Symposium Artificial Intelligence for Science, Industry and Society

Session

Submitted contributions

Submitted contributions

21 Oct 2019, 14:30

Conveners

Submitted contributions: Session 1

• Steven Schramm (Universite de Geneve (CH))

Submitted contributions: Session 2

• Boris Escalante-Ramírez (UNAM)

Submitted contributions: Session 3

• Federico Carminati (CERN)

Submitted contributions: Session 4

• Steven Schramm (Universite de Geneve (CH))

44. Galaxy Morphology classification using CNN

Dr Jose Antonio Vazquez-Mata (Instituto de Astronomia, UNAM)

21/10/2019, 14:30

Physics and astronomy

Oral

Galaxy morphology is one of the most important parameters to understand the assembly and evolution of galaxies in the universe. The most used classification nowadays was proposed by Hubble (1926). This classification is based on the present of disks-, arms-, bulges-like structures, and is carried out mostly by eye identification. Thanks to the upcoming large telescopes and surveys, the new...

7. Perspectives of Deep learning techniques in Lattice 1+1d Scalar Field Theory

Dr Kai Zhou (FIAS, Goethe-University Frankfurt am Main)

21/10/2019, 14:50

Physics and astronomy

Oral

We explore the perspectives of machine learning techniques in the context of quantum field theories based on our recent publication[1]. In particular, we discuss two-dimensional complex scalar field theory at nonzero temperature and chemical potential – a theory with a nontrivial phase diagram. A neural network is successfully trained to recognize the different phases of this system and to...

36. Studying the parton content of the proton with deep learning models

Dr Juan Manuel Cruz Martínez (University of Milan)

21/10/2019, 15:10

Physics and astronomy

Oral

Parton Distribution Functions (PDFs) model the parton content of the proton. Of the many collaborations which focus on PDF determination in the last 20 years, NNPDF was pioneer on the use of Neural Networks to model the probability of finding partons (quarks and gluons) inside the proton with a given energy and momentum.

In this work we introduce state of the art techniques to modernize the...

57. Machine Learning in Velo LHCb monitoring and calibration in Run I and II.

Maciej Witold Majewski (AGH University of Science and Technology (PL))

21/10/2019, 15:30

Physics and astronomy

Oral

Studies of High energy physics rely on cutting edge technology of particle detectors. These detectors are working under harsh conditions of constant radiations, and their operation must be strictly supervised to achieve maximum efficiency and quality of data taking. The LHCb's Vertex Locator is a strip silicon detector used in Run I and II of Large Hadron Collider. We study the application of...

54. Online Estimation of Particle Track Parameters based on Neural Networks for the Belle II Trigger System

Steffen Baehr (Karlsruhe Institute of Technology)

21/10/2019, 15:50

Physics and astronomy

Oral

The Belle II particle accelerator experiment is experiencing substantial background from outside of the interaction point. To avoid taking data representing this background, track parameters are estimated within the pipelined and dead time-free level 1 trigger system of the experiment and used to suppress such events. The estimation of a particle track’s origin with respect to the z-Axis,...

50. Similarity Hashing and Learning for Tracks Reconstruction

Tobias Golling (Universite de Geneve (CH))

21/10/2019, 16:10

Physics and astronomy

Oral

At the High Luminosity Large Hadron Collider (HL-LHC), many
proton-proton collisions happen during a single bunch crossing. This leads on average to tens of thousands of particles emerging from the interaction region. Two major factors impede finding charged particle trajectories from measured hits in the tracking detectors. First, deciding whether a given set of hits was produced by a common...

8. Generative Adversarial Networks for fast simulation: generalisation and distributed training in HPC

Federico Carminati (CERN)

21/10/2019, 16:30

Physics and astronomy

Oral

Deep Learning techniques have are being studied for different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. We will describe an R&D activity within CERN openlab, aimed...

45. Portraying Double Higgs at the Large Hadron Collider

K.C. Kong (University of Kansas)

21/10/2019, 16:50

Physics and astronomy

Oral

We examine the discovery potential for double Higgs production at the high luminosity LHC in the final state with two b-tagged jets, two leptons and missing transverse momentum. Although this dilepton final state has been considered a difficult channel due to the large backgrounds, we argue that it is possible to obtain sizable signal significance, by adopting a deep learning framework making...

55. From GigScience to Combined Intelligence of Machine and Crowd

Dr Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))

22/10/2019, 17:00

Artificial intelligence, data science, and machine learning

Oral

Online education captures all new frontiers. Modern platforms are scaled to transfer the necessary information to the student: illustrations of basic concepts, theoretical foundations, etc. At the same time, practical skills are difficult to convey online; they can only be acquired through personal experience. Such difficulties make it difficult to study natural sciences (physics, chemistry,...

37. A machine learning approach for the feature extraction of pulmonary nodules

Ms Cecilia Loeza (Instituto Tecnológico Superior de Misantla), CECILIA IRENE LOEZA MEJIA (Instituto Tecnológico Superior de Misantla)

22/10/2019, 17:20

Medicine, chemistry, pharmacology, and the life sciences

Oral

In recent times, computational studies have emerged as a viable alternative for complementing the efforts of experienced radiologists in disease diagnosis. Computed tomography (CT) studies are a common way of predicting the lung nodule malignancy for the early diagnosis and treatment of lung cancer in patients. Early detection of the type of nodule is the key to determining the appropriate...

63. Skin Lesion Detection in Dermatological Images using Deep Learning

José Carlos Moreno-Tagle (UNAM)

22/10/2019, 17:40

Oral

We demonstrate that it is possible to approach the skin lesion classification problem as a detection problem (multiple localization with classification), a much more complex and interesting problem with satisfactory and promising results. The image dataset used in the experiments comes from the ISIC Dermoscopic Archive, an open-access dermatology repository. In particular, the ISIC 2017...

46. The QUA³CK Machine Learning Development Process and the Laboratory for Applied Machine Learning Approaches (LAMA)

Mr Simon Stock (Karlsruhe Institute of Technology)

22/10/2019, 18:00

Artificial intelligence, data science, and machine learning

Oral

According to recent studies, Machine Learning has become a demanded skill in the world of engineering. Therefore, it is possible to find hundreds of online courses offering to teach these abilities. Some companies now call for ‘Machine Learning Engineers’ as a new hiring position. For many engineering students this is a very difficult situation, since taught courses are often focusing on the...

35. Randomness Characterization through Bayesian Model Selection

Rafael Diaz Hernandez Rojas (Sapienza University of Rome)

22/10/2019, 18:20

Artificial intelligence, data science, and machine learning

Oral

Random number generation currently plays a fundamental role due to its several applications in probabilistic algorithms (e.g. Monte Carlo methods, stochastic gradient descent, etc.), but mainly for its importance in cryptography. The most common methods for characterizing random numbers generators (RNG) either lack formality (e.g. the battery of tests provided by the NIST) or are not...

38. Regularization methods vs large training sets

Dr Juan Jaime Vega Castro (Instituto Nacional de Investigaciones Nucleares), Juan Jaime Vega Castro

22/10/2019, 18:40

Artificial intelligence, data science, and machine learning

Oral

Digital pulse shape analysis (DPSA) is becoming an essential tool to extract relevant information from waveforms arising from different source. For instance, in the particle detector field, digital techniques are competing very favorable against the traditional analog way to extract the information contained in the pulses coming from particle detectors. Nevertheless, the extraction of the...

47. Network science to analyse insecurity perturbations that undermine Mexican tourism industry

Dr Tanya Arenas (Centro de Ciencias de la Complejidad ), Tanya Arenas (UNAM)

24/10/2019, 14:30

Earth sciences and humanities

Oral

The increasing availability of data due to effective and fast sharing methods offered by technological advances has catalyzed new approaches like network science to process large data sets and transcend traditional statistical tools analysis; hence we introduce the tripartite: destinations-rates-advisories network to make connections between complex non-linear interrelations that affect...

52. AI Applications In Oil Extraction, Review and Case of Study: Convolutional Neural Networks Applied to Seismic Image Analysis

Ms Margarita Mayoral-Villa (ININ)

24/10/2019, 14:50

Earth sciences and humanities

Oral

Application of AI methods in the energy industry has been increasing in the last years. The introduction of AI in the different segments of the energy industry has turned out to be revolutionary. Some of these segments are considered as fundamental for the economic development of countries. This means, increasing their efficiency can highlight the difference between conservatory and...

40. Artificial Intelligence and Machine Learnig for Human Mobility in Megacities

Prof. Jose L. Mateos (Instituto de Fisica, UNAM)

24/10/2019, 15:10

Societal challenges

Oral

Human mobility in megacities is a fundamental problem to address and one of the most pressing societal challenges nowadays. Fortunately, we have now at our disposal a vast set of data, through mobile devices and geolocalized social networks, that allow us to explore, using Data Science, the patterns of mobility of tens of millions of people on a daily basis. We present here recent result for...

31. Machine Ethics Frameworks and Practical Implications for AI & DS

Prof. Jesus Ramos (Sociedad.Mexicana de Ciencia de Datos)

24/10/2019, 15:30

Societal challenges

Oral

Talk given at SXSW 2019 in Austin, TX.
Contrary to what tech news outlets like Mashable, Techcrunch, and Wired tell us about the future of work and the purported "rise of the machines", we're seeing that Machine Learning developments are still far away from general AI, and hence far away from becoming real problem solvers. However, it is precisely because of this early stage of development...

48. Arhuaco: Deep Learning and Isolation Based Intrusion Detection in High Energy Physics

Andres Gomez Ramirez (Johann-Wolfgang-Goethe Univ. (DE))

24/10/2019, 15:50

Societal challenges

Oral

High Energy Physics utilizes powerful distributed computational networks called grids, to process and analyze scientific data. Monitoring the security of these networks is a challenging task. Arbitrary and not-trusted applications can be executed inside the grid worker nodes by the scientists. Innovative methods and tools are required to reduce the risk associated with the
execution of users'...

56. Large-Scale Scientific endeavours: the production and dissemination of advance computer science's knowledge

Arturo Sanchez Pineda (Abdus Salam Int. Cent. Theor. Phys. (IT))

24/10/2019, 17:00

Data access and infrastructure

Oral

Perform big data analysis and visualisation on your own computer? Yes, you can! Commodity computers are now very powerful in comparison to only a few years ago. On top of that, the performance of today's software and data development techniques facilitates complex computation with fewer resources. Cloud computing is not always the solution, and reliability or even privacy is regularly a...

28. Machine Learning-Based System for the Availability and Reliability Assessment and Management of Critical Infrastructures

Luigi Serio (CERN)

24/10/2019, 17:20

Data access and infrastructure

Oral

A critical infrastructure is a complex interconnected system of systems providing basic and essential services to support the operation of particle accelerators but also industries and households for which they must guarantee high reliability of critical functions.
Model-based approaches are usually adopted to provide an early identification of failures and to reveal hidden dependencies among...

51. An assessment of an ConvNet Applied to Classify Animals in the Wild

Mr José Luis Gordillo (Center for Complexity Sciences, UNAM), José Luis Gordillo

24/10/2019, 17:40

Machine vision and robotics

Oral

The objective of this work is to show an analysis of the performance of a very well known Convolutional Neural Network applied to the classification of animals in the wild.
The interesting aspect of this application is that the set of images used have diverse characteristics from the training set of the ConvNet.
The analysis goes beyond the typical display of error rates, precision and...

42. Visual comparison of particle collisions in the TPC in the ALICE Experiment

Wiktor Kozakowski (Warsaw University of Technology (PL))

Physics and astronomy

Oral

The comparison of particle collisions is a key component of an understanding of high-energy physics experiments. It allows the user to check their theoretical knowledge with empirical results. The methods which currently are used mainly focus on properties of collision and collided particles itself. In this work, we present the new solution for this task, using real-life examples from the Time...