstation for Laboratory Automation in Life Sciences
2020
3D-printed laboratory devices can enable ambitious research purposes even at a low-budget level. To follow this trend, here we describe the construction, calibration, and usage of the FINDUS (Fully Integrable Noncommercial Dispensing Utility System). We report the successful 3D printing and assembly of a liquid-handling workstation for less than $400. Using this setup, we achieve reliable and flexible liquid-dispensing automation with relative pipetting errors of less than 0.3%. We show our system is well suited for several showcase applications from both the biology and chemistry fields. In support of the open-source spirit, we make all 3D models, assembly instructions, and source code ava…
Daudzspēlētāju kāršu spēle
2015
Kvalifikācijas darbs apraksta kāršu spēles izveidi. Spēles funkcionalitāte ietver lokālu spēli, datora pretinieku. Spēles aprakstā ietilpst daudzspēlētāju režīma projektējums. Programmai ir grafiska lietotāja saskarne. Tā veidota python programmēšanas valodā, ar pyglet grafiskās bibliotēkas palīdzību. Darbs sevī ietver spēles dokumentāciju un pašu programmu.
Vector coherent states and intertwining operators
2009
In this paper we discuss a general strategy to construct vector coherent states of the Gazeau-Klauder type and we use them to built up examples of isospectral hamiltonians. For that we use a general strategy recently proposed by the author and which extends well known facts on intertwining operators. We also discuss the possibility of constructing non-isospectral hamiltonians with related eigenstates.
Introducing libeemd: a program package for performing the ensemble empirical mode decomposition
2016
The ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN) are adaptive, noise-assisted data analysis methods that improve on the ordinary empirical mode decomposition (EMD). All these methods decompose possibly nonlinear and/or nonstationary time series data into a finite amount of components separated by instantaneous frequencies. This decomposition provides a powerful method to look into the different processes behind a given time series data, and provides a way to separate short time-scale events from a general trend. We present a free software implementation of EMD, EEMD and CEEMDAN and give an overview of the EMD methodology and the algorithms used in the deco…
Weak pseudo-bosons
2020
We show how the notion of {\em pseudo-bosons}, originally introduced as operators acting on some Hilbert space, can be extended to a distributional settings. In doing so, we are able to construct a rather general framework to deal with generalized eigenvectors of the multiplication and of the derivation operators. Connections with the quantum damped harmonic oscillator are also briefly considered.
NeoFox: annotating neoantigen candidates with neoantigen features
2020
Abstract Summary The detection and prediction of true neoantigens is of great importance for the field of cancer immunotherapy. Wesearched the literature for proposed neoantigen features and integrated them into a toolbox called NEOantigen Feature toolbOX (NeoFox). NeoFox is an easy-to-use Python package that enables the annotation of neoantigen candidates with 16 neoantigen features. Availability and implementation NeoFox is freely available as an open source Python package released under the GNU General Public License (GPL) v3 license at https://github.com/TRON-Bioinformatics/neofox. Supplementary information Supplementary data are available at Bioinformatics online.
Employing artificial neural networks to find reaction coordinates and pathways for self-assembly
2021
Capturing the autonomous self-assembly of molecular building blocks in computer simulations is a persistent challenge, requiring to model complex interactions and to access long time scales. Advanced sampling methods allow to bridge these time scales but typically require to construct accurate low-dimensional representations of the transition pathways. In this work, we demonstrate for the self-assembly of two single-stranded DNA fragments into a ring-like structure how autoencoder architectures based on unsupervised neural networks can be employed to reliably expose transition pathways and to provide a suitable low-dimensional representation. The assembly occurs as a two-step process throug…