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....
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...
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...
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...
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...
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...
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...
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...
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...
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.
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...
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...
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...
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...
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...
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...
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...
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...
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....
