uk: Комп'ютерні науки

description: For over a decade, there has been an open source map publication platform in the Netherlands, known as Tailormap (formerly Flamingo). That project is maintained largely by one company, B3Partners. Currently, Tailormap is being overhauled. Nah, not overhauled, I’d say completely rebuild. And this rebuild comes with a new approach on how to distribute this software project, and how to make it accessible to other developers to contribute, to organizations to roll out independently, and to other companies to use in their customer solutions. What we aim for in the long run is an online geospatial platform that is easy to use for all. For now, we publish an easy to install online GIS and mapviewing application, with features like mobile editing capabilities so that it can be used for maintenance purposes as well as for data dissemination. And here, at the FOSS4G in Firenze, we will celebrate this with our international launch presentation (and party in a not yet disclosed bar). We’d like to invite you to join us in this journey. We’re extremely happy to have been able to start this development without outside funding, and we’re looking for partners to grow Tailormap together. We’re currently looking into the OSGeo Community project program, to see if we can join. We’ll be at the B2B meeting as well, but this presentation is where we’ll show the goodies.

uk: вхід без авторизації

name: From a national map publication platform to infinity. And beyond, of course.


OpenLayers Feature Frenzy

Tim Schaub

type: Person

keywords: foss4g2022 generaltrack Stateofsoftware

cs: Informatika

de: Informatik

fi: tietojenkäsittelytiede

uk: Комп'ютерні науки

en: Computer Science

fr: Informatique

da: Datalogi

es: Ciencias de la Computación

nl: Informatica

cs: Technické vědy

de: Ingenieurwissenschaften

fi: tekniikka

uk: Інженерія

en: Engineering Sciences

fr: Ingénierie

da: Ingeniørvidenskab

es: Ingeniería

nl: Engineering

description: A decade ago, OpenLayers was the number one choice for a web mapping library. With the rewrite in 2012 and the rising competition of Leaflet and Mapbox GL JS, there was a phase when the project lost popularity. Today, it has found its niche as a full-featured, flexible, and high-performance geospatial JavaScript library that users can count on for the long haul, especially when their mapping needs get more complex. This talk will provide you with a tour of the latest features in the library, including daring live demonstrations. We will present our recent and ongoing work on adding new features and making the library more fun to work with. Whether you're a developer or decision maker, come to this talk to learn about the current status of OpenLayers. We’ll provide you with a glimpse into the future of the library and leave you motivated to get mapping with OpenLayers.

TIB AV-Portal

type: Service

type: WebContent

type: MediaObject

@language: en

cs: přihlášení není vyžadováno

de: keine Anmeldung erforderlich

uk: вхід без авторизації

en: no login required

nl: geen login vereist

type: Organization

name: Open Source Geospatial Foundation (OSGeo)

cs: Video

de: Video

fi: Video

uk: Відео

en: Video

fr: Vidéo

da: Video

nl: Video

es: Video


keywords:
Forschungsdatenmanagement
Data Wrangling
FDM
Skalenniveaus
Analysemethoden

uk: Бібліотекознавство, документознавство

en: Information and Library Sciences

de: Geisteswissenschaften

uk: Гуманітарні науки

en: Humanities

description: Das Video "Skalenniveaus. Eine kleine Entscheiungshilfe" erläutert das Konzept der Skalenniveaus in Zusammenhang mit dem Data Wrangling. Es wird beschrieben, welche Skalen- oder Messniveaus unterschiedlichen Arten von Daten zugeordnet werden können. Je nachdem welches Skalenniveau die Daten haben, können unterschiedliche Rechenoperationen durchgeführt werden.

@language: de

uk: вхід без авторизації

name: Skalenniveaus: Eine kleine Entscheidungshilfe


keywords:
Forschungsdatenmanagement
Data Wrangling
FDM
Reliabilität
Messgenauigkeit

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: Das Video "Reliabilität. Ein kleines (Küchen-)Beispiel" erläutert, welche Bedeutung die Stabilität eines Messinstrumentes für die Arbeit mit den daraus ermittelten Daten haben kann. Es wird beschrieben, wie sich die sogenannte Reliabilität von Messinstrumenten überprüfen und berechnen lässt.

uk: вхід без авторизації

name: Reliabilität: Ein kleines (Küchen-)Beispiel


Задачі близькості. Діаграма Вороного.

uk: Комп'ютерні науки

@language: uk

uk: вхід без авторизації

name: Taras Shevchenko National University of Kyiv

name: Projekt 'Open Education Resources with Ukraine'


Tabellenformate XLSX und CSV

keywords: Forschungsdatenmanagement Tabelle Dateiformat xlsx CSV

uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: Das Video stellt die Tabellenformate XLSX und CSV (Comma-separated values) kurz vor, erläutert den Aufbau von CSV und empfiehlt CSV für die Langzeitarchivierung.

uk: вхід без авторизації

Bergische Universität Wuppertal

addressCountry: DE

addressLocality: Wuppertal

addressRegion: North Rhine-Westphalia


uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: In a prior study (Cioffi & Peroni, 2022), we analysed the available reference extraction tools to understand their performances off-the-shelf – i.e. by using them as they have been configured, without prior training. We evaluated them against a corpus of 56 PDF articles (our gold standard) published in 27 subject areas (Computer Science, Arts and Humanities, Mathematics, etc.). From that analysis, we have identified the two most promising tools for bibliographic reference extraction and parsing, i.e. Anystyle and Grobid, which are CRF based. We want to extend such a study by training these two tools against the same gold standard used in the previous analysis to understand how much the performances improve. As a result, we are also going to revise the code used for testing and comparing the reference extraction software to make it available also for others to be reused for similar analysis. The final aim of this work would be to develop a reference extraction service which enables a user to provide a PDF of a scholarly article in input and to have, in return, citation data and bibliographic metadata from all the references that are cited by the given article in a format that enables their ingestion in OpenCitations (Peroni & Shotton, 2020). As a first step, a series of tests have been performed to check if the two software have been recently updated. During the testing phase, some differences emerged comparing the results obtained in the current study to the ones of (Cioffi & Peroni, 2022). With those differences in mind, we are going to modify the evaluation code (Cioffi, 2022) used in the prior study to adapt it to the current version of the tools. Afterwards, we will proceed with the training phase. To train Anystyle, the software documentation makes available a set of instructions for refining the training of the tool against a gold standard (ours, Cioffi, 2022). For what concerns GROBID, a series of instructions are available, too, for training the tool with specific data. The work is currently ongoing, thus in the presentation we will show the results obtained by mid-May. However, the next steps will consist in the evaluation of the trained versions of Anystyle and Grobid and the creation of a new gold standard, to compare the results of the evaluation and the training phases to the previous version of the standard. The results will be processed in order to integrate the data in the OpenCitations infrastructure. References Anystyle, https://archive.softwareheritage.org/swh:1:snp:92bc79eb31b8e7fd760c985aa62f313ced3976bf;origin=https://github.com/inukshuk/anystyle-cli. Cioffi, A. (2022). Data for Testing and Evaluating References Extraction and Parsing Tools (1.0). Zenodo. DOI:10.5281/zenodo.6182066. Cioffi, A. (2022). Code for converting different formats to TEI XML and evaluating results (1.0). Zenodo. DOI:10.5281/zenodo.6182128. Cioffi, A., & Peroni, S. (2022). Structured References from PDF Articles: Assessing the Tools for Bibliographic Reference Extraction and Parsing. In G. Silvello, O. Corcho, P. Manghi, G. M. Di Nunzio, K. Golub, N. Ferro, & A. Poggi (A c. Di), Linking Theory and Practice of Digital Libraries—26th International Conference on Theory and Practice of Digital Libraries, TPDL 2022, Padua, Italy, September 20–23, 2022, Proceedings (Vol. 13541, pp. 425–432). Springer International Publishing. DOI:10.1007/978-3-031-16802-4_42. Grobid, https://archive.softwareheritage.org/swh:1:snp:4f734b61d425809bfd1f1d8d7b8b160edf81ef2b;origin=https://github.com/kermitt2/grobid_client_python. Peroni, S., & Shotton, D. (2020). OpenCitations, an infrastructure organization for open scholarship. Quantitative Science Studies, 1(1), 428–444. DOI:10.1162/qss_a_00023.

uk: вхід без авторизації

name: Measuring the Performances of AnyStyle and Grobid against a Gold Standard

name: Max-Planck-Institut für Rechtsgeschichte und Rechtstheorie


de: Humanmedizin/Gesundheitswissenschaften

uk: Медицина / Науки про здоров'я

en: Human Medicine / Health Sciences

uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

uk: вхід без авторизації

name: Alle wollen Open Access, aber nicht alle setzen sich durch.

name: Deutsche Zentralbibliothek für Medizin (ZB MED)

addressLocality: Cologne


uk: Медицина / Науки про здоров'я

uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

uk: вхід без авторизації

name: OS-APS STEMO – medienneutrales Publizieren vor dem Hintergrund hin zu mehr Openness und Barrierefreiheit


OJS - Data privacy and Multilinguality

uk: Комп'ютерні науки

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: Data protection regulations like GDPR Privacy and Privacy Enhancing Technologies (PETs) are required to protect user rights for publishing platforms like Open Journal Systems (OJS). All stakeholders, from readers to journal managers must consent to private data collection. Consent related functions require explicit authorization for Data usage in OJS and in reviewing process. Journal consumer privacy should also be protected and respected, while ensuring that technical requirements remain intact to maintain smooth functionality. User creation and reviewer invitations must be revised and rewritten to support the requirements of the GDPR. The handling of cookies is another important area to be addressed. Institutional providers and small publishers have either neglected or used manual workflows to provide at least limited support for GDPR compliance, which may be unsustainable and less cost-effective. The weaknesses identified in the multilingual support of OJS are being thoroughly investigated and solutions are proposed and implemented. This proactive approach is geared towards ensuring smooth handling of multilingual content, enhancing interoperability and seamless synchronisation with external systems. By addressing these issues, we help journals to manage different languages efficiently and improve accessibility and usability across language boundaries. Eventhough OJS is the world's most widely used open source platform for publishing scholary and non-scholary content, there have been shortcomings and in some use-cases no built-in support for the two aspects mentioned above. In joint collaboration with PKP under European Grant CRAFT-OA, Technischerinformationsbibliothek and Federation of Finnish Learned Societies has taken the intiative to develop the above shortcomings for OJS. We are presenting the workflows, usability designs and the features developped during the project, which will be integrated to next OJS version 3.5. These commitments of european partners, underlines OJS' endeavour to meet the diverse needs of its users and promote a truly inclusive scholarly publishing environment.

uk: вхід без авторизації


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Метод групового урахування аргументів. Принципи та критерії.


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Еволюційні стратегії та мурашині алгоритми.


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Натурне 3D моделювання верхніх дихальних шляхів

name: Kharkiv National University of Radio Electronics (KNURE)


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Пошук і вимірювання відстані до ключової точки кадру за допомогою фреймворку ARCore


de: Biologie

uk: Біологія

en: Biology

de: Mathematik, Naturwissenschaften

uk: Математика, природничі науки

en: Mathematics, Natural Sciences

uk: вхід без авторизації

name: Ознайомлення з основами управління біотехнічними системами


Основи теорії управління. Лекція 1.

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Математика, природничі науки

uk: вхід без авторизації

name: Вступ до теорії керування. Біотехнічна система діагностики ольфактометричних порушень.


uk: Математика, природничі науки

uk: вхід без авторизації

name: Вступ до теорії керування. Біотехнічна система для дослідження магнітоакустичного впливу на модельовані ділянки біологічного середовища


uk: Математика, природничі науки

uk: вхід без авторизації

name: Вступ до теорії керування. Медичні системи візуалізації на прикладі ультразвукової діагностики.


keywords:
Open Access
Kumulative Dissertation
open.access-network
Promotion
CC-Lizenzen

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: Dieses Video dreht sich um die Frage, was Promovierende bei der Open-Access-Publikation ihrer kumulativen Dissertation beachten müssen. Es zeigt auf, wie Promovierende vorgehen können, wenn sie die Rechte an ihrem Text an einen Verlag übertragen haben und gibt praktische Tipps, an wen sie sich bei Unsicherheiten wenden können. Die Informationen sollen Promovierenden helfen, rechtliche Hürden bei der Veröffentlichung ihrer Dissertation erfolgreich zu meistern. Das Video wurde im Rahmen des BMBF-geförderten Projekts open-access.network erstellt.

uk: вхід без авторизації

name: Open-Access-Publikation einer kumulativen Dissertation - Was muss ich beachten?

Technische Informationsbibliothek (TIB)

addressLocality: Hanover

addressRegion: Lower Saxony


Стратегії використання гілок

uk: Комп'ютерні науки

uk: вхід без авторизації

name: National Aerospace University 'Kharkiv Aviation Institute'

addressCountry: UA

addressLocality: Kharkiv

addressRegion: Kharkivs’ka Oblast’


Системи контролю версій

uk: Комп'ютерні науки

uk: вхід без авторизації

name: Projekt 'Open Education Resources with Ukraine'


Основні команди Git

uk: Комп'ютерні науки

uk: вхід без авторизації


Основні команди Git. Частина 2

uk: Комп'ютерні науки

uk: вхід без авторизації


Гілки

uk: Комп'ютерні науки

uk: вхід без авторизації


Віддалені репозиторії

uk: Комп'ютерні науки

uk: вхід без авторизації


Усередині Git

uk: Комп'ютерні науки

uk: вхід без авторизації


keywords:
OpenAlex
Open Access
Open Science
Bibliometrics
CRAFT-OA
databases

uk: Комп'ютерні науки

description: While the citation databases Web of Science and Scopus are widely used in academic libraries to support research-related bibliometric services, there is growing interest in open alternatives that have emerged in recent years. In particular, the OpenAlex service from the provider OurResearch is considered very promising in both scientific research and applied bibliometrics. However, there are questions regarding the coverage and quality of the open data source in comparison to the established bibliometric databases. Bernhard Mittermaier, Head of the Central Library of the Forschungszentrum Jülich, will give a talk and explain the extent to which OpenAlex can be an alternative to proprietary databases in library practice and what its influence on Diamond OA publishing can be.

uk: вхід без авторизації

name: CRAFT-OA Webinar “OpenAlex – a game changer for citation databases?”

name: Staats- und Universitätsbibliothek Göttingen


uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: Im Rahmen des Konzeptes „Open Citizen Science“ arbeitet die SLUB Dresden seit mehreren Jahren an einer möglichst offenen Bereitstellung und Vernetzung von Kulturdaten, also digitalen bzw. digitalisierten Objekten aus Kultur- und Gedächtniseinrichtungen wie Bibliotheken, Archiven und Museen. Zum Einsatz kommen dabei vor allem die offenen Infrastrukturen des Wikiversums (hier: Wikisource, Wikidata, Wikimedia Commons, Wikiversity), aber auch lokale und thematische Wikis sowie andere Open-Source-Lösungen. Dabei geraten zunehmend auch erinnerungskulturelle Perspektiven in den Blick, die die SLUB in das sächsische Netzwerk „Erinnerungskultur digital“ (ERDi) einbringt. In einem kurzen Impuls sollen Ergebnisse einer gleichnamigen Tagung sowie die Erfahrungen der SLUB auf diesem Feld resümiert werden.

uk: вхід без авторизації

name: Mit Kulturdaten Gemeinschaften aufbauen und Geschichtsbewusstsein schärfen


de: Sozialwissenschaften/Soziologie

uk: Суспільні науки та соціологія

en: Social Sciences/Sociology

uk: Бібліотекознавство, документознавство

de: Rechts-, Wirtschafts- und Sozialwissenschaften

uk: Право, економіка та суспільні науки

en: Law, Economics and Social Sciences

uk: Гуманітарні науки

description: Gedenkstätten und Museen sind zentrale Orte erinnerungskultureller Praktiken. Digitale Transformationsprozesse verändern die Arbeit von Gedenkstätten und Erinnerungsorten and NS-Verbrechen in vielerlei Hinsicht. Die Digitalisierung der Erinnerungskultur in diesem Themenfeld bietet ein Instrumentarium, das neue Möglichkeiten schafft, aber auch Risiken beinhaltet. Dieser Vortrag diskutiert Potentiale digitalisierter Erinnerungskultur im Bereich der Gedenkstätten und verweist auf damit verbundene Herausforderungen. Er bietet einen Überblick zu digitalen Ressourcen und Formaten im Bereich der Forschung zur NS-Geschichte sowie der Bildungs- und Vermittlungsarbeit im Bereich der Gedenkstätten. Anhand ausgewählter Projekte werden Möglichkeiten einer partizipativen und interaktiven digitalen Erinnerungskultur zur NS-Geschichte ausgelotet.

uk: вхід без авторизації

name: Gedenkstätten und Digitale Erinnerungskultur


uk: Суспільні науки та соціологія

uk: Бібліотекознавство, документознавство

uk: Право, економіка та суспільні науки

uk: Гуманітарні науки

description: Die digitale Transformation wirkt sich maßgeblich auf die Art und Weise aus, wie wir unsere Geschichte erinnern. Die Wikimedia-Projekte stellen eine offene und kostenlose Infrastruktur für die gemeinsame Erstellung und Pflege von Wissen bereit. Ehrenamtliche und hauptamtliche Akteur*innen aus Kultur, Bildung und Wissenschaft können Wikipedia, Wikimedia Commons und Wikidata als Instrumente nutzen, um Wissen zu teilen, zu dokumentieren und zu bewahren. Anhand der Grundprinzipien – Community, Offenheit und Wissensgerechtigkeit – und konkreter Projektbeispiele zeigt Heike Gleibs, Leiterin Bildungspolitik und digitales Kulturgut bei Wikimedia Deutschland, in ihrem Impuls auf, welche Beiträge zur digitalen Erinnerungskultur Wikimedia und seine Projekte leisten können und wollen.

uk: вхід без авторизації

name: Gemeinsam erinnern: Wie die Wikimedia-Projekte und ihre Prinzipien die Öffnung und Offenheit digitaler Erinnerungskultur unterstützen


uk: Суспільні науки та соціологія

uk: Бібліотекознавство, документознавство

uk: Право, економіка та суспільні науки

uk: Гуманітарні науки

description: Die meisten Gedenkstätten und Initiativen zur Erinnerung an die nationalsozialistischen Verbrechen findet man heute auf Instagram oder Facebook. Dort und auf eigenen Websites stellen Vereine und Institutionen Informationen über Geschichte und Opfer bereit oder geben Einblicke in die eigene Arbeit. In der Regel sind es haupt- oder ehrenamtliche Mitarbeiter:innen, die diese Informationen zusammenstellen und veröffentlichen. Doch eine Suchanfrage zu den historischen Orten führt Nutzer:innen meistens zuerst auf die Seiten der Wikipedia. Hinter den Autor:innen dort verbergen sich jedoch überwiegend ganz andere Akteure. Wer verantwortet dort das Wissen über die historischen Orte und die Verbrechenskomplexe? Welche Probleme stellen sich dabei aus Sicht der Gedenkstätten und Initiativen? Inwiefern engagieren sich diese selbst als Autor:innen oder nutzen andere Wiki-Projekte? Welche Chancen und Herausforderungen gibt es aus dieser Perspektive?

uk: вхід без авторизації

name: Wer macht und wem gehört digitale Erinnerungskultur?


uk: Комп'ютерні науки

de: Allgemeine und vergleichende Literatur- und Sprachwissenschaft

uk: Загальне та порівняльне літературознавство та мовознавство

en: General and Comparative Literary Studies and Linguistics

uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

uk: вхід без авторизації

name: WikiRemembrance - Erinnerungskultur digital und partizipativ


uk: Бібліотекознавство, документознавство

uk: Гуманітарні науки

description: Systematische Erfassung von Daten ermöglicht es uns, Muster zu finden, Tendenzen abzulesen, Mengen genau zu bestimmen, Häufigkeiten darzustellen und zu Entwicklungen abzulesen. Daten helfen uns, eine Basis zu bilden und aufgrund dieser Basis Vergleiche anzustellen. Aber Daten sind immer auch mehr als das. Daten - auch die bereits Verstorbener - sind keine "tote Materie". Sie sind Zeugnisse von Leben. Und auch wenn das einzelne Leben mit dem Tod endet - so wirkt es in denen, die nachkommen weiter. Welche Verantwortung ergibt sich daraus für den Umgang mit Daten in der Erinnerungskultur? Welche Aussagen treffen wir, wenn wir aus den Daten neue Erkenntnisse über Geschichte(n) veröffentlichen? Wie kann ein verantwortungsvoller Umgang gelingen und welche Rolle spielen dabei diejenigen, die eine persönliche Beziehung zu den Urheber*innen dieser Daten haben? Auf diese Fragen möchte Nora Hespers in ihrem Vortrag eingehen und damit auch den Prinzipien des FAIR und CARE in der Wissenschaft auf den Grund gehen.

uk: вхід без авторизації

name: Das Leben in Daten - von wegen tote Materie


Inverse 2x2 Formel

cs: Matematika

de: Mathematik

fi: matematiikka

uk: Математика

en: Mathematics

fr: Mathématiques

da: Matematik

es: Matemáticas

nl: Wiskunde

uk: Математика, природничі науки

uk: вхід без авторизації

RWTH Aachen University

addressLocality: Aachen


Inverse 2x2 Beispiele

uk: Математика, природничі науки

uk: вхід без авторизації


Invertierbarkeit von Matrizen

uk: Математика, природничі науки

uk: вхід без авторизації


HPO and Bayesian Optimization

uk: Комп'ютерні науки

uk: вхід без авторизації


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Hand-On: DeepCAVE for Multi-Objective AutoML


uk: Комп'ютерні науки

description: Automated Machine Learning (AutoML) is a relatively young research area aiming at making high-performance machine learning techniques accessible to a broad set of users. This is achieved by identifying all design choices in creating a machine-learning model and addressing them automatically to generate performance-optimised models. In this opening lecture, we provide an extensive overview of the past and present, as well as future perspectives of AutoML. First, we introduce the concept of AutoML, formally define the problems it aims to solve and describe the three components underlying AutoML approaches: the search space, search strategy and performance evaluation. Next, we discuss hyperparameter optimisation (HPO) techniques commonly used in AutoML systems design, followed by providing an overview of the neural architecture search, a particular case of AutoML for automatically generating deep learning models. We further review and compare available AutoML systems. Finally, we provide a list of open challenges and future research directions.

uk: вхід без авторизації

name: Automated Machine Learning: Past, Present and Future


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Hands-On: PyExperimenter Computational: Experiments at Scale


uk: Комп'ютерні науки

description: Time series forecasting is an essential component of decision-making in domains such as energy, retail, and finance. Traditionally, machine learning practitioners have focused on developing task-specific forecasting models that are restricted to a certain dataset or application domain. Inspired by the success of pretrained Large Language Models (LLMs) in natural language processing, it becomes imperative to explore whether a similar approach can be applied to forecasting: Can we train a single large model on huge amounts of diverse time series data, that will generalize to new unseen time series tasks? In this talk, we introduce Chronos, a family of pretrained forecasting models based on minimal modifications to LLM architectures, that accomplishes this goal. Chronos demonstrates remarkable zero-shot performance on unseen datasets, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines. We discuss open challenges in the development and application of pretrained models for time series forecasting, and explore what role AutoML methods can play in overcoming them.

uk: вхід без авторизації

name: Chronos: Time series forecasting in the age of pretrained models


uk: Комп'ютерні науки

description: In numerous scientific disciplines, ML methods offers significant potential for advancing research in novel ways. However, a considerable challenge for domain experts lies in the necessity for extensive ML experience to effectively apply these methods. Similarly, the Centre for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW) encountered this obstacle when attempting to integrate ML into the work of their researchers. This talk will demonstrate how the ZSW applied AutoML to create new opportunities for renewable energy domain experts who lacked ML expertise. The session will also illustrate the use of AutoML through a variety of examples in this domain and present the ZSW's self-developed no-code AutoML tool, KI-Lab.EE.

uk: вхід без авторизації

name: Success stories on the use of AutoML for domain experts in the renewable energy context


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Opportunities for AutoML in Sustainable Energy: Tackling Misinformation, Building Control, and Power Systems Modelling


Basic Lecture - Green AutoML

uk: Комп'ютерні науки

uk: вхід без авторизації


uk: Математика, природничі науки

uk: вхід без авторизації

name: Skalarprodukt und Norm: Euklidischer Vektorraum 1 (V2)


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Пряма парадигма навчання нейромереж. RBF-нейромережа. МЗП-мережа.

name: Taras Shevchenko National University of Kyiv


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Еволюційні обчислення. Еволюційні алгоритми. Генетичні алгоритми.

name: Projekt 'Open Education Resources with Ukraine'


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Основи теорії нечітких множин. Операції над нечіткими множинами. Принцип узагальнення Заде


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Штучні нейронні мережі. Біологічний та штучний нейрони. Активаційні функції. Персептрон Розенблатта. Дельта-правило. Класифікація нейромереж.


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Алгоритм оберненого поширення помилки. Ітераційна парадигма навчання нейромереж


keywords:
Open Access
Kumulative Dissertation
Compilation Thesis
Cumulative Dissertation
PhD Thesis
open-access.network
Open Science
Open Research

uk: Комп'ютерні науки

description: This video addresses key considerations for doctoral candidates looking to publish their compilation thesis (cumulative dissertation) Open Access. It explains how to proceed if the rights to your articles have been transferred to a publisher and provides practical tips on whom to contact if you encounter uncertainties. The aim is to help doctoral researchers navigate legal challenges and successfully republish their articles as part of their compilation thesis. The video was created as part of the BMBF-funded open-access.network project.

uk: вхід без авторизації

name: Publishing a Compilation Thesis Open Access: What You Need to Know


keywords: superparamagnetic iron oxide nanoparticles

uk: Медицина / Науки про здоров'я

description: Cardiovascular diseases (CVD) are responsible for 17.9 million deaths per year and the global burden of atherothrombotic diseases continues to grow. To achieve a more effective primary prevention of cardiovascular morbidity and mortality, new diagnostic and therapeutic strategies are urgently needed. As the recent nanotechnology advances already enabled the development of nanosystems that found their way into the clinical trials and the therapeutic routine, nanomedicine is expected to help diagnose and treat the CVD patients in a more effective and personalized way. Iron oxide-based magnetic nanoparticles, a class of nanosized agents with superparamagnetic properties, are considered a promising tool in management of patients with CVD. Due to their inherent properties, their possible applications range from plaque imaging and thrombus detection using magnetic resonance imaging (MRI) to the magnetically-controlled targeted drug-delivery, as well as magnetic cell seeding-based stent endothelialisation and blood vessel regeneration. In this talk, the results of our preclinical studies focusing on the application of SPIONs for atherosclerotic plaque therapy using SPION-supported magnetic drug targeting, as well as atherosclerosis imaging using SPION-enhanced MRI will be highlighted. Furthermore, the translational considerations and safety hurdles related to the implementation of magnetic nanoparticles in the clinical studies will be addressed.

uk: вхід без авторизації

name: Magnetic nanoparticles: From bench to bedside of a cardiovascular disease patient

name: Beilstein-Institut zur Förderung der Chemischen Wissenschaften

addressLocality: Frankfurt am Main

addressRegion: Hesse


... and justice for AIl

uk: Комп'ютерні науки

description: „Everything’s science fiction until someone makes it science fact.“ - Marie Lu, Warcross We live in times that have quite a lot of those science facts- even if hoverboards sadly are not part of them- and now have to deal with this new world and all it’s changes for better or for worse. There are unsettling deepfakes, stunning Cap-Set-Problem-solving language models and the question of artificial conscience. Developers might be able to navigate the turbulences of AI evolution, but are you brave enough to take on the quest of untangling the nebulous scriptures of law that are known to the chaotic neutral wordwizards of the council of Europa as the „AI Act“? Accompany me on a journey through the valleys of risk-based AI categories, over the sea of subsectional articles and to the top of mount ethic, as we strive to understand the possibilities the AI Act gives our bold heroes to defy the boundaries of innovation, protect the villagers of the EU and construct (legally) safe software. The Proposal for the European Artificial Intelligence Act takes 224 Pages of legalese to work through. This talk will give a short overview of what the European AI Act is and about the purpose and necessity of a globally harmonised legal system. Hopefully it will give an understanding about the main goals of the act, which are, spoiler alert, ensuring AI safety, the protection of fundamental rights, and legal clarity for businesses and developers (which is probably you). Let us discuss, how developers can shield fundamental rights by writing ethical AI Systems whilst navigating the regulatory landscape and staying tuned with legal development as well.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Fully Homomorphic Encryption (FHE) is a privacy-enhancing technology that enables performing computations over encrypted data. FHE has recently seen a lot of progress, and commercial applications of FHE are now available. One of the main application domains for FHE is privacy-preserving machine learning. We introduce a Python interface for OpenFHE, a popular open-source FHE C++ software library that supports all common FHE schemes. OpenFHE is a NumFocus-sponsored open-source project that has been authored by a community of well-known FHE cryptographers and software engineers.The talk provides a high-level introduction to FHE and its applications, and then provides an overview of the Python API. Several examples are presented to both illustrate FHE concepts and show the practicality of the technology. More information about the OpenFHE project: * Main website: openfhe.org/ ; * OpenFHE discourse forum: openfhe.discourse.group/ ; * Main OpenFHE repository: GitHub: openfheorg/openfhe-development ; * OpenFHE organization: GitHub: openfheorg ; * Main OpenFHE design paper: eprint.iacr.org/2022/915

uk: вхід без авторизації

name: Encrypted computing in Python using OpenFHE


A Tale of Scaling Observability

uk: Комп'ютерні науки

description: What it’s like to keep the lights on in a rapidly growing business - how we’ve scaled our metrics, logging and tracing beyond processing 50TB+ of telemetry a day, and what we’ve learned along the way. During this session, we will discuss the challenges of scaling high load services, and give few pointers to developers to help your chosen open-source observebility tool function as a product.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Whether using threads or task-based event loops, running code concurrently is not without its challenges. This talk takes a look at the features provided by the Python programming language to solve problems of synchronization when dealing with concurrently executing code. Together we will take a look at the synchronization classes and functions provided by the Python threading and asyncio modules, what problems they aim to solve, and how we might use them effectively in our own code.

uk: вхід без авторизації

name: A Tour of Synchronization Primitives in Python


uk: Комп'ютерні науки

description: Are you curious about integrating the high-performance and memory efficiency of Rust into your Python applications? Rust can significantly enhance the speed and efficiency of Python programs, and this integration is made seamless with PyO3. This presentation will delve into the capabilities of PyO3, a tool that allows the creation of native Python modules using Rust. With PyO3, importing Rust code as a Python module is straightforward. It offers seamless type conversion between Python and Rust and includes macros that simplify the process of exposing Rust functions to Python. Moreover, with the growing trend towards asynchronous programming, PyO3-asyncio emerges as a vital tool for those working with async functions in Python or looking to generate Python bindings for an async Rust library. It streamlines the task of translating async functions between Python and Rust. Furthermore, PyO3 facilitates easy implementation of parallelism within Rust code, enhancing performance and efficiency.

uk: вхід без авторизації

name: Accelerating Python with Rust: The PyO3 Revolution


Adventures in not writing tests

uk: Комп'ютерні науки

description: Sometimes we write code that we don't expect to go to production; they are one-offs or analysis to understand our data. However, a good analysis may be worth repeating, and before we know it, the code that was never supposed to go to production is running every day and driving critical decisions – it is in production. Once our code is in production, we have to maintain it, and that means we need tests to ensure that changes made to the code while maintaining it do not change other behavior. Hypothesis is a Python library for creating inputs that are good for exercising code. Hypothesis tests create many different inputs for a single test case, with a special concentration on inputs that are likely to break your code. If the code was originally written to understand data, then new data we feed it over time could be very different from what was initially expected or planned for. With Hypothesis, we randomize our test outputs and they become just as unknown as our real-world outputs. Our tests then confirm certain properties to prove that the analysis was performed as expected. Ghostwriting is a feature of Hypothesis that writes tests based on the type hints in your code. This can not only save time, but also validate our type hints. The savings in time and toil can be significant, but the ghostwritten tests do also need some additions to truly test our code. We'll look at what is needed to both generate proper inputs and check our outputs.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Aggregating information is a common Django task, but using the aggregate method can be a bit cumbersome and in the case of large database tables, pretty slow as well. I will introduce the library django-pgviews-redux, which adds first-class support for database views (with Postgres), making that task much simpler. With that library, database views are wrapped around models, meaning you get many of the features you rely on with models for free, like querysets and filtering on those, admin, and any other feature which works with models. Defining a view is almost as simple as defining a model, by specifying what fields there are for the model and defining the SQL. This talk will walk through examples of aggregation in Django, and then show how one could simplify those examples using the library. Finally, we will get to materialized views as well, which stores the aggregation almost like a table in the database, providing big speed improvements on aggregation on large tables.

uk: вхід без авторизації

name: Aggregating data in Django using database views


uk: Комп'ютерні науки

description: Documenting Python project endpoints is a real challenge in API development. Poor readability, versioning, and lack of authentication information - this all reduce the usability of existing OpenAPI documentation. The different libraries offers us a good start for autogenerated OpenAPI documentation for our endpoints, but in this talk we will discuss an alternative way to auto generated documentation for our APIs.

uk: вхід без авторизації

name: An alternative view on the OpenAPI documentation.


Animations from first principles

uk: Комп'ютерні науки

description: How do you create an animation? What if you want to morph a circle into a figure eight? As it turns out, all you need is two or three functions and a loop! In this live-coded talk, we'll go over the basic concepts and code needed to create an animation _from first principles_. Because the talk presents the ideas and the code from first principles, you will be able to take the key concepts and build your own animations after! We'll start simple and build from there: - How can you draw a circle if all you can do is colour single pixels? - How can you animate the process of drawing a circle? - How can you animate the process of drawing something other than a circle? - How can you animate the process of morphing two figures? - How do you add colour to your animations? This visually appealing talk will show you all of the code without skipping a single line and by the time we're done you'll be jumping in your seat to create your own animations!

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Unlock the secrets of Python's async and await—tools that let you bend time itself! In this talk, we'll dive into the magic of asynchronous programming, making your code faster and more efficient. Learn to handle I/O-bound tasks like a pro, juggle multiple operations without breaking a sweat, and impress your peers with your newfound time-warping skills. Perfect for those ready to level up from mere mortals to Python sorcerers. People of all levels of experience are welcome, only requirement is curiosity and enthusiasm :) Join us, and let’s make time our playground!

uk: вхід без авторизації

name: Async Await: Mastering Python's Time-Bending Tricks


uk: Комп'ютерні науки

description: Hey there, fellow Python enthusiasts! Are you ready to dive into the exciting world of application observability without getting your hands too dirty with complex instrumentation? If that sounds like a journey you'd be interested in, then you're in for a treat! Observability is that magical window into the inner workings of our applications, allowing us to understand what's happening under the hood, troubleshoot issues, and ensure everything is running smoothly. However, achieving this level of insight can sometimes feel like a daunting task. That's where OpenTelemetry comes into play, simplifying the entire process and making it accessible to everyone, not just the observability wizards. In our session, we'll start with the basics: what OpenTelemetry is and the problems it aims to solve (and those it doesn't). We'll demystify the concept of instrumentation—the process of embedding observability into your applications—and show you how OpenTelemetry makes this not only possible but painless. The heart of our talk will be focused on autoinstrumentation, a magical feature of OpenTelemetry that automates the task of adding observability to your Python projects. Imagine being able to get detailed insights into your application's performance and behavior without having to manually instrument every nook and cranny. Sounds like a dream, right? And because we believe in learning by doing, we'll walk you through a small but mighty demo. You'll see firsthand how effortlessly you can implement OpenTelemetry in your own Python applications, turning the daunting into the doable.

uk: вхід без авторизації

name: Autoinstrumentation Adventures: enhancing Python apps with OpenTelemetry


uk: Комп'ютерні науки

description: Ever wished you had a smart fridge that both lists and alerts you with ingredients you have, to waste less food and make the best use of what you have through ingredient-recipe match? Bingo then that I'll share the story behind the creation of a Python-powered solution that maximizes ingredient usage, minimizes food waste by keeping track of your ingredients and streamlines the cooking process.

uk: вхід без авторизації

name: Automate Your Kitchen with Python & Applied AI


Automatic trusted publishing with PyPI

uk: Комп'ютерні науки

description: PyPI added support for “Trusted Publishing” last year, allowing package maintainers to create releases directly from their GitHub Actions pipelines without having to worry about token management. Trusted Publishing removes long-lived API tokens from the equation, removing a threat vector for supply chain attacks. In this talk, we’ll go through the details of how this works, how maintainers can easily take advantage of it with minimal changes to their existing setup, and the ongoing effort in the last 12 months to add support for publishers other than GitHub, such as GitLab, Google, and ActiveState.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: In this era of rapid technological advancement and AI, Ad prediction systems stand at the forefront of shaping online advertising, significantly impacting how content reaches its intended audience. In this session, I'll introduce the Ads prediction system from a user and algorithm view. We'll then walk through key concepts like targeting, bidding, ad ranking, click-through rate (CTR), and conversion rate. We'll deeply dive into connecting the dots, designing an ad prediction system, some ethical considerations, models, offline and online metrics, scaling and deployment decisions that enable handling high volumes of data and requests efficiently, and some case studies. At the end of this session, attendees will comprehensively understand the end-to-end process of developing an ads prediction system.

uk: вхід без авторизації

name: Behind the Scenes of an Ads Prediction System


uk: Комп'ютерні науки

description: The Python development landscape thrives on the extensive use of open-source libraries and frameworks. However, the growing prevalence of attacks targeting OSS underscores the need for robust security measures to consume open source. In this talk, we'll examine how the Secure Supply Chain Consumption Framework (S2C2F) can guide organizations in securely consuming Python OSS, utilizing tools such as pip, artifact managment, sboms and Dependabot. The S2C2F Framework was developed by Microsoft and later donated to the Open Source Security Foundation (OpenSSF). It provides a structured approach to enhancing the security of OSS consumption. We'll provide an overview of its core principles and maturity levels and discuss practical strategies for implementing S2C2F principles within Python projects, including dependency management with pip, artifact management, sboms, signatures, deny rules, forking policies and automated security updates with Dependabot. The S2C2F is a pragmatic approach to securing how you consume OSS. It emphasizes the fundamental principles of knowing your OSS, preventing the introduction of vulnerable packages, and maintaining robust patch management. You will come away from this talk with practical tips and best practices on how to securely consume open source in python.

uk: вхід без авторизації

name: Best practices for securely consuming open source in Python


uk: Комп'ютерні науки

description: In this talk, we dive into the world of Event Driven Architecture and Message Streaming, using Python and FastStream. You'll learn to integrate FastStream, a python framework, into your projects and leverage AsyncAPI to define contracts for asynchronous communication and event streaming. Through practical examples and insights, you'll discover the art of building scalable, responsive Python applications that thrive in real-time environments. #### About AsyncAPI AsyncAPI is an open standard/specification and growing set of open-source tools to help developers define, build, and maintain asynchronous APIs and Event-Driven Architectures. It describes message-driven APIs in a machine-readable format, and is protocol-agnostic. #### About FastStream FastStream is a powerful and easy-to-use Python framework for building asynchronous services interacting with event streams such as Apache Kafka, RabbitMQ, NATS and Redis. FastStream simplifies the process of writing producers and consumers for message queues, handling all the parsing, networking and documentation generation automatically. FastStream provides unified API across multiple brokers, built-in Pydantic validations, automatic AsyncAPI documentation, FastAPI like Dependency Injection System, built-in support for test and extensions.

uk: вхід без авторизації

name: Building Event-Driven Python service using FastStream and AsyncAPI


uk: Комп'ютерні науки

description: Many real-world problems are inherently multimodal, from the communicative modalities humans use such as spoken language and gestures to the force, sensory, and visual sensors used in robotics. For machine learning models to address these problems and interact more naturally and wholistically with the world around them and ultimately be more general and powerful reasoning engines, we need them to understand data across all of its corresponding images, video, text, audio, and tactile representations. In this talk, Zain Hasan will discuss how we can use open-source multimodal embedding models in conjunction with large generative multimodal models that can that can see, hear, read, and feel data(!), to perform cross-modal search(searching audio with images, videos with text etc.) and multimodal retrieval augmented generation (MM-RAG) at the billion-object scale with the help of open source vector databases. I will also demonstrate, with live code demos, how being able to perform this cross-modal retrieval in real-time can enables users to use LLMs that can reason over their enterprise multimodal data. This talk will revolve around how we can scale the usage of multimodal embedding and generative models in production.

uk: вхід без авторизації

name: Building Scalable Multimodal Search Applications with Python


Caching for Jupyter Notebooks

uk: Комп'ютерні науки

description: Caching data and calculation results in jupyter notebooks is a great way to speed up development by making expensive cells easier to re-run. Data scientists and developers using notebooks on a daily basis, can improve their notebook workflow with low-effort changes in the notebook code, cut the time spent waiting and reduce context switches. This talk targets developers and data scientist of all experience levels and will cover: Why caching in notebooks? Setting up the context in which developers and data scientists use notebooks for exploratory work and how caching is relevant in it. What is caching Quick definition of caching, introducing the different types of persistence (in-memory, on disk, database, object storage …), cache invalidation strategies (parameters, code changes, ttl, …), with some cautionary comments about data security when caching protected data. Caching Techniques Going through readily available options from the python standard library, and how to use them in notebooks. Introducing a few off-the-shelves options like ipython % magics, and cachetools. Showcasing how one would build their own mini-caching framework, that fits for their specific use case, using pandas and spark for the example Explaining when to stop trying to cache, and keeping the caching framework mini, what are the signs that caching went overboard.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: chDB, powered by the robust ClickHouse engine, is a high-performance in-process SQL engine designed for the Python data ecosystem. It supports direct interaction with databases like MySQL, PostgreSQL, SQLite, and MongoDB; streaming platforms such as Kafka; and key-value stores like Redis. With extensive support for various data sources and formats, chDB is highly adaptable to different data environments. It seamlessly integrates with structured formats like CSV and JSON as well as complex binary formats such as Parquet, Avro, and Arrow, enabling efficient data manipulation and analysis. The engine is ANSI SQL compliant, ensuring broad compatibility with existing SQL codebases and facilitating easy integration into diverse data pipelines. This comprehensive SQL support includes complex querying capabilities, enhancing the engine's utility in sophisticated data analysis tasks. chDB is particularly designed to accommodate the rapid prototyping of applications, allowing developers to transition smoothly and quickly from development to production environments with minimal adjustments. With its deep integration into Python, chDB interacts directly with popular Python libraries such as Pandas, NumPy, and others, streamlining the data science workflow. This integration enables direct data manipulation within Python scripts and notebooks, drastically reducing the need for data movement and conversion, thus accelerating the data analytics process. For organizations and developers leveraging Python, chDB offers an unmatched combination of performance, flexibility, and ease of use, making it an invaluable tool for building efficient, scalable, and robust data-driven applications.

uk: вхід без авторизації

name: chDB: The Blazing Fast SQL Engine for Data Science


uk: Комп'ютерні науки

description: Teaching how to program in Python has various approaches, but it becomes motivating when we work with concepts about diversity through poems. This talk will show how a project that aims to teach Python using poems and that grew hand in hand with the Python community has been changing the lives of children and, currently, teachers in a country in the global south. In order to demonstrate it, I will teach you how to create function blocks, conditional and looping structures in an easy and empowering way, while discussing topics such as racism, xenophobia, homophobia, and sexism, for example, through Python code integrated with poetry.

uk: вхід без авторизації

name: CompiledPoem.py: Teaching about diversity and Python through poem


uk: Комп'ютерні науки

description: There are a lot of resources on containerizing Python applications with Docker, but most are basic and outdated. Following them results in slow builds and potentially insecure applications. Let's see how we can build better containers using recent Docker features! This talk will show how to speed up your builds and make your images smaller and more secure. We'll use features such as multi-stage builds or cache mounts to build containers with Python apps. We will also discuss how to improve the security of your container. Tips from the talk are valid for applications of all sizes and kinds: from hobby projects to enterprises, from CLI tools to web applications and APIs. You will be able to apply them immediately after the talk. Basic knowledge of Docker and its key concepts (images, layers, Dockerfile commands) is required. You'll learn something new even if you have used Docker for some time.

uk: вхід без авторизації

name: Containerize your Python apps like it's 2024


uk: Комп'ютерні науки

description: The EU Commission is likely to vote on the Cyber Resilience Act (CRA) later this year. The CRA is an ambitious step towards protecting consumers from software security issues by creating a new list of responsibilities for software developers and providers. The Act also creates a new category of actor known as an “Open Source Steward” which we think makes important allowances for public open source repositories like CPython and Python Package Index (PyPI.) Once the dust settles, everyone who makes software will need to consider the CRA’s mandates in their security roadmaps. In this talk we will look at the timeline for the new legislation, any critical discussions happening around implementation and most importantly, the new responsibilities outlined by the CRA. We’ll also discuss what the PSF is doing for CPython and for PyPI and what each of us in the Python ecosystem might want to do to get ready for a new era of increased certainty – and liability – around security. ## Target audience Developers and maintainers whose project or product may be affected by the CRA. European legislation won’t just affect the European market, it will affect the software industry and the open source community globally as it is very hard to segregate one project or product from the EU market. So, this is for everyone in the Python community who shares their code with the world. ## Goal To educate the general public about CRA - how it can affect us and how to get ready for it. We also want to provide more information for the Python community about what has been done by the PSF regarding the CRA to reassure them that the Python community is aware and getting prepared for the CRA.

uk: вхід без авторизації

name: Counting down for CRA - updates and expectations


uk: Комп'ютерні науки

description: Have you ever wished for a feature in Jupyter Notebooks or JupyterLab that wasn't already there? Or perhaps you've found yourself doing complex or repetitive tasks and realized that you, and others, could benefit from integrating those tasks into JupyterLab? This is your chance to learn how to add that feature, or integrate that task, yourself. JupyterLab enables you to work with Jupyter notebooks, text editors, terminals, and custom components in a flexible, integrated, and extensible manner. This talk presents a practical tutorial about how to extend JupyterLab. We focus on understanding the underlying extension support infrastructure, as we walk through a step-by-step example of creating an app in JupyterLab. We will learn, among other things, how to launch that app from different places within JupyterLab, how to style our app, and how to pass parameters to our app to modify its behavior. Prerequisites: - Attendees should have some familiarity with Jupyter Notebooks and/or JupyterLab. - Attendees must have solid experience with _any_ typical object-oriented programming language (i.e. a good understanding of classes, objects, and inheritance).

uk: вхід без авторизації

name: Creating Your Own Extensions for JupyterLab


Cython and the Limited API

uk: Комп'ютерні науки

description: Cython's Limited API support is finally approaching a usable state. As an example, it is possible to produce a working version of Cython by compiling it in Limited API mode. For users the main advantage is to be able to reduce the number of wheels/binaries they have to build in order to be compatible across a range of versions of Python. For Cython itself there is also an advantage in future-proofing: being able to produce simpler code that should continue to work even as the Python interpreter evolves and which is more likely to work with alternative Python implementations, as well as hopefully placating the unease some of the core Python developers have at Cython's use of Python internals (in non-limited API mode). This talk will start off by looking at the subject from the users' perspective: * Why you might want to use the Limited API (from Cython). * What kind of projects are likely to benefit from it (as far as it's ever possible to predict how people will use a tool...). * What you actually need to do to build a Cython module with the Limited API. * What the limitations and disadvantages are: there are some features that don't work, some features that only work in recent versions of Python, some speed costs, and complete forward-compatibility might not be all you hope it would be. When that "general interest" section is done, I plan to talk about some of the gory implementation details - what "creative" solutions have been employed to work around missing features or things the Limited API was never intended to do.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Writing pipelines for processing large datasets has its challenges – processing data within an acceptable time frame, dealing with unreliable and rate-limited APIs, and unexpected failures that can cause data incompleteness. In this talk we’ll discuss how to design & implement modular, efficient, and manageable workflows with Celery, Redis, and signal-based triggering. We’ll begin by exploring the motivation behind segmenting pipelines into smaller, more manageable ones. The segmentation simplifies development, enhances fault tolerance, and improves modularity, making it easier to test and debug each component. By leveraging Redis as a data store and Celery’s signals, we introduce self-triggering (or looped) pipelines that efficiently manage data batches within API rate limits and system resource constraints. We will look at an example of how we did things in the past using periodic tasks and how this new approach, instead, simplifies and increases our data throughput and completeness. Additionally, this facilitates triggering pipelines with secondary benefits, such as persisting and reporting results, which allows analysis and insight into the processed data. This can help us tackle inaccuracies and optimise data handling in budget-sensitive environments. The talk offers the attendees a perspective on designing data pipelines in Celery that they may have not seen before. We will share the techniques for implementing more effective and maintainable data pipelines in their own projects.

uk: вхід без авторизації

name: Data pipelines with Celery: modular, signal-driven and manageable


uk: Комп'ютерні науки

description: The "data build tool" (DBT) was designed to unlock software engineering best practices for SQL-based data pipelines: pipelines as version controlled directed acyclic graphs (DAGs) consisting of testable and reusable nodes. With the increasing number of cloud data warehouses and data lakehouses that allow the native execution of Python code, DBT also added support for Python models. In this talk, I will explain how Flatiron Health uses DBT to improve and extend lives by learning from the experience of every person with cancer. We will discuss an example project setup that uses SQL as well as Python models. I will share our experiences with unit and data testing as well as with writing a reusable variable library. The talk is well-suited for anyone with prior data warehouse or data lakehouse experience who is curious how they can leverage DBT to write test-driven and reusable data piplines. The example project will use SQL, Python and Snowflake.

uk: вхід без авторизації

name: DBT & Python - How to write reusable and testable pipelines


uk: Комп'ютерні науки

description: No longer needed code creates technical debt if it is not removed from the code base. Unused code has to be maintained, it complicates code base and increases cognitive load. It might even depend on no longer necessary dependencies with vulnerabilities and might increase attack surface. Therefore, removing dead code saves time, money and reduces security risks. Recently, Ruff has became a de facto linter, which provides almost all existing linting rules from other linters. However, it is only capable to detect locally unused Python code, which is only a tiny portion of unused code. Vulture is the best known tool for detecting globally unused Python code. However, its configuration is not very flexible and disabling false positives in a larger code base might require a lot of effort. Also, unused code detection is sometimes inaccurate, because scopes are not taken into account, when detecting unused code. This presentation introduces a new Python package called `deadcode`, which tries to move globally unused Python code detection to the next level. First, it provides a large set of options to flexibly disable various types of false positives. Second, deadcode implements more rules for detecting unused code than Vulture. Third, an improved strategy which tracks scopes and namespaces into account is being used to more accurately identify unused code items. Fourth, a --fix option is provided, which allows to automatically remove detected unused code items. In addition, an idea to prune Python code in order to reduce its size will be consider, which might be relevant when serving Python code in a browser. Lets make Python ecosystem even more awesome!

uk: вхід без авторизації

name: Deadcode - a tool to find and fix unused (dead) Python code


uk: Комп'ютерні науки

description: Have you ever wondered why we are the way we are? Why some individuals develop diseases while others remain healthy? And what does Python have to do with all of this? Join this talk in which we will explore the interface between biology, technology and medicine, in the context of the research of rare genetic diseases. Learn what the Moore’s law has to do with advances in genetics and medicine, or why bigger is not always better.

uk: вхід без авторизації

name: Deciphering the mysteries of human genomes


uk: Комп'ютерні науки

description: Selecting the optimal text embedding model is often guided by benchmarks such as the Massive Text Embedding Benchmark (MTEB). While choosing the best model from the leaderboard is a common practice, it may not always align perfectly with the unique characteristics of your specific dataset. This approach overlooks a crucial yet frequently underestimated element - the tokenizer. We will delve deep into the tokenizer's fundamental role, shedding light on its operations and introducing straightforward techniques to assess whether a particular model is suited to your data based solely on its tokenizer. We will explore the significance of the tokenizer in the fine-tuning process of embedding models and discuss strategic approaches to optimize its effectiveness.

uk: вхід без авторизації

name: Deconstructing the text embedding models


Demystify Python Types for PEP 729

uk: Комп'ютерні науки

description: [PEP 729 – Typing governance process] proposes a new way to govern the Python type system. The PEP was endorsed by maintainers of all major type checkers. This talk aims to guide the audience to understand the reason more deeply of this new process after demystifying Python types. In this talk, the speaker will demystify python types from their theory to practice along with Python type systems. The theory includes the type theory by [Per Martin-Löf](en.wikipedia)'s and [gradual typing by Jeremy Siek], all theories will be explained with the Python code in the real world. The type system targets all major type checkers and CPython. The comparison will be based on the research: [Python 3 Types in the Wild: A Tale of Two Type Systems]. The practice covers how a new specification is done in type systems. In addition, the speaker will share their thoughts about the challenges behind the implementation, and connect the answer to the reason for the PEP 729.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: **AsyncIO** has emerged as a vital tool in Python's ecosystem, particularly in web development, IO-bound tasks, and network programming. However, its internal mechanics often remain obscure, even to seasoned Python developers. This talk aims to demystify AsyncIO by guiding you through creating your own event loop in Python, culminating in running a FastAPI application with it. In this talk, we’ll build an event loop from scratch in Python, capable of running an HTTP server through a FastAPI application. **Plan:** - **Introduction to AsyncIO** - **Core Concepts:** Deep dive into Event loop, Futures, Tasks, and coroutines - **Hands-On Building:** Constructing an event loop from scratch - Scheduling callbacks - Executing tasks and coroutines - Handling network calls - **Practical Application:** Running a FastAPI HTTP server with our loop - **Performance Insights:** Comparing our event loop with the fastest ones By the end of this talk, you'll be able to understand the internal workings of AsyncIO and create a basic event loop capable of running a FastAPI application.

uk: вхід без авторизації

name: Demystifying AsyncIO: Building Your Own Event Loop in Python


uk: Комп'ютерні науки

description: When your programs are configuration driven and used by PM/PO/Scientists etc you have to think slightly differently about how configuration files are used in your projects. For example, users may want to change/create configs on the fly and they may want to see all settings in one file without having to go through layers of indirection and library “configs” to see what their experiment/program will do. Rather than discussing config file formats (I’ll assume toml but it’s applicable for other formats), this talk will focus more on my ideas/tips on: how to structure config files, how to allow non technical people to contribute to config files, how to minimise potentially explosive number of config files, how to expose more control in config files, tools for testing these configs and checking if values are actually used, hints for good practices to make debugging problems easier.

uk: вхід без авторизації

name: Designing Config Files: The Conflicting Needs of Programmers and Users


uk: Комп'ютерні науки

description: Many software engineers, particularly web developers, recognize the critical importance of documentation for efficient collaboration. Yet, the challenge of maintaining up-to-date documentation remains a pervasive issue, often due to human errors such as failing to update documentation after changes in the codebase. This presentation introduces the philosophy of Documentation-First Development (DFD), a methodology I advocate for that leverages the code-based OpenAPI documentation generation capabilities of the FastAPI framework. I will discuss methods to embody this philosophy, including the use of a sub-application pattern to segregate API documents and the application of generic types for crafting reusable custom response models. Additionally, I will address the limitations of traditional approaches to API documentation and demonstrate how FastAPI, in conjunction with Pydantic, offers a more effective solution by automatically keeping documentation synchronized with the code. This presentation aims to enlighten attendees on the benefits of the FastAPI framework and provide practical insights into creating precise and well-maintained API documentation. It is designed for audiences interested in enhancing their documentation practices and those curious about the advantages of employing FastAPI for web development projects.

uk: вхід без авторизації

name: DFD (Documentation-First Development) with FastAPI


Don't fix bad data, do this instead

uk: Комп'ютерні науки

description: In a time where GenAI is quickly growing in popularity, along with prescriptive analytics and online ML models, the question is raised whether we still need to care about data quality? We strongly believe that the answer is yes, and even more so than before! Our expectations of data are high, and this often leads to frustrations when reality does not meet these expectations. In the pursuit of data quality, expectations must be grounded in reality. It is often the case that a gap exists between anticipated outcomes and the actual data reality, which leads to frustration and mistrust. This talk delves into pragmatic strategies that can be employed to bridge this gap. The talk will discuss both the technical (hard) and cultural (soft) measures implemented to uphold these standards. Key Takeaways: 1. Integration tests serve as a proactive barrier, preempting the violation of data contracts, unlike reactive data quality checks. 2. Prioritisation is crucial; a product-centric mindset is key when evaluating the balance between resource investment and potential gain. 3. Data quality management is requiring both hard and soft measures Are you a data scientist, software engineer, product manager, or data engineer? Join us in this discussion; data quality concerns us all.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Ever wondered how location planning is done to build city infrastructure? Or when there is a disaster, how do we determine the possible affected areas and send reinforcements there? We require overhead imagery for that, which we mainly obtain from satellites. European Space Agency has sent various satellites however, the dataset from these satellites is huge and may even contain multiple bands from the electromagnetic spectrum. Large AI models have a huge potential in this domain, if they are developed to work well with this dataset. There are a lot of pre-trained Generative & Large Vision models on platforms like HuggingFace, Kaggle, etc., but these models do not integrate well with a specific domain like satellite datasets, hence the need to train or fine-tune them. In this talk, we are going to see from where we can access open satellite datasets, fine-tune various Vision Models and Multimodals on it, and examine the following applications: - Perform Zero-Shot classification and object detection on satellite images with human language input using Multimodal models. - Image-to-image translation on satellite imagery using generative vision models.

uk: вхід без авторизації

name: Earth Observation through Large Vision Models


uk: Комп'ютерні науки

description: Building complex software projects is hard, but the fun part of doing it is that it often pushes you to find non-obvious solutions to problems which seem radical at first, yet almost obvious in hindsight. In this talk you learn some of the unusual Python tricks that we use at EdgeDB to write more efficient Python with fewer bugs, and which you can apply in your project next time you need to scratch a similar itch.

uk: вхід без авторизації

name: Edges of Python: Three Radical Python Hacks for Fun and Profit


uk: Комп'ютерні науки

description: In today's world, where disability affects a significant percentage of the population, it is crucial for open-source communities to address the challenges faced by persons with disabilities (PWDs) and work towards their inclusion. This talk will delve into practical measures such as referral programs, internal disability disclosures, and integrating disability into existing agendas rather than treating it as a separate issue. We will dive into disability mainstreaming with a focus on its role in promoting universal design and inclusivity. Attendees will gain insights into establishing disability mainstreaming committees, formulating action plans, implementing best practices, and monitoring and evaluating progress.

uk: вхід без авторизації

name: Effective Strategies for Disability Inclusion in Open Source Communities


uk: Комп'ютерні науки

description: Today's rapidly evolving software landscape requires agile and informed decision-making. This keynote will highlight the powerful combination of Python, Artificial Intelligence (AI), and heuristics for enabling dynamic decision-making processes. We will explore the strengths of Python as a versatile language, the potential of AI for intelligent decision support, and the value of heuristics derived from real-world experience. By embracing this synergistic trio, developers can navigate complex challenges, adapt to changing requirements, and make decisions that drive successful software outcomes. Through practical examples and insights, attendees will gain a deeper understanding of how to leverage Python, AI, and heuristics effectively, striking the right balance between technological innovation and tried-and-true techniques. Whether you're a seasoned developer or just starting your journey, this keynote will equip you with the tools and mindset to embrace agility and make informed decisions that propel your software projects forward.

uk: вхід без авторизації

name: Embracing Python, AI, and Heuristics: Optimal Paths for Impactful Software


uk: Комп'ютерні науки

description: Decorators are powerful, magical syntax sugar, offering a convenient way to wrap and enhance functions. But sometimes, it's not clear how to use a defined decorator. What arguments should we pass to a given decorator? What functions does it target? Does it change the return type of the wrapped function? Have you ever faced these questions? If proper type hints are defined for decorators, static type checkers like mypy and pyright IDEs will point out the errors in usage. Thus, guiding you on the right path by catching bugs earlier, reducing unnecessary debugging and unexpected runtime behaviour. This talk will step you through type definitions utilizing `typing.TypeVarTuple`, `typing.Protocol`, `typing.ParamSpec`, `typing.Concatenate`, `Type Parameter Syntax`, and more, all of which are practical to implement and can make your project robust!

uk: вхід без авторизації

name: Enhancing Decorators with Type Annotations: Techniques and Best Practices


uk: Комп'ютерні науки

description: Every day, as software continues to “eat the world”, applications increasingly grow in complexity. Nowhere else is this phenomenon more prevalent than in big organizations, which over time have hired more people to develop and maintain more features. There, it is no longer possible to have a complete mental model of what is going on. Enterprise software, when unchecked, bloats and becomes brittle. Paradoxically, engineers build software to keep complexity at bay, not to create it. When writing code, the goal is to make processes less labor intensive and more reliable. Yet, enterprise software has become a black hole for man-hours. Python disrupted Java to become the de facto programming language for enterprises precisely because it tackled this problem in a way that Java, or any other programming language, could not. How is that so? This talk will dive deep into this exact question. What does Python offer that radically changed how software gets built in organizations, both big and small?. And why is it that newer languages that have come along, such as Go and Rust, haven’t been able to put a dent on its dominions, and have been forced to recede into niche use cases. This talk is for engineers who want to understand and leverage Python to its maximum maintainability potential. They intuitively understand that Python is a great tool for that, but are unsure as to how to do it.

uk: вхід без авторизації

name: Enterprise Python: Software That Lives Long And Prosper


uk: Комп'ютерні науки

description: CPython Core Development Panel by Pablo Galindo Salgado, Łukasz Langa, Carol Willing, Hugo van Kemenade, Victor Stinner and Petr Viktorin. Python has been evolving very rapidly in the last few years, and this is because new ideas have been taking over the Core Development. New ideas require new people, a new background and lots of energy. This panel aims for the people to be aware of the changes that are coming in 3.13, and future versions, as well as ways people can contribute by testing features, fixing issues, or even sharing their own ideas. Host: Konstantin Ignatov Panelists: Pablo Galindo Salgado, Łukasz Langa, Carol Willing, Hugo van Kemenade, Victor Stinner and Petr Viktorin

uk: вхід без авторизації

name: EuroPython 2024 — CPython Core Development Panel


uk: Комп'ютерні науки

description: Enjoy Fridays lightning talks. Short talks about everything by everyone. Hosted by Elise Kasai and Mark Smith: 00:00 *Welcome to Friday's Lightning Talks* 01:52 *Community Conference / Events Announcements & Celebration!* 07:12 *OpenFHE - Matrix Arithmetic Library* — Sukanya Mandal 10:05 *A mysterious case of distorted 3D models* — Miro Hrončok 15:28 *How to `from italia import

uk: вхід без авторизації

name: EuroPython 2024 — Lightning talks Friday


uk: Комп'ютерні науки

description: Enjoy Thursdays's lightning talks. Short talks about everything by everyone. Hosted by Velda Kiara and Angel Ramboi: 00:00 *Welcome to Thursday's Lightning Talks* 00:31 *Community Conference / Events Announcements & Celebration!* 17:55 *What to expect at the social event* — Moisés Guimarães 24:50 *AutoQuizzer: Can you beat an LLM in a pub quiz?* — Bilge Yucel 29:38 *Launching Crawlee for Python: Special beta version for EuroPython* — Saurav Jain 32:33 *Should Python adopt CalVer?

uk: вхід без авторизації

name: EuroPython 2024 — Lightning talks Thursday


uk: Комп'ютерні науки

description: Enjoy Wednesday's lightning talks. Short talks about everything by everyone. Hosted by Jodie Burchell and Rodrigo Girao Serrao: 00:00 *Welcome to Wednesday's Lightning Talks* 00:46 *EuroPython Game Tournament Announcement* — Neil Vaytet 04:31 *Czech wines* — Jacob Hallén 09:07 *How to make a Python meeting with friends* — Inácio Medeiros 15:16 *Make cron expressions readable again* — Fabrizio Damicelli 20:15 *Searching For Celebrities With Your Face* — Mark Smith 27:33 *Named constructors* — Austin Bingham 32:16 *A backend developer’s entry point to the UI* — Luke Purnell 38:05 *The Art of Puzzle Solving* — Olga Vinogradova

uk: вхід без авторизації

name: EuroPython 2024 — Lightning talks Wednesday


uk: Комп'ютерні науки

description: Open Source Sustainability Panel by Armin Ronacher, Çağıl Uluşahin Sönmez, Artur Czepiel, Deb Nicholson, Anwesha Das, Samuel Colvin The motivation behind this panel is to provide insights to the audience with regards to funding open source projects, manage the community interaction, and options people might find attractive in order to be paid while doing Open source. We can also observe the sustainability of a project by the amount of contributors, even if it’s code or activities around it like conferences, communities, and NGOs that support the ecosystem.

uk: вхід без авторизації

name: EuroPython 2024 — Open Source Sustainability Panel


Event Sourcing in production

uk: Комп'ютерні науки

description: Event Sourcing (ES) is a powerful concept that lets you adapt your business logic without losing data and past states. Whether your domain understanding changes or new requirements land on your lap, there is a path forward. Join us as we talk about some real-world tactics we relied on to manage Event Sourcing in production. We will accumulate a handful of patterns throughout the talk that will hopefully help you avoid pitfalls and bottlenecks. Our use cases build on the `eventsourcing` library, a mature and well-rounded Python library that deserves more attention. We will tackle the three key aspects of a successful event-sourced application: evolution, projection, and runtime. Software does not run in a vacuum, models need to change and evolve to reflect the world they live in. ES records the evolution of how we abstract our domain, how we see things. Eventually these abstractions can become clumsy or simply inappropriate. We can deal with that without breaking stride (losing data). ES also gives us the ability to revisit our perspective and change how we present the application state — by creating new projections and replaying the history. We will look at how it offers a cheap way to support optimal read-models, which we can can tweak and rebuild in the blink of an eye. Finally, we will present how such a system actually runs in a typical web application. Whether in the request loop (synchronous), or through eventual consistency (asynchronous). As a single process, or distributed for parallel processing. --- This talk assumes some familiarity with _Event Sourcing_ and its friends _Domain Driven Design_ (DDD) and _Command Query Responsibility Seggregation_ (CQRS).

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Bloomberg is a leading provider of financial data, with information spanning multiple decades. Handling and organizing these huge datasets can be challenging, with typical concerns including sluggish query performance, high storage costs, and data consistency problems. This talk will describe how Apache Iceberg is revolutionizing big data management, offering ACID transactions, time travel, and seamless schema evolution that enable lightning-fast query performance and robust data consistency for even our largest workloads. The session will introduce Apache Iceberg, an open-source table format that enables incremental updates, versioning, and schema evolution. The discussion will focus on how these features address common big data management challenges, improve query performance, and reduce storage costs. Finally, the session will outline how our Enterprise Data Lake Applications engineering team has harnessed the capabilities of Apache Iceberg (especially PyIceberg) to revolutionize our data management and analytical processing workflows. Attendees will be able to apply the best practices discussed in the talk to build better infrastructure for their growing data demands and spur innovation within their organization.

uk: вхід без авторизації

name: Exploring Apache Iceberg: A Modern Data Lake Stack


FastAPI Internals

uk: Комп'ютерні науки

description: FastAPI became one of the most web frameworks in Python. It has an amazing documentation, and easy to use API, which made it very popular. It's easy to start, and as a developer you have a lot of power on what you can do. But... How does it work internally? In this talk, we will explore the internals of FastAPI. We'll explore the dependency injection system, what are the benefits, and limitations. We'll also see how the routing system works, when the middleware stack runs, how the request and response are handled in detail, how the OpenAPI schema is generated, and the differences between async and non-async endpoints, and how WebSockets fit in the whole picture. Furthermore, we'll also see how the dependencies Pydantic and Starlette help FastAPI on its job. At the end of this talk, the attendee will understand what's underneath of this very popular package.

uk: вхід без авторизації


FastUI - panacea or pipe dream?

uk: Комп'ютерні науки

description: Are web interfaces defined in Python a genius idea, a complete folly, or (like most technologies) a good fit for some use cases but not all? I'll give a brief tour of packages that let you build web interfaces by writing only Python, including Streamlit, Gradio, NiceGUI, reflex, Solara, dominate, ReactPy and FastUI (recently released by the Pydantic team). The main three questions I'll be asking are: 1. Is building a web UI in Python really a good idea at all? 2. What fundamental trade-offs are required to make such a tool successful? 3. If someone can answer point 1 and 2, when's the right time to use these tools? Over the last couple of years, lots of different libraries have emerged to let you develop web interfaces without getting your hands dirty with HTML, CSS, the JS ecosystem; but so far none have got as popular as "traditional" template rendering (Jinja, Django) or modern SPA frameworks like React. So are we at the dawn of a new way era — and one of these frameworks will become ubiquitous. Or is the whole idea that you can build such an interface without engaging with the fundamental technologies that power them mistaken? One important question is "what kind of interface are we aiming at?" If we are trying to give complete control over the browser, allowing Python developers to do everything raw JavaScript can do; our solution will look very different to something that is "just" trying to allow Python developers to plug common components together to build 80% of UIs with 20% of the effort. Looking at the question through this lens will help explain the design choices of the above libraries, and might even allow us to guess at which approaches will be most

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Fine-tuning big neural nets like Large Language Models (LLMs) has traditionally been prohibitive due to high hardware requirements. However, Parameter-Efficient Fine-Tuning (PEFT) and quantization enable the training of large models on modest hardware. Thanks to the PEFT library and the Hugging Face ecosystem, these techniques are now accessible to a broad audience. Expect to learn: - what the challenges are of fine-tuning large models - what solutions have been proposed and how they work - practical examples of applying the PEFT library

uk: вхід без авторизації

name: Fine-tuning large models on local hardware


Forecasting the future with EarthPT

uk: Комп'ютерні науки

description: We introduce EarthPT -- an open source Earth Observation (EO) pretrained transformer written in Python and PyTorch. EarthPT is a 700 million parameter decoding transformer foundation model trained in an autoregressive self-supervised manner and developed specifically with EO use-cases in mind. EarthPT is trained on time series derived from satellite imagery, and can accurately predict future pixel-level surface reflectances across the 400-2300 nm range well into the future. For example, forecasts of the evolution of the Normalised Difference Vegetation Index (NDVI) have a typical error of approximately 0.05 (over a natural range of -1 - 1) at the pixel level over a five month test set horizon, out-performing simple phase-folded models based on historical averaging. We also demonstrate that embeddings learnt by EarthPT hold semantically meaningful information and could be exploited for downstream tasks such as highly granular, dynamic land use classification, crop yield, and drought prediction. Excitingly, we note that the abundance of EO data provides us with -- in theory -- quadrillions of training tokens. Therefore, if we assume that EarthPT follows neural scaling laws akin to those derived for Large Language Models (LLMs), there is currently no data-imposed limit to scaling EarthPT and other similar ‘Large Observation Models.’ EarthPT is released under the MIT licence here: GitHub: aspiaspace/EarthPT.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: This talk is specifically designed for Python developers and data practitioners who wish to deepen their skills in asynchronous code execution, from single CPU applications to complex distributed systems with thousands of cores. We'll provide a detailed exploration and explanation of Python's asynchronous execution models and concurrency primitives, focusing on `Future` and `Executor` interfaces within the `concurrent.futures` module, and the event-driven architecture of `asyncio`. Special attention will be given to the processing of large datasets, a common challenge in data science and engineering. We will start with the fundamental concepts and then explore how they apply to large scale, distributed execution frameworks like Dask or Ray. On step-by-step examples, we aim to demonstrate simple function executions and map-reduce operations. We will illustrate efficient collaboration between different concurrency models. The session will cover the transition to large-scale, distributed execution frameworks, offering practical guidelines for scaling your computations effectively and addressing common hurdles like data serialization in distributed environments. Attendees will leave with a solid understanding of asynchronous code execution underpinnings. This talk will empower you to make informed practical decisions about applying concurrency in your data processing workflows. You will be able to seamlessly integrate new libraries or frameworks into your projects, ensuring optimal development lifecycle, performance and scalability.

uk: вхід без авторизації

name: From built-in concurrency primitives to large scale distributed computing


uk: Комп'ютерні науки

description: Most Python programmers are probably aware that Python supports multiple inheritance. However, few are likely to be aware of its implications and inner workings. This talk aims to shed light on this commonly overlooked topic. In the first part of the talk we will start by reviewing the “diamond problem,” where a class inherits from two classes that have a common ancestor, and contrast how this issue is handled in Python compared to other object oriented languages. Next, we will discuss the Method Resolution Order (MRO) to see how Python determines the sequence in which classes are considered when searching for a method or attribute. We will also review the use of the `super()` function that allows a subclass to call a method from its superclass in a way that adheres to the MRO. During the second part of the talk, we will explore real-world scenarios related to the benefits, problems, and alternatives of using multiple inheritance in our programs. We will dedicate some time to examining the concept of a _mixin_ and how to implement it effectively in Python. Finally, we will delve into the _Interface Segregation Principle_ and explore collaboration and composition as mechanisms for avoiding the pitfalls of inheritance in general.

uk: вхід без авторизації

name: From Diamonds to Mixins: Demystifying Multiple Inheritance in Python


From Pandas to production: ELT with dlt

uk: Комп'ютерні науки

description: We created the “data load tool” (dlt), an open-source Python library, to bridge the gap between data engineers and data scientists. In this talk you will learn about how dlt can help you overcome typical roadblocks in your data science workflows, and how it streamlines the transition from data exploration to production. We will also discuss the pains of maintaining data pipelines and how dlt can help you to avoid common engineering headaches. Join us to learn best practices around data handling and managing failures with real-life examples!

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Customers only buy the products they are able to find. Improving the search functions on the website is crucial for user-friendliness. In our talk we present the lessons learnt from improving the search of our global online marketplace, which sells 20 million products per year. We moved from a traditional word-match based approach (BM25) to a modern hybrid solution that combines BM25 with a semantic vector model, an open-source language model that we fine-tuned to our domain. With numerous references to current literature, we will explain how we designed our new system and solved the multiple challenges we encountered on both the ML and engineering side (data pipeline encoding documents, live service encoding queries, integration with search engine). Our system is based on OpenSearch, the lessons can be applied to other search engines as well. In particular the presentation will cover: - Status and Short-Comings of our old Search - Introduction of Hybrid Search - Our Machine Learning Solution - Architecture and Implementation (with special consideration of latency) - Learnings and Next Steps

uk: вхід без авторизації

name: From Text to Context: How We Introduced a Modern Hybrid Search


uk: Комп'ютерні науки

description: Retrieval Augmented Generation (RAG) has emerged in recent years as a popular technique at the crossroads of Information Retrieval and Natural Language Generation. It represents a promising new approach that combines the strengths of both retrieval-based systems and generative AI models, aiming to address the limitations of each, while enhancing their overall performance on document intelligence tasks. This talk will introduce the key frameworks, methodologies and advancements in RAG, exploring its ability to empower Large Language Models with a deeper comprehension of context, by leveraging pre-existing knowledge from external corpora. We will review the theoretical foundations, practical applications, and technical challenges associated with RAG, showcasing its potential to impact various fields, such as document summarization or database management. Through this talk, attendees will gain insights into the most relevant topics related to RAG, including token embedding, vector indexing and semantic similarity search.

uk: вхід без авторизації

name: Fundamentals of Retrieval Augmented Generation


GeoPandas 1.0 and beyond

uk: Комп'ютерні науки

description: GeoPandas is one of the core packages in the Python ecosystem to work with geospatial vector data. By combining the power of several open source geo tools (GEOS/Shapely, GDAL/pyogrio, PROJ/pyproj) and extending the pandas data analysis library to work with geographic objects, it is designed to make working with geospatial data in Python easier. Recently, the development that started more than ten years ago resulted in version 1.0. This talk will give an overview of what is new in GeoPandas 1.0 and of recent developments in the broader ecosystem of packages on which GeoPandas depends, or that extend GeoPandas. We will highlight some changes and new features in GeoPandas 1.0, such as the new default IO based on pyogrio, closer integration of Shapely 2.0 leading to a range of new methods, and the removal of other geometry engines and consequences of that. We will look at the journey of the GeoPandas from its start at SciPy 2013 to the current 1.0 and discuss the plans moving forward, covering support of spherical geometries, native support for GeoArrow, and more. You will get a sense of what is coming in future, where to help the development and how to prepare your code for upcoming changes.

uk: вхід без авторизації


GraalPy - Fast Python Implementation

uk: Комп'ютерні науки

description: GraalPy is the fast Python implementation built on GraalVM. We run PyTorch and TensorFlow and ML models from Huggingface.co. We execute the test suites of the top 600 PyPI packages every day and are the most compatible alternative implementation of Python to date. We can JIT pure Python code to the same speed as code rewritten in Cython: https://twitter.com/timfelgentreff/status/1760597779250839820. We are the most seamless and performant choice for integration with Java in both directions, including Jython compatibility mode. In this talk, we want to show what's possible today with GraalPy and why you might choose it for your projects: for its performance, integration with Java and other languages, or sandboxing and distribution features.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Codebases often disappoint us, but we keep creating new ones with the absurd hope that it will be different this time. Spoiler alert: it never is. Notably, research codebases need to fulfil the oxymoron of being robust for long and large computations and highly flexible to experiment with outlandish ideas quickly. But what if there was a way to break the cycle and build code that endures, even for groundbreaking research. This talk isn't about magic bullets or silver linings. It's about the powerful connection between healthy teams and healthy code. We'll explore how a healthy team improved the health of a codebase by introducing type annotations and runtime type checking for JAX arrays.

uk: вхід без авторизації

name: Healthy code for healthy teams (or the other way around)


uk: Комп'ютерні науки

description: Nowadays, if you attend an event you're bound to end up with a catalogue of photographs to look at. Formal events are likely to have a professional photographer, and modern smartphones mean that it's easy to make a photographic record of just about any gathering. It can be fun to look through the pictures, to find yourself or your friends and family, but it can also be tedious. At our company get-together earlier in the year, the photographers did indeed take a lot of pictures. Afterwards the best of them were put up on our internal network - and like many people, I combed through them looking for those in which I appeared (yes, for vanity, but also with some amusement). In this talk, I'll explain how to automate finding the photographs I'm in (or at least, mostly so). I'll walk through Python code that extracts faces using OpenCV, calculates vector embeddings using imgbeddings and OpenAI, and stores them in PostgreSQL® using pgvector. Given all of that, I can then make an SQL query to find which pictures I'm in. Python is a good fit for data pipelines like this, as it has good bindings to machine learning packages, and excellent support for talking to PostgreSQL. You may be wondering why that sequence ends with PostgreSQL (and SQL) rather than something more machine learning specific. I'll talk about that as well, and in particular about how PostgreSQL allows us to cope when the amount of data gets too large to be handled locally, and how useful it is to be able to relate the similarity calculations to other columns in the database - in our case, perhaps including the image metadata.

uk: вхід без авторизації

name: How I used pgvector and PostgreSQL® to find pictures of me at a party