2018-10-19
10:53 |
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Our Chief Weapon in Programming
/ Van Eerd, Tony (speaker) (Christie Digital)
If I had time to convey just one* piece of programming advice, what would it be? Over the years I've often wondered "If I could only follow one bit of programming advice, what would it be", and have asked many people the same. I think I have narrowed down the one* rule I follow and have actually always followed, and which I find has helped me write correct, fast, understandable, maintainable code. [...]
2018 - 1:05:05.
CERN Computing Seminar
External link: Event details
In : Our Chief Weapon in Programming
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2018-10-19
10:42 |
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Learning New Physics from a machine
/ Wulzer, Andrea (speaker) (CERN)
We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The model-independent nature of our approach, and its ability to deal with rare signals such as those expected at the LHC, is quantitatively assessed in toy examples..
2018 - 0:41:15.
Machine Learning; IML Machine Learning Working Group: unsupervised searches and unfolding with ML
External links: Talk details; Event details
In : IML Machine Learning Working Group: unsupervised searches and unfolding with ML
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2018-10-19
10:34 |
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2018-10-19
10:29 |
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2018-10-19
10:23 |
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2018-10-18
17:05 |
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2018-10-18
16:11 |
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Guiding New Physics Searches with Unsupervised Learning
/ De Simone, Andrea (speaker) (SISSA)
I will describe an approach to search for new phenomena in data, by detecting discrepancies between two datasets. These could be, for example, a simulated standard-model background, and an observed dataset containing a potential hidden signal of New Physics.
I will propose a new statistical test, built upon a test statistic which measures deviations between two samples, using a Nearest Neighbors approach to estimate the local ratio of the density of points.
The test is model-independent and non-parametric, requiring no knowledge of the shape of the underlying distributions, and it does not bin the data, thus retaining full information from the multidimensional feature space.
As a by-product, the technique is also a useful tool to identify regions of interest for further study.
As a proof-of-concept, I will show the power of the method when applied to synthetic Gaussian data, and to a simulated dark matter signal at the LHC..
2018 - 0:47:08.
Machine Learning; IML Machine Learning Working Group: unsupervised searches and unfolding with ML
External links: Talk details; Event details
In : IML Machine Learning Working Group: unsupervised searches and unfolding with ML
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2018-10-18
15:51 |
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EARLY-CAREER RESEARCHERS IN MEDICAL APPLICATIONS @ CERN – SHORT TALKS
/ Stringhini, Gianluca (speaker) (Universita & INFN, Milano-Bicocca (IT)) ; Kozlowska, Wioletta Sandra (speaker) (CERN) ; Nowak, Marie (speaker) (UNIL - Universite de Lausanne (CH)) ; Gowtham , ADITHYA RAGHUPATHI (speaker) ; HUSSEIN, SHAUKATALI ZAHIR (speaker) ; MOHAMEDALI , Maryam (speaker)
Discover how technological advances for high-energy physics have become essential tools for modern medicine.
This series of short talks is given by Early-Career Researchers working on projects related to diverse medical applications that arise from technology developed at CERN and in high-energy physics.
The short talks from CERN researchers will focus on PET, Simulations and Dosimetry in Surgery
Team 2.7 from the recent Medtech:Hack will also present their winning project which aims to find solutions for people in rural areas to get better access to vital healthcare.
See more about the Medtech: Hack here.
Attendance is open to all without registration. If you require a CERN access card please contact us.
Coffee will be served at 15.30
For information about the next Knowledge Transfer Seminar, sign up to our e-group at http://cern.ch/go/F9cX
2018 - 0:02:55.
KT Seminars
External link: Event details
In : EARLY-CAREER RESEARCHERS IN MEDICAL APPLICATIONS @ CERN – SHORT TALKS
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2018-10-18
15:35 |
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2018-10-18
15:31 |
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