Neural Monkey: The Current State and Beyond

Neural Monkey is an open-source toolkit for sequence-to-sequence learning. The focus of this paper is to present the current state of the toolkit to the intended audience, which includes students and researchers, both active in the deep learning community and newcomers. For each of these target groups, we describe the most relevant features of the toolkit, including the simple configuration scheme, methods of model inspection that promote useful intuitions, or a modular design for easy prototyping. We summarize relevant contributions to the research community which were made using this toolkit and discuss the characteristics of our toolkit with respect to other existing systems. We conclude with a set of proposals for future development

Neural Monkey: The Current State and Beyond

Neural Monkey is an open-source toolkit for sequence-to-sequence learning. The focus of this paper is to present the current state of the toolkit to the intended audience, which includes students and researchers, both active in the deep learning community and newcomers. For each of these target groups, we describe the most relevant features of the toolkit, including the simple configuration scheme, methods of model inspection that promote useful intuitions, or a modular design for easy prototyping. We summarize relevant contributions to the research community which were made using this toolkit and discuss the characteristics of our toolkit with respect to other existing systems. We conclude with a set of proposals for future development

Méthodes d'apprentissage profond pour le transfert de style musical

Récemment, les méthodes d'apprentissage profond ont permis d'effectuer des transformations du matériel musical basées sur les données (data-driven). L'objet de cette thèse est le transfert de style musical, dont le but est de transférer de manière automatique le style d'un morceau à un autre. Dans la première partie de ce travail, nous nous concentrons sur les méthodes supervisées pour le transfert de style des accompagnements dans une représentation symbolique, visant à transformer un morceau donné en lui générant un nouvel accompagnement. La méthode proposée est basée sur l'apprentissage supervisé de séquence à séquence à l'aide de réseaux de neurones récurrents (RNN) et s'appuie sur une base de données synthétiques parallèle (alignée par paires) générée à cet effet à l'aide d'un logiciel de génération d'accompagnement existant. Nous proposons ainsi un ensemble de mesures objectives pour évaluer la performance sur cette nouvelle tâche et nous montrons que le système réussit à générer un accompagnement dans le style souhaité tout en suivant la structure harmonique de l'entrée. Dans la deuxième partie, nous étudions une question plus fondamentale : le rôle des encodages positionnels (PE) dans la génération de musique à l'aide des Transformers. Nous proposons l'encodage positionnel stochastique (SPE), un nouveau PE capable de coder des positions relatives et compatible avec une classe récemment proposée de Transformers efficaces. Nous démontrons que le SPE permet, mieux que la méthode conventionnelle (le PE absolu), de modéliser des séquences plus longues que celles rencontrées pendant l'entraînement.Enfin, dans la troisième partie, nous passons de la musique symbolique à l'audio et abordons le problème du transfert de timbre. Plus précisément, nous nous intéressons à transférer le timbre d'un enregistrement audio à un autre, tout en préservant le contenu mélodique et harmonique de ce dernier. Nous présentons une nouvelle méthode pour cette tâche, basée sur une extension de l'autoencodeur variationnel quantifié (VQ-VAE), ainsi qu'une stratégie d'apprentissage auto-supervisé conçue pour obtenir des représentations démêlées du timbre et de la hauteur. Comme dans la première partie, nous concevons un ensemble de métriques objectives pour la tâche. Nous montrons que la méthode proposée est capable de surpasser des méthodes existantes.Recently, deep learning methods have enabled transforming musical material in a data-driven manner. The focus of this thesis is on a family of tasks which we refer to as (one-shot) music style transfer, where the goal is to transfer the style of one musical piece or fragment onto another.In the first part of this work, we focus on supervised methods for symbolic music accompaniment style transfer, aiming to transform a given piece by generating a new accompaniment for it in the style of another piece. The method we have developed is based on supervised sequence-to-sequence learning using recurrent neural networks (RNNs) and leverages a synthetic parallel (pairwise aligned) dataset generated for this purpose using existing accompaniment generation software. We propose a set of objective metrics to evaluate the performance on this new task and we show that the system is successful in generating an accompaniment in the desired style while following the harmonic structure of the input.In the second part, we investigate a more basic question: the role of positional encodings (PE) in music generation using Transformers. In particular, we propose stochastic positional encoding (SPE), a novel form of PE capturing relative positions while being compatible with a recently proposed family of efficient Transformers.We demonstrate that SPE allows for better extrapolation beyond the training sequence length than the commonly used absolute PE.Finally, in the third part, we turn from symbolic music to audio and address the problem of timbre transfer. Specifically, we are interested in transferring the timbre of an audio recording of a single musical instrument onto another such recording while preserving the pitch content of the latter. We present a novel method for this task, based on an extension of the vector-quantized variational autoencoder (VQ-VAE), along with a simple self-supervised learning strategy designed to obtain disentangled representations of timbre and pitch. As in the first part, we design a set of objective metrics for the task. We show that the proposed method is able to outperform existing ones

A vízjogi engedélyezés automatizálhatóságának vizsgálata öntözési projektek vízjogi engedélyezési folyamatain keresztül.

A diplomadolgozatban megvizsgált probléma az öntözési vízjogi engedélyezés automatizációjának vizsgálata. A felvázolt kérdésre, hogy az engedélyezési folyamat mennyire automatizálható a felhasználó oldal tekintetében, részletesen kifejtve egyértelmű választ: részeiben automatizálható. Az általam megadott megoldási javaslatokkal és felvázolt egyéb beavatkozásokkal a VIZEK rendszer működése hosszú távon javítható, a folyamatok később tovább fejleszthetők és ezzel mind munkaerőgazdálkodási mind gazdasági szempontból kisebb terheléssel végezhetők el a növekvő feladatok. A felvázolt automatizálások megteremthetik az utat az emberi beavatkozás nélkül végrehajtható engedélyhosszabbítási folyamatok kialakítása felé, mellyel már egy, a jelenleginél jóval magasabb digitalizációs nívót érhetük el és olyan új technológiának nyitunk kaput mind az (AI) mesterséges értelem, blokklánc vagy deep learning.Mezőgazdasági vízgazdálkodási mérnökMSc/M

Radiolysis of 131 I‐O‐iodohippurate: A kinetic study

Although radiolytic decomposition of several radiopharmaceuticals has exhibited apparent first‐order kinetics, the dependence of decomposition on drug concentration precludes a first‐order rate process. A kinetic study of the decomposition of 131 I‐o‐iodohippurate in aqueous solution was carried out to accurately assess the dependence of radiopharmaceutical decomposition on absorbed radiation dose, initial radiopharmaceutical concentration and, in addition, on the concentration of added free radical inhibitors. The fraction of 131 I‐o‐iodohippurate remaining, (A/A o ), was found to decrease exponentially with increased absorbed radiation dose. The radiolytic decomposition rate, however, exhibited a Langmiurian relationship with respect to the initial concentration of the radiopharmaceutical present (A o ) and the initial concentration of free‐radical inhibitor present (I o ). was derived to characterize these dependencies and demonstrated good correlation with experimental results. This equation may be of value in assessing the radiolytic stability of other radiopharmaceuticals.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90323/1/2580170515_ftp.pd

Working within the system: teachers of English learners negotiating a literacy instruction mandate

In an effort to reverse the reading crisis purported to plague public education, schools and districts are mandating prescriptive reading programs and teacher-centered instructional practices in hopes of improving the academic achievement of minority students, including English learners (ELs). The wide-spread implementation of these programs in schools and classrooms serving ELs is particularly striking in California, where there are large numbers of ELs, as these programs were developed for monolingual, English-speaking children, not ELs. Drawing on interviews with 32 teachers in four Northern California elementary schools serving primarily ELs from Latino backgrounds, we found that most teachers required to use one such program, Open Court Reading (OCR), did not think that it addressed the needs of ELs or tapped into their interests and/or understandings. That is, the top-down, one-size-fits-all policy mandate was not grounded in an understanding of ELs’ language and literacy instructional needs. In light of our findings, we support policies that enable teachers to provide quality instruction that addresses the needs, interests, and understandings of all students, particularly ELs, who are often the, most underserved. This includes policies that promote the development of reflective, inquiring, and knowledgeable teachers who, in collaboration with colleagues and other educational stakeholders, play a key role in the policy making process