![](\"https://storage.googleapis.com/kaggle-media/competitions/kaggle/4488/media/standardmodel2.png\")
In this competition, you are given a list of collision events and their properties. You will then predict whether a τ → 3μ decay happened in this collision. This τ → 3μ is currently assumed by scientists not to happen, and the goal of this competition is to discover τ → 3μ happening more frequently than scientists currently can understand.
\r\nIt is challenging to design a machine learning problem for something you have never observed before. Scientists at CERN developed the following designs to achieve the goal.
\r\nThis is a labelled dataset (the label ‘signal’ being ‘1’ for signal events, ‘0’ for background events) to train the classifier. Signal events have been simulated, while background events are real data.
\r\nThis real data is collected by the LHCb detectors observing collisions of accelerated particles with a specific mass range in which τ → 3μ can’t happen. We call these events “background” and label them 0.
\r\nThe test dataset has all the columns that training.csv has, except mass, production, min_ANNmuon, and signal.
\r\nThe test dataset consists of a few parts:
\r\nreal data for the control channel (ignored for scoring, used by agreement test)
\r\nYou need to submit predictions for ALL the test entries. You will need to treat them all the same and predict as if they are all the same channel's collision events.
\r\nA submission is only scored after passing both the agreement test and the correlation test.
\r\nThis dataset contains simulated and real events from the Control channel Ds → φπ to evaluate your simulated-real data of submission agreement locally. It contains the same columns as test.csv and weight column. For more details see agreement test.
This dataset contains only real background events recorded at LHCb to evaluate your submission correlation with mass locally. It contains the same columns as test.csv and mass column to check correlation with. For more details see correlation test.
Like last year's Higgs Boson Machine Learning Challenge, this competition deals with the physics at the Large Hadron Collider (LHC). However, the subject of last year's challenge, the Higgs Boson, was already known to exist. The aim of this year's challenge is to find a phenomenon that is not already known to exist – charged lepton flavour violation – thereby helping to establish \"new physics\".
\r\nThe laws of nature ensure that some physical quantities, such as energy or momentum, are conserved. From Noether’s theorem, we know that each conservation law is associated with a fundamental symmetry. For example, conservation of energy is due to the time-invariance (the outcome of an experiment would be the same today or tomorrow) of physical systems. The fact that physical systems behave the same, regardless of where they are located or how they are oriented, gives rise to the conservation of linear and angular momentum.
\r\nSymmetries are also crucial to the structure of the Standard Model of particle physics, our present theory of interactions at microscopic scales. Some are built into the model, while others appear accidentally from it. In the Standard Model, lepton flavour, the number of electrons and electron-neutrinos, muons and muon-neutrinos, and tau and tau-neutrinos, is one such conserved quantity.
\r\n
Interestingly, in many proposed extensions to the Standard Model, this symmetry doesn’t exist, implying decays that do not conserve lepton flavour are possible. One decay searched for at the LHC is τ- → μ+μ-μ- (or τ → 3μ). Observation of this decay would be a clear indication of the violation of lepton flavour and a sign of long-sought new physics.
\r\nYou will be working with real data from the LHCb experiment at the LHC, mixed with simulated datasets of the decay. The metric used in this challenge includes checks that physicists do in their analysis to make sure the results are unbiased. These checks have been built into the competition design to help ensure that the results will be useful for physicists in future studies.
\r\nTo get started, review the Data Page, and be sure to download the Starter Kit. The Starter Kit will help you to get used to the unique submission procedure for this competition.
\r\nYou've got lots of questions. Researchers at CERN & LCHb have the answers.
\r\n- What is the goal of this competition? (1:56)
- Why is finding τ → μμμ exciting? (2:18)
- What are flavours? (4:10)
- Why use machine learning to find τ → μμμ? (4:57)
- How did you decide on the size of the dataset? (5:31)
- Why is weighted AUC the evaluation metric? (6:09)
- Why use Ds → φπ data for the Agreement Test? (7:53)
- Why do we need a Correlation Check? (8:44)
- How will the competition results impact what you do? (11:38)
- How will the competition results be used at CERN? (12:17)
Flavour of Physics, Research Documentation
\r\nRoel Aaij et al., Search for the lepton flavour violating decay τ → µµµ, 2015, JHEP, 1502:121, 2015
\r\nNew approaches for boosting to uniformity
\r\nThis competition is brought to you by:
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Co-sponsored by:
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Additional support from:
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The evaluation metric for this competition is Weighted Area Under the ROC Curve. The ROC curve is divided into sections based on the True Positive Rate (TPR). To calculate the total area, multiply the area with TPR in [0., 0.2] by weight 2.0, the area with TPR in [0.2, 0.4] by 1.5, the area with TPR [0.4, 0.6] with weight 1.0, and the area with TPR [0.6, 0.8] with weight 0.5. Anything above a TPR of 0.8 has weight 0.
\r\n![](\"https://storage.googleapis.com/kaggle-media/competitions/kaggle/4488/media/roc_optimistic.png\")
These weights were chosen to match the evaluation methodology used by CERN scientists. Note that the weighted AUC is calculated only for events (simulated signal events for tau->µµµ and real background events for tau->µµµ) with min_ANNmuon > 0.4 (see details in section 2.2 Physics background).
\r\nBefore your predictions are scored with weighted AUC, they also must pass two addition checks: first an agreement test and then the correlation test. Please refer to their respective pages to learn about these tests and what is needed to pass them.
\r\nFor every event in the dataset, submission files should contain two columns: id and prediction. The prediction should be a floating point value between 0 and 1.0, indicating the probability that this event is τ → 3μ decay.
The file should contain a header and have the following format:
\r\nid,prediction", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Prizes", "content": "
14711831,0.3
16316387,0.3
6771382,0.3
686045,0.3
8755882,0.3
10247299,0.3
etc.
Participants with the best score on the private leaderboard who meet the eligibility criteria detailed in the competition rules will be awarded:
\r\nAn award will be given to the team that, as judged by the LHCb collaboration members on the organizing committee, creates a model that is most useful for the LHCb experiment. The selection criteria include:
\r\nThe winning team will be awarded $2000 and invited to meet the LHCb collaboration physicists at dedicated workshop held by University of Zurich on Feb. 18-19, 2016.
\r\nStrong performers in this competition may be invited to contribute to a NIPS workshop associated with this competition (pending acceptance from NIPS) with limited travel and conference support.
\r\nThe winners of the cash prizes must provide a paper & code for presentation at the NIPs workshop. However, they do not need to submit a speaker's proposal.
\r\nTo be eligible for the HEP Meets ML Award, the team must submit their model with code and a textual description (500-1000 words) at most one week after the end of the main competition.
\r\nThe committee will then select up to 10 submissions, based on the description and the place that the team took, and invite them to the NIPS workshop provided they prepare a talk & paper for the workshop. The committee will select the best paper/solution from these 10 papers and award them the HEP Meets ML Award prize at the workshop.
\r\nThe team will decide how the amount of the HEP Meets ML Award will be divided internally, it being understood that the Award will not cover the travel expenses of team members who belong to the LHCb collaboration or are based at CERN.
\r\nA single team can win more than one prize.
\r\nThe authors of the 3 most up-voted scripts for this competition will get their choice of an official Kaggle sweatshirt, t-shirt, or mug!
\r\nYour position on the leaderboard does not factor into winning Scripts for Swag.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "\r\n\t\r\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\r\n\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n
\r\n\r\n Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.
\r\n
The maximum size of a team is 4 participants.
\r\nYou may submit a maximum of 5 entries per day.
\r\nYou may select up to 2 final submissions for judging.
\r\nStart Date: 7/20/2015 10:50 PM UTC
\r\nMerger Deadline: 10/5/2015 11:59 PM UTC
\r\nEntry Deadline: 10/5/2015 11:59 PM UTC
\r\nEnd Date: 10/12/2015 11:59 PM UTC
\r\n\t\r\nCOMPETITION NAME (the 'Competition'): Flavours of Physics: Finding τ → μμμ
\r\nCOMPETITION SPONSOR: Yandex/CERN
\r\nCOMPETITION WEBSITE: https://www.kaggle.com/c/flavours-of-physics
\r\nPRIZES: $15,000
\r\nWINNER LICENSE TYPE: Open Source License
\r\nThese are the complete, official rules for the Competition (the 'Competition Rules') and incorporate by reference the contents of the Competition Website listed above.
\r\nBy downloading a dataset linked from the Competition Website, submitting an entry to this Competition, or joining a Team in this Competition, you are agreeing to be bound by these Competition Rules which constitute a binding agreement between you and the Competition Sponsor.
\r\nThe Competition is sponsored by the Competition Sponsor listed above and hosted on the Sponsor's behalf by Kaggle Inc ('Kaggle').
\r\nThe Competition will run from Start Date to End Date as listed on the Competition Website. Note that Competition deadlines are subject to change and additional hurdle deadlines may be introduced during the Competition. Any additional or altered deadlines not already described in these Competition Rules will be publicized to the Participants via the Competition Website. It is the Participant's responsibility in this Competition to check the website regularly throughout the Competition to stay informed of any new or updated deadlines. Neither Kaggle nor Competition Sponsor are responsible for any Participant's failure to do so.
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\r\nThe Competition is open to all individuals over the age of 18 at the time of entry and to all validly formed legal entities that have not declared or been declared in bankruptcy.
\r\nOfficers, directors, employees and advisory board members (and their immediate families and members of the same household) of the Competition Sponsor, Kaggle and their respective affiliates, subsidiaries, contractors (with the express exception of Kaggle's authorized Kaggle Community Evangelists), agents, judges and advertising and promotion agencies are not eligible to participate in the Competition.
\r\nYou are not eligible to receive any Prize in the Competition if you are a resident of a country designated by the United States Treasury’s Office of Foreign Assets Control (see <http://www.treasury.gov/resource-center/sanctions/SDN-List/Pages/default.aspx> for additional information).
\r\n'Submission' means the material submitted by you in the manner and format specified on the Website via the Submission form on the Website. You (or if you are part of a Team, your Team) may submit up to the maximum number of Submissions per day as specified above. All Submissions must be uploaded to the Website in the manner and format specified on the Website. Submissions must be received prior to the Competition deadline and adhere to the guidelines for Submissions specified on the Website.
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\r\nIf the Competition is a multi-stage Competition with temporally separate training data and/or leaderboard data, one or more valid Submissions must be made and selected during each stage of the Competition in the manner described on the Competition Website.
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\r\nUnless otherwise permitted by the terms of the Competition Website, Participants must use the Data solely for the purpose and duration of the Competition, including but not limited to reading and learning from the Data, analyzing the Data, modifying the Data and generally preparing your Submission and any underlying models and participating in forum discussions on the Website. Participants agree to use suitable measures to prevent persons who have not formally agreed to these Competition Rules from gaining access to the Data and agree not to transmit, duplicate, publish, redistribute or otherwise provide or make available the Data to any party not participating in the Competition. Participants agree to notify Kaggle immediately upon learning of any possible unauthorized transmission or unauthorized access of the Data and agree to work with Kaggle to rectify any unauthorized transmission. Participants agree that participation in the Competition shall not be construed as having or being granted a license (expressly, by implication, estoppel, or otherwise) under, or any right of ownership in, any of the Data.
\r\nUnless otherwise expressly stated on the Competition Website, Participants must not use data other than the Data to develop and test their models and Submissions. Competition Sponsor reserves the right in its sole discretion to disqualify any Participant who Competition Sponsor discovers has undertaken or attempted to undertake the use of data other than the Data, or who uses the Data other than as permitted according to the Competition Website and in these Competition Rules, in the course of the Competition.
\r\nParticipants are prohibited from privately sharing source or executable code developed in connection with or based upon the Data, and any such sharing is a breach of these Competition Rules and may result in disqualification.
\r\nParticipants are permitted to publicly share source or executable code developed in connection with or based upon the Data, or otherwise relevant to the Competition, provided that such sharing does not violate the intellectual property rights of any third party. By so sharing, the sharing Participant is thereby deemed to have licensed the shared code under the MIT License (an open source software license commonly described at <http://opensource.org/licenses/MIT>).
\r\nA Submission will be ineligible to win a prize if it was developed using code containing or depending on software licensed under an open source license:
\r\n* other than an Open Source Initiative-approved license (see <http://opensource.org/>); or
* an open source license that prohibits commercial use.
This Competition is a challenge of skill and the final results are determined solely by leaderboard ranking on the private leaderboard (subject to compliance with these Competition Rules). Participants' scores and ranks on the Competition Website at any given stage of the Competition will be based on the evaluation metric described on the Competition Website, as determined by applying the predictions in the Submission to the ground truth of a validation dataset whose instances were a fixed set sampled from the Data.
\r\nThe evaluation metric used for scoring and ranking Submissions will be displayed on the Competition Website.
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\r\n* disqualify your Submission(s); or
* require that you remediate within one week all issues identified in your Submission(s) (including, without limitation, the resolution of license conflicts, the fulfillment of all obligations required by software licenses, and the removal of any software that violates the software restrictions).
A tie between two or more valid and identically ranked submissions will be resolved in favour of the tied submission that was submitted first.
\r\nA Participant may decline to be nominated as a Winner by notifying Kaggle directly within one week following the Competition deadline, in which case the declining Participant forgoes any prize or other features associated with winning the Competition. Kaggle reserves the right to disqualify a Participant who so declines at Kaggle's sole discretion if Kaggle deems disqualification appropriate.
\r\nAs a condition of receipt of the Prize, the Prize winner must deliver the final model’s software code as used to generate the winning Submission and associated documentation (consistent with the winning model documentation template available on the kaggle wiki at https://www.kaggle.com/wiki/WinningModelDocumentationTemplate) to the Competition Sponsor. The delivered software code must be capable of generating the winning Submission and contain a description of resources required to build and/or run the executable code successfully.
\r\nIf a potential winning Participant or Team member is a U.S. citizen, potential winner must also sign and return an IRS W-9 form, or if a foreign resident, an IRS W-8BEN form, within the stated time in order to claim the prize.
\r\nPotential winners will be disqualified and the prize may be awarded to an alternate winner (the next-ranked qualified Participant on the leaderboard) if
\r\n* the required documentation is not returned within 14 days after receipt of any request to provide documentation;
* prize notification letter/email or prize is returned as undeliverable; or
* potential winner or winning Team member is disqualified for any reason.
By accepting any Prize, each Participant receiving a Prize thereby agrees to use of his/her name, address, likeness and/or Prize information by Kaggle and Competition Sponsor for promotional purposes in any medium without additional compensation.
\r\nAs a further condition of receipt of a Prize, each winning Participant thereby licenses their winning Submission and the source code used to generate the Submission according to the Winner License Type specified above (note: if no Winner License Type is specified above, the Winner License Type is deemed to be Non-Exclusive License).
\r\nIf the Winner License Type for the Competition (see Winner License Type above) is a Non-Exclusive License then each Winner by accepting a Prize thereby:
\r\n* grants to Competition Sponsor and its designees a worldwide, non-exclusive, sub-licensable, transferable, fully paid-up, royalty-free, perpetual, irrevocable right to use, not use, reproduce, distribute, create derivative works of, publicly perform, publicly display, digitally perform, make, have made, sell, offer for sale and import their winning Submission and the source code used to generate the Submission, in any media now known or hereafter developed, for any purpose whatsoever, commercial or otherwise, without further approval by or payment to Participant; and
* represents that he/she/it has the unrestricted right to grant that license.
If the Winner License Type for the Competition (see Winner License Type above) is Open Source License, then each Winner by accepting a Prize thereby:
\r\n* licenses their winning Submission and the source code used to generate the Submission under the MIT License (an open source software license commonly described at <http://opensource.org/licenses/MIT>), unless another open source license is chosen explicitly from the list of those approved by the Open Source Initiative at <http://opensource.org/licenses/>; and
* represents that he/she/it has the unrestricted right to grant that license.
If the Competition is classed above as a Recruiting Competition, Participant agrees to grant and hereby grants to Competition Sponsor a perpetual, irrevocable, worldwide, royalty-free, transferable, and sublicenseable right to use, review, reproduce, and internally distribute the Submission (including any and all submitted source code) in connection with evaluating Participant's suitability for employment, provided that the Submission licensed as set forth above will not be used for Competition Host’s commercial purposes except as may otherwise be set forth in this Agreement.
\r\nParticipating using more than one Kaggle account is deemed cheating and, if discovered, will result in disqualification from the Competition and any other affected Competitions and may result in banning or deactivation of affected Kaggle accounts.
\r\nAfter verification of eligibility, each Prize winner will receive the prize in the form of a check or wire transfer made out to the Prize winner (if an individual, or to the individual Team members if a Team). Allow 30 days from final confirmation for Prize delivery. Any winners who are U.S. citizens will receive an IRS 1099 form in the amount of their prize at the appropriate time. Prize winners are responsible for any taxes, fees or other liability resulting from their receipt of a Prize.
\r\nMultiple individuals or entities may collaborate as a team (“Team”). You may not participate on more than one Team. Each Team member must be a single individual operating a separate Kaggle account. You must register individually for the Competition before joining a Team. You must confirm your Team membership to make it official by responding to the Team notification message which will be sent to your Account.
\r\nTeam membership may not exceed the Maximum Team Size.
\r\nTeams may be permitted to merge at Kaggle’s discretion, so long as the merged Team meets all requirements in these Competition Rules. Kaggle will review any merger request and will either approve or reject the request within three business days. Merger requests may be rejected if the combined number of Entries made by the merging Teams exceeds the number of Entries permissible at the date of the merger request. Team merger requests will not be permitted within seven days of any deadline listed on the Competition Website.
\r\nIf a Team wins a monetary Prize, Competition Sponsor will allocate the Prize money in even shares between the Team members unless the Team unanimously contacts Kaggle via the Competition Website within three business days following the Submission deadline to request an alternative prize distribution.
\r\nBy registering, you agree that (a) your Account is complete, correct and accurate and (b) your registration may be rejected or terminated and all Entries submitted by you and/or your Team may be disqualified if any of the information in your Account is (or Competition Sponsor has reasonable grounds to believe it is) incomplete, incorrect or inaccurate. You are solely responsible for your Account. All registration information is deemed collected in the United States.
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* does not contain confidential information or trade secrets and is not the subject of a registered patent or pending patent application;
* does not violate or infringe upon the patent rights, industrial design rights, copyrights, trademarks, rights of privacy, publicity or other intellectual property or other rights of any person or entity;
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* does not trigger any reporting or royalty obligation to any third party; and
* was not previously published and has not won any other prize/award.
A breach of any of these warranties will result in the corresponding Submission being invalid.
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\r\nNeither Kaggle nor Competition Sponsor are responsible for (a) late, lost, stolen, damaged, garbled, incomplete, incorrect or misdirected Entries or other communications, (b) errors, omissions, interruptions, deletions, defects, or delays in operations or transmission of information, in each case whether arising by way of technical or other failures or malfunctions of computer hardware, software, communications devices, or transmission lines, or (c) data corruption, theft, destruction, unauthorized access to or alteration of Submission materials, loss or otherwise. Neither Kaggle nor Competition Sponsor are responsible for electronic communications or emails which are undeliverable as a result of any form of active or passive filtering of any kind, or insufficient space in any email account to receive email messages. Competition Sponsor disclaims any liability for damage to any computer system resulting from participation in, or accessing or downloading information in connection with, the Competition.
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", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Timeline", "content": "All deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The organizers reserve the right to update the contest timeline if they deem it necessary.
\r\nSince the classifier is trained on a mix of simulated signal and real data background, it is possible to reach a high performance by picking features that are not perfectly modeled in the simulation. We require that the classifier not have a large discrepancy when applied to real and simulated data.
\r\nTo verify this, we use a control channel, Ds → φπ, that has a similar topology as the signal decay, τ → 3μ. Ds → φπ is a much more well-known, well-observed behavior, as it happens more frequently. We provide both data and simulation samples for this decay, to which the classifier is applied. A Kolmogorov–Smirnov (KS) test is used to evaluate the differences between the classifier distribution on each sample.
\r\nDs → φπ data is not included in the training set. This control channel data is used purely in the test set as \"ignored\" data, meaning that they don’t contribute to the final scoring. The control channel dataset is used purely in Kolmogorov–Smirnov test. We require the KS-value of the test to be smaller than 0.09. Only when a submission can pass the agreement test on these ignored data, we can then regard the predictions as valid, and score on the submission.
\r\nIn particle physics experiments, it is well-known how to split the signal/background by their “weights”. The method is called sPlot. This weight is a measurement derived from the likelihood/probability that the event is a Ds → φπ and the weights range from -∞ to ∞. Higher weight means likely this event is signal, lower weights means it’s likely to be background.
\r\nThe Control channel, Ds → φπ, is split into two main parts, real (data1) and simulated (data2):
\r\nweight from sPlot function and signal = 0weight = 1 and signal = 1We use K-S test to tell that classifier outputs for data1 and data2 are from the same distribution.
\r\nPlease check out the github repo to learn more about how the agreement test is implemented.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Correlation Test", "content": "Each particle has its own mass. In an ideal world, one would just the mass of a particle to tell which particle it is. However, in reality, mass is an estimation, and it isn't a feature that scientists trust when building a model. Correlations with mass can cause an artificial signal-like mass peak or lead to incorrect background estimations.
\r\nThe mass column is not included in the test dataset. However, we use the hidden mass information to perform a Cramer-von Mises (cvm) test. This test iteratively compares two distributions of a) predicted values from submission for entire dataset and b) predicted values within a certain mass region in rolling window fashion along the whole mass range. Then we get average value for those checks. Getting similar distributions for all mass sub-regions means that your classifier is not correlated with the mass. Your submission must give a cvm value less than 0.002 to pass the test.
\r\nBelow is an illustration of this correlation test. On the left shows no correlation between prediction and mass, while on the right there is correlation.
\r\n![\"\"](\"https://storage.googleapis.com/kaggle-media/competitions/kaggle/4488/media/corr_test.png\")
Please check out the github repo to learn more about how the correlation test is implemented.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Starter Kit", "content": "There are several starter kits prepared for making submissions using different computing languages (Python, C++/ROOT).
\r\nAll the starter kits are available in the following GitHub repository:
https://github.com/yandexdataschool/flavours-of-physics-start
Also you can find implementations of the competition Agreement and Correlation checks there.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "About The Sponsors", "content": "The European Organization for Nuclear Research is the world’s largest high energy physics laboratory.
\r\nLHCb is an experiment set up to explore what happened after the Big Bang that allowed matter to survive and build the Universe we inhabit today.
\r\nThe Yandex School of Data Analysis (YSDA) is a free Master’s-level program in Computer Science and Data Analysis, which is offered by Yandex since 2007. The aim of the School is to train specialists in data analysis and information retrieval to be able to solve cutting edge industry problems as well as fundamental research challenges. YSDA is associated member of LHCb since December 2014.
\r\nYandex Data Factory are the Machine Learning and data analytics experts that use data science to improve business’ operations, revenues and profitability. By building upon the real-time personalisation and predictive analytics technology of parent company, Yandex, the fourth largest search engine in the world, Yandex Data Factory helps clients improve their business awareness through the exploitation of their own data.
\r\nYandex Data Factory’s proven data science and technology continually analyses, tests, refines and reapplies hundreds of hypotheses to the customers’ datasets to determine the best next course of action. It offers tailored, scalable, SaaS-driven Machine Learning services to a wide variety of data-reliant verticals, such as retail, financial services, travel and telecoms, who wish to use their data for purposes such as improving personalisation, segmentation, churn prevention or fraud detection.
\r\nYandex Data Factory was founded in 2014 by Yandex and is headquartered in Amsterdam, operating throughout Europe.
\r\n![\"\"](\"https://storage.googleapis.com/kaggle-media/competitions/kaggle/4488/media/intel_logo_crop.png\"){kind=logo}
Intel (NASDAQ: INTC) is a world leader in computing innovation. The company designs and builds the essential technologies that serve as the foundation for the world’s computing devices. As a leader in corporate responsibility and sustainability, Intel also manufactures the world’s first commercially available “conflict-free” microprocessors. Additional information about Intel is available at http://newsroom.intel.com and http://blogs.intel.com.
\r\nThe University of Zurich is one of the leading research universities in Europe and offers the widest range of degree programs in Switzerland. It was founded in 1833 and currently has seven faculties: Philosophy, Human Medicine, Economic Sciences, Law, Mathematics and Natural Sciences, Theology and Veterinary Medicine.
\r\nWarwick is one of the UK's leading universities, with an acknowledged reputation for excellence in research and teaching, for innovation, and for links with business and industry.
\r\nInstitute of Nuclear Physics, Polish Academy of Sciences. Founded in 1955 Institute of Nuclear Physics has become leading Particle Physics research institution and ranked as class A+ by Polish Ministry of Higher Education.
\r\nConsistently ranked as one of Russia’s top universities, the Higher School of Economics is a leader in Russian education and one of the preeminent economics and social sciences universities in eastern Europe and Eurasia.
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