"Can programming of multi-core systems be easier, please? The ALMA Approach"
By Oliver Oey, Karlsruhe Institute of Technologie - KIT for ScilabTEC 2015
Modelica Tutorial with PowerSystems: A tutorial for Modelica simulation智哉 今西
A simulation for electricity transmission using Modelica language.
Since all the tools come from OpenModelica (a free tool), you can easily start and test the simulation in any OS.
See the following link about OpenModelica: https://www.openmodelica.org/
"Can programming of multi-core systems be easier, please? The ALMA Approach"
By Oliver Oey, Karlsruhe Institute of Technologie - KIT for ScilabTEC 2015
Modelica Tutorial with PowerSystems: A tutorial for Modelica simulation智哉 今西
A simulation for electricity transmission using Modelica language.
Since all the tools come from OpenModelica (a free tool), you can easily start and test the simulation in any OS.
See the following link about OpenModelica: https://www.openmodelica.org/
The purpose of this document is to guide you step by step in exploring the various basic features of Scilab for a user who has never used numerical computation software.
Simulating Large-scale Aggregate MASs with Alchemist and ScalaDanilo Pianini
Recent works in the context of large-scale adaptive systems, such as those based on opportunistic IoT-based applications, promote aggregate programming, a development approach for distributed systems in which the collectivity of devices is directly targeted, instead of individual ones.
This makes the resulting behaviour highly insensitive to network size, density, and topology, and as such, intrinsically robust to failures and changes to working conditions (e.g., location of computational load, communication technology, and computational infrastructure).
Most specifically, we argue that aggregate programming is particularly suitable for building models and simulations of complex large-scale reactive MASs.
Accordingly, in this paper we describe Scafi (Scala Fields), a Scala-based API and DSL for aggregate programming, and its integration with the Alchemist simulator, and usage scenarios in the context of smart mobility.
Simulation video available at https://vid.me/BNVx
Presented at Multi Agent Systems & Simulation 2016, Gdansk, Poland
What did functional programming ever do for us (software engineers)? An overview of advantages of functional programming, with code examples in Scala. See recorded presentation at https://youtu.be/glDudJ3fqLk
Lightning talk : OCL's 4-valued logic is a source of much unhappiness. We attempt to recreate the design decisions that led to the current design and thereby suggest solutions to escape from it.
TMPA-2017: Modeling of PLC-programs by High-level Coloured Petri NetsIosif Itkin
TMPA-2017: Tools and Methods of Program Analysis
3-4 March, 2017, Hotel Holiday Inn Moscow Vinogradovo, Moscow
Modeling of PLC-programs by High-level Coloured Petri Nets
Dmitriy Ryabukhin, Egor Kuzmin, Valery Sokolov, Yaroslavl State University
For video follow the link: https://youtu.be/XJoKuCNrTi0
Would like to know more?
Visit our website:
www.tmpaconf.org
www.exactprosystems.com/events/tmpa
Follow us:
https://www.linkedin.com/company/exactpro-systems-llc?trk=biz-companies-cym
https://twitter.com/exactpro
Image Caption Generation: Intro to Distributed Tensorflow and Distributed Sco...ICTeam S.p.A.
Tech talk by Luca Grazioli (https://www.linkedin.com/in/luca-grazioli-a74927bb/) in the event ''Tensorflow and Sparklyr: Scaling Deep Learning and R to the Big Data ecosystem'', May 15, 2017 at ICTeam Grassobbio (BG). The event was part of the Data Science Milan Meetup (https://www.meetup.com/it-IT/Data-Science-Milan/).
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Scilab Technical Talk at NTU, TP and HCMUT (Dr Claude Gomez)TBSS Group
A very comprehensive set of slides presented by CEO, Scilab Enterprise, Dr Claude Gomez. TBSS-Scilab Singapore Center is the partner of Scilab Enterprise in Singapore and TBSS Khai Kinh Co. Ltd. is the partner in Vietnam. Both companies are the TBSS Group of Companies.
The purpose of this document is to guide you step by step in exploring the various basic features of Scilab for a user who has never used numerical computation software.
Simulating Large-scale Aggregate MASs with Alchemist and ScalaDanilo Pianini
Recent works in the context of large-scale adaptive systems, such as those based on opportunistic IoT-based applications, promote aggregate programming, a development approach for distributed systems in which the collectivity of devices is directly targeted, instead of individual ones.
This makes the resulting behaviour highly insensitive to network size, density, and topology, and as such, intrinsically robust to failures and changes to working conditions (e.g., location of computational load, communication technology, and computational infrastructure).
Most specifically, we argue that aggregate programming is particularly suitable for building models and simulations of complex large-scale reactive MASs.
Accordingly, in this paper we describe Scafi (Scala Fields), a Scala-based API and DSL for aggregate programming, and its integration with the Alchemist simulator, and usage scenarios in the context of smart mobility.
Simulation video available at https://vid.me/BNVx
Presented at Multi Agent Systems & Simulation 2016, Gdansk, Poland
What did functional programming ever do for us (software engineers)? An overview of advantages of functional programming, with code examples in Scala. See recorded presentation at https://youtu.be/glDudJ3fqLk
Lightning talk : OCL's 4-valued logic is a source of much unhappiness. We attempt to recreate the design decisions that led to the current design and thereby suggest solutions to escape from it.
TMPA-2017: Modeling of PLC-programs by High-level Coloured Petri NetsIosif Itkin
TMPA-2017: Tools and Methods of Program Analysis
3-4 March, 2017, Hotel Holiday Inn Moscow Vinogradovo, Moscow
Modeling of PLC-programs by High-level Coloured Petri Nets
Dmitriy Ryabukhin, Egor Kuzmin, Valery Sokolov, Yaroslavl State University
For video follow the link: https://youtu.be/XJoKuCNrTi0
Would like to know more?
Visit our website:
www.tmpaconf.org
www.exactprosystems.com/events/tmpa
Follow us:
https://www.linkedin.com/company/exactpro-systems-llc?trk=biz-companies-cym
https://twitter.com/exactpro
Image Caption Generation: Intro to Distributed Tensorflow and Distributed Sco...ICTeam S.p.A.
Tech talk by Luca Grazioli (https://www.linkedin.com/in/luca-grazioli-a74927bb/) in the event ''Tensorflow and Sparklyr: Scaling Deep Learning and R to the Big Data ecosystem'', May 15, 2017 at ICTeam Grassobbio (BG). The event was part of the Data Science Milan Meetup (https://www.meetup.com/it-IT/Data-Science-Milan/).
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Scilab Technical Talk at NTU, TP and HCMUT (Dr Claude Gomez)TBSS Group
A very comprehensive set of slides presented by CEO, Scilab Enterprise, Dr Claude Gomez. TBSS-Scilab Singapore Center is the partner of Scilab Enterprise in Singapore and TBSS Khai Kinh Co. Ltd. is the partner in Vietnam. Both companies are the TBSS Group of Companies.
ScyllaDB Open Source 5.0 is the latest evolution of our monstrously fast and scalable NoSQL database – powering instantaneous experiences with massive distributed datasets.
Join us to learn about ScyllaDB Open Source 5.0, which represents the first milestone in ScyllaDB V. ScyllaDB 5.0 introduces a host of functional, performance and stability improvements that resolve longstanding challenges of legacy NoSQL databases.
We’ll cover:
- New capabilities including a new IO model and scheduler, Raft-based schema updates, automated tombstone garbage collection, optimized reverse queries, and support for the latest AWS EC2 instances
- How ScyllaDB 5.0 fits into the evolution of ScyllaDB – and what to expect next
- The first look at benchmarks that quantify the impact of ScyllaDB 5.0's numerous optimizations
This will be an interactive session with ample time for Q & A – bring us your questions and feedback!
« Le « Machine Learning » – « Apprentissage statistique » ou « Analyse prédictive » - sort des labos de recherche et des cercles de spécialistes pour être de plus en plus être utilisé au sein des entreprises, et pas seulement les startups. En témoigne l’essor de la toolkit OpenSource Scikit-learn très vite répandue internationalement comme l’un des nouveaux standards de cette nouvelle façon de faire du logiciel, mais aussi la disponibilité depuis juillet 2014 d’Azure ML, le service de Machine Learning de Microsoft Azure. Dans cette session nous vous proposons un aperçu du développement de logiciel d’apprentissage statistique en Python avec SciKit-Learn. Nous invitons l'un des principaux contributeurs de cette toolkit, Olivier Grisel , ingénieur de recherche dans l’équipe équipe Inria PARIETAL à Saclay, à venir nous en présenter un aperçu dans une session interactive et basée sur de nombreux exemples et démos. Pour en savoir plus: http://scikit-learn.org https://team.inria.fr/parietal/ https://twitter.com/ogrisel
Pitfalls of machine learning in productionAntoine Sauray
Going from POC to production with Machine Learning can lead to many unexpected problems. We explore some of them in this presentation at the Nantes Machine Learning Meetup.
TensorFlow meetup: Keras - Pytorch - TensorFlow.jsStijn Decubber
Slides from the TensorFlow meetup hosted on October 9th at the ML6 offices in Ghent. Join our Meetup group for updates and future sessions: https://www.meetup.com/TensorFlow-Belgium/
So you've been deploying Java in the cloud and are wondering how to handle the new world of containers, microservices, and memory constraints. Cold starts got you down? Come to this session to learn about how the OpenJ9 and the JVM in general can help you on your Cloud Native journey.
Porting a Streaming Pipeline from Scala to RustEvan Chan
How we at Conviva ported a streaming data pipeline in months from Scala to Rust. What are the important human and technical factors in our port, and what did we learn?
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Join this workshop and accelerate your journey to production-ready Kubernetes by learning the practical techniques for reliably operating your software lifecycle using the GitOps pattern. The Weaveworks team will be running a full-day workshop, sharing their expertise as users and contributors of Kubernetes and Prometheus, as well as followers of GitOps (operations by pull request) practices.
Using a combination of instructor led demonstrations and hands-on exercises, the workshop will enable the attendee to go into detail on the following topics:
• Developing and operating your Kubernetes microservices at scale
• DevOps best practices and the movement towards a “GitOps” approach
• Building with Kubernetes in production: caring for your apps, implementing CI/CD best practices, and utilizing the right metrics, monitoring tools, and automated alerts
• Operating Kubernetes in production: Upgrading and managing Kubernetes, managing incident response, and adhering to security best practices for Kubernetes
XConf 2022 - Code As Data: How data insights on legacy codebases can fill the...Alessandro Confetti
In complex legacy modernization projects, rebuilding company-wide knowledge about and around business processes is one of the most challenging tasks. Engaging business stakeholders and capturing their actual needs is paramount, but not always enough to get all the underlying complex business logics, and, most of all, assessing the impact of changes.
Webinar: Começando seus trabalhos com Machine Learning utilizando ferramentas...Embarcados
Nesse webinar será apresentado o passo a passo de como criar projetos com Machine Learning utilizando ferramentas de terceiros como Sensi ML e Edge Impulse.
Tópicos que serão apresentados:
Kits de desenvolvimento para Machine Learning:
EV18H79A: SAMD21 ML Evaluation Kit with TDK 6-axis MEMS
EV45Y33A: SAMD21 ML Evaluation Kit with BOSCH IMU
SAMC21 xPlained Pro evaluation kit (ATSAMC21-XPRO) plus its QT8 xPlained Pro Extension Kit (AC164161)
Ferramentas de desenvolvimento:
MPLAB X
Data Visualizer
Ambiente de terceiros: Sensi ML e Edge Impulse
Coleta de dados
Como desenvolver um projeto usando Machine Learning sem conhecimentos específicos sobre o assunto e com conhecimentos sobre Machine Learning.
Clipper: A Low-Latency Online Prediction Serving SystemDatabricks
Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy serving loads. However, most machine learning frameworks and systems only address model training and not deployment.
Clipper is a general-purpose model-serving system that addresses these challenges. Interposing between applications that consume predictions and the machine-learning models that produce predictions, Clipper simplifies the model deployment process by isolating models in their own containers and communicating with them over a lightweight RPC system. This architecture allows models to be deployed for serving in the same runtime environment as that used during training. Further, it provides simple mechanisms for scaling out models to meet increased throughput demands and performing fine-grained physical resource allocation for each model.
In this talk, I will provide an overview of the Clipper serving system and then discuss how to get started using Clipper to serve Spark and TensorFlow models in a production serving environment.
Why electric vehicles need model-based design?
Because of the rising complexity in new vehicles, model-based design & systems engineering is needed to cascade the requirements and trace back any modification along the engineering lifecycle. Find out more in this presentation of a customer case about electric motor optimization.
Keynote of the French Space Agency CNES on the Asteroidlander MASCOT boarding the Hayabusa2 mission in collaboration with the Japanese Space Agency JAXA and the German Aerospace Center DLR
Faster Time to Market using Scilab/XCOS/X2C for motor control algorithm devel...Scilab
Rapid Prototyping becomes very popular for faster algorithm development. With a graphical representation of the algorithm and the possibility to simulate complete designs, engineers can help to reduce the time to market. A tight integration with MPLAB-X IDE allows the combination with standard C-coding to easily get mass production code. This solution was used to optimise a sensorless field oriented controlled PMSM motor driven pump efficiency. A model for closed loop simulation was developed using X2C blocks [1][2] for the FOC algorithm based on the existing application note AN1292 [3]. Enhancements to the original version were implemented and verified with simulation. The X2C Communicator was used to generate code of the new algorithm. With the online debugging capabilities and the scope functionality the algorithm was further tuned and optimized to achieve the highest possible efficiency of the pump.
Scilab and Xcos for Very Low Earth Orbits satellites modellingScilab
Very Low Earth Orbits are orbits in altitudes lower than 450 km. The interaction between the atmosphere particles and the surfaces of the spacecraft is responsible for the aerodynamic torques and forces. Simulating several aspects of the performance of a satellite flying in VLEO is very important to make decisions about the design of the spacecraft and the mission.
X2C -a tool for model-based control development and automated code generation...Scilab
Peter Dirnberger, Stefan Fragner
Nowadays, the market demands compact, stable, easy maintain-and customizable embedded systems. To meet these requirements, afast, simple and reliable implementation of control algorithms is crucial. This paper demonstrateshow model-based design with the help of Scilab/Xcosand X2C, developed by LCM,simplifiesand speedsup the development and implementation of controlalgorithms. As an example, acontrol schemefor a bearingless motoris presented.
A Real-Time Interface for Xcos – an illustrative demonstration using a batter...Scilab
As part of an EU-founded research project, the Scilab based development tool LoRra (Low-Cost Rapid Control Prototyping Platform) was created. This allows the realization of the continuously model based and highly automated Rapid Control Prototyping (RCP) design process for embedded software within the Scilab / Xcos environment (cf. Figure 1). Based on the application battery management system (BMS), this paper presents a Real-Time interface for Scilab.
Aircraft Simulation Model and Flight Control Laws Design Using Scilab and XCosScilab
The increasing demand in the aerospace industry for safety and performance has been requiring even more resourceful flight control laws in all market segments, since the airliners until the newest flying cars. The de facto standard for flight control laws design makes extensive use of tools supporting numerical computing and dynamic systems visual modeling, such that Scilab and XCos can nicely suit this kind of development.
Multiobjective optimization and Genetic algorithms in ScilabScilab
In this Scilab tutorial we discuss about the importance of multiobjective optimization and we give an overview of all possible Pareto frontiers. Moreover we show how to use the NSGA-II algorithm available in Scilab.
Collapsing Narratives: Exploring Non-Linearity • a micro report by Rosie WellsRosie Wells
Insight: In a landscape where traditional narrative structures are giving way to fragmented and non-linear forms of storytelling, there lies immense potential for creativity and exploration.
'Collapsing Narratives: Exploring Non-Linearity' is a micro report from Rosie Wells.
Rosie Wells is an Arts & Cultural Strategist uniquely positioned at the intersection of grassroots and mainstream storytelling.
Their work is focused on developing meaningful and lasting connections that can drive social change.
Please download this presentation to enjoy the hyperlinks!
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
Mastering the Concepts Tested in the Databricks Certified Data Engineer Assoc...SkillCertProExams
• For a full set of 760+ questions. Go to
https://skillcertpro.com/product/databricks-certified-data-engineer-associate-exam-questions/
• SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
• It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
• SkillCertPro updates exam questions every 2 weeks.
• You will get life time access and life time free updates
• SkillCertPro assures 100% pass guarantee in first attempt.
7. 7 - ScilabTec 2015
Performances
Scilab
Scilab
+
COLD
(kernel)
Scilab
+
COLD
(main
script)
COLD
(executable)
average
kernel
duraAon
390
ms
24
ms
12
ms
12
ms
total
Ame
for
10000
cycles
1
h
3
min
39
s
1
min
56
s
1
min
52
s
speed-‐up
x
16
x
31
x
32
Experimental
condiAons:
• Intel
Core
i7-‐3770
@
3.40GHz,
16Go,
Ubuntu
14.04
• Scilab
5.5.2,
COLD
2.1,
gcc
4.8.2
8. 8 - ScilabTec 2015
Some
more
performances…
▶ Series
of
benchmarks
from
an
industrial
partner
(Richelieu
Project)
▶ Raw
code,
no
calls
to
Scilab
library
funcAons
or
vector
operaAons
Scilab
5.4.1
COLDTM
1.6.3
Speed-‐up
Mat_mul
(100)
Mat_vec_mul
(1000)
Mat_mul2
(100)
2.38
s
2.13
s
2.08
s
0.0011
s
0.0016
s
0.0016
s
x2000
x1300
x1300
Mymesh
Skyline
Matrice
0.64
s
14.55
s
11.96
s
0.0014
s
0.035
s
0.054
s
x450
x400
x200
Mymesh
triangular
mesh
generaAon
Skyline
morse
matrix
indexing
building
Matrice
morse
matrix
building
9. 9 - ScilabTec 2015
COLD:
a
Scilab-‐to-‐C++
compiler
Inputs
▶ Scripts
and/or
funcAons
▶ Large
subset
of
language
basics
– scalar
types
(int,
real,
complex),
N-‐D
arrays,
sparse
matrices,
structures
– all
control
flow
structures
– user
funcAons…
▶ Large
subset
of
Scilab
funcAons
(>
200)
Outputs
▶ C++11
high-‐performance
code
▶ Standalone
executables
from
scripts
▶ Scilab
libraries
▶ Libraries
with
specific
interfaces
(for
integraAon
in
user
libraries)
▶ Human-‐readable
or
obfuscated
code
10. 10 - ScilabTec 2015
COLD:
specific
features
High-‐performance
C++
code
▶ Strong
type
inference
mechanism:
no
dynamic
run-‐Ame
overhead
▶ CompilaAon
opAmizaAons
▶ MathemaAcal
opAmizaAons
▶ ParallelizaAon
▶ Extends
input
data-‐size
/
parameter
values
coverage
User
friendliness
▶ Windows
/
Linux
▶ Compliance
analysis
to
detect
unsupported
features
and
opAmizaAon
obstacles
▶ Fast
compilaAon
(5000
lines
of
Scilab:
<
0.5s
to
generate
C++
code)
11. 11 - ScilabTec 2015
COLDTM
…
…
generates
high-‐performance
code
from
Scilab
codes
…
▶ stand-‐alone
applica7ons
▶ op7mized
libraries,
re-‐usable
from
Scilab
environment
…
offers
…
▶ reducAon
of
Ame-‐to-‐demo
or
Ame-‐to-‐product
▶ code
obfuscaAon,
intellectual
property
protec7on
…
evolves
con?nuously
and
benefits
from
…
▶ industrial
partnerships
(real
use-‐cases)
▶ collaboraAve
research
projects
(FUI
Richelieu,
FUI
Similan,
ITEA2
Mach)
…
is
a
scalable
product
…
▶ on-‐demand
customiza7on
for
specific
needs
Contact
us
on
cold-‐support@silkan.com