uk: Комп'ютерні науки

description: Parks & Equity: Using PyQGIS and Rgeoda for Evaluating Localized Equitable Park Access across the United States Across urbanized areas in the US, who are the communities being served by parks, and who is left out? In park system planning, the traditional measure of equitable access to parks has been the 10-minute walk isochrone, determining what percentage of residents live within a short walk of a park. However, these metrics do not capture inequities in park access for different demographic groups, nor do they consider the quality of parks that communities have access to. This research project explored a methodology to layer in considerations of park size, population density, and demographic characteristics to allow a more nuanced look at the equity of park access in urbanized areas across the United States. PyQGIS and Rgeoda are two open source platforms used for this analysis to allow for localized analysis at the national scale, as well as to make this analysis available to cities, towns, and other researchers interested in parks equity. Parks are an essential part of a city’s social life, supporting health and wellness for communities across our country. Access to high-quality parks with diverse amenities and programs is particularly critical for communities in urbanized areas with backyard deficits. Ensuring equitable access to great parks for all communities, especially those that are majority people of color or low-income, is increasingly the focus of many cities looking to plan civic parks and resilience practices for the future. Building on existing efforts to measure park access by the Trust for Public Land (TPL)’s “ParkServe” tool and the National Recreation and Park Association (NRPA)’s “Park Metrics”, this research project explored methods for measuring park access that go beyond the 10-minute walk circle. Using publicly available nation-wide datasets, we analyzed how access varies for different kinds of Americans. How equitable are our nation’s urban parks? How does access vary for people of color and low income residents? This presentation will showcase our approach to understanding parks and equity beyond the 10-minute walk measure and how we used PyQGIS and Rgeoda to scale up the analysis to all 487 urbanized areas in the US. Find out more at https://github.com/sasakiassociates/park-equity-analysis Authors and Affiliations – Jill Allen Dixon (1) Laura Marett (2) Elaine Limmer (3) Kai Ying Lau (4) Ken Goulding (5) Raj Adi Raman (6) (1) Sasaki Associates, Boston, USA (2) Sasaki Associates, Boston, USA (3) Sasaki Associates, Boston, USA (4) Sasaki Associates, Boston, USA (5) Sasaki Associates, Boston, USA (6) Sasaki Associates, Boston, USA Track – Use cases & applications Topic – Data visualization: spatial analysis, manipulation and visualization Level – 1 - Principiants. No required specific knowledge is needed. Language of the Presentation – English

uk: вхід без авторизації


Symmetry Breaking

Thomas Tucker

type: Person

cs: Matematika

de: Mathematik

fi: matematiikka

uk: Математика

en: Mathematics

fr: Mathématiques

da: Matematik

es: Matemáticas

nl: Wiskunde

de: Mathematik, Naturwissenschaften

uk: Математика, природничі науки

en: Mathematics, Natural Sciences

description: Given a group A acting on a set X, the distinguishing number, or asymmetric coloring number, denoted D(A,X) or ACN(A,X), is the smallest k such that X has a k-coloring where the only elements of A preserving the coloring fix all elements of X, thus "breaking" the symmetry of X under A. Albertson and Collins [1996] introduced and named D(G) in the context of a graph G with A=Aut(G),X=V(G), but precedents include Babai's work [1977] on regular trees, Cameron et al.\ [1984] and Seress [1997] on regular orbits for a primitive permutation group acting on the set of subsets of X, and work of many authors on the graph isomorphism problem using colors to "individualize" vertices. This talk will survey various aspects of symmetry breaking: contexts other than graphs (such as maps), bounds relating D(G) and the maximal degree of G, variations of D(G) where the coloring is proper or where edges are colored instead of vertices. An underlying theme is the role of the elementary "Motion Lemma'' (Cameron et al. [1984] and Russel and Sundaram [1997]) that D≤2 when m(A)>2log2(|A|), where m(A) is the minimum number of elements of X moved by any element of A not acting as the identity on X.

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

name: Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

type: Organization

cs: Video

de: Video

fi: Video

uk: Відео

en: Video

fr: Vidéo

da: Video

nl: Video

es: Video


uk: Математика, природничі науки

description: A graph is said to have infinite motion, if every automorphism moves infinitely many vertices. Tucker's Infinite Motion Conjecture asserts that if a locally finite graph has infinite motion, then there is a 2-colouring of its vertex set which is only preserved by the identity automorphism. We show that this is true for graphs whose maximum degree is at most 5. In case the maximum degree is 3, we can even drop the assumption of infinite motion.

uk: вхід без авторизації

name: Distinguishing numbers of infinite graphs with bounded degrees


uk: Математика, природничі науки

description: Apart from finitely many exceptions, connected vertex-transitive graphs of valency at most 3 have distinguishing number 2. Recently, Florian Lehner and I determined the distinguishing number of all vertex-transitive graphs of valency 4. In this case, there are infinitely many examples with distinguishing number 3. I will discuss this recent work, as well as some intriguing related questions.

uk: вхід без авторизації

name: Distinguishing number of vertex-transitive graphs of valency 4


uk: Математика, природничі науки

description: The distinguishing index D′(G) of a graph G is the least number of colours in an edge colouring that breaks all nontrivial automorphisms of G. This invariant is defined only for graphs with at most one isolated vertex and without K2 as a component (we call them admissible graphs). The general upper bound for connected admissible graphs is D′(G)≤Δ(G) except for small cycles C3,C4,C5. Moreover, it was proved by the second author that the equality holds only for cycles of length at least six, symmetric and bisymmetric trees and for two small graphs K4,K3,3. Recently, we proved with Imrich and Woźniak that D′(G)≤⌈Δ(G)√⌉+1 for every connected graph of order at least seven and without pendant edges. There are classes of graphs with the distinguishing index at most two (perhaps with few known exceptions). For instance, traceable graphs, claw-free graphs, Cartesian powers of connected graphs, and 3-connected planar graphs (the latter result was recently proved by Pilśniak and Tucker). A number of open problems will be formulated.

uk: вхід без авторизації

name: Bounds for the distinguishing index of finite graphs


uk: Математика, природничі науки

description: If we want to distinguish all vertices of the graph by coloring its elements, then we have the following possibilities. We can use the concept of coloring that breaks non-trivial automorphisms, or coloring that induces different color palettes for each vertex. These approaches are not independent. Always distinguishing using automorphisms is stronger than using palettes. And, very often, the corresponding parameters are quite distant from each other. We will show several situations when the corresponding parameters are close to each other. The talk is based on the papers [1] and [2]. [1] R. Kalinowski, M. Pilśniak, J. Przybyło and M. Woźniak, How to personalize the vertices of a graph?, European Journal of Combinatorics 40 (2014), 116-123. [2] R. Kalinowski, M. Pilśniak, M. Woźniak, Distinguishing graphs by total colourings, Ars Mathematica Contemporanea 11 (2016), 79-89.

uk: вхід без авторизації

name: Distinguishing vertices of a graph: automorphisms and palettes


uk: Математика, природничі науки

description: A vertex coloring of a graph G is called distinguishing if no non-identity automorphism of G preserves it. The distinguishing number of G, denoted by D(G), is the smallest number of colors required for such a coloring. We are intent to count number of different distinguishing colorings with a set of k colors for some certain kinds of graphs. An application to distinguishing lexicographic products of graphs is also presented. Coauthors: Bahman Ahmadi, Fatemeh Alinaghipour.

uk: вхід без авторизації

name: The number of different distinguishing colorings of a graph


uk: Математика, природничі науки

description: The distinguishing number (index) D(G) (D′(G)) of a graph G is the least integer d such that G has a vertex (edge) labeling with d labels that is preserved only by a trivial automorphism. In this talk we consider the maximal outerplanar graphs (MOP graphs) and show that MOP graphs, except K3, can be distinguished by at most two vertex (edge) labels. We also consider the distinguishing number and distinguishing index of regular and Cayley graphs. In particular, we present a family of Cayley graphs, graphical regular representations of a group, for which the distinguishing number and the distinguishing index is two. Coauthor: Samaneh Soltani

uk: вхід без авторизації

name: Symmetry breaking in maximal outerplanar, regular and Cayley graphs


uk: Математика, природничі науки

description: The minimal degree of a permutation group G is the minimum number of points not fixed by non-identity elements of G. Lower bounds on the minimal degree have strong structural consequences on G. In 2014 Babai proved that the automorphism group of a strongly regular graph with n vertices has minimal degree at least cn, with known exceptions. Strongly regular graphs correspond to primitive coherent configurations of rank 3. We extend Babai's result to primitive coherent configurations of rank 4. We also show that the result extends to non-geometric primitive distance-regular graphs of bounded diameter. The proofs combine structural and spectral methods. The results have consequences to primitive permutation groups that were previsouly known using the classification of finite simple groups (Cameron, Liebeck).

uk: вхід без авторизації

name: Minimal degree of the automorphism group of primitive coherent configurations


uk: Математика, природничі науки

description: This talk is based on the paper by Xiaorui Sun and John Wilmes, "Structure and automorphisms of primitive coherent configurations" (2015). Coherent configurations (CCs) are edge-colorings of the complete directed graph satisfying certain regularity conditions, modeling the orbitals (orbits on ordered pairs) of permutation groups. Introduced by Schur in 1934 for the study of permutation groups, CCs also include strongly regular graphs and association schemes, structures that do not necessarily arise from groups. Primitive CCs (PCCs) are a combinatorial model of primitive permutation groups. A base of a permutation group is a subset of the permutation domain of which the pointwise stabilizer is the identity. A base of a structure (such as a graph or a CC) is a base of its automorphism group. The minimum size of a base is a measure of the cost of destroying all symmetry. 37 years ago Babai gave a nearly tight O(n−−√logn) upper bound on the minimum base size of PCCs with the trivial exception of the clique (Annals of Math, 1981). As a corollary, this result solved the then century-old problem of bounding the order of primitive permutation groups in an unexpected, purely combinatorial way. In a major recent development, Sun and Wilmes reduced the upper bound to O(n1/3(logn)4/3), with known exceptions. As a corollary, they classify the largest primitive permutation groups, a result previously only known through the classification of finite simple groups (CFSG) (Cameron 1981). At the same time, the scope of their result goes significantly beyond primitive permutation groups, into a combinatorial domain where the CFSG has little relevance. To prove their result, Sun and Wilmes develop a combinatorial structure theory of PCCs. A crucial element of their theory is the discovery of "asymptotically uniform clique geometries" in PCCs with parameters in a certain range. Like Babai's, the Sun-Wilmes result is algorithmic: the proof is an analysis of the individualization/refinement method.

uk: вхід без авторизації

name: The Sun-Wilmes classification of primitive coherent configurations with many automorphisms


Symmetries of Homogeneous Structures

uk: Математика, природничі науки

description: Homogenous structures exhibit a high degree of symmetry. In particular their automorphism group is transitive, and any partial isomorphism between two finite substructures extends to an automorphism of the entire structure. It is thus natural to better understand these symmetries, and one approach is by trying to break them. The distinguishing number provides an interesting tool to do so and at the same time providing structural information. Two such well known homogeneous structures are the rationals and the Rado graph. The first one is easily seen to have unbreakable symmetry in this setting, its distinguishing number is infinite. On the other hand Imrich et al. showed that the Rado graph has distinguishing number 2. We will present an overview of the distinguishing number of the homogeneous simple and directed graphs through their classification, and discuss recent results for the countable Urysohn homogenous metric spaces of given spectrum.

uk: вхід без авторизації


uk: Математика, природничі науки

description: All but four finite vertex-transitive cubic graphs are 2-distinguishable. We discuss the corresponding amount, resp. density, of points needed to break all the symmetries for the latter ones. This will be done by splitting the problem into three cases depending on the number of edge orbits the graph has. In the cases of one or three edge orbits the number needed is always finite. The talk will be focused on the case of two edge orbits which will prove to be a bit more diverse.

uk: вхід без авторизації

name: The costs of symmetry breaking vertex-transitive cubic graphs


uk: Математика, природничі науки

description: The Sierpiński product of graphs was introduced as a generalization of Sierpiński graphs, which is a fractal-like family of graphs. The main building blocks of Sierpiński graphs are complete graphs and each next iteration is built in the fractal-like manner of a complete graph. This idea was recently generalized to a graph product, where instead of initially taking just one graph, we build a fractal-like structure with two arbitrary graphs, say G and H. Intuitively this is done in such a way that the product G⊗H has |G| copies of graph H which are connected among themselves according to the edges in G. So we get a graph with local structure of H, but global structure of G (i.e., if we contract all copies of H to a vertex, we get a copy of graph G). This construction is interesting because it may yield graphs with distinguishing number greater than 2. In the talk I will describe the Sierpiński products and list some of their basic properties, which may be useful to answer the open question(s) about the distinguishing number of Sierpiński products.

uk: вхід без авторизації

name: Distinguishing Sierpiński products of graphs


uk: Математика, природничі науки

description: A permutation group is semiprimitive if each normal subgroup is transitive or semiregular. This class of groups generalises the classes of primitive and quasi primitive groups, and has attracted most interest due to problems in algebraic graph theory. In this talk, I will survey some recent joint work with Kyle Rosa and Cheryl Praeger where we explored the question of whether certain bounds (in terms of degree) that hold for primitive groups could be adapted to the class of semiprimitive groups. Motivated by "useful'' results for the class of primitive groups, we investigated bounds on order, base size and fixity. In general, we found that similar bounds hold, or, one can establish even stronger bounds if we exclude certain families of primitive groups.

uk: вхід без авторизації

name: Bounds and invariants of semiprimitive groups


uk: Математика, природничі науки

description: The distinguishing number of a permutation group G≤\Sym(X) is the smallest number of colours required to colour the points of X such that only the identity of G preserves the colouring. The distinguishing number of a graph, in the traditional sense, is simply the distinguishing number of its automorphism group. Seress proved that every primitive group of degree n other than \Alt(n) and \Sym(n) has distinguishing number 2, except for a short list of known examples (with distinguishing number 3 or 4). In this talk, I will overview previous work on the distinguishing number of groups, before discussing recent joint work with Alice Devillers and Luke Morgan on the distinguishing number of semiprimitive groups. I will highlight the application of our result to graphs.

uk: вхід без авторизації

name: The distinguishing number of semiprimitive groups


uk: Математика, природничі науки

description: In 2011 Ellingham and Schroeder introduced the idea of a "distinguishing partition" for an action of a group Γ on a set X, namely a partition of X that is preserved by no nontrivial element of Γ. As a special case, a distinguishing partition of a graph is a partition of the vertex set that is preserved by no nontrivial automorphism. Distinguishing partitions are weaker at breaking symmetry than distinguishing colourings, and not every graph has a distinguishing partition. We discuss our work with Schroeder which linked distinguishing partitions of complete equipartite graphs with asymmetric uniform hypergraphs. We find a function f(n) such that a distinguishing partition for Km(n)=Kn,n,…,n, or equivalently for the wreath product action Sn≀Sm, exists if and only if m≥f(n). We also discuss some other work on distinguishing partitions of complete multipartite graphs by Michael Goff.

uk: вхід без авторизації

name: Distinguishing partitions and asymmetric uniform hypergraphs


uk: Математика, природничі науки

description: We introduce the distinguishing numbers of partially ordered sets. This study has given us the opportunity to apply classic results in poset theory to obtain results about this parameter. We present both an elementary proof of the distinguishing number of divisibility lattices and a more general result that employs Birkoff's Fundamental Theorem of Distributive Lattices. In other distinguishing bounds, we use a fundamental result about straight line embeddings of ranked planar posets with a minimum element and a maximum element to find bounds on planar posets.

uk: вхід без авторизації

name: The Distinguishing Number and Posets, Part I


uk: Математика, природничі науки

description: We introduce two distinguishing chromatic numbers of partially ordered sets, one based on incomparability and the other on comparability. This study has given us the opportunity to apply classic results in poset theory to obtain results about these parameters. We use Dilworth's Theorem to obtain a bound for the parameter based on incomparability. In distinguishing chromatic bounds based on comparability, we use the proof of Birkoff's Fundamental Theorem of Distributive Lattices. In contrast, we provide a bound for the Boolean lattice, Bn, based on its high degree of symmetry.

uk: вхід без авторизації

name: The Distinguishing Number and Posets, Part II


uk: Математика, природничі науки

description: The symmetry of a discrete structure can easily be altered (or in the case of maps, destroyed) by changing just a small part of it. But there are other contexts where a large degree of symmetry is desirable without achieving the maximum possible symmetry, for example with maps and polytopes that are maximally symmetric by orientation-preserving automorphisms but are chiral (admitting no refections). In a similar vein, sometimes restricting the symmetry can help achieve things that are either impossible or difficult when full symmetry is assumed. I will outline a few recent discoveries that explain and illustrate these phenomena.

uk: вхід без авторизації

name: Restricting symmetry (rather than breaking it)


A view of uncountable graphs

uk: Математика, природничі науки

description: Many results on distinguishing finite and locally finite graphs have easy extensions to graphs without degree restrictions or to uncountable graphs. Some other results are very difficult to generalize, and many hopelessly difficult. The talk presents a selection of results and problems for graphs without degree restrictions or uncountable graphs, and how they relate to the locally finite case. It concludes with challenging, interesting problems.

uk: вхід без авторизації

name: Banff International Research Station (BIRS) for Mathematical Innovation and Discovery


uk: Математика, природничі науки

description: In the talk we discuss a recently introduced finite element numerical method for the solution of partial differential equations on evolving domains. The approach uses a completely Eulerian description of the domain motion. The physical domain is embedded in a triangulated computational domain and can overlap the time-independent background mesh in an arbitrary way. The numerical method is based on finite difference discretizations of time derivatives and a standard geometrically unfitted finite element method with an additional stabilization term in the spatial domain. The performance and analysis of the method rely on the fundamental extension result in Sobolev spaces for functions defined on bounded domains. Theoretical findings include a complete stability and error analysis, which accounts for discretization errors resulting from finite difference and finite element approximations as well as for geometric errors coming from a possible approximate recovery of the physical domain. We show numerical examples that illustrate the theory and demonstrate the practical efficiency of the method.

uk: вхід без авторизації

name: A Finite Element Method For PDEs in Time-Dependent Domains


uk: Математика, природничі науки

description: In this paper we present a derivation of the surface Helmholtz decomposition, discuss its relation to the surface Hodge decomposition, and derive a well-posed stream function formulation of a class of surface Stokes problems. The surface gradient, divergence, curl and Laplace operators are defined in terms of the standard differential operators of the ambient Euclidean space. These representations are very convenient for the implementation of numerical methods for surface partial differential equations. Recently we derived a surface Helmholtz decomposition, in terms of these surface differential operators, based on elementary differential calculus. Using this decomposition the variational form of the surface Stokes equation can be reformulated as a well-posed variational formulation of a fourth order equation for the stream function. A particular finite element method for the latter formulation is explained and results of a numerical experiment with this method are presented.

uk: вхід без авторизації

name: Stream function formulation of surface Stokes equations


uk: Математика, природничі науки

description: In this talk, we will explore cell aggregation techniques for finite element numerical approximations with unfitted meshes, when using conforming spaces. We will present and analyze the method, show how it solves the small cut cell problem, and discuss its numerical implementation. We have combined this strategy with octree meshes to deal with large scale problems. We will also show parallel simulations with unfitted methods based on aggregation, and comment on the linear solver and octree handling steps. Finally, we will consider the Stokes problem, and analyze the well-posedness of the aggregated approach when using mixed finite element methods. It will motivate the introduction of a minimal stabilization and the use of new types of extension operators in the aggregated method.

uk: вхід без авторизації

name: Unfitted finite element simulations based on cell aggregation


uk: Математика, природничі науки

description: Radial basis functions are highly effective meshfree methods for the solution of PDEs problems. In this talk, we first solve a stationary and evolutionary Stokes problem by global and local divergence free RBFs techniques. We prove that, unlike global techniques, local methods are capable of solving large PDEs Stokes problem in an efficient way. We then, use these RBFs methods to solve null control problems for the Stokes system with few internal scalar controls, which are supported in small sets. We recall, that up to the best of our knowledge, Stokes control problems have not been treated in the literature by radial basis functions (RBFs) methods. The RBFs results are compared with classical mixed finite element methods, proving that the proposed meshfree techniques are competitive and more flexible, due to the lack of mesh, than these classical techniques. We close the talk with some open problems and perspectives in this field. Coauthors: Louis David Breton & Cristhian Montoya

uk: вхід без авторизації

name: Stokes problem, controlability and radial basis function methods


uk: Математика, природничі науки

description: We present a stable finite-element scheme for incompressible flows in time-dependent domains. The time step is independent of the mesh size, and only one linear system is solved on each time step. We consider fluid-structure interaction (FSI) and Navier-Stokes equations in time-dependent domains. The properties of the scheme are shown on several benchmarks and hemodynamic applications. This is the joint work with Maxim Olshanskii (University of Houston), Alexander Danilov, Alexander Lozovskiy and Victoria Salamatova (INM RAS, MIPT). An unconditionally stable semi-implicit FSI finite element method. Comput.Methods Appl.Mech.Engrg., V.297, pp.437-454, 2015 A.Danilov, A.Lozovskiy, M.Olshanskii, Yu.Vassilevski. A finite element method for the Navier-Stokes equations in moving domain with application to hemodynamics of the left ventricle. Russian J. Numer. Anal. Math. Modelling, V.32, N4, pp.225-236, 2017 A.Lozovskiy, M.Olshanskii, Yu.Vassilevski. A quasi-Lagrangian finite element method for the Navier-Stokes equations in a time-dependent domain. Comput.Methods Appl.Mech.Engrg., V.333, 55-73, 2018

uk: вхід без авторизації

name: A stable scheme for simulation of incompressible flows in time-dependent domains and hemodynamic applications


uk: Математика, природничі науки

description: We present accurate and efficient numerical methods to simulate the deformation of drops and bubbles in Stokes flow with surfactant. Both insoluble and soluble surfactant will be considered. For insoluble surfactant, a new boundary integral (BI) method is presented in which a special quadrature originally developed by Helsing and Ojala is used to retain accuracy for close drop-drop interactions (joint work with Sara Palsson and Anna-Karin Tornberg, KTH). For soluble surfactant, we describe longstanding work and recent results on the development of a hybrid or multiscale BI method that resolves a transition layer of bulk soluble surfactant near the interface.

uk: вхід без авторизації

name: Accurate and efficient boundary integral computations for interfacial flow with surfactant


uk: Математика, природничі науки

description: We propose a fully eulerian model for simulating the interaction of fluid-fluid and fluid-solid structures for incompressible fluids and a black-box type numerical scheme for computing its solution. The physical model consists of two Hamilton-Jacobi type PDEs: the fluid-fluid and fluid-solid interfaces are implicitly tracked by a level set equation, and the fluid motion is modeled by the vorticity formulation of Navier-Stokes equation, also of Hamilton-Jacobi type. This approach simplifies the task by allowing us to implement a single numerical scheme for solving both equations. We present the proposed scheme along with several numerical experiments simulating the coalescence of gas bubbles and the oscillations of an elastic membrane that demonstrate the simplicity and robustness of this approach.

uk: вхід без авторизації

name: A Simple Solver for Simulating Fluid-Structure Interactions in 2D


uk: Математика, природничі науки

description: In this talk we discuss the finite element approximation of fluid-structure interaction problems. After presenting a finite element version of the Immersed Boundary Method (originally developed by Peskin in the finite difference framework), we show how the introduction of a distributed Lagrange multiplier allows the design of a Fictitious Domain formulation. The properties of our approach include a superior mass conservation, unconditional stability for the time discretization, and rigorous convergence analysis for the steady state solution.

uk: вхід без авторизації

name: FSI: from the Immersed Boundary Method to a Fictitious Domain approach with Lagrange multiplier


uk: Математика, природничі науки

description: Interface problems arise in several applications including heart models, cochlea models, aquatic animal locomotion, blood cell motion, front-tracking in porous media flows and material science, to name a few. One of the difficulties in these problems is that solutions are normally not smooth across interfaces, and therefore standard numerical methods will lose accuracy near the interface unless the meshes align to it. However, it is advantageous to have meshes that do not align with the interface, especially for time dependent problems where the interface moves with time. Remeshing at every time step can be prohibitively costly, can destroy the structure of the mesh, or can deteriorate the well-conditioning of the stiffness matrix, and affect the stability of the problem. For a simple moving interface fluid-membrane interaction, we present a formal second-order finite element discretization in space and first-order in time where the finite element triangulation does not fit the interface and it is unconditionally stable in time independently of mesh parameters and fluid viscosity and membrane stiffness.

uk: вхід без авторизації

name: Unconditionally Stable CutFEM for Dynamic Interfaces in a Fluid-Structure Problem


uk: Математика, природничі науки

description: Motivated by the modeling of thin structures we study the Stokes problem under punctual forcing. We show the well-posedness of this problem on weighted spaces over general Lipschitz domains. We develop an a posteriori error estimator for this problem and show its reliability and efficiency. Numerical experiments illustrate and complement our theory.

uk: вхід без авторизації

name: The Stokes problem under singular forcing


uk: Математика, природничі науки

uk: вхід без авторизації

name: Some Simple Preconditioners for Unfitted Nitsche discretizations of interface elliptic problems


uk: Математика, природничі науки

description: In this talk we consider the formulation of FSI problems based on a Fictitious Domain method with a Lagrange multiplier. The time discretization of the resulting system of partial differential equations leads to the solution of a stationary problem with a saddle point structure. We present a rigorous analysis of existence, uniqueness and stability of the solution of the continuous problem and of its finite element discretization. Moreover, we introduce splitting schemes and discuss their stability and convergence properties.

uk: вхід без авторизації

name: Analysis of the finite element discretization of FSI with a fictitious domain approach


uk: Математика, природничі науки

description: In fluid mechanics the interaction of fluids with distinguishable material properties (e.g. water and air) is referred as multiphase flow. In this work we consider two-phase incompressible flow and concentrate on the representation and time evolution of the interface. There is an extensive list of methods to treat material interfaces. Popular choices include the volume of fluid and level set techniques. We propose a novel level-set like methodology for multiphase flow that preserves the initial mass of each phase. The model combines and reconciles ideas from the volume of fluid and level set methods by solving a non-linear conservation law for a regularized Heaviside of the (distance function) level-set. This guarantees conservation of the volume enclosed by the zero level-set. The equation is regularized by a consistent term that assures a non-singular Jacobian. In addition, the regularization term penalizes deviations from the distance function. The result is a nonlinear monolithic model for a phase conservative level-set where the level-set is given by the distance function. The continuous model is monolithic; meaning that only one equation is needed, doesn’t require any post-processing like: numerical stabilization, re-distancing, artificial compression, flux limiting and others, all of which are commonly used in either level-set or volume of fluid methods. In addition, we have only one parameter that controls the strength of regularization/penalization in the model. We start the presentation reviewing the main ingredients of this model: 1) a conservative level-set method by [Kees et all (2011)], which combines a distanced, non-conservative level-set method with the volume of fluid method via a non-linear correction and 2) elliptic re-distancing by [Basting and Kuzmin(2014)]. Afterwards, we manipulate the conservative level-set method by [Kees et all (2011)] to motivate our formulation. We present a first model which we then modify to resolve some difficulties. Finally, we present a full discretization given by continuous Galerkin Finite Elements in space and a high-order Implicit-Explicit time integration. We demonstrate the behavior of this model by solving different benchmark problems in the literature of level-set methods. Then, we present results of this model coupled with a Navier-Stokes solver to simulate water-air interaction problems in two and three dimensions.

uk: вхід без авторизації

name: A phase conservative, monolithic level-set method (with built-in redistancing) for multiphase flow


uk: Математика, природничі науки

description: Simulations of fluid and porous media couplings, multi-particles & multi-scale problems are important and challenging because of different governing equations, muilti-scales and multi-connected domain, and complicated interface conditions such as BJ and BJS relations. Most of numerical methods in the literature are based on direct approaches that leads large system of equations, often coupled and ill-conditioned, with lower order accuracy near the interface or boundary. In this talk, we propose a finite difference approach with unfitted meshes. By introducing several augmented variables along the interface, we can decouple the original problem as several Poisson/Helmholtz equations with intermediate jump conditions in the solution and the normal derivatives. One obvious advantage is that a fast Poisson/Helmholtz solver can be utilized. The augmented variables should be chosen such that the Beavers-Joseph-Saffman (BJS) and other interface conditions are satisfied. Another significant strategy is to enforce the divergence condition at the interface from the fluid side. We have shown that the original and transformed systems are equivalent. Because the interface conditions are enforced in strong form, we have observed second order convergence for both of the velocity and the pressure for our constructed non-trivial analytic solutions with circular interfaces. The proposed new method has also been utilized to simulate different flow/porous media setting with complicated interfaces which leads to some interesting simulations results such as effect of corners, orientation effect etc.

uk: вхід без авторизації

name: LS Augmented Methods for Fluid and Porous Media Couplings


uk: Математика, природничі науки

description: We discuss modeling, analysis, and simulation of a free boundary problem arising in one of two applications of current interest. First, we consider methane gas is mixed with water under low temperatures and high pressure conditions, leading to the presence of dissolved gas, free gas, and hydrate crystals. Second, we describe biofilm growth at the poreescale around the grains of porous medium. Depending on the time and spatial scale of interest, the model(s) can be of equilibrium (variational inequality) or kinetic type, and can be described with a phase field model. This is joint work with many students and collaborators to be named in the talk.

uk: вхід без авторизації

name: Phase interface in multicomponent mixtures at porescale: What matters at macro (Darcy) scale?


uk: Математика, природничі науки

description: We design and analyze a Hybrid High-Order (HHO) method on unfitted meshes to approximate elliptic interface problems. The curved interface can cut through the mesh cells in a very general fashion. As in classical HHO methods, the present unfitted method introduces cell and face unknowns in uncut cells, but doubles the unknowns in the cut cells and on the cut faces. The main difference with classical HHO methods is that a Nitsche-type formulation is used to devise the local reconstruction operator. We prove stability estimates and optimal error estimates in the H1-norm. Robustness with respect to cuts is achieved by a local cell-agglomeration procedure taking full advantage of the fact that HHO methods support polyhedral meshes.

uk: вхід без авторизації

name: Hybrid High-Order methods for interface problems


uk: Математика, природничі науки

description: We present stable and convergent mixed finite element methods for the discretisation of poroelasticity and elasticity/poroelasticity coupled equations in a new formulation, where the volumetric contributions to the total stress are merged into an additional unknown. The resulting saddle point formulations can be analysed by means of a Fredholm alternative, after realising that they are compact perturbations of Stokes-like invertible systems. Galerkin schemes are then proposed, and given that specific finite dimensional spaces are chosen adequately, the methods remain stable and optimally convergent in the incompressible limit.

uk: вхід без авторизації

name: Locking-free, three-field formulations for coupled elasticity-poroelasticity

name: Banff International Research Station (BIRS) for Mathematical Innovation and Discovery


uk: Математика, природничі науки

description: Problems in which immersed elastic structures interact with the surrounding fluid abound in science and engineering. Despite their scientific importance, analysis and numerical analysis of such problems are scarce or non-existent. In this talk, we consider the problem of an elastic filament immersed in a 2D or 3D Stokes fluid. We first discuss our recent results on the analysis of the immersed filament problem in a 2D Stokes fluid (the Peskin problem). We prove well-posedness and immediate regularization of the elastic filament configuration, and discuss the implication of these results for numerical analysis. We will then discuss the immersed filament problem in a 3D Stokes fluid (the Slender Body problem). Here, it has not even been clear what the appropriate mathematical formulation of the problem should be. We propose a mathematical formulation for the Slender Body problem and discuss well-posedness for the stationary version of this problem. Furthermore, we prove that the Slender Body approximation, introduced by Keller and Rubinow in the 1980's and used widely in the fluid-structure interaction community, provides an approximation to the Slender Body problem with some error bound. This is joint work with Analise Rodenberg, Laurel Ohm and Dan Spirn.

uk: вхід без авторизації

name: Analysis of the Dynamics of Immersed Elastic Filaments in Stokes Flow


uk: Математика, природничі науки

description: The Localized Spectral Decomposition finite element method is based on a hybrid formulation of elliptic partial differential equations, that is then transformed via several space decompositions. Such decompositions make the fomulation embarrassingly parallel and efficient, in particular in the presence of multiscale coefficients. It differs from most of the methods out there since it requires solution's minimum regularity. Also, it is robust with respect to high contrast coefficients.

uk: вхід без авторизації

name: Localized Spectral Decomposition (LSD): a robust and efficient finite element method for solving elliptic PDEs


uk: Математика, природничі науки

description: A novel conservative level set method is introduced. The method builds on recent conservative level set approaches and utilizes an entropy production to construct a balanced artificial diffusion and artificial anti-diffusion. The method is self-tuning, maximum principle preserving, suitable for unstructured meshes, and neither re-initialization of the level set function or reconstruction of the interface is needed for long-time simulation. Computational results in one, two and three dimensions are presented for finite element and finite volume implementations of the method.

uk: вхід без авторизації

name: An conservative anti-diffusion technique for the level set method


uk: Математика, природничі науки

description: Simulations of precipitating convection are usually carried out with cloud resolving models, which typically represent all the different phases of water: water vapor, cloud water, rain water and ice. Here we investigate the question: what is the minimal possible representation of water processes that is sufficient for these models? The simplified models that we present use a Boussinesq approximation, assume fast auto conversion and neglect ice. To test the simplified models, we present simulations of squall lines and scattered convection and show that they qualitatively capture observations made in nature and also seen in more comprehensive cloud resolving models, such as propagation of squall lines with tilted profiles, cold pools, and scattered convection.

uk: вхід без авторизації

name: Water vapour and rain dynamics in precipitating turbulent convection


uk: Математика, природничі науки

description: Many advanced engineering problems require the numerical solution of multidomain, multidimension, multiphysics and multimaterial problems with interfaces. When the interface geometry is highly complex or evolving in time, the generation of conforming meshes may become prohibitively expensive, thereby severely limiting the scope of conventional discretization methods. For instance, the simulation of blood flow dynamics in vessel geometries requires a series of highly non-trivial steps to generate a high quality, full 3D finite element mesh from biomedical image data. Similar challenging and computationally costly preprocessing steps are required to transform geological image data into conforming domain discretizations which respect complex structures such as faults and large scale networks of fractures. Even if an initial mesh is provided, the geometry of the model domain might change substantially in the course of the simulation, as in, e.g., fluid-structure interaction and free surface flow problems, rendering even recent algorithms for moving meshes infeasible. Similar challenges arise in more elaborated optimization problems, e.g. when the shape of the problem domain is subject to the optimization process and the optimization procedure must solve a series of forward problems for different geometric configuration. In this talk, we focus on recent finite element methods on cut meshes (CutFEM) as one possible remedy. CutFEM technologies allow flexible representations of complex or rapidly changing geometries by decomposing the computational domain into several, possibly overlapping domains. Alternatively, complex geometries only described by some surface representation can easily be embedded into a structured background mesh. In the first part of this talk, we briefly review how finite element schemes on cut and composite meshes can be designed by using Nitsche-type imposition of interface and boundary conditions. To make the formulations robust, optimally convergent and to avoid ill-conditioned linear algebra systems, so-called ghost-penalties are added in the vicinity of the boundary and interface. In the second part we demonstrate how CutFEM techniques can be employed to address various challenges from mesh generation to fluid-structure interaction problems, solving PDE systems on embedded manifolds of arbitrary co-dimension and PDE systems posed on and coupled through domains of different topological dimensionality.

uk: вхід без авторизації

name: CUTFEM: Discretizing Geometry and Partial Differential Equations


uk: Математика, природничі науки

description: Weight-adjusted inner products are easily invertible approximations to weighted L2 inner products and mass matrices. These approximations make it possible to formulate very simple time-domain discontinuous Galerkin (DG) discretizations for wave propagation based on the the energy of the system. The resulting methods are low storage, energy stable, and high-order accurate for acoustic and elastic wave propagation in arbitrary heterogeneous media and curvilinear meshes. We conclude with numerical results confirming the stability and high-order accuracy of weight-adjusted DG for acoustic, elastic, and coupled acoustic-elastic waves.

uk: вхід без авторизації

name: Energy-based methods for time-dependent acoustic and elastic wave propagation


uk: Математика, природничі науки

description: In tracer tests in oil reservoirs, a fluid (water) with a radioactive substance pulse is injected into the porous medium and it is monitoring until its arrival in neighboring extraction wells. From the observations of each extraction well, a tracer breakthrough curve is generated (time vs. tracer concentration). These curves are used to determine the existence of communication channels in underground formations and to characterize the porous medium properties, such as porosity, dispersivity, thickness of the production layer and residual oil saturation, among other applications. In this talk we will present the numerical modeling of the dynamics of the tracers, when the injection/extraction tests are performed in a single-well. We will show results for bipolar flow and a study for the injection phase in partially penetrating wells. Numerical simulations are generated by finite element with bilinear elements. Bibliography [1] J. Cosler: Effect of rate-limited Mass-Transfer on Water Sampling with Partially Penetrating Wells. Ground Water, 42(2) (2004), pp. 203--222 . [2] J.-Sh Chen, Ch.-L. Wu and Ch.-W. Liu: Analysis of contaminant transport towards a partially penetrating extraction well in an anisotropic aquifer. Hydrological Processes, 44 (2010), pp. 2125--2136. [3] J.S. Chen, C.W. Liu. Effect of transverse dispersion on solute transport in a vertical dipole flow test with a tracer. Journal of Hydrology, (2011), pp. 1--11. [4] Coronado M., Sandoval M. L., Escobar-Alfaro G. S. Modeling fluid flow and tracer transport in partially penetrating injection wells. Corrections are submitted to Journal of Petroleum Science and Engineering. March 5, 2018. [5] Sandoval M. L., Coronado M., Grande-Sánchez S. Dynamics of vertical dipole tracer tests in a sandy-clay reservoir. Paper in preparation.

uk: вхід без авторизації

name: Numerical modeling of tracer tests in single injection/extraction wells


uk: Математика, природничі науки

description: The porosity of a compacting sedimentary basin satisfies a nonlinear pseudo-parabolic partial differential equation. This equation is distinguished from the classical porous medium equation by a third order term, a degenerate elliptic operator in spatial variables acting on the time derivative of the solution. We describe the derivation of the model equation, review classical results for pseudo-parabolic equations and their relation to parabolic equations, present new existence and regularity results for this nonlinear PDE, and show they are consistent with expected behavior of solutions in this context.

uk: вхід без авторизації

name: A Pseudo-Parabolic PDE for Compaction of a Sedimentary Basin


uk: Математика, природничі науки

description: We develop exact polynomial sequences on Alfeld splits in any spatial dimension and for any polynomial degree. An Alfeld split of a tetrahedron is obtained by connecting the vertices of an n-simplex with its barycenter. We show that, on these triangulations, the kernel of the exterior derivative has enhanced smoothness. Byproducts of this theory include characterizations of discrete divergence-free subspaces for the Stokes problem, commutative projections, and simple formulas for the dimensions of smooth polynomial spaces. This is joint work with Guosheng Fu and Johnny Guzmán.

uk: вхід без авторизації

name: Exact smooth piecewise polynomial sequences on Alfeld splits


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: Correlation does not imply causation. It turns out, however, that with some simple ingenious tricks one can unveil causal relationships within standard observational data, without having to resort to expensive random control trials. I introduce the basic concepts of causal inference demonstrating in an accessible manner using visualisations. My main message for data analysts is that by adding causal inference to your statistical toolbox you are likely to conduct better experiments and ultimately get more from your data. E.g, by introducing Simpson’s Paradox, a situation where the outcome of all entries is in conflict with that of its cohorts, I shine a light on the importance of using graphs to model the data which enables identification and managing confounding factors. This talk is targeted towards anyone making data driven decisions. The main takeaway is the importance of the story behind the data is as important as the data itself. My ultimate objective is to whet your appetite to explore more on the topic, as I believe that it will enable you to go beyond correlation calculations and extract more insights from your data, as well as avoid common misinterpretation pitfalls like Simpson’s Paradox.

uk: вхід без авторизації

name: A Gentle Introduction To Causal Inference


uk: Комп'ютерні науки

description: Through its 1000mercis Impacts initiative, Numberly has been actively working with tech and non tech associations over the years for social, environmental and educational causes. As part of these commitments, we want to hand over the stage of this talk to some of our close partners. - Margaux from Latitudes.cc will introduce you to the various ways technology can be used for general interest. - Brieuc from Code Phoenix will share his great experience in coding teaching in prisons to help rehabilitation. - Souad from Descodeuses will explain how her association drives more women from disadvantaged neighborhoods in tech jobs. This talk is for all of you who are curious to know how to use your skills for a greater good. Join us to hear and share about Tech for Good!

uk: вхід без авторизації

name: Tech for Good: Build the world you want to live in


uk: Комп'ютерні науки

description: Open-source project "The Pattern" is the platform to inspire collaboration for building modern natural language processing tools and techniques and making them practically useful for end-users. In this session, I will walk through creating 3 Machine Learning NLP pipelines and deploying them using Redis and Redis Modules: RedisGears, RedisGraph and RedisAI. The first pipeline demonstrates how to use traditional NLP techniques, such as the Aho-Corasic algorithm to build a knowledge graph from medical literature using a medical thesaurus (UMLS). The second pipeline demonstrates how to build and deploy BERT Question/Answering model and create API for text to speech interface. Leveraging Redis Cluster sharding and capturing Redis Gears "keymiss" event to trigger processing of BERT QA in RedisAI. The third pipeline demonstrates how to deploy Google's T5 (text to text transfer transformers) to build summary of each article.

uk: вхід без авторизації

name: The Pattern: Machine Learning Natural Language Processing meets VR/AR


uk: Комп'ютерні науки

description: Learn how to build Flask apps for Cloud Run, one of Google Cloud's serverless platforms. To accelerate the Cloud Run development loop, we'll be using Cloud Code, an IDE plugin that makes working with Cloud Run easy. We'll also cover how to integrate products like Cloud SQL, Pub/Sub, and Secret Manager.

uk: вхід без авторизації

name: Developing Flask Applications for Google Cloud


Introduction to Quantum Deep Learning

uk: Комп'ютерні науки

description: The aim of the lightning talk is to shed light into the field of Quantum computation in the field of Deep Learning. Qubits , which form the fundamental units of quantum computing can be used to create quantum variational circuits which can be placed over traditional deep learning networks to create hybrid quantum-deep learning models. These models not only rely on the gradient convergence properties of general backpropagation technique, but also on the final probabilistic states of the Qubits. Essentially there has been quite a development to optimize the gradient convergence of these hybrid models with the help of Fischer approximation and Natural Gradient Descent.The talk would focus on the importance of Quantum Variational Deep Learning Circuits and how they provide an advantage over traditional Autograd based Circuits. The application of Quantum Variational circuits in the field of Reinforcement Learning as well as NLP would be one of the main points of the talk. There has been sufficient development in the field of quantum computing and this talk aims to throw light on how to exploit the probabilistic states of Qubits to enhance deep learning models. Topics: Introduction to Quantum Computing and Qubit system Quantum Variational Circuits Creating Hybrid Circuits (Classical-Quantum-Classical etc.) Realizing Performance of Hybrid Circuits Applications in the field of Quantum RL and Quantum NLP (research) Democratizing adoption of Quantum Circuits over traditional deep learning circuits Resources

uk: вхід без авторизації


Python in a world of Pan-Africanism

uk: Комп'ютерні науки

description: The use of Python in Africa is widely spread daily. In this talk, I will take you on a journey of what Python means to us in a Pan-African setting, lessons learned from organizing PyCon Africa, the people behind it, challenges we are facing and reflections on what works in our ecosystem.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: In this talk, we will show you the use of GRASS GIS (Geographic Resources Analysis Support System - Geographical Information System) and other geospatial Python libraries within a Jupyter Notebook to simulate wildfire spread in Yosemite National Park California USA. We will also show you a straightforward workflow to obtain and save input geospatial data for wildfire simulation using Google Earth Engine (GEE) Python API, GeoPandas, and geemap (a Python package for interactive mapping with GEE). GRASS GIS commands are generally run into bash shells, thus in this talk, we will demonstrate how we run GRASS GIS commands from Jupyter notebook to model wildfire behavior and display the resulting maps. We expect the audience has a basic understanding of GIS, Remote Sensing, and Python programming.

uk: вхід без авторизації

name: Wildfire Modeling in Yosemite National Park


uk: Комп'ютерні науки

description: The lifecycle of a machine learning model only begins once it's in production. In this talk we provide a practical deep dive on best practices, principles, patterns and techniques around production monitoring of machine learning models. We will cover standard microservice monitoring techniques applied into deployed machine learning models, as well as more advanced paradigms to monitor machine learning models with Python leveraging advanced monitoring concepts such as concept drift, outlier detector and explainability. We'll dive into a hands on example, where we will train an image classification machine learning model from scratch using Tensorflow, deploy it, and introduce advanced monitoring components as architectural patterns with hands on examples. These monitoring techniques will include AI Explainers, Outlier Detectors, Concept Drift detectors and Adversarial Detectors. We will also be understanding high level architectural patterns that abstract these complex and advanced monitoring techniques into infrastructural components that will enable for scale, introducing the standardised interfaces required for us to enable monitoring across hundreds or thousands of heterogeneous machine learning models. Benefits to ecosystem This talk will benefit the ecosystem by providing cross-functional knowledge, bringing together best practices from data scientists, software engineers and DevOps to tackle the challenge of machine learning monitoring at scale. During this talk we will shed light into best practices in the python ecosystem that can be adopted towards production machine learning, and we will provide a conceptual and practical hands on deep dive which will allow the community to both, tackle this issues and help further the discussion.

uk: вхід без авторизації

name: Production ML Monitoring: Outliers, Drift, Explainers & Statistical Performance


uk: Комп'ютерні науки

description: In this talk I will walk you through all the steps needed to create and host feature rich and localized documentations (and even websites) using MkDocs + Github Pages. MkDocs is a famous Python powered project used to build documentation for a lot of projects. It's based on Markdown and supports Markdown extensions, custom themes and plugins to extend its rendering capabilities. But it did not support building documentations in multiple languages and this was a wanted feature since 2014... After building documentations using MkDocs I decided to use it to switch my website and blog from Wordpress to a static page generator. I thus took on the challenge to contribute localization support to the MkDocs project because I needed it and it could benefit the community. After 2 months of work, 175 comments and 85 commits over 55 files I'm glad that my PR have been merged and is part of the MkDocs 1.2 version. I learned a lot along the way and I wanted to share my experience and a comprehensive walk through to get you ready and happy to use these new MkDocs features!

uk: вхід без авторизації

name: Create beautiful and localized documentations and websites using MkDocs + Github


uk: Комп'ютерні науки

description: What would the world look like if Russia won the cold war? If the Boston Tea Party never happened? And where would we be if Guido van Rossum had decided to go to music school? Unfortunately we don't have the technology to slide into parallel worlds and explore alternative histories. However it turns out we do have the tools to simulate parallel realities and give decent answers to intriguing 'what if' questions. This talk will provide a gentle introduction to these tools, professionally known as Causal Inference. The talk is aimed at data practitioners, preferably with basic knowledge of Python and statistics. That said, the focus of the talk is to nurture an intuitive understanding of the subject first, and implementation second. By the end of the talk I hope audience members could identify causal inference problems, have an intuitive understanding of the different tools they can apply to these problems, and have the appetite to further their learning! The talk will cover the problem of answering causal questions (The Fundamental Problem of Causal Inference) and the main tools to address it. The emphasis will be on intuitive understanding how the different tools work, rather than pesky underlying assumptions, complex notation or convoluted literature. Just enough theory and lines of code to get the message across. Outline: - Introduction to parallel universes and "what if?" questions? - The golden standard for causal inference. We'll discuss randomised controlled experiments and also set the scene for cases these aren't possible. [6 mins] Three key tools: - Differences-in-Differences - Propensity score methods - Synthetic Controls (or: creating an alternate universe on your machine) What's next: A wrap up that includes: - What we didn't cover (a few words about other techniques, DAGs, etc.) - Quick overview of Python tools for causal inference - Where do we go from here - resources, curriculums, readings and communities. Slides and code: https://github.com/alonnir/EuroPython-2021-Talk

uk: вхід без авторизації

name: Sliding into Causal Inference, with Python!


We build a ML pipeline after we deploy

uk: Комп'ютерні науки

description: This talk covers the importance of building end-to-end machine learning pipelines from day one. What you will learn: - why we need a machine learning pipeline and when to use it; - ML pipeline building blocks covering training and inference; - engineering around failures and engineering for performance; - ML pipelines debugging and monitoring; - open-source Python libraries to save your time. For whom: - data scientists, data analysts, data engineers, machine learning engineers, data product owners, Python developers, working or willing to work with machine learning. Prerequisites: to get the most out of this talk, Data Science, ML, and Python experience is recommended

uk: вхід без авторизації


Continuous Documentation for your code

uk: Комп'ютерні науки

description: Do you document your code? Do you think it is important? Imagine that you need to get back to your code in 6 month after you wrote it, there is always a big possibility that you will have to spend some time to find out how this code works. Or if someone else wrote some code, which is already in production and your task is to fix a bug in it and there is no documentation and no one actually knows what this code does. There are more benefits of implementing continuous documentation for the code: easy to onboard new team members, easy to share knowledge, if this code is open source - easy to start contributing, easy to see purpose and motivation of each piece of code, easy to keep versioning for each new release of the code. In this talk you will see the difference between documentation types and a demo in the end of the talk.

uk: вхід без авторизації


Heartbeats for Hackers

uk: Комп'ютерні науки

description: Heartbeats for Hackers [EuroPython 2021 - Talk - 2021-07-29 - Brian] [Online] By Andreas Klostermann Pulse sensors have found their way into a surprising number of devices, and they are useful in many kinds of contexts, most of all in medical ones. This talk will cover mostly "electrocardiography", the electrical way to listen to your heart. I will cover briefly how the heart works, how ECG sensors work and how to build a simple ECG with an Arduino or similar microcontrollers. Then I will show you a bit about how to analyze such data and do some machine learning. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

uk: вхід без авторизації


The spec you never knew you needed

uk: Комп'ютерні науки

description: Many of the systems we build fail to account for performance, error rates, data correctness, or other customer reliability needs. This talk discusses the basics of service level objectives by comparing them to ordinary functional specifications. It also serves as an introduction to System Reliability Engineering (SRE).

uk: вхід без авторизації


Finding Magic in Python

uk: Комп'ютерні науки

description: This talk was inspired by a personal project I worked on a while ago where I created my own magical universe in order to learn and apply Python concepts I hadn’t used before. Some of these concepts are included in the talk in a clear, precise and well-explained manner, including examples from the magical universe. The talk tells a story about how our passions can be used for learning to create something magical. Level: The talk is suited both for beginners and intermediates. It might even contain some new knowledge for professionals. Required knowledge: Only basic Python knowledge is required, all other concepts will be introduced and explained clearly in the talk. The goal of the talk is two-fold: a) the audience should understand that learning becomes much more fun and engaging when they look for a topic they are interested in and use it for learning. b) the presented Python features should become clear such that everyone is able to use them in future projects or come back to the talk if they need a well-explained reference.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: "Smart people sometimes like to show off their smarts by demonstrating their mental juggling abilities. After all, if you can reliably remember that r is the lower-cased version of the url with the host and scheme removed, then you must clearly be very smart. One difference between a smart programmer and a professional programmer is that the professional understands that clarity is king." — Robert C. Martin, "Clean Code", p.25 Robert C. Martin says it’s hard to mentally translate one name (e.g. “r”) to another, and you can avoid it by using a better name (e.g. “urlPath”). But the problem is not just lost brainpower; it can also be misunderstanding. I’ve seen experienced Python programmers trying to teach Python “metaclasses” by explaining that a metaclass is the class of a class—which it isn’t. Metaclasses have a reputation of being difficult, but they actually become easy once you use the right term for them. Bad terminology can be far more damaging than just confusing people. In April 2021, the European Commission published a proposal for an Artificial Intelligence Act, which they proudly advertise as the “first ever legal framework on AI”. It has provisions such as prohibiting "an AI system that deploys subliminal techniques beyond a person’s consciousness in order to materially distort a person’s behaviour in a manner that causes or is likely to cause that person or another person physical or psychological harm.” What do they mean with "AI system"? Well, more or less... statistics. None of this would happen if we didn't use misleading terms such as "artificial intelligence". While I do get to metaclasses, the main part of this talk is a tour of bad names. The talk is suitable for all audiences, beginners and advanced alike.

uk: вхід без авторизації

name: Why you always had trouble understanding metaclasses


Innovation in the newsroom

uk: Комп'ютерні науки

description: In BBC News Labs, we're always looking for ways to innovate without requiring production teams to change their workflows. We investigated ways we could enrich our digital output by extracting detailed information about the contents of our TV and radio programmes from their running orders. Like many traditional broadcasters, the BBC uses the MOS Protocol for communications between newsroom computers and servers where media files such as audio and video are stored. Production teams create running orders using enterprise broadcasting tools, and every time they add details or make changes, XML messages are emitted by the newsroom computer systems. We've created a way to process these messages to build up a machine-readable version of the running order. We developed a general solution for dealing with MOS messages - a Python library called mosromgr (MOS running order manager). It's based on open standards and could be useful to other broadcasting organisations, so we've released it under an open source licence. This library was developed by the BBC News Labs team as part of a series of projects based around extracting information from running orders from the BBC's TV and radio programmes. This talk aims to share the detail of our innovation projects investigating personalisation, automatic chapterisation and live notifications, and showcases our Python library and AWS pipeline.

uk: вхід без авторизації


Python monorepos: what, why and how

uk: Комп'ютерні науки

description: As organizations and repos grow, we have to choose how to manage codebases in a scalable way. We have two architectural alternatives: Multirepo: split the codebase into increasing numbers of small repos, along team or project boundaries. Monorepo: Maintain one large repository containing code for many projects and libraries, with multiple teams collaborating across it. In this talk we'll discuss the advantages of monorepos for Python codebases, and the kinds of tooling and processes we can use to make working in a Python monorepo effective. At the end of this talk you will understand the tradeoffs of different codebase architecture choices, and how to evaluate tooling and processes that keep your repo humming along at scale. No prior knowledge is required.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Dependency Injection: Stealing Cool stuff from the Weird Kids [EuroPython 2021 - Talk - 2021-07-29 - Brian] [Online] By Bojan Miletic While dependency injections is widely used in other programming languages, it's a design pattern that is nowhere near as popular in Python. During this talk I would like to introduce you to this concept and show you how it can improve your engineering skills. I would also like to share couple of examples and ways you can implement them in python. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

uk: вхід без авторизації


uk: Комп'ютерні науки

description: It is well known that Python 3 is an object-oriented language. But if we look back at the roots of the language, functions as first-class objects were part of the initial design. Recently, functional programming has gained considerable popularity because it is robust, concise, and easier to test. That set of benefits are not only reserved for functional pure programming languages such as Haskell. In this talk, I will run into a series of concepts, such as shared state, pure functions, side effects, and some other functional programming lingo that will help us understand how to use some not well-known Python features influenced by functional principles. We'll see how to implement our functional patterns in the code we write every day. In the end, you will see why this paradigm is relevant right now and why most of the existing languages and libraries actually evolved and keep going in that direction.

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name: Functional Programming inside OOP? It’s possible with Python


uk: Комп'ютерні науки

description: In the ultra-competitive sporting world, the value of data and computing has been on the rise. When the difference between winning and losing, success and failure hinges on the smallest of margins, being able to calculate and implement optimal, timely decisions is literally game-changing, and potentially life-changing. Football, or Soccer to others, as the most popular sport in the world, perfectly illustrates the importance of computational data-empowered decisions, both on and off the pitch. This talk, accessible to beginner Pythonistas with intermediate football knowledge, provides the audience with an overview of popular applications of Python Scientific Computing & Data Science within the ecosystem of typically well-funded clubs, but emphasising the opportunities available to democratise such enabling tech more inclusively, and benefiting a more diverse base. Example takeaways are real-world use-cases involving spatio-temporal tracking & event data, Computer Vision/video AI, tactical analysis, and player insights. This talk ultimately supports the audience discovering this fast-growing domain and the role Python has and continues to play in advancing it globally.

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name: How Scientific Computing is advancing the world of Football


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description: When building data pipelines at scale, it is crucial to design data pipelines that are reliable, scalable and extensible according to evolving business needs. Designing data pipelines for reproducibility and maintainability is a challenge, as testing and debugging across compute units (threads/cores/computes) are often complex and time-consuming due to dependencies and shared states at runtime. In this talk, Chin Hwee will be sharing about common challenges in designing reproducible and maintainable data pipelines at scale, and exploring the use of functional programming in Python to build scalable production-ready data pipelines that are designed for reproducibility and maintainability. Through analogies and realistic examples inspired by data pipeline designs in production environments, you will learn about: What is Functional Programming, and how it differs from other programming paradigms Key Principles of Functional Programming How "control flow" is implemented in Functional Programming Functional design patterns for data pipeline design in Python, and how they improve reproducibility and maintainability Whether it is possible to write a purely functional program in Python This talk assumes basic understanding of building data pipelines with functions and classes/objects. While the main target audience are data scientists/engineers and developers building data-intensive applications, anyone with hands-on experience in imperative programming (including Python) would be able to understand the key concepts and use-cases in functional programming.

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name: Designing Functional Data Pipelines for Reproducibility and Maintainability


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description: If you are managing developer teams or departments a pet project may be a helpful tool to stay out of your developers hair and learn about the technologies used in your department without blocking your developers to babysit you. If you are like me and have a development background, you will want to get your hands dirty and start fixing bugs and implementing stories. However, this can have many negative side effects. Breaking established rules is easier in a leadership position and sets a bad precedent. By becoming a last instance for each technical decision you take away personal identification with the work of your engineers. It may also lead to distrust. With this talk, I want to share some of my experiences in that regard.

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name: Why You Should Consider Getting a Python Pet Project


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description: HPC, Scientific Big Data, co-design, Python: beneath the buzzwords, bringing together academics from the most disparate research fields to work on a common product is no easy feat. What worked, what didn't, and lessons learned from the Helmholtz Analytics Framework experience.

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name: Connecting Communities: the Helmholtz Analytics Framework and the making of Heat


Learn CPython by breaking it

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description: One of the main reasons that stop people to write Python C extensions and even attempt to contribute to CPython is due to the fact that you need to know another programming language, C. Wait, don't go! don't be scared by the fact that you will hear about C, this talks objective is not that! but only to learn the essential bits to get more comfortable around CPython. By understanding the internals, you will feel like the same as that time in your childhood when you took something apart, then re-assemble it again and it worked (if that was not the case, this will be your first time I promise). During this talk, you will go on a journey to understand how Python works, and which are the places you can look to understand the implementation of different Python details, from the whole interpretation process, until look what's lies under the module/types functions you use everyday. You will get the power to add new functionality to Python and even how to create your first C-based extension module. If you are not familiar with C, don't worry, I will guide you every time some C code is shown, and if you know some C, then you will be able to sit back and relax during this ride.

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description: When Python 3.10 comes out in October 2021, it will include a new feature called “Structural Pattern Matching”. Structural Pattern Matching has been a staple of functional programming languages, but it has recently appeared or been proposed as future additions to imperative and OOP programming languages (like JavaScript, C++, and Rust). It provides an elegant way of processing and deconstructing heterogeneous data structures, and it enables an alternative paradigm to method dispatch. As one of this feature's contributors, my goals in this presentation are to describe the motivation for this new functionality, present some of the problems that its use will effectively solve, and give a brief introduction of how to use it. This talk is aimed at intermediate Python developers (although a beginner will be able to understand it). After attending you should be able to understand not just how to use pattern matching, but also when it’s a good idea to use it and what are the possible pitfalls to avoid. There will be a Q&A session which could be a good chance to discuss your questions about why certain design decisions were made when introducing pattern matching into Python.

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name: Pattern Matching in Python: Learning why Python and patterns are a perfect match


Moving Fast with FastAPI

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description: FastAPI is the new kid on python’s web frameworks block. Don’t let its novelty fool you - it is feature packed and it has top notch documentation. In this talk, we’ll build an application using FastAPI - leveraging our knowledge from existing frameworks. We’ll see how the “fast” FastAPI is for fast development.

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description: Imagine a world where all the data generated on your phone and on your wearables never left these devices without compromising your user experience. Or, a world where AI collaboration between data owners and developers did not have to be slowed by bureaucracy and data compliance laws like the GDPR. Federated Machine Learning, originally introduced in 2015, aims to create this world. It is the gift of distributed computing to Artificial Intelligence for training models without compromising data privacy. Federated Machine Learning is a model training technique in which the data never leaves the source (for example, a data silo, a smartphone, or an IoT device) and a copy of a global model is trained at source while sharing only the learning with the global model. In this talk, I will present an introduction to privacy preserving machine learning with a focus on federated ML, and make a case for federated ML beyond just Gboard and into enterprises. The session will aim to demystify the jargon around Federated Machine Learning, specifically terms such as horizontal vs vertical federated ML, cross device/cross silo training, centralized vs decentralized training, and data centric vs model centric federated learning. I will briefly introduce existing FedML implementations in Python and also demonstrate a live code walk-through for a minimal centralized federated learning system built using python-socketio and confluent kafka. Finally, I will discuss the challenges faced by enterprises in incorporating federated machine learning. The most notorious difficulty with Federated ML is non-IID data. Other technical difficulties include network latencies affecting training coordination, single point of failure in centralized federated learning systems, computational limitations at source nodes, etc. Additionally, in data centric federated ML, where a data provider allows AI developers to use their data to build models, federated ML poses challenges for early stage EDA and development. I’m excited to talk about federated ML, which is ripe with opportunities for research and development, and the tech community is finally ready to adopt it.

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name: Federated Machine Learning with Python: Training models without looking at data


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description: Development tools such as Jupyter are prevalent among data scientists because they provide an environment to explore data visually and interactively. However, when deploying a project, we must ensure the analysis can run reliably in a production environment like Airflow or Argo; this causes data scientists to move code back and forth between their notebooks and these production tools. Furthermore, data scientists have to spend time learning an unfamiliar framework and writing pipeline code, which severely delays the deployment process. Ploomber solves this problem by providing: A workflow orchestrator that automatically infers task execution order using static analysis. A sensible layout to bootstrap projects. A development environment integrated with Jupyter. Capabilities to export to production systems (Kubernetes, Airflow, and AWS Batch) without code changes. * Who and why * This talk is for data scientists (with experience developing Machine Learning projects) looking to enhance their workflow. Experience with production tools such as Airflow or Argo is not necessary. The talk has two objectives: Advocate for more development-friendly tools that let data scientists focus on analyzing data and take off the overhead of popular production tools. Demonstrate an example workflow using Ploomber where a pipeline is developed interactively (using Jupyter) and deployed without code changes.

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name: Develop and deploy a Machine Learning pipeline in 30 minutes with Ploomber


uk: Комп'ютерні науки

description: Ever wondered about the difference between is and ==, or why you might need to use deepcopy? Maybe you’ve been stumped by an interview question about object lifetimes, or overriding the __eq__ method. Or, perhaps, you just really want to see some tuples behaving badly. Join me for a tour of some of CPython’s dark memory magic and unintuitive behaviours, where we’ll find out: What pointers are and where you’ll see them in your code; What the id of a Python object is, and why it matters; How CPython can tell when you’re done using an object, and what happens next. Python is a wonderful thing that takes all the complication of memory management away from us. We don’t have to worry about pre-allocating memory for our objects, or remember to free it once we’re done. So, given that we’re not doing it manually, how do these things happen? Do we have to care? Well, sometimes. Maybe. In this talk, we’ll discuss what a pointer is, where they crop in in Python, and what you need to know in order to avoid getting tripped up by them. We’ll also cover the id of an object, why it's implemented the way it is, and why 'equal' is different from 'identical'. Finally, we’ll talk about garbage collection, what reference counting is, and how CPython knows when it’s time to get rid of unused objects. Whether you just want to understand what’s going on under the hood without having to dive into the implementation, or you’re on the hunt for some new diabolical things to do with code (self-referential lists are only the beginning!), come along to this talk and learn about the pointers in your Python. This talk is aimed at anyone with an interest in how Python handles objects in memory, or who’s ever been confused by side-effects popping up in their code. Absolutely no knowledge of C is required, and Python beginners are extremely welcome.

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name: Pointers? In my Python? It's more likely than you think


Writing a python web framework in 2021

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description: Django, Flask and Pyramid are among the most popular Python web frameworks and for good reasons. But... Those projects are over 10 years old and tied to decisions made at the time of the first commit. The fact the web was a server-rendered world of request/response cycle is a big part in those decisions. Today's web has shifted with a special attention to API's and websockets. It still needs to care for the "regular" server-rendered page but it is not its only focus anymore! Not only that, but (well-documented) tools are available today that were not there 10 years ago. So, if you wanted to write the next big webframefork in 2021, what tools and libraries would be at your disposal? Somewhat ironically, those 3 main frameworks I mentioned have given rise to, or at least inspired some, "derivate" standalone libraries like ORM's, or schema serializers that can be used by a third party framework. Other libraries have also spawned as ports from other languages and, finally, the python WSGI ecosystem (like middlewares) has also grown in that time frame. So, is writing a python web framework in 2021 mostly a matter of picking up existing tools and putting them together? Let's take a closer look!

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EuroPython 2021 - Lightning Talks 07/28

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description: Lightning Talk 1

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EuroPython 2021 - Lightning Talks 07/29

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description: Lightning Talk 2

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EuroPython 2021 - Lightning Talks 07/30

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description: Lightning Talk 3

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description: A major trend in data science is the rapid adoption of Bayesian statistics for data analysis and modeling. With modern data-sets growing by orders of magnitude in size, the focus is now on developing methods capable of applying contemporary inference techniques to extremely large datasets. To this aim, I present PyAutoFit, an open-source probabilistic programming language for automated Bayesian inference that was recently published in the Journal of Open Source Software. I will begin by giving an overview of PyAutoFit’s core features, in particular how it: Makes it simple to compose and fit probabilistic models using a range of Bayesian inference libraries, such as emcee and dynesty. Handles the 'heavy lifting' that comes with model-fitting, including model composition & customization, outputting results, model-specific visualization and posterior analysis. Is built for big-data analysis, whereby results are output as a sqlite database which can be queried after model-fitting is complete. PyAutoFit was developed by Astronomers seeking to fit large libraries of galaxy images to better understand the nature of dark matter. Using this science-case, I will describe PyAutoFit’s advanced features, such as multi-level models, automated model-fitting pipelines and support for massively parallel computing infrastructures. The goal of this talk is to introduce the audience to PyAutoFit so they can adopt it for their use-case. The only prerequisite is a basic understanding of object oriented programming in Python.

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name: PyAutoFit: A Classy Probabilistic Programming Language For Data Science


Formalizing a Language

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description: What is formal grammar, how does it create a programming language and what does Python's grammar look like. In Python 3.9 the grammar parsing engine was changed; find out some of the reasons why and what this change has allowed in terms of future language evolution.

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description: I like to remember the places I have been and I always liked the idea of having a map of those places, so as any developer would do, I built a web application for that. In this talk, we will see how you can use Python and Auth0 together to build your very own "Where Have I Been" map! I will walk you through all the steps we will need starting from scratch. From building the first API endpoints, protecting the endpoints that create new markers, all the data manipulation, and even deployment!

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name: Traveling through a secure API in Python


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description: Time series are everywhere in science and business, and the ability to forecast them accurately and efficiently can provide decisive advantages. For much of its history, time series forecasting has mostly been relying on "classical" statistical methods such as ARIMA. These methods work very well in many cases, but they are not appropriate for capturing patterns in large quantities of data. Very recently, some deep learning techniques have been proposed as a way to build very advanced and accurate models from large quantities of time series data. In our work, it has become very important to quickly develop and compare these new learning based methods against the more established statistical ones. Unfortunately there were no easy way to do that in Python and that is why we developed Darts. Darts is an open-source Python library that provides ready-to-use implementations of all sorts of forecasting models under a unified and simple API. It puts emphasis on reducing the experiment cycle duration and improving the ease of using, comparing and combining different models. In this talk, we will give a tour of Darts and show how it can be used to obtain great forecasting results in few lines of code. Goal of the talk: Introduce how one can tackle forecasting problems Overview of best practices from pre-processing to backtesting Obtain great results quickly in few line of codes

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name: Darts: Unifying time series forecasting models from ARIMA to Deep Learning


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description: A recent study by CheckPoint Research has recorded over 1,50,000 cyber-attacks every week during the COVID-19 pandemic. There has been an increase of 30% in cyber-attacks compared to previous weeks. The pandemic has been the main reason for job loss and pay cuts of people and has led to an increase in cybercrimes. Examples of cyber-attacks include phishing, ransomware, fake news, fake medicine, extortion, and insider frauds. Cyber forensics is a field that deals with the investigation of digital crimes by analyzing, examining, identifying, and recovering digital evidence from electronic devices and producing them in the court of law. Python has a great collection of built-in modules for digital forensics tasks. The talk begins with an introduction to digital crimes, digital forensics, the process of investigation, and the collection of evidence. Next, I will cover the various python modules and built-in functions required to build your first cyber forensic application. The modules covered in the discussion are pyscreenshot, PIL, secrets, argparse, hashlib, os,csv, logging, time, sys, stat and NLTK. Finally, I will demonstrate using code walk through the sample cyber forensic application. Outline 1. Introduction to digital crimes, digital forensics, the process of investigation, and the collection of evidence. (03 Minutes) 2. Setting up Python for forensics application development (02 Minutes) 3. Built-in functions and modules for forensic tasks (05 Minutes) 4. Forensic Indexing and searching (03 Minutes) 5. Forensic Evidence extraction (03 Minutes) 6. Using Natural Language Tools in Forensics (05 Minutes) 7. Code walkthrough of sample forensic application (08 Minutes) 8. Conclusion and Next steps (01 Minutes) 9. Questions and Answers (05 Minutes)

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name: Build Your First Cyber Forensic Application using Python


Driving 3D Printers with Python

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description: With OctoPrint I've created an open source web interface for consumer 3d printers that allows to control all aspects of the printer and ongoing print job and is extensible by plugins. OctoPrint is written in Python and installed by end users on their own devices. In this talk I want to provide some insights into the various challenges of developing and maintaining such a piece of end user facing software in Python and how I tackled them. I have found that maintaining software targeted at end users that are not necessarily very tech affine in general can be a somewhat challenging experience with unique problems that you wouldn't expect when developing software for more tech affine groups. I want to share my experiences so that others may learn from them and hopefully also don't repeat my mistakes. No knowledge about 3d printing or particular Python tooling is required. This is an updated edition of my talk of the same name at PyCon.DE 2019.

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From Research Project to PyPI Release

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description: Halfway through my first postdoc it was clear it would be very difficult to submit a paper to a journal before my contract ended. How do I make something useful in the time allotted that keeps me motivated enough to finish a paper after my contract ends (and useful on a CV)? Answer: package my code into a tested library via GitHub, The Python Package Index (PyPI) and Zenodo for citations. Goals: - Pros and cons of rearranging a project for public release (5 min) - Python cookiecutter templates (5 min) - Options for testing (standard library unittest vs pytest) (5 min) - Continuous Integration (Travis vs GitHub Actions) (5 min) - Documentation (5 min) - Release on Zotero for citation (5 min) Prerequisites: - Intermediate Python - Command line

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description: Build Serverless Python Applications using AWS Chalice [EuroPython 2021 - Talk - 2021-07-29 - Ni] [Online] By Harsh Bardhan With the rise of Serverless Applications, Developers now focus on developing the Code Logic and allow their Cloud Service Provider to focus on the deployment. Serverless exactly doesn’t mean that there are no servers. It just means that we don’t need to provision and maintain them. The Cloud Service Provider takes care of packaging and deployment almost instantaneously. Amazon Web Services (AWS) released an interesting Micro Web-Framework named: Chalice. It features everything would want from a Serverless Framework to develop and deploy applications using Python. With an easy to understand syntax, a unified Command Line Interface to create and deploy applications with ease of development experience, Chalice is a must. The talk would introduce you to the Serverless Environment on Amazon Web Services and how you can develop and deploy Python-based Production Grade RESTful API Services using Chalice. What is Serverless? Background to AWS Lambda Setting up a Project on Chalice Developing a REST API Service Deploying the Application on AWS Testing the API By the end of the talk, we will learn how you can run your Code on Serverless Platforms like AWS. We will also have set up an API on AWS tied up with various other AWS Services at ease. Finally, we will be taking a look at how Chalice aims to improve the developer experience with ease through the intuitive use of Serverless Technologies. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

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description: In the current technology era, all kind of applications work on data. Data is used to represent a set of information. The healthcare apps, e-commerce apps etc works on data. Sometimes, this data needs to be get updated to reflect new changes across the platform. This action can be performed manually but what if platform data is getting updated in realtime or let’s say in every 1 hour? Such kind of problem can be solved by implementing a service based on Producer Consumer model. In this talk, I will be covering how Producer Consumer models work and how such design pattern can be implemented with Python. I will be explaining the whole implementation process using other tools such as Kafka as data streamer and Elasticsearch as data store. Talk Outline: 1. Problem Statement (2 mins) Introduction to problem statement. 2. Introduction to Producer Consumer Model (3 mins) Basics of Producer Consumer Model Applications 3. Deep-dive explanation of Producer Consumer model using example (5 mins) Elasticsearch Kafka 4. Explaining parts of our Producer Consumer model (5 mins) What kind of data are we updating in our data store? Why it’s a high performance solution? Implementation in Python as end-to-end framework. 5. Code walkthrough (5 mins) Produce data Stream data Consume data 6. Conclusion and Learnings (5 mins) Learnings Performance Pros and Cons 7. Q/A Session (5 mins) Target Audience - Beginner / Intermediate Proposal Section - Web based Systems Prerequisites - Python & System Design

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name: High Performance Data Processing with Python, Kafka and Elasticsearch