Contribution of small and medium enterprises to economic development of Algeria during period (2001-2017)

     تهدف الورقة البحثية إلى التعرف على واقع المؤسسات الصغيرة والمتوسطة في الجزائر، بالإضافة إلى إبراز مدى مساهمتها في تحقيق التنمية الاقتصادية، وباستخدام  المنهج الوصفي والمنهج التحليلي في عرض المعلومات تبين لنا، أن المؤسسات الصغيرة والمتوسطة تساهم بشكل كبير في الاقتصاد الوطني من خلال توفير مناصب الشغل، زيادة في الناتج الداخلي الخام، زيادة في القيمة المضافة وتنمية الصادرات وبالتالي تحقيق فائض في الميزان التجاري.     The purpose of this paper is to identify the reality of small and medium enterprises in Algeria, in addition to highlighting their contribution to economic development, and using the descriptive and analytical approaches in the presentation of information, we found that SMEs contribute significantly to the national economy by providing jobs, Increase in GDP, increase in value added and export development, thus achieving a trade surplus

TrackML high-energy physics tracking challenge on Kaggle

The High-Luminosity LHC (HL-LHC) is expected to reach unprecedented collision intensities, which in turn will greatly increase the complexity of tracking within the event reconstruction. To reach out to computer science specialists, a tracking machine learning challenge (TrackML) was set up on Kaggle by a team of ATLAS, CMS, and LHCb physicists tracking experts and computer scientists building on the experience of the successful Higgs Machine Learning challenge in 2014. A training dataset based on a simulation of a generic HL-LHC experiment tracker has been created, listing for each event the measured 3D points, and the list of 3D points associated to a true track.The participants to the challenge should find the tracks in the test dataset, which means building the list of 3D points belonging to each track.The emphasis is to expose innovative approaches, rather than hyper-optimising known approaches. A metric reflecting the accuracy of a model at finding the proper associations that matter most to physics analysis will allow to select good candidates to augment or replace existing algorithms

Track reconstruction at LHC as a collaborative data challenge use case with RAMP

Charged particle track reconstruction is a major component of data-processing in high-energy physics experiments such as those at the Large Hadron Collider (LHC), and is foreseen to become more and more challenging with higher collision rates. A simplified two-dimensional version of the track reconstruction problem is set up on a collaborative platform, RAMP, in order for the developers to prototype and test new ideas. A small-scale competition was held during the Connecting The Dots / Intelligent Trackers 2017 (CTDWIT 2017) workshop. Despite the short time scale, a number of different approaches have been developed and compared along a single score metric, which was kept generic enough to accommodate a summarized performance in terms of both efficiency and fake rates

Machine Learning Techniques for Charged Particle Tracking at the ATLAS Experiment

The Large Hadron Collider (LHC) uses proton-proton collisions to probe the fundamental building blocks of matter. Each collision produces thousands of particles scattering away from the detector center at nearly the speed of light. Reconstructing the trajectories of particles is a crucial task in most physics analysis. However, due to the rise in the number of simultaneous proton-proton interactions at the High Luminosity LHC (HL-LHC), the current tracking techniques will be the dominant component in CPU requirements. This thesis proposes the extension of existing as well as the design of novel Machine Learning (ML) approaches for the tracking of particles in the ATLAS experiment. We propose to describe and extend the similarity search problem in particle tracking through Approximate Nearest Neighbors (ANNs). In this context, the distance between data points is redefined with a tracking aware metric learning model termed TrackNet. Additionally, ANNs and metric learning models are evaluated on the TrackML dataset and on the ATLAS Inner Tracker Phase II dataset. We propose the Dynamic Tracking Linkage (DTL) clustering algorithm to process the output of the TrackNet model and to retrieve the final particle trajectories. This tracking inspired algorithm encapsulates physics constraints in its pairwise distance as well as a trained classifier that acts as an automatic stopping criteria

Hashing and metric learning for charged particle tracking

We propose a novel approach to charged particle tracking at high intensity particle colliders based on Approximate Nearest Neighbors search. With hundreds of thousands of measurements per collision to be reconstructed e.g. at the High Luminosity Large Hadron Collider, the currently employed combinatorial track finding approaches become inadequate. Here, we use hashing techniques to separate measurements into buckets of 20-50 hits and increase their purity using metric learning. Two different approaches are studied to further resolve tracks inside buckets: Local Fisher Discriminant Analysis and Neural Networks for triplet similarity learning. We demonstrate the proposed approach on simulated collisions and show significant speed improvement with bucket tracking efficiency of 96% and a fake rate of 8% on unseen particle events

Hashing and similarity learning for tracking with the HL-LHC ATLAS detector

At the High Luminosity Large Hadron Collider (HL-LHC), up to 200 proton-proton collisions happen during a single bunch crossing. This leads on average to tens of thousands of particles emerging from the interaction region. The CPU time of traditional approaches of constructing hit combinations will grow exponentially as the number of simultaneous collisions increases at the HL-LHC, posing a major challenge. A framework for similarity hashing and learning for track reconstruction will be described where multiple small regions of the detector, referred to as buckets, are reconstructed in parallel within the ATLAS simulation framework. New developments based on metric learning for the hashing optimisation will be introduced and new results obtained both with the TrackML dataset [1] as well as ATLAS simulation will be presented. [1] Rousseau. D, et al. "The TrackML challenge." 2018

The TrackML challenge

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