HEPTECH AIME19 AI & ML

Contribution List

69. Opening

István Szabó

25/11/2019, 13:00

74. Machine Learning in High-Energy Physics: Successes and Future Directions

Graeme Stewart (CERN)

25/11/2019, 13:15

Lecture

Machine learning has been used in high-energy physics for several decades, with considerable success. With the advent of modern machine learning the range of applications HEP has exploded, with techniques being applied in almost every area of the field. I shall review here some of the advances and successes that have been achieved in event classification, simulation and reconstruction domains....

68. One Year Into the ESS Control System Machine Learning Project

Karin Rathsman (ESSS)

25/11/2019, 14:00

Lecture

The European Spallation Source ERIC (ESS) is a joint European organisation committed to building and operating the world's leading facility for research using neutrons. The facility design and construction includes a powerful linear proton accelerator, a helium-cooled tungsten target wheel and two dozen state-of-the-art neutron instruments.

ESS is made up of a large number of diverse...

58. Image-based classification of variable stars

Attila Bódi (Konkoly Thege Miklós Astronomical Institute, Research Centre for Astronomy and Earth Sciences)

25/11/2019, 14:25

Lecture

Among the astronomical community it is well-known that different types of variable stars can be recognised based on their light curve properties. However, with the advent of large astronomical sky surveys, the amount of measurements increases dramatically, reaching the limits of the human capabilities of stellar classification. During my talk I will give a brief introduction to variable star...

57. Variable star classification

Dóra Tarczay-Nehéz (CSFK CSI)

25/11/2019, 14:50

Lecture

The most recent telescopes (e.g. Kepler, K2, Gaia, TESS) and sky surveys (e.g. SSDS, and the forthcoming LSST) provide huge amount of data, that leads to the challenge of data processing. This huge volume of data needs to be analyzed with fast and effective automated computer programming techniques. Therefore, machine learning algorithms become popular in astronomy, as they can play a key role...

71. Life beyond the pixels: machine learning and image analysis methods for HCS

Péter Horváth (Hungarian Academy of Sciences)

25/11/2019, 15:40

Lecture

In this talk I will give an overview of the computational steps in the analysis of a single cell-based high-content screen. First, I will present a novel microscopic image correction method designed to eliminate vignetting and uneven background effects which, left uncorrected, corrupt intensity-based measurements. I will present deep learning-based image segmentation methods. I will discuss...

63. Learning structured deep generative models from incomplete data in biomedicine

Péter Antal (BME)

25/11/2019, 16:25

Lecture

Deep probabilistic generative models demonstrated superior and scalable performance in multiple domains. However, their application in biomedicine is still hindered by the following challenges, incorporation of prior knowledge, interpretation and explanation, and learning from highly incomplete data, especially from sparsely populated time-series data. At first, I illustrate standard solutions...

65. What can we learn about learning through human-machine interactions?

Ferdinando Mussa-Ivaldi (Northwestern Univ and Shirley Ryan Ability Lab)

25/11/2019, 16:50

Lecture

A growing body of evidence suggests that when we interact physically with our environments our brains form models of the deterministic connection between our actions and the ensuing sensory information. Theories of motor learning posit that the formation of internal models is a key mechanism though which the brain forms predictions about the outcomes of actions, overcoming certain limitations...

62. Complete Inference of Causal Relationships in Dynamical Systems

Zoltán Somogyvári (Wigner Research Centre for Physics, Department of Computational Sciences)

25/11/2019, 17:15

Lecture

Inference of causal structure between multiple observations of a complex system gained large interest in wide range of scientific disciplines, from pharmaceutics to economy, where it earned Nobel-prize for Clive Granger in 2003. In this talk, we present a new analysis method called Dimensional Causality (DC). We belive, our method is the first one, which is able to detect and distinguish all...

60. Hindrances to adaptation of Machine Learning in Healthcare

Richárd Nagyfi (Cursor Insight)

25/11/2019, 17:40

Lecture

Artificial Intelligence is over-hyped and anthropomorphised by the media which has led to numerous unfulfilled expectations and fear-mongering throughout the decades. Despite experts prophesying omnipotent robot AGIs, there is seldom any discourse about the actual hindrances to adaptation of current narrow solutions. Black-box functionality, lack of transparency and interpretability, and...

72. AI ecosystem and AI strategy in Hungary

Gergely Szertics

26/11/2019, 09:00

Lecture

The Minister of Innovation and Technology announced the 2020 action plan of AI in October and the elaboration of the AI strategy is in progress. We will take a look at the cornerstones of the strategy and the elements that are already announced that are focusing on both the ecosystem, the institutions and the markets that has to be developped to be able to live with the opportunities that AI...

64. AI based algo-trading using quantization and volatility information

János Levendovszky (BME)

26/11/2019, 09:45

Lecture

Novel algorithms are developed for algorithmic trading on financial time series by using quantization and volatility information to achieve High Frequency Trading (HFT). The proposed methods are estimation based and trading actions are carried out after estimating the Forward Conditional Probability Distribution (FCPD) on the quantized return values. For estimating FCPD, a FeedForward Neural...

49. Hb-graphs and their applications to the ranking of information in an information space.

Xavier Ouvrard (CERN / University of Geneva)

26/11/2019, 10:10

Lecture

Hb-graphs have been previously introduced to handle redundancy in the m-uniformisation process used for the construction of an e-adjacency tensor for general hypergraphs. In this talk, we present an application of hb-graphs to co-occurrence networks of an information space, allowing a multi-diffusion scheme for ranking information.

53. The Hungarian National AI Center of Excellence

Andras Benczur (SZTAKI)

26/11/2019, 11:00

Lecture

The initiative to found the Hungarian National AI Center of Excellence led by the Institute for Computer Science and Control (SZTAKI) was announced in October 15 this year. The Center is planned to start operation in early 2020 in the areas of basic and applied research, education, technology development as well as acting as a key Hungarian player in international cooperation.

In my...

50. Machine Learning and Reasoning for Exploratory Data Analysis and Model Extraction in Cyber-Physical Systems

András Földvári

26/11/2019, 11:45

Lecture

Modern cyber-physical system (CPS) design relies on the paradigm of component integration. Assurance of the compliance with extra-functional requirements of critical CPS applications necessitates empirical identification before integrating components, and validation during the final acceptance test and operation, respectively.

Benchmarking and operational log analysis are the primary means...

54. Adding artificial Intelligence into process control systems – implementation and integration by open source tools

Tamas Ruppert

26/11/2019, 12:10

Lecture

We demonstrate how the toolbox of artificial intelligence (AI) and machine learning (ML) can support the monitoring of processes. We highlight how these functions can be implemented in existing process control systems and how open source solutions (e.g., Python toolboxes) can be goal-oriented tailored for their development.
We give an in-depth overview of the steps of the workflow of the...

73. Artificial Intelligence for industry at the Hartree Centre

Andrew Gargett

26/11/2019, 14:00

Lecture

There would seem to be good reason to be optimistic about the uptake of Artificial Intelligence in real-world settings; despite the hype, there seems to be genuine progress with the long-standing problem of how to map areas of often obscure research to more mundane and practical challenges, at least for those organisations large enough to absorb any risks involved as well as supply the...

70. Computer vision methods for understanding human movements in daily living and clinical settings

Maura Casadio (U Genova)

26/11/2019, 14:40

Lecture

Measuring and understanding human motion is crucial in several domains, ranging from neuroscience, to rehabilitation and sports biomechanics. The study of human motion is commonly done through marker-based techniques and motion capture systems. If on one hand these methods are precise and reliable, on the other they present some disadvantages, in particular they are expensive, encumbering, and...

56. Enhancement of 2D and 3D Medical Imaging Data acquired with Mediso SPECT Cameras

Gábor Légrádi (physicist-engineer, AI expert, freelancer)

26/11/2019, 15:05

Lecture

Mediso Medical Imaging Systems is a Hungarian company which develops, manufactures and sells 3D medical imaging tools. Beside Computer Tomography (CT), Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) tools, Single Photon Emission Tomography (SPECT) is the leading technology of the Company.

Since the very beginning of Mediso, SPECT technology has always been...

52. HEP Graph Machine Learning for Industrial & Humanitarian Applications

Daniel Dobos (Visiting Researcher at CERN with Lancaster University)

26/11/2019, 16:00

Lecture

The power of Machine Learning for graph structured data is used more and more for HEP applications like particle tracking or jet analysis. We studied different ML tracking approaches (including GNN's) used in the TrackML and HEPTrkX challenges and used a simplified game model of Nine Men's Morris to study Graph ML using quantum algorithms. While the quantum implementation is motivated by...

55. Overview of applied machine learning projects at the Department of Automation and Applied Informatics

Gabor Gulyas (BME-AUT)

26/11/2019, 16:45

Lecture

In this talk we cover some research areas at our department where we use machine learning as our main tool. We provide insights to these by discussing some of the key projects in details. More particularly, we discuss works in the conjunction of machine learning and data collection and analysis, image- and natural language processing.

Regarding data collection and analysis, we present a...

67. The breakdown of photon blockade: a first order dissipative quantum phase transition (cloud-based simulation of open quantum systems)

András Vukics (Wigner FK)

26/11/2019, 17:10

Lecture

First-order phase transitions characterized by the coexistence of phases are commonly observed in the surrounding world, e.g. in the freezing of water. Continuous – second-order – phase transitions also exist in classical physics, e.g. the transition between ferro- and paramagnetism at the Curie temperature. Whereas the latter class has seen straightforward generalizations to quantum systems...

66. Comparing the Markowitz Portfolio Model and the Market Graph of Pardalos

Sándor Szabó (Univ Pécs)

26/11/2019, 17:35

Lecture

The Markowitz portfolio selection model is an optimization problem for investments to achieve a good return while control the risk of losses. On the technical side it is a quadratic or in other later variants a linear program. The so-called market graph is a graph based model to capture certain aspects of the inner working and structure of a stock market. More specifically it uses concepts...

61. Nonlinear decoders are efficient for discriminating structured stimuli in V1

Marcell Stippinger (Wigner Research Centre for Physics, System Level Neuroscience Research Group, Budapest, Hungary)

Poster

Complex stimuli are represented by the activations of populations of neurons in the visual cortex. While the response pattern of a single neuron provides limited stimulus information, the collection of the responses from a population can be used to reliably identify the image eliciting the responses. A key question regarding the neural code is whether the information carried by a population...

59. Simulating high-energy heavy-ion collisions in the cloud

Gábor Bíró (Wigner FK)

Poster

At the world largest particle accelerators, such as the Large Hadron Collider at CERN, or the Relativistic Heavy Ion Collider at BNL, hundreds of thousands of interesting interactions may occur in every second. A special subset of these events are the high-energy heavy-ion collisions, aiming to investigate the birth of the Universe itself. These experimental measurements are always accompanied...

51. Test-time augmentation for deep learning-based cell segmentation on microscopy images

Mr Nikita Moshkov (Biological Research Centre, Szeged, Hungary; University of Szeged, Szeged, Hungary; Higher School of Economics, Moscow, Russia)

Poster

Recent advancements in deep learning have revolutionized the way microscopy images of cells are processed. Deep learning network architectures have a large number of parameters, thus, in order to reach high accuracy, they require massive amount of annotated data. A common way of improving accuracy builds on the artificial increase of the training set by using different augmentation techniques....